Characterizing spatiotemporal white matter hyperintensity pathophysiology in vivo to disentangle vascular and neurodegenerative contributions

Domain: biology
Published: 2026-03-31
Editorial outcome: major_revision

Abstract
White matter hyperintensities (WMHs) are neuroimaging markers widely interpreted as caused by cerebral small vessel disease, yet emerging evidence suggests that a subset may have a neurodegenerative etiology. Current imaging methods have lacked the specificity to disentangle biological processes underlying WMHs in vivo. Here, we used voxel-level normative modeling and seven microstructural MRI markers with complementary biophysical sensitivities to generate single-subject high-resolution WMH pathophysiology maps in a large cohort (n=32,526). We calculated data-driven spatial patterns of similar WMHs, revealing distinct periventricular, posterior, and anterior clusters. We identified a reproducible WMH signature linked to dementia and Alzheimer’s disease, characterized by a posterior predominance and a pathophysiological pattern indicative of selective fiber degeneration. Posterior WMHs connected cortical regions vulnerable to tau pathology. Our framework helps parsing vascular and neurodegenerative contributions of WMHs in vivo, which could alter the course of treatment strategies and provide nuanced interpretations of research findings.

Introduction
White matter hyperintensities (WMHs) are among the most common neuroradiological findings in older adults.1,2 They are present in most individuals over 65 years old and appear as bright regions in fluid-attenuated inversion recovery (FLAIR) images during magnetic resonance imaging (MRI) exams.3 The presence of WMHs is widely considered a downstream consequence of cerebral small vessel disease and is typically used to detect this disorder, as neuroimaging methods able to directly investigate small blood vessels (<500 μm in diameter) are lacking.4 The extent of visible WMHs is used by neurologists and radiologists to monitor the cerebrovascular disease burden and for diagnoses such as vascular dementia,5 by researchers as a quantifiable marker of cerebrovascular dysfunction,6,7 and in some clinical trials as outcome measures of vascular treatments (e.g., antihypertensives).8,9 Importantly, the pathogenesis of WMHs can vary, with a growing body of evidence suggesting that some WMHs may be caused in part by neurodegenerative processes rather than solely vascular disease.10–13 For example, a post-mortem examination found no evidence of hypoperfusion-related damage within the WMHs of brain specimens with Alzheimer’s disease (AD) pathology, and instead observed signs of Wallerian degeneration.11 This pathological heterogeneity is currently invisible to conventional in vivo imaging and could result in misinterpretation or misdiagnosis of cerebral pathology, hindering the application of precision medicine in diagnosis and treatment as well as complicating the interpretation of research findings. FLAIR images lack the specificity to parse the biological processes underlying WMHs, as these contain various levels of inflammation,14 edema,15,16 demyelination,11,17 and axonal degeneration11 that cannot be distinguished with a single contrast. A deep in vivo phenotyping of WMH pathophysiology could help parse neurodegenerative from vascular etiologies.

There is evidence that some of the pathophysiological heterogeneity observed in WMHs may be dependent on their neuroanatomical location. Studies suggest that periventricular WMHs are associated to a larger extent with vascular risk factor burden and contain more edema and less demyelination relative to deep WMHs.18 Consequently, periventricular and deep WMHs are often assessed separately using heuristic methods with criteria that can vary significantly between studies.19 Other parcellation schemes include cortical lobes, vascular territories, or white matter tracts.20 In contrast to these hypothesis-driven parcellations, data-driven methods can uncover more complex patterns of WMH spatial heterogeneity, and while such examinations have been done based on the co-occurrence of WMHs,12,21 these have not taken into account the underlying pathophysiological alterations. Given the sparsity of WMH prevalence at the voxel-level, such an analysis requires a very large dataset. Furthermore, the pathophysiological cascade of WMHs remains speculative, and it is unclear whether pathological events follow the same sequence in vascular and neurodegeneration-related WMHs. The prevailing hypothesis suggests that fluid infiltration from ventricles and blood vessels triggers inflammation, leading to tissue damage such as demyelination, oligodendrocyte loss, and, in more severe cases, axonal degeneration;3 however, this is one of several pathways through which WMHs can emerge.11 Generally, WMH pathophysiology is characterized post-mortem using histological and immunohistochemical stains sensitive to different aspects of cellular, molecular, and pathological features. Multi-contrast microstructural MRI, while lacking the biological specificity of these techniques, has established sensitivities to various tissue properties,22,23 providing a window into biophysical tissue alterations in the brain and allowing for the non-invasive, large-scale investigation of WMH pathophysiology. This in vivo approach could provide clinicians with supplemental information about WMH pathogenesis that could alter the course of treatment and disease prevention strategies.

Our observational study aimed to parse the pathophysiological, spatial, temporal, and ultimately etiological heterogeneity of WMHs. We generated high-resolution maps of WMH pathophysiology by performing voxel-wise normative modeling of seven MRI markers sensitive to fluid, fiber, myelin, and iron content in a large population cohort (UK Biobank; n=32,526) and transferred these models to an independent cohort of subjects along the AD spectrum. We calculated spatial regions of WMHs that showed similar pathophysiological features with unsupervised clustering, allowing us to then estimate region-specific pathophysiological cascades with machine learning-based disease modeling techniques. With an improved understanding of these spatiotemporal dynamics, we identified reproducible disease-specific signatures of WMH pathophysiology in vascular and neurodegenerative disorders.

Results
We used cross-sectional data from 32,526 UK Biobank (UKB) participants, a population-based prospective study of middle- to late-aged individuals. Exclusion criteria (Methods) included disrupted MRI data used in downstream analyses (missing modality, motion artifacts, or processing failures), missing demographic information, and a diagnosis of multiple sclerosis to not confound white matter lesions from WMHs (step-by-step exclusions detailed in Supplementary Fig. 1; demographic statistics detailed in Supplementary Table 1). While MRI images have already been processed by UKB,24 we updated key processing steps to ensure the highest data quality possible for our specific use case. A customized UKB template was generated25 (available at https://github.com/CoBrALab/WMH_patho_UKB) and MRI images were aligned to this template with multispectral registration (Methods), providing highly accurate voxel-by-voxel correspondence across subjects, particularly in the periventricular white matter, which is an important area of interest. Second, to ensure accurate delineation of WMHs and normal-appearing white matter (NAWM) while leveraging multi-contrast information, we retrained the BraIn SegmentatiON (BISON) algorithm26 with a manually defined reference.

To investigate tissue properties, the multi-shell diffusion MRI and two-echo susceptibility-weighted MRI acquisitions were used to derive seven quantitative microstructural markers characterizing important aspects of white matter biophysical composition that are altered in aging, broadly categorized as fluid-sensitive (mean diffusivity [MD] and isotropic volume fraction [ISOVF]), fiber-sensitive (fractional anisotropy [FA], intracellular volume fraction [ICVF], and orientation dispersion [OD]), and myelin- and iron-sensitive (T2* and quantitative susceptibility mapping [QSM]). However, their biological underpinnings are complex and potentially disease-dependent (summarized in Table 1). For example, diffusion tensor imaging (DTI) indices such as FA and MD are particularly difficult to interpret since they assume a main axis of diffusion within voxels, while most voxels instead contain multiple fiber populations with different orientations.27 Different multi-compartment modeling schemes of the diffusion signal have been developed to handle the crossing-fiber problem and increase the interpretability of microstructural metrics, one of which is the Neurite Orientation Dispersion and Density Imaging (NODDI) model used here.28 While no metric perfectly captures individual biological processes (e.g., inflammation, demyelination, edema), interpreting these markers together can help generate hypotheses about the potential biological substrates of the observed effects.23

Pathophysiology was operationalized as the deviation in microstructural values in WMHs relative to the expected microstructure in normal-appearing white matter (NAWM). In other words, we aimed to estimate pathophysiological processes such as water accumulation/edema as opposed to microstructural properties like water density. High-resolution normative modeling of white matter microstructure was performed for the seven markers, only including individual-level voxels that were labeled as NAWM (Methods; Fig. 1A).37 We note that NAWM may still be pathological to a certain extent, as has been previously demonstrated.16 However, pathological NAWM remains ill-defined, and there is currently no established framework to identify it. We thus focus on contrasting WMH from non-WMH tissue, considering that some tissue alterations are part of the NAWM variations. From these normative models, we generated age- and sex-specific maps of expected normal microstructure, which are then used to standardize WMH microstructure using a z-score procedure (example in Fig. 1B). Voxels labeled as NAWM were not included in any other analyses. The resulting seven high-resolution pathophysiology maps represent, in essence, an individual-level signature of the biophysical processes at play within WMHs. These maps have many desirable properties: 1) they isolate the effect of the pathology within WMHs on the microstructure (i.e., the WMH pathophysiology), 2) since all markers are on the same scale, they do not require further normalization, and 3) they remove the spatial microstructural contrast driven by normal anatomical variations, which is a necessary property for our spatial clustering analyses (see below). In other words, regional variations in normal white matter microstructure are removed. As an example, the single-subject raw OD map in WMHs has a high level of spatial variation, but these are not present in the z-scored map (Fig. 1B, first row compared to last row), highlighting that spatial variations were due to the anatomy and not caused by differences in WMH-related alterations.

From this rich dataset, we first aimed to characterize group-level spatial patterns of WMH pathophysiology in the general population. Mean WMH pathophysiology maps were calculated by averaging all subject-level maps (excluding all voxels labelled as NAWM), resulting in one between-subject map per marker. We excluded voxels with low WMH or NAWM prevalence, which have higher noise levels (Fig. 2A; Methods; Supplementary Fig. 2 and 3), and these maps were used as inputs to a spectral clustering algorithm.38 This data-driven approach identified spatial regions that are most similar between themselves solely in terms of WMH pathophysiology, independent of voxel location. Yet, all clusters were mostly spatially contiguous, suggesting that WMHs appearing in specific areas shared pathophysiological signatures. We opted for a solution of three clusters (Fig. 2B) in the interest of parsimony since the four-cluster solution (Supplementary Fig. 4) was less spatially contiguous with similar pathophysiological differentiation across clusters. The first cluster surrounded the ventricular wall and showed low abnormality on all metrics (median z-scores: MD = 0.49, ISOVF = -0.18, FA = -0.61, ICVF = -1.26, OD = -0.3, T2* = 0.63, QSM = 0.06). These voxels may be contaminated by partial volume effects (i.e., ventricle signal) and include small caps WMHs that are likely benign.39 The second cluster was located in periventricular-posterior regions and showed abnormality related to fluid accumulation, fiber alterations, and myelin and iron loss (see Table 1 for more complex biological interpretations; median z-scores: MD = 4.01, ISOVF = 1.07, FA = -1.56, ICVF = -3.22, OD = -0.3, T2* = 1.47, QSM = 0.23). The third cluster was located in deep-anterior regions and showed a similar pathophysiological pattern to the posterior cluster but with higher absolute abnormality on most metrics (median z-scores: MD = 5.89, ISOVF = 1.47, FA = -1.99, ICVF = -4.37, OD = -0.34, T2* = 1.6, QSM = 0.29). We also observed that the clustering solution explained most of the variance in the average pathophysiology spatial maps of MD, ICVF, and ISOVF (R2 of 0.81, 0.78, and 0.63, respectively; Supplementary Fig. 5), a moderate amount for FA and T2* (R2 of 0.39 and 0.29, respectively), and almost none for QSM and OD (R2 of 0.04 and 0.00, respectively). The posterior and anterior clusters mostly differed in the severity of pathophysiological markers, but still revealed distinct spatial signatures. Further characterization of specific pathophysiological alterations is needed to assess potential etiological signatures.

To quantify and contextualize the localization of each spatial cluster, we compared the overlap of our custom parcellation with other heuristic parcellations used in the field: periventricular/deep, lobar regions, cerebral artery territories as estimated from a probabilistic atlas,40 and fiber type (Fig. 2C). We observed that the first cluster was more periventricular (Dice similarity coefficient [DSC] = 0.37), located in temporal white matter (DSC = 0.31) and in commissural fibers (DSC = 0.21), and supplied by the posterior cerebral artery (DSC = 0.27). This limited overlap between cluster 1 and the periventricular a priori region is due to the much broader coverage of the a priori region (<8mm from the ventricle, as is typically done),19 as compared to the data-driven cluster. The second cluster was more periventricular (DSC = 0.54), located in parietal white matter (DSC = 0.37) and commissural fibers (DSC = 0.45), and supplied by the middle cerebral artery (DSC = 0.37), although it also showed overlap with the posterior and anterior cerebral artery territories (DSC of 0.30 and 0.26, respectively). The third cluster was mostly located in deep and frontal white matter (DSC of 0.76 and 0.66, respectively) as well as association and projection fibers (DSC of 0.56 and 0.41, respectively), and largely supplied by the middle cerebral artery (DSC = 0.68). The lack of consistent agreement with often-used WMH parcellations suggests an improved pathophysiological specificity in these data-driven clusters that is not captured with the more heuristic approaches. For the rest of the manuscript, these spatial clusters will be referred to as periventricular (cluster 1), posterior (cluster 2), and anterior (cluster 3).

To investigate if these patterns simply reflected severity stages (e.g., spreading of WMHs from periventricular to posterior to anterior regions), we separated our sample into three groups based on total WMH volume and ran the clustering analysis within each group (Supplementary Fig. 6A). Remarkably, the spatial overlaps between the subgroup clusters and the original clustering solution in the complete dataset were very high (DSCs: Periventricular > 0.87; Posterior > 0.78; Anterior > 0.88; Supplementary Fig. 6B) and the associated pathophysiological patterns were highly congruent. We also found congruent spatial and pathophysiological patterns between males and females (DSCs: Periventricular > 0.95; Posterior > 0.91; Anterior > 0.96; Supplementary Fig. 7). This demonstrated that the observed spatial variations in WMH pathophysiology are robust to the severity of WMH burden and are consistent across sexes.

Finally, voxels that were previously excluded from the clustering were assigned to the closest spatial cluster using a search area strategy (Methods; Supplementary Fig. 8A). This yielded a parcellation that was comparable but more spatially contiguous relative to a clustering solution where voxels were not excluded based on WMH prevalence (Supplementary Fig. 8B). This parcellation enabled a pathophysiology-informed dimensionality reduction of the high-resolution WMH maps, as we extracted subject-wise region-of-interest (ROI) measures by sampling the median pathophysiological estimates within WMHs in each spatial cluster (Methods). This reduced the complex voxel information into a set of 24 values (8 measures * 3 regions) for each subject. All further analyses are made at the ROI level and not at the voxel level. To simplify the interpretation, we inverted the values of markers that showed negative effects in WMHs (FA, ICVF, and OD) so that an increase in z-scores is consistently interpreted as worsening WMH pathophysiology for all markers.

Next, we investigated the temporal heterogeneity of WMH pathophysiology by modeling pathophysiological cascades in our three derived spatial regions using the Subtype and Stage Inference (SuStaIn) technique.41 SuStaIn is an event-based unsupervised machine learning technique that infers temporal progression patterns from cross-sectional data and is further able to find progression subtypes, essentially sorting abnormality events in the order in which they tend to appear across different clusters of subjects.41 Pathophysiological events were determined empirically on a per-marker basis (at least 1% of participants needed to reach the threshold; Methods; Supplementary Fig. 9).

Interestingly, we did not find evidence for sub-trajectories of WMH pathophysiological progression in any of the regions, as is evidenced by nearly identical biomarker progression curves even when investigating cross-validated three-trajectory solutions (Supplementary Fig. 10). We interpret this finding as evidence that most of the pathophysiological heterogeneity is spatial and not temporal, and was adequately accounted for in our spatial clustering step. We thus describe here the pathophysiological cascades shared across all subjects within each WMH region (Fig. 3A). In periventricular WMHs, we observed low abnormality in most markers, with the maximum z-score thresholds being set to 1.5 SD (for MD and ICVF) according to our heuristic. ICVF was the earliest marker to be altered. Posterior WMHs displayed early alterations of the ICVF and MD markers, followed by FA, T2*, and ISOVF in the intermediate stages, and OD and QSM in the later stages. Anterior WMHs showed a similar progression pattern, but with most markers reaching higher abnormality. Positional variance diagrams showed very low stage uncertainty, demonstrating the stability of these temporal trajectories (Supplementary Fig. 11). Using WMH volume as a simpler empirical definition of temporal disease severity, we recapitulate results from our data-driven findings of disease progression (Supplementary Fig. 12A). SuStaIn stages were correlated with regional WMH volumes (Spearman ρ: Periventricular = 0.41, Posterior = 0.66, Anterior = 0.85; all p<0.001; Supplementary Fig. 12B) but still showed substantial variation not explained by WMH volumes. The subject-level stage from SuStaIn provided a summary measure of the temporal progression of WMH pathophysiology and is included in further analyses.

Previous studies have reported higher WMH burdens in females that manifest after midlife, which is hypothesized to be due to the menopausal transition and the consequent reduction in the neuroprotective effects of estrogen.42,43 We investigated whether a specific signature of WMH pathophysiology exists in females, providing standardized betas and false discovery rate (FDR)-corrected p-values.44 Interestingly, we found higher WMH volumes in the periventricular (β=0.17, p (FDR) < 0.001) and anterior (β=0.09, p (FDR) < 0.001) regions in females, but higher WMH volumes in posterior (β=-0.05, p (FDR) < 0.001) regions in males (Fig. 3B). Investigating differences in WMH pathophysiology when males and females have equivalent WMH volumes (Fig. 3C), we observed a clear pattern of strong effects in females restricted to periventricular and posterior WMHs for most pathophysiological markers. This was not the case for the OD marker, which showed a significantly higher effect in males in the periventricular region but no significant effects in other regions. Taken together, our results show nuanced sex-specific effects in WMHs. There are clear spatial differences, with females having more WMHs but similar pathophysiological effects in anterior regions, while males show higher WMH volumes but lower pathophysiological effects in posterior regions.

With a deeper understanding of WMH spatiotemporal dynamics, we were now well-positioned to elucidate complex disease-specific phenotypes in vascular and neurodegenerative disorders. We first compared WMHs across different diagnostic categories with a case-control design using UKB lifetime diagnosis data (i.e., prevalent and incident cases; Fig. 4A). Control subjects had no endocrine, circulatory, behavioral, nervous, or metabolic disease-related lifetime diagnoses (n=8,528; Methods). Case subjects were defined by groupings of ICD-10 codes without comorbidities among the diagnoses analyzed (groups described in Supplementary Fig. 13).

In individuals with ischemic heart disease (n=2,283), WMH alterations were small but significant, with stronger effects in anterior regions (average β = 0.17; p (FDR) < 0.05 for all markers) than posterior regions (average β = 0.12; p (FDR) < 0.05 for all markers except OD and T2*). The stroke group (n=526) showed moderate-to-large effects, particularly in anterior WMHs (average β = 0.47; p (FDR) < 0.05 for all markers) relative to posterior WMHs (average β = 0.31; p (FDR) < 0.05 for all markers). In contrast, the dementia group excluding vascular dementia (n=31) showed moderate effects, particularly in posterior WMHs (average β = 0.24; p (FDR) < 0.05 for WMH volume, MD, ISOVF, OD) relative to anterior WMHs (average β = 0.09; p (FDR) < 0.05 for OD). Notably, FA effects were more pronounced in stroke, whereas OD effects were more pronounced in dementia. This suggests a signature of neurodegenerative WMHs indicative of specific disruptions to the fiber organization captured by the OD marker. However, this analysis is limited by the uncertainty in dementia type (n=23 were diagnosed with unspecified dementia).

To better isolate disease-specific effects, we investigated polygenic risk scores (PRS) indexing the genetic susceptibility to a certain disease,45 comparing high genetic risk (top 1%) and low genetic risk (bottom 50%) individuals (Fig. 4B). For cardiovascular disease and ischemic stroke, WMHs showed more severe alterations in high PRS individuals in anterior regions (average β = 0.12 and 0.18, respectively; p (FDR) < 0.05 for all markers except OD, T2*, QSM, and for all markers except QSM, respectively) than posterior regions (average β = 0.09 and 0.17, respectively; p (FDR) < 0.05 for WMH volume, stage, MD, FA, and for all markers except OD, respectively). For AD, WMHs showed more severe alterations in high PRS individuals in posterior regions (average β = 0.08; p (FDR) < 0.05 for stage, MD, ICVF, OD, T2*) relative to anterior regions (average β = 0.01; no significant associations at p (FDR) < 0.05). The inverse pattern of FA and OD effects in stroke and Alzheimer’s PRS echoed findings from the diagnosis analysis. This analysis was replicated using different cut-off values to determine high genetic risk individuals (top 3% and top 5%; Supplementary Fig. 14).

We replicated the Alzheimer’s-specific findings using ADNI3 data (Fig. 4C), which included fiber- and fluid-sensitive metrics but not myelin- and iron-sensitive metrics. Normative models were transferred to compute WMH pathophysiology maps (Methods). Among individuals with amyloid positron emission tomography (PET) data, 28% of cognitively normal individuals, 53% of people with mild cognitive impairment, and 94% of people with dementia were amyloid-positive (demographic statistics detailed in Supplementary Table 2). Compared with cognitively normal control participants (n=125), individuals with MCI (MCI; n=66) and individuals with dementia (n=21) showed a similar pattern of higher effect sizes in posterior WMHs, lower effect sizes for FA, and higher effect sizes for OD. In the dementia group, some effects did not reach significance, likely due to the lower sample size. For example, the posterior WMH OD effect size was almost identical in the MCI group (β = 0.37; p = 0.01) and dementia group (β = 0.38; p = 0.08), hence the difference in significance likely stems from differences in sample sizes.

We investigated sex by group interactions to investigate if some effects were higher in males or females, but no effect was significant at the p (FDR) < 0.05 level (Supplementary Fig. 15).

We quantified the replicability across the eight reported effect size patterns (Fig. 4D), observing high correlations within vascular and neurodegenerative groupings, which clustered together (Fig. 4D). For example, the UK Biobank dementia signature was highly correlated with the ADNI dementia signature (r=0.81). We additionally compared amyloid-positive MCI and dementia subjects (n=42) to amyloid-negative cognitively normal subjects from ADNI (Supplementary Fig. 16A) in order to isolate biomarker-confirmed cases. Effects were similar to the other ADNI patterns and showed higher correlations with effect size patterns in the neurodegenerative grouping (average r = 0.63) compared to the vascular grouping (average r = 0.32; Supplementary Fig. 16B). These findings were also reproduced using a parcellation that was made without including rare WMH voxels using our search area strategy (Supplementary Fig. 17), with minimal differences that do not alter our conclusions. This demonstrated the reproducibility of the disease-specific WMH pathophysiological signatures, which clearly differed in vascular and neurodegenerative pathologies. However, these results remain correlational and require validation using gold-standard ex vivo methods.

As a proof of concept, we investigated whether a machine learning model could differentiate vascular from neurodegenerative pathologies based on our derived spatial and pathophysiological WMH data. We trained Lasso logistic regression models to classify individuals with stroke diagnoses (n=642) from individuals with AD-related dementia or MCI diagnoses (n=127), combining data from UKB and ADNI. WMH variables were additionally harmonized across datasets using ComBat (see Methods).46 Models were trained using 5-fold cross-validation repeated 100 times. We used 6 sets of predictors: WMH volumes only or WMH volumes and microstructure, either unparcellated, following the periventricular/deep parcellation, or using our custom clusters (distributions of area under the receiver operating characteristic curve (AUROC), balanced accuracy, and F1 score are available in Supplementary Fig. 18). Performance was compared using paired Wilcoxon tests. Unparcellated WMH volumes performed poorly in differentiating stroke from cognitive impairment (AUROSC mean[SD]: 0.562[0.001]) and improved using clustered volumes (periventricular/deep: 0.630[0.005]; custom clusters: 0.666[0.006]). Performance was noticeably elevated when adding microstructural data (unparcellated: 0.653[0.007]; periventricular/deep: 0.697[0.009]; custom clusters: 0.706[0.011]). All performance differences were significant at p(Bonferroni) < 0.001. Results were similar for balanced accuracy and F1 score. Thus, we observed that 1) WMH microstructural information adds meaningful predictive value for disentangling pathologies and 2) our custom clusters performed better than the traditional periventricular/deep parcellation on this task.

Next, we investigated the grey matter correlates of our spatial WMH clusters, with the hypothesis that Wallerian degeneration stemming from grey matter Alzheimer’s pathology would affect the associated white matter fibers which could be linked to our observed pattern of fiber disorganization in posterior WMHs in neurodegenerative disorders. Using a high-resolution tractogram derived from 1001 subjects from the Human Connectome Project,47 we extracted streamlines passing through each white matter spatial cluster and calculated normalized cortical connectivity profiles (Fig. 5A; Methods). Since larger ROIs will have more streamlines, and thus higher raw connectivity metrics, we further normalized the values by dividing by the number of streamlines of each ROI across all three clusters. This analysis showed that the posterior WMH region disproportionately connects inferior temporal and occipital regions, while the anterior WMH region connects dorsal frontal regions.

The cortical patterns were compared with average maps of amyloid-beta (Aβ) and tau positron emission tomography (PET) standardized uptake value ratio (SUVR) in Aβ-positive individuals from the PREVENT-AD cohort (Fig. 5B).48 This dataset was chosen due to the high representation of individuals at early stages in the AD spectrum. We observed high correlations between cortical connectivity profiles of the posterior WMH region and the tau PET distribution (r=0.65, p (Bonferroni) = 0.005), which was significantly different from a spatial permutation-based null (Fig. 5C).49 No significant associations were observed for Aβ PET. These results demonstrate that the posterior WMH region structurally connects cortical regions prone to early tau pathology, but not amyloid pathology.

Methods
Study design and participants
The UK Biobank (UKB) dataset is a cohort study comprised of over 500,000 individuals from midlife to advanced age (application #45551). The study was approved by the North West Multicenter Research Ethics Committee (United Kingdom). A subset of ~40,000 participants underwent a comprehensive brain MRI protocol. Data from UK Biobank is publicly available via material transfer agreements (https://www.ukbiobank.ac.uk/enable-your-research/register). The Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset is a longitudinal observational study composed of participants along the Alzheimer’s disease (AD) spectrum (http://adni-info.org/) and was approved by the institutional review board at each participating institution. We focused on the ADNI3 dataset since it included the multi-shell diffusion MRI acquisition necessary to derive NODDI metrics. Data from ADNI is publicly available via a data-sharing application (https://adni.loni.usc.edu/data-samples/adni-data/#AccessData). The Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease (PREVENT-AD) is a longitudinal study comprised of individuals who have parents or siblings diagnosed with AD and were cognitively normal at recruitment.48 It was approved by the institutional review board at McGill University. Data from PREVENT-AD is publicly available via a data user agreement (https://openpreventad.loris.ca/). All participants gave written informed consent prior to participation in the studies. Sex was self-reported.

Image processing
MRI acquisition parameters for UKB and ADNI3 are available in Supplementary Methods 1. Microstructural maps were processed by the UKB team,24,72 and we processed ADNI microstructural maps ourselves, matching the UKB processing steps when possible. Processing steps to derive microstructural metrics from diffusion and susceptibility-weighted imaging are detailed in Supplementary Methods 2.

Since voxel-by-voxel correspondence and accurate segmentation of WMHs were crucial for our application, we updated key processing steps from UKB and applied those same steps to the ADNI dataset. T1w and FLAIR images were denoised,73 corrected for N3 inhomogeneities,74 and intensity normalized. Brain masks were calculated using the BEaST algorithm.75 A UKB study-specific template space was generated using the processed T1w images of 200 subjects with representative age and sex distributions (available at https://github.com/CoBrALab/WMH_patho_UKB), and an ADNI template was generated similarly from all cross-sectional T1w images.25 All modalities were then aligned at the subject level by performing rigid registration of other modalities (FLAIR, diffusion, and susceptibility) to T1w subject space using the Advanced Normalization Tools software.76 We then performed multispectral non-linear registration to the study-specific templates using preprocessed T1w images and fractional anisotropy maps supersampled to 1 mm isotropic77 as inputs.76 The ADNI template was non-linearly registered to the UKB template. All microstructural maps were transformed in UKB space using 4th-order B-spline interpolation at a 2 mm isotropic resolution to match the diffusion-weighted imaging resolution (of note, the susceptibility-weighted imaging acquisition had an original resolution of 0.8 x 0.8 x 3 mm). The rich spatial contrast of fractional anisotropy maps allowed for increased registration accuracy of white matter tracts,78 a crucial aspect for our application. WMHs were segmented in native space with the Brain tIssue SegmentatiON (BISON) algorithm, a multi-contrast random forest classifier15 that was retrained on 60 manually-labeled WMH masks on UKB data using both T1-weighted and FLAIR images as inputs.

Quality control
The MRI data underwent multiple steps of quality control. We visually inspected T1w and FLAIR images according to guidelines established by our group and excluded scans showing significant motion artifacts (https://github.com/CoBrALab/documentation/wiki/Motion-Quality-Control-(QC)-Manual). Motion during diffusion MRI scanning was quantified using an automated tool79 and scans with a mean absolute head motion (UKB field 24450) above 3 mm were excluded. Participants were further excluded if they failed any image processing steps or had a multiple sclerosis diagnosis according to the first-occurrence field (UKB field 131042). The final UKB sample was composed of 17,382 females (53.4%) and 15,144 males (46.6%) aged 45 to 81 years old (mean [SD] = 63.52 [7.49]; Supplementary Table 1). The final ADNI sample was composed of 123 females (58%) and 89 males (42%) aged 51 to 93 years old (mean [SD] = 73.41 [8.44]; Supplementary Table 2).

Normative modeling and WMH pathophysiology
Normative modeling of NAWM was performed on a voxel-wise basis in the space of our UKB template for each of the seven markers only using UKB microstructural data (Fig. 1A). In other words, only individual-level voxels that were labeled as NAWM by the BISON algorithm were included in the normative models. We used Bayesian linear regression from the PCN toolkit/v0.3537 with the covariates of age (modeled with 3rd-order B-splines with 4 knots) and sex. Age- and sex-specific maps of average and standard deviations of NAWM microstructure were then generated and used to z-score the microstructure of individual-level voxels labeled as WMH (example in Fig. 1B). This process crucially isolates the impact of the presence of a WMH on the microstructure and removes the spatial microstructural contrast driven by normal anatomical variations.

The normative model hyperparameters were then transferred to ADNI data in order to account for site and scanner variations. The adaptation sample to estimate the site effect was comprised of the first timepoint available of cognitively normal individuals within the UKB age range (45-81 years old; n=114), which is deemed a large enough sample for accurate transferring of normative model hyperparameters.80 For the ADNI subjects older than 81 years old (n=35), pathophysiology maps were calculated from normative values of 81-year-olds in order to not extrapolate the models.

Analyses
All linear models were processed in R/v4.1.2 using the lm function, scaling all continuous variables to obtain standardized beta coefficients. p-values were corrected using the false discovery rate (FDR)44 on a per-analysis basis unless otherwise indicated. For example, in Figure 4A, FDR correction is applied on 81 p-values (9 measures * 3 regions * 3 diagnoses).

Spatial clustering
Between-subject averages of WMH pathophysiology for each of the seven microstructural markers were computed (Fig. 2A). Voxels with a low prevalence of WMH (<30) or NAWM (<5000) labels were excluded (32,424 excluded voxels out of 54,257 total voxels). This was necessary to 1) accurately model the NAWM microstructure trends using our Bayesian linear regression approach, and 2) obtain robust estimates of the WMH average pathophysiology. These thresholds were informed by prevalence by standardized mean error (SEM) plots (Supplementary Fig. 3), since we observed SEM plateaus starting at these cutoff values. While excluding voxels with low NAWM prevalence caused by almost ubiquitous presence of WMH in older adults could induce a bias, WMHs in these areas (which are primarily in immediately periventricular regions, as seen in Supplementary Fig. 2) are likely benign and would not be informative for our goal of parsing heterogeneity. Included voxels were thus stable, robust estimates of the average WMH pathophysiology and still covered large portions of the white matter, permitted by the very large sample size. These maps were used as inputs to a spectral clustering algorithm using the Spectrum/v1.1 package in R38 resulting in spatial clusters of pathophysiologically similar WMHs (Fig. 2B). The eigengap method38 determined that the optimal number of clusters was 4 (Supplementary Fig. 4A); however, the three-cluster solution resulted in more spatially contiguous clusters with similar pathophysiological differentiation across clusters (Supplementary Fig. 4B).

We calculated the variance explained (R2) in the maps of between-subject averaged WMH pathophysiology (Figure 2A) by the spatial clustering solution (Figure 2B) for each microstructural metric using ANOVAs (Supplementary Fig. 5). We calculated the Dice similarity coefficient (DSC) overlaps between our derived spatial cluster and other often-used parcellations (Fig. 2C): periventricular/deep (derived manually by defining a distance from the ventricles of >8 mm as deep), lobar,81,82 cerebral arterial territory,40 and an atlas of fiber type. We only included clustered voxels in the overlap calculations. The fiber atlas was derived using the high-resolution population-averaged streamline tractography obtained from the Fibre Data Hub, comprising 34 association, 2 commissural, and 34 projection fibre bundles derived from 1,065 Human Connectome Project participants and processed using DSI Studio.83,84 For each fibre bundle, a voxelwise tract density map was computed by calculating the tract density index (TDI) as the total number of streamlines in the bundle passing through each voxel. Within each fibre class (association, commissural, projection), voxelwise TDIs were aggregated by taking the maximum TDI across all constituent fibre bundles, yielding one class-specific density map per class. A three-class fibre atlas was then generated using a winner-take-all voting approach.

The clustering analysis was repeated in different subgroups: based on WMH volume (low: <=5000 mm3, n=27,511; medium: >5000 and <=10000 mm3, n=3,103; high: >10000 mm3, n=1,192), excluding voxels with prevalence of WMH<10 (Supplementary Fig. 6A), and across sexes, excluding voxels with prevalence of WMH<15 (Supplementary Fig. 7A). DSCs between the original clusters and subgroup clusters were calculated, only including voxels that were present in both clustering solutions in the calculation (Supplementary Fig. 6B and Supplementary Fig. 7B).

To derive a final parcellation covering all possible WMH locations (i.e., including voxels that were previously excluded based on low WMH or NAWM prevalence), we used a search area strategy: for each excluded voxel, we sampled the assigned clusters of the surrounding voxels and calculated the most prevalent cluster assignment. If the excluded voxel was not immediately surrounded by voxels with an assigned cluster, we extended the search area iteratively by steps of one voxel until there were voxels with an assigned cluster in the search area. This final parcellation covering the whole white matter is shown in Supplementary Fig. 8A and is made publicly available in both our custom UKB template space and in standard MNI ICBM152 09c symmetric space (https://github.com/CoBrALab/WMH_patho_UKB).

We then calculated region-of-interest measures of WMH pathophysiology by computing the median pathophysiological value for each metric within WMHs in each spatial cluster. In cases with fewer than 5 WMH voxels within a spatial cluster, data was imputed as 0, indicating no WMH-related microstructural abnormality. Region-wise WMH volumes were calculated in common space (inherently normalized for brain size) and log-transformed, as recommended.85 To simplify the interpretation, we inverted the values of markers that showed negative effects in WMHs (FA, ICVF, and OD) so that an increase in z-scores is consistently interpreted as worsening WMH pathophysiology for all markers.

Temporal modeling
To model the temporal progression of WMH pathophysiological events, we used the Subtype and Stage Inference (SuStaIn) technique,41,86 specifically the piecewise linear z-score model since our data was expressed as abnormality z-scores (Fig. 3A). Pathophysiological events were determined based on z-score thresholds (Z = 0.5, 1, 1.5, 2, 3, 4, 5) and the maximum z-score was determined empirically on a per-marker basis (at least 1% of participants needed to reach the threshold; Supplementary Fig. 9). We note that subjects without WMHs in a cluster (< 5 voxels) will have all 0 values and will be assigned the stage and subtype of 0, thereby not contributing to the variance in SuStaIn stages used in subsequent analyses. SuStaIn was performed using 10-fold cross-validation with 15 startpoints and 10,000 Monte Carlo Markov Chain resamples at each fold using pySuStaIn. We compared the one-subtype solutions to the three-subtype solutions and concluded that there was no evidence of sub-trajectories (Supplementary Fig. 10). We also modeled the relationships between WMH pathophysiology and volume (i.e., using WMH volume as an empirical proxy of WMH temporal progression). Non-linear relationships with log-transformed WMH volume were modeled with 4th-order B-splines (Supplementary Fig. 12A). Spearman correlations were calculated for associations between SuStaIn stages and regional WMH volumes (Supplementary Fig. 12B).

Sex differences
Sex differences in WMH measures were investigated using linear models 1) for regional WMH volumes, correcting for age (to investigate simple sex differences; Fig. 3B), and 2) for WMH pathophysiological estimates, additionally correcting for region-specific WMH volume (to investigate differences in WMH pathophysiology when males and females have equivalent extents of WMHs; Fig. 3C).

Associations with vascular and neurodegenerative disorders
UK Biobank diagnosis data was used through the first occurrences field (category #1712) which mapped data from self-reported medical conditions, primary care, hospital inpatient, and death register records to ICD-10 codes together with a date of diagnosis. Control subjects were defined as subjects with no endocrine, circulatory, behavioral, nervous, or metabolic disease-related lifetime diagnoses (n=8,528). Case subjects were defined by groupings of ICD-10 codes (detailed in Supplementary Fig. 13) regardless of the timing relative to the MRI visit (i.e., prevalent and incident cases). We excluded subjects with comorbidities on the studied diagnoses (e.g., for dementia, we excluded individuals with a diagnosis of stroke or ischemic heart disease). We then compared WMHs between cases and controls using linear models while covarying for age and sex (Fig. 4A).

We investigated the associations between WMH pathophysiology and genetic susceptibility to vascular and neurodegenerative diseases, as indexed with polygenic risk scores (PRS) computed by UK Biobank.45,87 We used the standard PRS calculated on all UK Biobank participants. Briefly, the authors used genome-wide association study (GWAS) meta-analysis results external to UK Biobank to train trait-specific PRS algorithms and then calculated subject-wise PRS values as the sum of the per-variant effect size multiplied by allele dosage. The first 5 genetic principal components were used to obtain distributions with approximately zero means and unit variances per ancestry group. We used a similar case-control design as the diagnosis analysis, where controls were defined as individuals in the bottom 50% of a PRS and cases as individuals above the top 1% of a PRS. Groups were compared using linear models with no covariates (Fig. 4B). Results with other cutoff values for the case group (top 5% and top 3%) are presented in Supplementary Fig. 14.

For the ADNI replication analysis, our sample was composed of cognitively normal participants (n=125), participants with a diagnosis of mild cognitive impairment (n=66), and participants with a diagnosis of dementia (n=21), excluding participants where the cause was determined to be due to non-AD factors. We kept the last timepoint available with the required MRI acquisitions for each participant in order to maximize the number of individuals diagnosed with mild cognitive impairment or dementia. Groups were compared with linear models controlling for age and sex (Fig. 4C). Amyloid and tau PET data availability in ADNI was insufficient to investigate associations with WMH markers with adequate statistical power separately by cognitive group (Amyloid PET: 127 controls, 75 MCI, 33 dementia; tau PET: 43 controls, 20 MCI, 9 dementia). However, we compared amyloid-positive MCI and dementia subjects combined (n=42) to amyloid-negative cognitively normal subjects from ADNI (n=73; Supplementary Fig. 16). Amyloid positivity was determined from PET data ([18F] florbetaben (FBB) or [18F] florbetapir (FBP)) using tracer-specific cerebellum-normalized cortical uptake SUVR thresholds.88

Each analysis was repeated with a group by sex interaction term to investigate sex differences in the reported effects (Supplementary Fig. 15).

Each effect size pattern was correlated to all other patterns with Pearson’s correlations, excluding modalities that were not present for the correlations with ADNI patterns. Hierarchical clustering was performed on the resulting correlation matrix using the hclust function in R (Fig. 4D).

We then used a machine learning classification framework to classify individuals with stroke from individuals with MCI or dementia purely based on their WMH characteristics (Supplementary Fig. 18). The input data was region-of-interest measures following three parcellations (unparcellated, periventricular/deep, custom clusters) derived as specified in section Spatial clustering, with either volumes only or volumes and microstructural variables. From UKB, we selected individuals with stroke or dementia diagnoses, excluding vascular dementia as in the previous analyses, and removed overlapping participants. From ADNI, we selected participants with a clinical diagnosis of MCI or dementia, excluding participants where the cause was determined to be due to non-AD factors as in the previous analysis. The WMH data (volumes and microstructure) were harmonized across ADNI and UKB using the ComBat method with the neuroHarmonize/v2.4.5 python package.89 Specifically, controls from UKB and ADNI were used to calculate site effects with the covariates of age and sex, and corrections were applied to the diseased groups. Lasso logistic regression models were fit using the LogisticRegression function from sklearn/v1.7.2 in python (parameters: L1 penalty, C = 1, saga solver, balanced class weights (to address class imbalances), and max_iter = 1000). We performed 5-fold cross-validation, calculated performance metrics using all out-of-fold predictions, and repeated the process 100 times. Input data was normalized using a scaler fit on the train set and predicted on the test set at each fold. We did six runs across six sets of predictors as specified above and compared their performance using paired Wilcoxon tests using the rstatix R package, adjusting p-values with Bonferroni.

Cortical grey matter connectivity profiles
To investigate the cortical regions that were structurally connecting through our three WMH spatial clusters, we used a high-resolution tractogram of 10 million streamlines derived from the Human Connectome Project dataset with diffusion MRI-based tractography.47,90 We extracted the streamlines passing through each cluster using mrtrix3’s tckedit command.91 Using the CerebrA atlas (a precisely-registered and manually-corrected version of the MindBoggle101 atlas in MNI ICBM152 space),92 connectivity matrices were constructed and summed (to calculate the total connectivity of each ROI) for each of the three streamline sets using the tck2connectome command. Since larger ROIs will have more streamlines, and thus higher raw connectivity metrics, we further normalized the values by dividing by the number of streamlines of each ROI across all three clusters (Fig. 5A).

To calculate AD pathology maps, we used the PREVENT-AD dataset, a longitudinal cohort of individuals with a parental history of AD who were cognitively unimpaired at recruitment,48 and calculated average maps of amyloid-beta (Aβ) and tau positron emission tomography (PET) standardized uptake value ratio (SUVR) in Aβ positive individuals using cross-sectional data from the last available timepoint (n=35). Aβ PET imaging was performed with the [18F]NAV4694 (NAV) tracer and tau PET was performed with the [18F]AV1451 (flortaucipir) tracer. Processing details are available in McSweeney et al., 2020.93 Briefly, PET images were registered to each subject’s T1w scan, masked to exclude CSF signal, and smoothed with a 6 mm3 Gaussian kernel. Standardized uptake value ratios (SUVRs) were normalized with cerebellum grey matter values for Aβ PET94 and inferior cerebellum grey matter values for tau PET.95 Aβ positivity was determined based on global Aβ SUVR in a data-driven fashion using Gaussian mixture modeling with two components.94 PET volumetric maps were registered to MNI space and averaged. ROI values were extracted using the CerebrA atlas (Fig. 5B).92

We computed Pearson correlations between the cortical connectivity maps and averaged PET maps, performed spin tests to calculate 10,000 permutation-based nulls preserving the spatial autocorrelation,49 and derived p-values using the neuromaps package in Python (Fig. 5C).96 p-values were corrected with Bonferroni instead of FDR due to the lower amount of comparisons (n=6).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Parente and colleagues assessed multiple MRI parameters within areas of WMH in a large sample of participants from the UK Biobank (n > 300,000) to test whether WMH cluster into spatially and microstructurally heterogeneous subtypes. The rationale was that combining multiple quantitative MRI measures (diffusion and susceptibility measures) may reveal the underlying biological substrates of WMH and thus detect different subtypes, such as those more characteristic of vascular vs. neurodegenerative etiology. The authors employed spectral clustering to derive WMH clusters and plotted the z-values of abnormality for each MRI measure. Pseudotemporal evolution was estimated using event-based modeling. MRI measures were subsequently compared between control subjects and different diagnostic groups in the UK Biobank, as well as across levels of PRS scores for cardiovascular/stroke and AD. Furthermore, MR measures were compared between AD and control groups in an external dataset from the ADNI study. Spatial associations between WMH areas of each cluster and typical tau deposition were assessed based on group-level connectivity analyses. In addition, sex differences were investigated. Results showed three clusters of WMH: periventricular, posterior periventricular, and anterior-deep white matter types. Each cluster was characterized by distinct z-score profiles across MRI parameters, suggesting microstructural alterations specific to different WMH types. Event-based modeling with SuStaIn indicated a similar temporal ordering of the development of abnormalities among the different MR measures, with mean diffusivity and intracellular volume fraction (ICVF) appearing first. Comparisons of MR parameters across groups for each MRI measure revealed disease-dependent differences. Overall, this is a comprehensive study involving multi-parameter characterization of microstructural alterations in WMH. The approach is innovative is a step forward from simply mapping the spatial locations to different diseases, given that the underlying microstructural changes can be heterogenous as revealed in prior post-mortem studies. Yet the conclusiveness of the findings remains somewhat unclear.\n\nComments\n1. A concern is that the MRI measures assessed in the current study cannot be unambiguously linked to the pathophysiological events underlying WMH, and the chosen approach may not resolve these uncertainties. In the introduction, the authors state that “fluid infiltration from ventricles and blood vessels triggers inflammation, leading to tissue damage such as demyelination, oligodendrocyte loss, and, in more severe cases, axonal degeneration,” which may lead to WMH. They also suggest that multiparametric MRI provides homologs of post-mortem brain stains and can therefore reveal WMH pathophysiology. However, MR measures such as FA, MD, OD, ICVF, ISOVF, T2*, and QSM are ambiguous in terms of the underlying substrate when considered individually. It remains unclear how combining these measures resolves such ambiguities, especially when nearly all indices show alterations, as observed in this study. For example, free water (edema), fiber loss, macromolecules (inflammation), or myelin loss may all affect MD, FA, or OD. The authors are advised to critically discuss these issues and moderate their conclusions.\n2. Visual inspection of cluster 1 suggests that it covers a small rim around the ventricles, an area prone to partial volume effects (PVE), which may result from inadequate segmentation and spatial normalization. Additionally, for this cluster, z-scores for most MR measures are close to zero, making interpretation difficult. Did the authors exclude voxels directly adjacent to ventricles to minimize PVE or took other measures to reduce PVE?\n3. Clusters two and three show the same pattern of alterations in MRI measures but differ quantitatively in z-scores and spatial location. Do the authors conclude that these two signatures reflect similar etiology or underlying pathophysiology?\n4. The analysis of group differences and associations with PRS scores remains unclear.\na. Conceptually, what was the rationale for comparing groups across each MRI measure separately after identifying cluster-specific MRI signatures? Why not test whether patients with a particular disease are more strongly associated with specific WMH clusters? Such an analysis could clarify whether WMH clusters and their microstructural alterations are disease-specific.\nb. Technically, it is unclear how statistical analyses and corresponding FDR corrections were performed. For example, for ischemic heart disease the authors report: “WMH alterations were small but significant, with stronger effects in anterior regions (average beta = 0.19; p (FDR) < 0.05 for all markers) than posterior regions (average beta = 0.13; p (FDR) < 0.05 for all markers except OD and T2*).” Does that mean a voxel-based analysis was performed for each MRI marker, with beta coefficients subsequently averaged across all markers within each cluster mask? What exactly does the FDR correction refer to?\nc. For controls, several diseases were defined as exclusion criteria. Were the same criteria applied to disease cases (apart from the disease of interest)?\n5. Related to point 4: Figure 4: What determined the location of the asterisks?\n6. The authors highlight OD differences in MCI/AD, yet alterations in other indices were observed as well. Furthermore, OD differences were seen in MCI but not in AD dementia. How do the authors interpret these findings?\n7. Why dichotomize PRS when it is inherently a continuous measure? Dichotomization introduces arbitrary cut-offs, reduces statistical power, and, as in the present study, results in exclusion of a substantial amount of data.\n8. Why were no covariates controlled for when comparing AD vs. controls in the ADNI sample? Were the MCI subjects in ADNI amyloid-positive?\n9. Overall, the number of AD/dementia cases is small, and in the UK Biobank includes mixed dementia types. The results should be interpreted with caution.\n10. How do the authors interpret the observed sex effects? Were sex differences in MRI measures evident across diagnostic groups?", "reviewer_2": "I thank the authors for the considerable effort invested in this manuscript. From the very first read, it was clear to me that this work is the product of years of dedicated research. I am thus sincerely grateful to both the authors and the editors for the opportunity and privilege to review this wonderful piece of work. To match the authors’ dedication, I have sought to provide feedback that was both thorough and (hopefully) constructively critical.\n\nLine of argumentation. The authors argue that attributing white matter hyperintensities (WMH) to a vascular origin, as the expression “of presumed vascular origin” does, neglects their heterogeneous origins. While I partly agree with the authors’ line of argumentation, I would also contend that the qualifier itself is not the core problem. The expression “of presumed vascular origin” does not categorically assert a vascular cause for all WMH, but rather implies that some—though not necessarily all—may arise from vascular mechanisms. The authors of the STRIVE criteria themselves acknowledged that, although the vascular hypothesis provided a plausible explanation for the origin of WMH, WMH were possibly multifactorial and emphasised that the qualifier “of presumed vascular origin” was a way to distinguish CSVD-related WMH from similar lesions in disorders such as multiple sclerosis and leukodystrophies (see page 826 of Wardlaw et al. (2013) for exact phrasing). Instead, I would argue that the core limitation of STRIVE’s definition lies in its broad characterisation of WMH of presumed vascular origin as T2-weighted MRI hyperintense white matter regions, without providing additional criteria to reliably differentiate vascular from non-vascular aetiologies. The leap from “WMH of presumed vascular origin” to assuming that all WMH fall under this vascular category is another issue entirely. My main recommendation for the manuscript is to move from this initial line of argumentation and place greater emphasis on the subtyping of WMH per se. Sure, WMH are multifactorial, but how can we meaningfully differentiate between their underlying causes in vivo and develop robust methods to quantify them accordingly? This is exactly what the authors aimed to accomplish: to define WMH subtypes using multimodal data, assess their clinical relevance, and try to determine how the underlying processes unfold—and whether these differ across the identified subtypes.\n\nAcknowledging previous work. The second paragraph of the introduction provides context by highlighting recent findings suggesting that the observed heterogeneity may, to some extent, be connected to the anatomical location of WMH within the brain. There might be a limit on the number of references, but several claims in this section would really benefit from a more comprehensive acknowledgment of prior work and accurate phrasing. To the best of my knowledge, the earliest work to distinguish WMH based on their anatomical location is the same study that introduced what is now known as the Fazekas scale (Fazekas et al., 1987). Although based on a small sample, this visual rating offered some of the earliest neuroimaging evidence suggesting that anatomical location relates to the heterogeneity of WMH. However, as later discussed by Kim et al. (Kim et al., 2008), this distinction requires “complementary rules” to effectively classify periventricular and deep WMH, as both the terminology, definitions, and implementations used were inconsistently—the limitation the authors are also highlighting. While I agree with this point, I would like to point out that this is not the only approach to study WMH; other approaches include, for instance, the increasingly popular assessment of the so-called multispot WMH pattern for CAA diagnosis (Charidimou et al., 2022; Charidimou & Baron, 2025) as well as lobe-wise and voxel-wise analyses (see (Botz et al., 2023) for a systematic revision of the literature up to 2023). To ensure accuracy, I kindly encourage the authors to broaden their current literature review to acknowledge prior work in the field.\n\nNovelty claims. I find it difficult to understand why the authors claim that “no study has used unbiased, data-driven methods to systematically investigate spatial patterns of WMH pathophysiology”. In recent years, we have seen a couple of papers using data-driven clustering methods for examining spatial WMH patterns, e.g., (Bachmann et al., 2024; Phuah et al., 2022; Rosbergen et al., 2024). I understand that spatial patterns and “pathophysiology” are not synonymous—the former relates to spatial distribution, while the latter concerns microstructural integrity. Nonetheless, there is already a paper published last year that incorporates microstructural measurements into the analysis (Rosbergen et al., 2024). One can say that this one has more metrics, but I wonder whether that would be enough to claim novelty. The term “unbiased” should not be used lightly here—it represents a substantial claim that, to the best of my knowledge (and apologies if I have overlooked something), has not been adequately verified. Since the methods are trained on specific datasets acquired using particular protocols and targeting certain populations, inherent biases are inevitable. Using a large cohort for training does not, in itself, guarantee the absence of bias. One thing is to show that pattern generalise and a completely different one that they are unbiased. The use of the term “systematic” is also problematic and may work against the authors, as it requires strong experimental justification. In this context, “systematic” would imply that the authors, for instance, investigated the minimal set of sequences needed to achieve the observed subtyping (i.e., whether all sequences are essential or if some can be omitted) and assessed the sensitivity of the subtyping to each subset of images (e.g., number of groups). However, I could not find any evidence supporting this “systematic” aspect. Apologies if I have overlooked it. Overall, I believe the current claims of novelty are not sufficiently substantiated and would benefit from revision. To be clear, I am not questioning the quality of the work—it is undoubtedly a strong contribution—but rather pointing out that the way it is currently described lacks precision and does not fully reflect the actual work presented.\n\nMRI vs post-mortem. In the introduction, the authors suggest that multi-contrast microstructural MRI sequences can serve as “homologs” of post-mortem stains. I’m not entirely convinced by this claim. While microstructural measurements undoubtedly bring us closer to understanding the underlying nature of these lesions, but they are not true homologs in the strict biological or pathological sense. MRI signals are indirect, sensitive to a mix of tissue properties (e.g., water content, diffusion, susceptibility), and their interpretation is often ambiguous and context-dependent (Both table 1 and the discussion actually talk about this; see also (Figley et al., 2022) for more references and further discussion on the topic). It would be more accurate (and cautious) for authors to say that multi-contrast microstructural MRI can approximate or reflect some aspects of histopathology—rather than calling them “homologs” which implies a much stronger equivalence than is currently supported by evidence—which is not the aim of the study.\n\nEstimation of WMH pathophysiology. The authors generate age- and sex-specific normative maps and compare each individual image against them to produce personalised abnormality maps. I find this approach interesting, as it directs the clustering towards individual abnormalities rather than shared features that might be more closely linked to ageing. My main concern, however, lies in how this is operationalised: the authors appear to assume that voxels labelled as normal-appearing white matter (NAWM) represent truly healthy measurements. This is a strong assumption, considering that the distinction between normal and abnormal is highly context-dependent and often lacks a clear boundary. In segmentation algorithms, for instance, the choice of threshold has effects on the segmentation of WMH and, by extension, on NAWM. A more stringent threshold tends to result in undersegmentation of WMH, while a more lenient one leads to oversegmentation. As a consequence, the definition of NAWM can vary depending on the selected threshold. At the same time, I believe the authors would agree that the abnormality associated with WMH extends beyond the visibly affected tissue, esp. in microstructural sequences (Duering et al., 2023; Ter Telgte et al., 2018). I thus truly wonder whether the assumption is correct. Could the authors please elaborate further on the correctness of their assumptions?\n\nAssumptions in spectral clustering. The spectral clustering results reveal regions that align with previous findings in the literature, with anterior WMH more closely associated with vascular problems and posterior WMH more linked to neurodegenerative processes. This is a valuable contribution to the literature, as it provides large-scale validation of previously observed patterns. Interestingly, even the authors appeared somewhat surprised that, despite using multiple imaging modalities, the clusters emerged in just three specific brain regions. Given that these patterns largely mirror those identified in earlier unimodal studies, I wonder whether the inclusion of multiple modalities—which is one of the key points of the work—offered any substantial value. This ties back to my earlier point about the need for a systematic evaluation of the proposed approach. It would be highly beneficial for the authors to thoroughly assess the individual contribution of each modality to the clustering itself. The spatial layout of the clusters is to some extent unsurprising. First, the authors selected a three-cluster solution rather than four, prioritising a pattern in which the clusters remained more spatially coherent. Second, it is well known that white-matter microstructure differs across brain regions (Brander et al., 2010; Lawrence et al., 2021), because local fibre architecture varies—from highly aligned, single-direction bundles to regions with dense fibre crossings. Could this very anatomical structure, by design, influence how the clustering unfolds? It is also interesting to see that apparently there are no differences in the “temporal dynamics”. If they unfold in the same manner, what distinguishes them from each other?\n\nNote: While I talk about many points raised in the introduction, it is clear that many of them apply equally to the discussion.\n\nRecommendations. Assuming these patterns are indeed valid, the pertinent issue becomes their practical implementation. Must we acquire every imaging modality used in the study, or would a carefully selected subset—or simply applying the resulting cluster masks—be adequate? The authors argue that the prevailing misinterpretation of WMH as being of vascular origin poses a significant obstacle to implementing precision medicine in both diagnosis and treatment. I share this view—if a patient's WMH do not stem from a vascular cause, prioritising the management of cardiovascular risk factors may not be a rational approach. Since this is a recurring theme throughout the manuscript, I would encourage the authors to verbalise it further. What are the concrete benefits of understanding the aetiology of WMH?\n\nMinor\n• Introduction – “[…] For example, … Wallerian degeneration […]”: it is clear that not all WMH are a product of hypoperfusion, they are multifactorial. Could the authors please elaborate on the importance of this example?\n• I wonder whether it is necessary to reference some of the hypotheses mentioned in lines 119 to 121, as it is already clear at that point that these represent only one of several possible pathways through which WMH may develop.\n• Introduction - “[…] most common brain radiological findings […]”: brain radiological -> neuroradiological\n• Introduction - “[…] They appear in most […]”: perhaps “are present” instead.\n• Introduction - “[…] are typically detected […]”: “appear” perhaps\n• Discussion – The account of how the process unfolds still needs sharper focus, subtler reasoning, and a well-curated thesis. With so much work already completed, what new understanding can we add about the breakdown of white-matter integrity that has not yet been articulated?", "reviewer_3": "The study aims to disentangle vascular and neurodegenerative contributions to white matter hyperintensities (WMHs), which are commonly interpreted as markers of cerebral small vessel disease but may also reflect neurodegenerative processes. Traditional FLAIR imaging lacks specificity to distinguish these mechanisms in vivo. To address this, the authors propose a multimodal MRI framework combining seven microstructural markers with voxel-wise normative modelling to generate subject-level pathophysiology maps.\n\nMain comments:\nIntroduction:\n1. Lines 95–98: The authors appear to misinterpret the meaning of the nomenclature “of presumed vascular origins.” This terminology was adopted because the primary underlying pathology—cerebral small vessel disease—cannot be directly observed in vivo, rather than to imply variability in the pathophysiological mechanisms of WMHs. This fact is another crucial point, that the authors also should ackowledge.\n2. Lines 115-117: the authors claim that no study has attempted to investigate spatial pattern of WMH pathophysiology. This is only partially true, as there are indeed studies finding different spatial WMH pathology related to pathophysiological underpinnings and underlying pathology, as the authors acknowledge in the discussion section. A more complete contextualisation at this stage would probably be more valuable.\n\nMethods and Results:\n3. The central construct—“pathophysiology” defined as the z scored deviation from normative NAWM—relies on the assumption that NAWM is sufficiently disease free to serve as a reference. This is unlikely to hold, as cerebral small vessel disease is diffuse and not confined to WMHs. Failing to account for this risks biasing estimates toward zero, particularly in participants with high WMH burden, and the magnitude of this bias is difficult to predict given the unknown contribution of each metric. A sensitivity analysis excluding individuals with high WMH burden could strengthen confidence in the approach and this should issue should be ackowledged in the discussion and/or the limitations.\n4. Clusters 2 (posterior) and 3 (anterior) show very similar pathophysiological profiles, differing mainly in the magnitude of abnormality rather than in distinct marker patterns. This similarity is further reinforced by the SuStaIn modeling, which shows nearly identical pseudo-time progression of biomarkers for these two clusters. This raises concerns about whether the clustering truly captures biologically meaningful heterogeneity or simply partitions WMHs by spatial location.\n5. The clustering approach is based on a population-averaged pathophysiology map restricted to voxels with high WMH prevalence. While this ensures stability, it assumes a single canonical pattern of WMH pathophysiology across individuals and does not allow for subject-level heterogeneity or co-pathology. This design choice may also explain why the posterior and anterior clusters exhibit very similar pathophysiological signatures, differing mainly in magnitude and location rather than in distinct biological profiles.\n6. For both UK Biobank and ADNI, neurodegenerative dementia diagnoses appear to be based on clinical criteria rather than biomarker-confirmed status (e.g., amyloid or tau PET, CSF). This distinction is important because clinical diagnosis alone can misclassify cases, especially in mixed or atypical presentations, and may dilute disease-specific WMH signatures. The potential for misclassification is particularly relevant when interpreting the posterior WMH pattern as Alzheimer’s-related. Please clarify.\n7. The reported correlation between the posterior WMH cluster's cortical connectivity profile and tau PET distribution is intriguing, but its biological specificity warrants caution. Occipital and posterior association cortices-regions contributing the largest number of fibres to posterior white matter-are also heavily connected hubs in the brain. If these regions naturally exhibit high streamline density and are simultaneously among the earliest or most prominent sites of tau deposition in Alzheimer's disease, the observed association could reflect network topology rather than a mechanistic link between WMHs and tau pathology. Please clarify: how did the analysis account for baseline connectivity density or hubness? Could the correlation be driven by the fact that posterior WMH clusters inherently connect to high-degree nodes (e.g., visual association cortices)? Would the association remain significant after normalising for streamline count or using a null model that preserves degree distribution (beyond the spin test)? Consider tempering the interpretation: the result may indicate spatial co-localization of two processes in highly connected regions, rather than direct evidence of Wallerian degeneration from tau-positive cortex. Including these caveats or performing a sensitivity analysis would strengthen the claim and avoid overinterpretation.\n\nDiscussion:\n1. The statement on lines 415-417 claims that the data-driven approach \"revealed that deep-anterior WMHs had more severe pathophysiology than periventricular-posterior WMHs, which does not seem to be caused by spreading patterns.\" While this is technically accurate in terms of severity, the interpretation appears to overstate novelty. Based on the presented results: a) the most salient axis of heterogeneity still aligns with the traditional periventricular vs deep WMH distinction, as the periventricular cluster clearly differs from the other two in both magnitude and pattern, b) the additional anterior/posterior split adds spatial nuance but does not fundamentally overturn the established classification; rather, it refines it, c) furthermore, the posterior and anterior clusters share highly similar pathophysiological signatures and nearly identical SuStaIn trajectories, suggesting that the main biological contrast remains between periventricular and deep WMHs. We recommend tempering the claim and clarifying that the findings largely confirm the periventricular vs deep dichotomy, while providing additional granularity within the deep WMH category. A brief discussion of how this refinement compares to prior heuristic classifications would strengthen the interpretation.\n2. Lines 438-442: while the authors interpret the correlation between posterior WMHs and tau-prone cortical regions as evidence of a mechanistic link, this association could equally reflect the inherent topology of white matter pathways (see previous comments). Without tract-specific degeneration evidence or longitudinal data, the claim of a causal chain from tau accumulation to fibre loss and WMH formation remains speculative.\n3. Although the authors argue that spatiotemporal pathophysiological signatures can disentangle vascular and neurodegenerative WMHs, they also acknowledge that anterior WMHs are associated with both small vessel disease and neurodegeneration (lines 450–454), which “cannot purely be distinguished by neuroanatomical location.” This admission appears to contrast with their spatial clustering approach, as their primary evidence for differentiation relies heavily on location (posterior vs anterior) combined with microstructural profiles that are largely similar in pattern and differ mainly in magnitude.\n4. The discussion section ventures into clinical implications (lines 455-464; e.g., antihypertensive treatment decisions and eligibility for amyloid-clearing drugs) that are not directly supported by the presented data. Given the cross-sectional design and the modest, partially overlapping pathophysiological patterns, these statements risk overstating the immediate clinical applicability of the findings. While we concur that a more nuanced approach may have potential clinical relevance, it should be framed more cautiously as a future perspective rather than as a practical implication.\n\nMinor comments:\n- Voxel inclusion thresholds (Methods, Spatial clustering section, lines 710, 724-725): please justify the fixed cut‑offs used for voxel inclusions (WMH prevalence < 30, NAWM < 5,000). In a sample of 32,526, these map to 0.09% and 15.4% of participants, respectively, which appears arbitrary. We recommend adopting either (a) percentage‑based thresholds (e.g., WMH ≥ 0.5%, NAWM ≥ 15%) or, preferably, (b) precision/estimate-stability based thresholds (e.g. thresholds that bind the SEM of voxelwise mean z‑scores).\n- We suggest rephrasing lines 323–324 for clarity. The current sentence reads “Control subjects had no endocrine, circulatory, behavioural, nervous, or metabolic-disease related lifetime diagnoses, with some exceptions (n = 10,629).” The phrase “with some exceptions” is ambiguous and does not align well with the explicit sample size. Instead, we recommend specifying the rationale for these exceptions, for example: “Control subjects had no endocrine, circulatory, behavioural, nervous, or metabolic disease-related lifetime diagnoses, except for common conditions considered unlikely to impact WMH burden (e.g., migraine, depressive episode).” Additionally, please note that the assumption that migraine has no influence on WMH burden is debatable and should be acknowledged or justified with supporting evidence.\n- The analysis reports standardised betas for volume differences by sex (age adjusted), and for pathophysiology (additionally volume adjusted). These are small to modest effects but consistent. Consider adding absolute volume differences (mL) alongside betas for interpretability.\n\nSumming up, the study demonstrates spatial heterogeneity (posterior vs anterior predominance linked to AD vs vascular risk), but the underlying microstructural profiles differ mainly in magnitude rather than composition, and temporal divergence is not supported (SuStaIn found no sub-trajectories, with nearly identical progression curves). Disease-specific contrasts and connectivity analyses are suggestive but remain correlational and limited by cross-sectional design, diagnostic granularity, and metric specificity. Overall, the work advances spatial characterization and provides preliminary etiologic insights, yet falls short of delivering robust, diverging spatiotemporal signatures as originally hypothesised.", "reviewer_4": "The manuscript by Parent et al. called ‘Characterizing spatiotemporal white matter hyperintensity pathophysiology in vivo to disentangle vascular and neurodegenerative contributions” describes a study in which based on multi-modal brain MRI the authors have identified different spatial patterns of white matter hyperintensity pathology using a data driven and voxel-wise approach on a large population-based dataset (UKBB). To assess the tissue properties of white matter hyperintensity pathology, the following microstructural markers were used: MD, FA, ISOVF, ICVF, OD, T2* and QSM. The extensive analyses performed in this study broadly consist of the following steps:\n1) Using voxel-based normative modelling of each of these markers based on only the NAWM voxels, resulted in age- and sex- specific maps of expected healthy microstructure. Using these maps for the normalization, individual-level pathophysiology maps are created.\n2) To assess patterns of shared pathophysiological properties, spectral clustering was performed. Averaging the individual-level pathophysiology maps, only using the WMH voxels, creates seven between-subject averaged WMH pathophysiology maps, where the value of the voxel in a map represents the average deviation from healthy NAWM (expressed in SD) at that voxel location. These between subject maps are the input of the spectral clustering, which essentially leads to clustering the voxels based on the values of the 7 microstructural markers in those voxels. This resulted in the following spatial patterns: Periventricular (Cluster 1), Posterior (Cluster 2) and Anterior (Cluster 3). There was no consistent agreement of these spatial patterns and often-used WMH parcellations (periventricular vs deep, lobar regions, cerebral artery territories).\n3) temporal pathological cascades per spatial cluster were determined using SuStaIn (disease progression model which performs subtyping as well as staging) and provides information on the order at which changes per microstructural marker occurs within each cluster. In general the progression patterns was similar across the clusters, except for the amount of ‘events’ that take place (corresponds with the amount of stages) in each cluster.\nThe authors conclude that these spatial patterns show that periventricular-anterior WMHs have more severe pathophysiology than periventricular-posterior WMHs. Temporal progression pattern of WMHs were similar across the different clusters and is in general in agreement with the hypothesis that water infiltration and inflammation are the early events in the progression pattern of WMH. Based on the dominance of WMHs in the posterior cluster in patients with dementia/AD and differences in OD, they conclude that the WMHs in individuals with dementia/AD are neurodegeneration-related.\nThis paper is an important contribution to the field, where the advanced data-driven approach provides relevant new insights in the pathophysiology behind WMH. The authors did not stop when spatial clusters with similar pathophysiological signatures were identified, but also assessed temporal progression of the microstructural markers and evaluated in which WMH clusters the WMH volume occurred in vascular and neurodegenerative disorders and which microstructural markers deviated most compared to controls. Putting all this information together the authors suggest that WMH in patients with AD/dementia is the result of selective degeneration of fibers.\nI believe it is an excellent paper and it was a pleasure to read it. I only have a few minor concerns/questions for the authors:\n- Regarding the exclusion of voxels for the between-subject averaged pathophysiology maps, used as input for the spectral clustering, how are the cut off values of 30 (WMH) and 5000 (NAWM) chosen? A prevalence of only 30 individuals with WMH at a certain voxel in the large UKBB dataset seems still very low to me.\n- Did the authors explore the relationship between stage and WMH volume? Since there is not a very strong relationship between WMH volume and Z-score values of the 7 markers within the periventricular cluster (as shown in Extended Figure 4), perhaps comparing the staging with the volumes provides a clearer picture of the meaning of WMH volume in this location.\n- In the results section where spatial clustering is described, I’d suggest to change the sentence: “Subject-level maps of WMH pathophysiology were first collapsed into an average for each of the seven markers, ……” to something like: “Between-subject averaged WMH pathophysiology maps were retrieved by averaging all subject-level maps, resulting in one between-subject map per marker”. I found the description in the methods much clearer and the term ‘collapsed’ and then only stating ‘average of each of the seven markers’ in the results section are confusing in my opinion.\n- I struggle a bit with the interpretation or the way the authors describe the level of pathophysiology, especially in the results section of the temporal pathophysiological cascades. For example It is stated that: ‘Periventricular WMHs showed low pathophysiology across temporal stages’. I am not sure whether ‘low pathophysiology’ makes sense here, I don’t understand what the authors meant by this. The pathological events are defined as Z-scores, or in this case steps of 0.5, 1, 1.5 SD. Using SuStaIn you could also choose to label 0.25 SD as an event, so the number of events does not perse reflect severity or level of pathology, in case that is wat the authors meant with low pathophysiology. Furthermore, Although in this region it only goes until 1.5 SD, it could in terms of ‘pathology’ perhaps be as severe as reaching 5SD in another region? Later on in the paragraph the authors see the Z-score as level of abnormality, however assigning the biological meaning of a certain deviation from the mean is not something SuStaIn does, as user of the algorithm you have to choose what you define as an event. To me it makes sense here to use the Z-scores as the authors did (with the addition of 0.5 and 1.5 SD), but in my opinion the authors should be careful with the interpretation and formulation.\n- Did the authors assess whether the staging within each of the spatial clusters correlated with WMH volume in that region? You’d expect that individuals with low WMH in a spatial cluster would be staged early in the temporal cascade. It would also be interesting to see whether individuals with no visible WMH yet are always ending up in stage=0, or that they already show signs of deviations in some of the markers. In that case the staging could be an interesting predictive marker of WMH in the future potentially?"}, "author_response": "Summary of the main changes\nMany of the changes requested by reviewers were regarding some specific wording choices.\nTo address these issues raised, we added caveats to the biological interpretation of MRI\nmicrostructural markers, clarified the specific novelty of our spatial clustering approach\nwithin the context of the existing literature, indicated that our results were observational and\nremain to be causally established, changed “healthy” to “normal-appearing” white matter,\nclarified our interpretation of the clustering solution, and softened our wording comparing\nour novel parcellation to the PV/deep parcellation scheme.\nSome changes to existing analyses were made, which did not alter our previous\ninterpretations. Mainly, for the UKB diagnosis analysis (Fig 4A), we excluded participants\nwith comorbidities among the diseases analyzed (e.g., people with a stroke or a ischemic\nheart disease diagnosis were excluded from the dementia group). This reduced the group\nsample sizes but all interpreted significant effects remained.\nAdditional analyses were performed to investigate if our chosen WMH and NAWM\nprevalence threshold were appropriate (Supp. Fig. 2), see which microstructural measures\nwere driving the clustering solution (Supp. Fig. 4), compare our results to a clustering\nsolution done without excluding any voxels based on low WMH prevalence (Supp Fig. 7B),\ninvestigate different PRS cutoffs (Supp. Fig. 13), analyze sex-by-group interactions for\ndisease effects (Supp. Fig. 14), and derive the WMH pathophysiological signature of ADNI\namyloid positive MCI and AD participants (Supp. Fig. 15).\nWe further deepened our discussion regarding the uncovered spatial clustering, SuStaIn\nlimitations which could explain why no temporal WMH pathophysiology subtrajectories\nwere detected, how non-vascular WMHs could result in FLAIR hyperintensities, how our\nresults can be used by different groups, and we now interpret the observed sex differences.\nResponses to individual comments are detailed below.\nReviewer 1\nSummary: Parent and colleagues assessed multiple MRI parameters within areas of WMH in\na large sample of participants from the UK Biobank (n > 300,000) to test whether WMH\ncluster into spatially and microstructurally heterogeneous subtypes. The rationale was that\ncombining multiple quantitative MRI measures (diffusion and susceptibility measures) may\nreveal the underlying biological substrates of WMH and thus detect different subtypes, such\nas those more characteristic of vascular vs. neurodegenerative etiology. The authors\nemployed spectral clustering to derive WMH clusters and plotted the z-values of abnormality\nfor each MRI measure. Pseudotemporal evolution was estimated using event-based modeling.\nMRI measures were subsequently compared between control subjects and different diagnostic\ngroups in the UK Biobank, as well as across levels of PRS scores for cardiovascular/stroke\nand AD. Furthermore, MR measures were compared between AD and control groups in an\nexternal dataset from the ADNI study. Spatial associations between WMH areas of each\ncluster and typical tau deposition were assessed based on group-level connectivity analyses.\nIn addition, sex differences were investigated. Results showed three clusters of WMH:\nperiventricular, posterior periventricular, and anterior-deep white matter types. Each cluster\nwas characterized by distinct z-score profiles across MRI parameters, suggesting\nmicrostructural alterations specific to different WMH types. Event-based modeling with\nSuStaIn indicated a similar temporal ordering of the development of abnormalities among the\ndifferent MR measures, with mean diffusivity and intracellular volume fraction (ICVF)\nappearing first. Comparisons of MR parameters across groups for each MRI measure\nrevealed disease-dependent differences. Overall, this is a comprehensive study involving\nmulti-parameter characterization of microstructural alterations in WMH. The approach is\ninnovative is a step forward from simply mapping the spatial locations to different diseases,\ngiven that the underlying microstructural changes can be heterogenous as revealed in prior\npost-mortem studies. Yet the conclusiveness of the findings remains somewhat unclear.\nComment 1: A concern is that the MRI measures assessed in the current study cannot be\nunambiguously linked to the pathophysiological events underlying WMH, and the chosen\napproach may not resolve these uncertainties. In the introduction, the authors state that “fluid\ninfiltration from ventricles and blood vessels triggers inflammation, leading to tissue damage\nsuch as demyelination, oligodendrocyte loss, and, in more severe cases, axonal\ndegeneration,” which may lead to WMH. They also suggest that multiparametric MRI\nprovides homologs of post-mortem brain stains and can therefore reveal WMH\npathophysiology. However, MR measures such as FA, MD, OD, ICVF, ISOVF, T2*, and\nQSM are ambiguous in terms of the underlying substrate when considered individually. It\nremains unclear how combining these measures resolves such ambiguities, especially when\nnearly all indices show alterations, as observed in this study. For example, free water\n(edema), fiber loss, macromolecules (inflammation), or myelin loss may all affect MD, FA,\nor OD. The authors are advised to critically discuss these issues and moderate their\nconclusions.\nResponse: We thank the reviewer for their time and thorough and comprehensive review of\nour paper. We acknowledge and agree that microstructural MRI lacks biological specificity.\nWhile this fact was pointed out in some places throughout the manuscript, we have added the\nfollowing text to further highlight and emphasize this point in the Introduction section (p. 4,\nlines 112-115):\n“Multi-contrast microstructural MRI, while lacking the biological specificity of these\ntechniques, has established sensitivities to various tissue properties,22,23 providing a window\ninto biophysical tissue alterations in the brain and allowing for the non-invasive, large-scale\ninvestigation of WMH pathophysiology.”,\nas well as in the Results section (p. 6, lines 153-162):\n“For example, diffusion tensor imaging (DTI) indices such as FA and MD are particularly\ndifficult to interpret since they assume a main axis of diffusion within voxels, while most\nvoxels instead contain multiple fiber populations with different orientations.27 Different\nmulti-compartment modeling schemes of the diffusion signal have been developed to handle\nthe crossing-fiber problem and increase the interpretability of microstructural metrics, one of\nwhich is the Neurite Orientation Dispersion and Density Imaging (NODDI) model used\nhere.28 While no metric perfectly captures individual biological processes (e.g., inflammation,\ndemyelination, edema), interpreting these markers together can help generate hypotheses\nabout the potential biological substrates of the observed effects.23”,\nand in the Discussion section (p. 23, lines 481-483):\n“Of note, these biological interpretations remain speculative and will need to be confirmed\nwith gold standard ex vivo methods such as histology.”.\nHowever, we argue that combining these metrics can give us hints regarding the underlying\ntissue alterations. For example, the lack of QSM abnormality combined with positive T2*\nabnormality points to myelin AND iron loss, as is discussed in the Discussion section (p. 22-\n23, lines 476-481):\n“The combination of positive T2* abnormality, consistent with our previous work,49 and very\nlow QSM positive abnormality potentially indicates myelin and iron loss, as these two\nprocesses would have an additive positive impact on T2* but would cancel each other out on\nQSM.34,35 While iron accumulation occurs in aging, especially in subcortical grey matter\nregions,50 iron loss in white matter could be caused by the death of oligodendrocytes, which\ncontain large amounts of paramagnetic iron.51”.\nAs a follow-up study, we aim to quantify the biological sensitivities of these 7 markers\nspecifically in WMHs using a rich dataset combining MRI and histology, which will\nultimately bridge the gap between gold-standard ex vivo histology and in vivo MRI markers\nused here. However, we believe this is well outside the scope of the current study.\nComment 2: Visual inspection of cluster 1 suggests that it covers a small rim around the\nventricles, an area prone to partial volume effects (PVE), which may result from inadequate\nsegmentation and spatial normalization. Additionally, for this cluster, z-scores for most MR\nmeasures are close to zero, making interpretation difficult. Did the authors exclude voxels\ndirectly adjacent to ventricles to minimize PVE or took other measures to reduce PVE?\nResponse: We did not exclude voxels around the ventricles, and we agree with the reviewer\nthat PVE effects likely drive the observed effects of minimal microstructural abnormality.\nHowever, we argue that our spatial clustering step essentially removes these noisy voxels,\nwhich include PVE-contaminated voxels and very small WMH rims which are very likely\nbenign, as now discussed in the Results section (p. 10, lines 218-219):\n“These voxels may be contaminated by partial volume effects (i.e., ventricle signal) and\ninclude small caps WMHs that are likely benign.39”\nComment 3: Clusters two and three show the same pattern of alterations in MRI measures but\ndiffer quantitatively in z-scores and spatial location. Do the authors conclude that these two\nsignatures reflect similar etiology or underlying pathophysiology?\nResponse: We agree with the reviewer that the pathophysiological patterns mostly differ in\noverall severity. We don’t believe etiological differences can be determined purely from this\nfinding. We added this clarification in the Results section (p. 11, lines 230-233):\n“The posterior and anterior clusters mostly differed in the severity of pathophysiological\nmarkers, but still revealed distinct spatial signatures. Further characterization of specific\npathophysiological alterations is needed to assess potential etiological signatures.”,\nand in the Discussion section (p. 24, lines 523-530):\n“This is supported by our finding that WMH volume was significantly elevated relative to\ncontrols across all three spatial regions in both cerebrovascular disorders and dementia,\nhighlighting the need for a deeper phenotyping of WMH pathophysiology to parse etiologies.\nWe still encourage other groups to use our publicly available parcellation scheme\n(https://github.com/CoBrALab/WMH_patho_UKB), but note that this is informative but not\nsufficient to disentangle vascular from neurodegenerative contributions. Based on our\nobservations, metrics derived from multi-shell diffusion MRI acquisitions in particular show\npromise for identifying WMH pathogenesis.”\nComment 4: The analysis of group differences and associations with PRS scores remains\nunclear.\nComment 4a: Conceptually, what was the rationale for comparing groups across each MRI\nmeasure separately after identifying cluster-specific MRI signatures? Why not test whether\npatients with a particular disease are more strongly associated with specific WMH clusters?\nSuch an analysis could clarify whether WMH clusters and their microstructural alterations are\ndisease-specific.\nResponse: From our understanding, the analysis proposed by the reviewer is already\nincluded in our diagnosis analyses in Figure 4. We used linear models to relate diseases to\nWMH measures within each cluster, as is proposed (e.g., WMH FA in cluster 1 ~ Group (DX\nvs HC) + Age + Sex). We apologize for this ambiguity. To clarify, the clustering analysis\nleveraged microstructural MRI measures to derive a parcellation at the group level, but\ninformation regarding the pathophysiological alterations present for each individual subject\nis not represented in the clusters. We hope that our analysis scheme is clearer following our\nanswer to Comment 4b.\nComment 4b: Technically, it is unclear how statistical analyses and corresponding FDR\ncorrections were performed. For example, for ischemic heart disease the authors report:\n“WMH alterations were small but significant, with stronger effects in anterior regions\n(average beta = 0.19; p (FDR) < 0.05 for all markers) than posterior regions (average beta =\n0.13; p (FDR) < 0.05 for all markers except OD and T2*).” Does that mean a voxel-based\nanalysis was performed for each MRI marker, with beta coefficients subsequently averaged\nacross all markers within each cluster mask? What exactly does the FDR correction refer to?\nResponse: All analyses performed after the section “Spatial regions of pathophysiologically\nsimilar WMHs” are ROI-based, and not voxel-based. To do so, we sampled the median\nmicrostructural value across all voxels within the WMH mask within each spatial cluster\nmask for each subject. This is now clarified in the Results section (p.13, lines 280-282):\n“This reduced the complex voxel information into a set of 24 values (8 measures * 3 regions)\nfor each subject. All further analyses are made at the ROI level and not at the voxel level.”.\nFDR correction is performed across all effects calculated in one analysis scheme. For\nexample, Figure 4A shows 81 effects (9 measures * 3 regions * 3 diagnoses) and FDR\ncorrection is calculated across these 81 p-values. Now clarified in the Methods section (p.\n30, lines 651-652):\n“For example, in Figure 4A, FDR correction is applied on 81 p-values (9 measures * 3\nregions * 3 diagnoses).”.\nComment 4c: For controls, several diseases were defined as exclusion criteria. Were the same\ncriteria applied to disease cases (apart from the disease of interest)?\nResponse: The reviewer raises an important point and limitation of our current work, and\nmore broadly speaks to the difficulty in dealing with complex comorbidity profiles in\npopulation-based cohort studies like UK Biobank. We could not use the strict exclusion\ncriteria that we used to design the control group (excluding 247 unique ICD-10 diagnoses),\nas very few “clean” participants would be left. However, we have now rerun the diagnosis\nanalysis, excluding individuals who had comorbidities among the diagnoses analyzed (e.g.,\nfor dementia, we now excluded individuals with a diagnosis of stroke or ischemic heart\ndisease). Results are now updated in the Results section “Pathophysiology of WMHs across\nvascular and neurodegenerative disorders” (p. 19-20), in Figure 4A, in Supplementary\nFigure 8, and in the Methods section (p. 33, lines 727-729):\n“We excluded subjects with comorbidities on the studied diagnoses (e.g., for dementia, we\nexcluded individuals with a diagnosis of stroke or ischemic heart disease).”.\nNo substantial changes in our interpretation of the results were necessary following this\nupdate.\nComment 5: Related to point 4: Figure 4: What determined the location of the asterisks?\nResponse: The location of the asterisks is determined by which WMH marker and cluster\nshowed a significant effect after FDR correction.\nComment 6: The authors highlight OD differences in MCI/AD, yet alterations in other\nindices were observed as well. Furthermore, OD differences were seen in MCI but not in AD\ndementia. How do the authors interpret these findings?\nResponse: While the posterior OD effect reaches significance in MCI and not AD in the\nADNI analysis, the effect sizes are moderate and of similar amplitude (MCI β = 0.37; AD β =\n0.38). It is likely that the AD effect does not reach significance because of the small sample\nsize (n=21); however we still believe that the almost identical effect sizes between MCI and\nAD still demonstrates a consistent effect, which was also observed in the UKB diagnosis and\nPRS analyses. This is now clarified in the Results section (p. 19, lines 396-399):\n“For example, the posterior WMH OD effect size was almost identical in the MCI group (β =\n0.37; p = 0.01) and dementia group (β = 0.38; p = 0.08), hence the difference in significance\nlikely stems from differences in sample sizes.”\nComment 7: Why dichotomize PRS when it is inherently a continuous measure?\nDichotomization introduces arbitrary cut-offs, reduces statistical power, and, as in the present\nstudy, results in exclusion of a substantial amount of data.\nResponse: Dichotomization of PRS scores is an often-used method to identify high-risk\nindividuals (eg. Lennon et al., 2024, Nature Medicine, https://doi.org/10.1038/s41591-024-\n02796-z). We hypothesize that this is likely because PRS scores are noisy in the middle of the\ndistribution, and may show complex non-linear associations with disease outcomes.\nAttempting to model these more complex associations would complicate the interpretation of\nfindings and limit the quantitative comparison between effect size patterns we performed in\nFigure 4D. Instead, we have now replicated our findings using different PRS cutoffs (top 5%\nand top 3%), now available in Supplementary Figure 9 and in the Results section (p. 18, lines\n385-386):\n“This analysis was replicated using different cut-off values to determine high genetic risk\nindividuals (top 3% and top 5%; Supplementary Figure 13).”\nComment 8: Why were no covariates controlled for when comparing AD vs. controls in the\nADNI sample? Were the MCI subjects in ADNI amyloid-positive?\nResponse: As in the UKB diagnosis analysis, covariates of age and sex were included in the\nlinear models (see Methods p. 34, lines 748-749: “Groups were compared with linear models\ncontrolling for age and sex (Fig. 4C)”). We clarified the number of amyloid positive\nindividuals in each group in the main text, although it is important to note that not all\nincluded subjects had amyloid PET data available. Results section (p. 18, lines 390-392):\n“Among individuals with amyloid positron emission tomography (PET) data, 28% of\ncognitively normal individuals, 53% of people with mild cognitive impairment, and 94% of\npeople with dementia were amyloid-positive (demographic statistics detailed in\nSupplementary Table 2).”.\nLastly, we added a sensitivity analysis where we compared controls to amyloid-positive MCI\nand AD subjects in ADNI, as discussed in the Results section (p. 19, lines 406-411):\n“We additionally compared amyloid-positive MCI and dementia subjects (n=42) to amyloid-\nnegative cognitively normal subjects from ADNI (Supplementary Figure 15A) in order to\nisolate biomarker-confirmed cases. Effects were similar to the other ADNI patterns and\nshowed higher correlations with effect size patterns in the neurodegenerative grouping\n(average r = 0.63) compared to the vascular grouping (average r = 0.32; Supplementary\nFigure 15B).”\nComment 9: Overall, the number of AD/dementia cases is small, and in the UK Biobank\nincludes mixed dementia types. The results should be interpreted with caution.\nResponse: We agree with the reviewer that this is an important limitation of the UK Biobank\ndiagnosis analysis, as we mention in the Results section (p. 17, lines 361-362):\n“However, this analysis is limited by the uncertainty in dementia type (n=23 were diagnosed\nwith unspecified dementia).”.\nThis is what prompted the replication of the signatures using genetic risk and the ADNI data.\nThe replicability of the UK Biobank dementia signature is exemplified with a very high\ncorrelation of the effect size patterns with the ADNI dementia pattern (r=0.81; Figure 4D),\nwhich we now added in the Results section (p. 19, lines 405-406):\n“For example, the UK Biobank dementia signature was highly correlated with the ADNI\ndementia signature (r=0.81).”\nComment 10: How do the authors interpret the observed sex effects? Were sex differences in\nMRI measures evident across diagnostic groups?\nResponse: We have now added a paragraph in the Discussion interpreting the observed sex\ndifferences (p. 25, lines 531-547):\n“Investigating sex differences, we observed that females had lower WMH volumes but higher\npathophysiological effects in the posterior region, and higher WMH volume but similar\npathophysiological effects in the anterior region relative to males. This novel finding adds\nnuance to the reported higher WMH load in women,62 further highlighting the relevance of\ndifferentiating posterior and anterior WMHs. While this pattern could be due to the\nmenopausal transition and the reduction in estrogen’s neuroprotective effects,43 a recent\ninvestigation from our group found no differences in WMH volumes in women before and\nafter the menopausal transition when accounting for the effect of age in UK Biobank,63\nalthough examining region-specific effects using our novel parcellation would be an\ninteresting follow-up. The observed higher WMH volumes in females in the more vascular-\nrelated anterior region has been previously reported by other groups.64 While this is\ncounterintuitive, given that cerebrovascular diseases are more prevalent in males, this could\nbe due to the higher frequency of arterial stiffness in females.65 The pattern of higher WMH\nvolumes in males but worse pathophysiology in females in the more neurodegeneration-\nrelated posterior region is intriguing, and may be related to female-specific AD dynamics.\nFor example, inflammatory microglial activation has been reported to play a more important\nrole in the AD pathological cascade in females versus males, which could also impact WMH\npathophysiology.66”.\nWe also investigated sex by group interactions and found no significant effects (p. 19, lines\n400-402):\n“We investigated sex by group interactions to investigate if some effects were higher in males\nor females, but no effect was significant at the p (FDR) < 0.05 level (Supplementary Figure\n14).”\nReviewer 2\nSummary: I thank the authors for the considerable effort invested in this manuscript. From\nthe very first read, it was clear to me that this work is the product of years of dedicated\nresearch. I am thus sincerely grateful to both the authors and the editors for the opportunity\nand privilege to review this wonderful piece of work. To match the authors’ dedication, I\nhave sought to provide feedback that was both thorough and (hopefully) constructively\ncritical.\nComment 1: Line of argumentation. The authors argue that attributing white matter\nhyperintensities (WMH) to a vascular origin, as the expression “of presumed vascular origin”\ndoes, neglects their heterogeneous origins. While I partly agree with the authors’ line of\nargumentation, I would also contend that the qualifier itself is not the core problem. The\nexpression “of presumed vascular origin” does not categorically assert a vascular cause for all\nWMH, but rather implies that some—though not necessarily all—may arise from vascular\nmechanisms. The authors of the STRIVE criteria themselves acknowledged that, although the\nvascular hypothesis provided a plausible explanation for the origin of WMH, WMH were\npossibly multifactorial and emphasised that the qualifier “of presumed vascular origin” was a\nway to distinguish CSVD-related WMH from similar lesions in disorders such as multiple\nsclerosis and leukodystrophies (see page 826 of Wardlaw et al. (2013) for exact phrasing).\nInstead, I would argue that the core limitation of STRIVE’s definition lies in its broad\ncharacterisation of WMH of presumed vascular origin as T2-weighted MRI hyperintense\nwhite matter regions, without providing additional criteria to reliably differentiate vascular\nfrom non-vascular aetiologies. The leap from “WMH of presumed vascular origin” to\nassuming that all WMH fall under this vascular category is another issue entirely. My main\nrecommendation for the manuscript is to move from this initial line of argumentation and\nplace greater emphasis on the subtyping of WMH per se. Sure, WMH are multifactorial, but\nhow can we meaningfully differentiate between their underlying causes in vivo and develop\nrobust methods to quantify them accordingly? This is exactly what the authors aimed to\naccomplish: to define WMH subtypes using multimodal data, assess their clinical relevance,\nand try to determine how the underlying processes unfold—and whether these differ across\nthe identified subtypes.\nResponse: We thank the reviewer for their kind words, their recognition of our scientific\neffort, and their dedication to a detailed review that will undoubtedly improve the\nmanuscript. We acknowledge that the wording used may have unjustly criticized the STRIVE\ncriteria, which was not our intent, and have thus removed references to the wording “of\npresumed vascular origins” in the introduction, as this was also pointed out by Reviewer 3.\nComment 2: Acknowledging previous work. The second paragraph of the introduction\nprovides context by highlighting recent findings suggesting that the observed heterogeneity\nmay, to some extent, be connected to the anatomical location of WMH within the brain.\nThere might be a limit on the number of references, but several claims in this section would\nreally benefit from a more comprehensive acknowledgment of prior work and accurate\nphrasing. To the best of my knowledge, the earliest work to distinguish WMH based on their\nanatomical location is the same study that introduced what is now known as the Fazekas scale\n(Fazekas et al., 1987). Although based on a small sample, this visual rating offered some of\nthe earliest neuroimaging evidence suggesting that anatomical location relates to the\nheterogeneity of WMH. However, as later discussed by Kim et al. (Kim et al., 2008), this\ndistinction requires “complementary rules” to effectively classify periventricular and deep\nWMH, as both the terminology, definitions, and implementations used were inconsistently—\nthe limitation the authors are also highlighting. While I agree with this point, I would like to\npoint out that this is not the only approach to study WMH; other approaches include, for\ninstance, the increasingly popular assessment of the so-called multispot WMH pattern for\nCAA diagnosis (Charidimou et al., 2022; Charidimou & Baron, 2025) as well as lobe-wise\nand voxel-wise analyses (see (Botz et al., 2023) for a systematic revision of the literature up\nto 2023). To ensure accuracy, I kindly encourage the authors to broaden their current\nliterature review to acknowledge prior work in the field.\nResponse: We have now updated the paragraph addressing prior literature in WMH spatial\nanalysis in the Introduction (p. 4, lines 97-104):\n“Consequently, periventricular and deep WMHs are often assessed separately using heuristic\nmethods with criteria that can vary significantly between studies.19 Other parcellation\nschemes include cortical lobes, vascular territories, or white matter tracts.20 In contrast to\nthese hypothesis-driven parcellations, data-driven methods can uncover more complex\npatterns of WMH spatial heterogeneity, and while such examinations have been done based\non the co-occurrence of WMHs,12,21 these have not taken into account the underlying\npathophysiological alterations. Given the sparsity of WMH prevalence at the voxel-level,\nsuch an analysis requires a very large dataset.”.\nComment 3: Novelty claims. I find it difficult to understand why the authors claim that “no\nstudy has used unbiased, data-driven methods to systematically investigate spatial patterns of\nWMH pathophysiology”. In recent years, we have seen a couple of papers using data-driven\nclustering methods for examining spatial WMH patterns, e.g., (Bachmann et al., 2024; Phuah\net al., 2022; Rosbergen et al., 2024). I understand that spatial patterns and “pathophysiology”\nare not synonymous—the former relates to spatial distribution, while the latter concerns\nmicrostructural integrity. Nonetheless, there is already a paper published last year that\nincorporates microstructural measurements into the analysis (Rosbergen et al., 2024). One\ncan say that this one has more metrics, but I wonder whether that would be enough to claim\nnovelty. The term “unbiased” should not be used lightly here—it represents a substantial\nclaim that, to the best of my knowledge (and apologies if I have overlooked something), has\nnot been adequately verified. Since the methods are trained on specific datasets acquired\nusing particular protocols and targeting certain populations, inherent biases are inevitable.\nUsing a large cohort for training does not, in itself, guarantee the absence of bias. One thing\nis to show that pattern generalise and a completely different one that they are unbiased. The\nuse of the term “systematic” is also problematic and may work against the authors, as it\nrequires strong experimental justification. In this context, “systematic” would imply that the\nauthors, for instance, investigated the minimal set of sequences needed to achieve the\nobserved subtyping (i.e., whether all sequences are essential or if some can be omitted) and\nassessed the sensitivity of the subtyping to each subset of images (e.g., number of groups).\nHowever, I could not find any evidence supporting this “systematic” aspect. Apologies if I\nhave overlooked it. Overall, I believe the current claims of novelty are not sufficiently\nsubstantiated and would benefit from revision. To be clear, I am not questioning the quality\nof the work—it is undoubtedly a strong contribution—but rather pointing out that the way it\nis currently described lacks precision and does not fully reflect the actual work presented.\nResponse: We addressed the following points from the reviewer's comment. First, we now\nmore comprehensively compare our results with other data-driven WMH clustering\napproaches in the literature, as suggested. This was done briefly in the Introduction (p. 7),\nas in the previous reviewer comment’s response, and now also in the Discussion (p. 22, lines\n463-470):\n“The investigation by Phuah et al. (2022) used spectral clustering on WMH probability maps\nfrom FLAIR images, resulting in a 5-cluster solution and found that deep frontal WMHs were\nassociated with hypertension and diabetes, while parietal WMHs were associated with\namyloid SUVR.12 Habes et al. (2018) used non-negative matrix factorization to uncover\nclusters of covarying WMH volumes and found that high blood pressure and female sex were\nassociated with higher frontal WMH load, while AD genetic risk was associated with higher\ndorsal WMH load.21 Our pathophysiology-based approach to parsing WMH spatial\nheterogeneity is thus consistent with this emerging data-driven literature.”.\nWe also note that the paper by Rosbergen et al. (2024) clustered subjects and not WMH\nregions, thus did not address the spatial heterogeneity of WMHs as we have done here.\nSecond, we have now removed the term “unbiased” from our claims in the introduction. We\nspecify that we only meant “unbiased” in the context of our sample (data-driven approach\nwithout any assumptions), not as a guarantee of the generalizability of our results. Third, we\nalso removed the term “systematic”. We hope that the new wording and motivation of our\nspatial clustering analysis is now more precise and representative of the work done and its\nnovelty.\nComment 4: MRI vs post-mortem. In the introduction, the authors suggest that multi-contrast\nmicrostructural MRI sequences can serve as “homologs” of post-mortem stains. I’m not\nentirely convinced by this claim. While microstructural measurements undoubtedly bring us\ncloser to understanding the underlying nature of these lesions, but they are not true homologs\nin the strict biological or pathological sense. MRI signals are indirect, sensitive to a mix of\ntissue properties (e.g., water content, diffusion, susceptibility), and their interpretation is\noften ambiguous and context-dependent (Both table 1 and the discussion actually talk about\nthis; see also (Figley et al., 2022) for more references and further discussion on the topic). It\nwould be more accurate (and cautious) for authors to say that multi-contrast microstructural\nMRI can approximate or reflect some aspects of histopathology—rather than calling them\n“homologs” which implies a much stronger equivalence than is currently supported by\nevidence—which is not the aim of the study.\nResponse: This was also pointed out by Reviewer 1 (Comment 1). We have added the\nfollowing statements to emphasize the lack of biological specificity of microstructural MRI in\nthe manuscript in the Introduction section (p. 4, lines 112-115):\n“Multi-contrast microstructural MRI, while lacking the biological specificity of these\ntechniques, has established sensitivities to various tissue properties,22,23 providing a window\ninto biophysical tissue alterations in the brain and allowing for the non-invasive, large-scale\ninvestigation of WMH pathophysiology. ”,\nin the Results section (p. 6, lines 153-162):\n“For example, diffusion tensor imaging (DTI) indices such as FA and MD are particularly\ndifficult to interpret since they assume a main axis of diffusion within voxels, while most\nvoxels instead contain multiple fiber populations with different orientations.27 Different\nmulti-compartment modeling schemes of the diffusion signal have been developed to handle\nthe crossing-fiber problem and increase the interpretability of microstructural metrics, one of\nwhich is the Neurite Orientation Dispersion and Density Imaging (NODDI) model used\nhere.28 While no metric perfectly captures individual biological processes (e.g., inflammation,\ndemyelination, edema), interpreting these markers together can help generate hypotheses\nabout the potential biological substrates of the observed effects.23”,\nand in the Discussion section (p. 23, lines 481-483):\n“Of note, these biological interpretations remain speculative and will need to be confirmed\nwith gold standard ex vivo methods such as histology.”.\nComment 5: Estimation of WMH pathophysiology. The authors generate age- and sex-\nspecific normative maps and compare each individual image against them to produce\npersonalised abnormality maps. I find this approach interesting, as it directs the clustering\ntowards individual abnormalities rather than shared features that might be more closely\nlinked to ageing. My main concern, however, lies in how this is operationalised: the authors\nappear to assume that voxels labelled as normal-appearing white matter (NAWM) represent\ntruly healthy measurements. This is a strong assumption, considering that the distinction\nbetween normal and abnormal is highly context-dependent and often lacks a clear boundary.\nIn segmentation algorithms, for instance, the choice of threshold has effects on the\nsegmentation of WMH and, by extension, on NAWM. A more stringent threshold tends to\nresult in undersegmentation of WMH, while a more lenient one leads to oversegmentation.\nAs a consequence, the definition of NAWM can vary depending on the selected threshold. At\nthe same time, I believe the authors would agree that the abnormality associated with WMH\nextends beyond the visibly affected tissue, esp. in microstructural sequences (Duering et al.,\n2023; Ter Telgte et al., 2018). I thus truly wonder whether the assumption is correct. Could\nthe authors please elaborate further on the correctness of their assumptions?\nResponse: The reviewer is right to point out this limitation of our work. We now removed\nwording related to “healthy” white matter, replacing it with “normal appearing”, and\nacknowledge the limitation in the Results section (p. 8, lines 174-177):\n“We note that NAWM may still be pathological to a certain extent, as has been previously\ndemonstrated.16 However, pathological NAWM remains ill-defined, and there is currently no\nestablished framework to identify it. We thus focus on contrasting WMH from non-WMH\ntissue, considering that some tissue alterations are part of the NAWM variations.”.\nWe argue that the framework implemented here does not rely on the assumption of healthy\nwhite matter to be efficient. Given the sample size of the dataset (n=32,000), the presence of\nsome pathological NAWM in some subjects included in the normative modeling should have\na relatively small effect. This could still result in the z-score metrics being a little bit higher\nor lower relative to the hypothetical scenario of excluding these pathological voxels.\nHowever, the effect of age and sex would still be accurately removed. Furthermore, the most\nimportant feature of this voxel-wise normative modeling step is removing the spatial contrast\ninduced by the anatomy, leaving spatial patterns that are caused by the presence of WMHs\nand not by the anatomy. This crucial aspect should not be significantly affected by the\npresence of some pathological values in the normative modeling, particularly given that even\nwhen present, they would be at mild (sub-threshold) severity levels.\nComment 6: Assumptions in spectral clustering. The spectral clustering results reveal regions\nthat align with previous findings in the literature, with anterior WMH more closely associated\nwith vascular problems and posterior WMH more linked to neurodegenerative processes.\nThis is a valuable contribution to the literature, as it provides large-scale validation of\npreviously observed patterns. Interestingly, even the authors appeared somewhat surprised\nthat, despite using multiple imaging modalities, the clusters emerged in just three specific\nbrain regions. Given that these patterns largely mirror those identified in earlier unimodal\nstudies, I wonder whether the inclusion of multiple modalities—which is one of the key\npoints of the work—offered any substantial value. This ties back to my earlier point about the\nneed for a systematic evaluation of the proposed approach. It would be highly beneficial for\nthe authors to thoroughly assess the individual contribution of each modality to the clustering\nitself. The spatial layout of the clusters is to some extent unsurprising. First, the authors\nselected a three-cluster solution rather than four, prioritising a pattern in which the clusters\nremained more spatially coherent. Second, it is well known that white-matter microstructure\ndiffers across brain regions (Brander et al., 2010; Lawrence et al., 2021), because local fibre\narchitecture varies—from highly aligned, single-direction bundles to regions with dense fibre\ncrossings. Could this very anatomical structure, by design, influence how the clustering\nunfolds? It is also interesting to see that apparently there are no differences in the “temporal\ndynamics”. If they unfold in the same manner, what distinguishes them from each other?\nResponse: We added a supplementary analysis investigating the variance explained in the\naverage pathophysiology maps by the spatial clusters for each marker, from which we can\ninfer the contribution of each marker to the clustering solution. Details are available in the\nResults section (p. 10-11, lines 226-230):\n“We also observed that the clustering solution explained most of the variance in the average\npathophysiology spatial maps of MD, ICVF, and ISOVF (R2 of 0.81, 0.78, and 0.63,\nrespectively; Supplementary Figure 4), a moderate amount for FA and T2* (R2 of 0.39 and\n0.29, respectively), and almost none for QSM and OD (R2 of 0.04 and 0.00, respectively).”,\nin the Discussion section (p. 22, lines 458-459):\n“These spatial patterns were mostly influenced by the MD, ICVF, and ISOVF markers (...)”\nand in the Methods section (p. 30, lines 668-670):\n“We calculated the variance explained (R2) in the maps of between-subject averaged WMH\npathophysiology (Figure 2A) by the spatial clustering solution (Figure 2B) for each\nmicrostructural metric using ANOVAs (Supplementary Figure 4).”.\nSecond, the variations in white matter microstructure (what we refer to as spatial anatomical\ncontrast in the text) are removed by the voxel-wise normalization approach, which minimizes\nthe impact on the clustering. This clarification was added in the Results section (p. 8, lines\n186-187):\n“In other words, regional variations in normal white matter microstructure are removed.”.\nThird, while SuStaIn did not find temporal subtrajectories, this doesn’t exclude the possibility\nthat differences exist at the more granular level in terms of smaller differences in the\namplitude of abnormality for different markers, while the general ordering between markers\nremains relatively stable, which we find in our disorders analyses. We now discuss other\npossibilities as to why SuStaIn did not detect subtrajectories in the Discussion section (p. 23,\nlines 483-491):\n“Furthermore, we hypothesize that SuStaIn did not uncover subtrajectories because the\nsubgroup of individuals with neurodegeneration-related WMHs could be very small and\nvastly over-weighted by the subgroup with vascular-related WMHs (e.g., a few hundred\nversus 10s of thousands). Another limitation of SuStaIn is that it depends on the sensitivity of\nthe markers. It is possible that small deviations in fiber-sensitive metrics like OD are\nbiologically meaningful (as evidenced by the differential OD effects in vascular and\nneurodegenerative diseases) but were overshadowed by less disease-specific markers that\nshow very high abnormality (like the MD marker). Further analyses are needed to establish if\nour results are due to modeling limitations.”\nComment 7: Note: While I talk about many points raised in the introduction, it is clear that\nmany of them apply equally to the discussion.\nResponse: Modifications to the Discussion are specified in the other comment’s responses,\nwhere appropriate.\nComment 8: Recommendations. Assuming these patterns are indeed valid, the pertinent issue\nbecomes their practical implementation. Must we acquire every imaging modality used in the\nstudy, or would a carefully selected subset—or simply applying the resulting cluster masks—\nbe adequate? The authors argue that the prevailing misinterpretation of WMH as being of\nvascular origin poses a significant obstacle to implementing precision medicine in both\ndiagnosis and treatment. I share this view—if a patient's WMH do not stem from a vascular\ncause, prioritising the management of cardiovascular risk factors may not be a rational\napproach. Since this is a recurring theme throughout the manuscript, I would encourage the\nauthors to verbalise it further. What are the concrete benefits of understanding the aetiology\nof WMH?\nResponse: We added a passage describing how our findings can be used by other groups who\nwant to parse WMH etiological heterogeneity in the Discussion section (p. 24, lines 523-\n530):\n“This is supported by our finding that WMH volume was significantly elevated relative to\ncontrols across all three spatial regions in both cerebrovascular disorders and dementia,\nhighlighting the need for a deeper phenotyping of WMH pathophysiology to parse etiologies.\nWe still encourage other groups to use our publicly available parcellation scheme\n(https://github.com/CoBrALab/WMH_patho_UKB), but note that this is informative but not\nsufficient to disentangle vascular from neurodegenerative contributions. Based on our\nobservations, metrics derived from multi-shell diffusion MRI acquisitions in particular show\npromise for identifying WMH pathogenesis.”\nWe also clarified our amyloid-clearing drug use case and added two specific use cases in the\nDiscussion section (p. 25-26, lines 554-564):\n“Second, the use of amyloid-clearing drugs like lecanemab is currently restricted to\nindividuals without comorbid cerebrovascular pathology, including a criteria of ”no\nsignificant white matter hyperintensities”,68 since they are at higher risk of developing\npotentially severe amyloid-related imaging abnormalities (ARIA).68 Clarifying WMH\npathogenesis could improve the selection of patients, potentially including people with\nneurodegeneration-related WMHs who could benefit most from these treatments. Third,\nidentifying neurodegenerative WMHs could complement other AD-specific biomarkers, such\nas amyloid and tau protein markers, and help detect the disease at earlier stages, when\ntreatments are more likely to be effective. Lastly, our framework could help with the\ndifferential diagnosis of vascular and Alzheimer’s disease dementia.”\nComment 9: Introduction – “[…] For example, … Wallerian degeneration […]”: it is clear\nthat not all WMH are a product of hypoperfusion, they are multifactorial. Could the authors\nplease elaborate on the importance of this example?\nResponse: We now elaborate in the following sentence in the Introduction (p. 3, lines 86-89):\n“This pathological heterogeneity is currently invisible to conventional in vivo imaging and\ncould result in misinterpretation or misdiagnosis of cerebral pathology, hindering the\napplication of precision medicine in diagnosis and treatment as well as complicating the\ninterpretation of research findings”\nComment 10: I wonder whether it is necessary to reference some of the hypotheses\nmentioned in lines 119 to 121, as it is already clear at that point that these represent only one\nof several possible pathways through which WMH may develop.\nResponse: We added the requested changes in the Introduction (p. 4, lines 107-110):\n“The prevailing hypothesis suggests that fluid infiltration from ventricles and blood vessels\ntriggers inflammation, leading to tissue damage such as demyelination, oligodendrocyte loss,\nand, in more severe cases, axonal degeneration;3 however, this is one of several pathways\nthrough which WMHs can emerge.11”\nComment 11: Introduction - “[…] most common brain radiological findings […]”: brain\nradiological -> neuroradiological. Introduction - “[…] They appear in most […]”: perhaps\n“are present” instead. Introduction - “[…] are typically detected […]”: “appear” perhaps\nResponse: The requested changes were made.\nComment 12: Discussion – The account of how the process unfolds still needs sharper focus,\nsubtler reasoning, and a well-curated thesis. With so much work already completed, what\nnew understanding can we add about the breakdown of white-matter integrity that has not yet\nbeen articulated?\nResponse: We thank the reviewer for challenging us in going further in our interpretation of\nour results. We have added the following paragraph in the Discussion section (p. 23-24, lines\n504-512):\n“The question remains as to why neurodegeneration would cause hyperintense signals on\nFLAIR images, which are mostly sensitive to increased fluid content. In vascular-related\nWMHs, hypoxia/ischemia, potentially leading to blood-brain barrier dysfunction, may result\nin fluid leakage from the ventricles or the blood vessels, as evidenced by recent works.15,56 In\nneurodegeneration-related WMHs, the death of axons and myelin (captured by the altered\nOD marker) could leave more space to be filled by extracellular fluid content or\ninflammatory cells, but without important fluid leakage from ventricles or blood vessels,\nwhich could explain the lower MD abnormality in posterior WMHs versus anterior WMHs.\nMore hypotheses are discussed in a recent review.57”\nReviewer 3\nSummary: The study aims to disentangle vascular and neurodegenerative contributions to\nwhite matter hyperintensities (WMHs), which are commonly interpreted as markers of\ncerebral small vessel disease but may also reflect neurodegenerative processes. Traditional\nFLAIR imaging lacks specificity to distinguish these mechanisms in vivo. To address this,\nthe authors propose a multimodal MRI framework combining seven microstructural markers\nwith voxel-wise normative modelling to generate subject-level pathophysiology maps.\nSumming up, the study demonstrates spatial heterogeneity (posterior vs anterior\npredominance linked to AD vs vascular risk), but the underlying microstructural profiles\ndiffer mainly in magnitude rather than composition, and temporal divergence is not supported\n(SuStaIn found no sub-trajectories, with nearly identical progression curves). Disease-specific\ncontrasts and connectivity analyses are suggestive but remain correlational and limited by\ncross-sectional design, diagnostic granularity, and metric specificity. Overall, the work\nadvances spatial characterization and provides preliminary etiologic insights, yet falls\nshort of delivering robust, diverging spatiotemporal signatures as originally hypothesised.\nComment 1: Lines 95–98: The authors appear to misinterpret the meaning of the\nnomenclature “of presumed vascular origins.” This terminology was adopted because the\nprimary underlying pathology—cerebral small vessel disease—cannot be directly observed in\nvivo, rather than to imply variability in the pathophysiological mechanisms of WMHs. This\nfact is another crucial point, that the authors also should ackowledge.\nResponse: We thank the reviewer for their time and thorough and comprehensive review of\nour paper. This issue was also pointed out by Reviewer 2 (Comment 1) and we now have\nremoved all references to the wording “of presumed vascular origins” in the introduction.\nWe also now note that direct investigation of the brain’s small vessels in vivo is currently not\nfeasible in the Introduction section (p. 3, lines 75-78):\n“The presence of WMHs is widely considered a downstream consequence of cerebral small\nvessel disease and is typically used to detect this disorder, as neuroimaging methods able to\ndirectly investigate small blood vessels (<500 μm in diameter) are lacking.4”\nComment 2: Lines 115-117: the authors claim that no study has attempted to investigate\nspatial pattern of WMH pathophysiology. This is only partially true, as there are indeed\nstudies finding different spatial WMH pathology related to pathophysiological underpinnings\nand underlying pathology, as the authors acknowledge in the discussion section. A more\ncomplete contextualisation at this stage would probably be more valuable.\nResponse: This comment is similar to Reviewer 2’s comments 2 and 3. Accordingly, we now\ndiscuss prior work in that field in more detail and clarify the specific value added of our set\nof analyses. The details are added in the Introduction section (p. 4, lines 99-104):\n“Other parcellation schemes include cortical lobes, vascular territories, or white matter\ntracts.20 In contrast to these hypothesis-driven parcellations, data-driven methods can uncover\nmore complex patterns of WMH spatial heterogeneity, and while such examinations have been\ndone based on the co-occurrence of WMHs,12,21 these have not taken into account the\nunderlying pathophysiological alterations. Given the sparsity of WMH prevalence at the\nvoxel-level, such an analysis requires a very large dataset.”\nand Discussion section (p. 22, lines 463-470):\n“The investigation by Phuah et al. (2022) used spectral clustering on WMH probability maps\nfrom FLAIR images, resulting in a 5-cluster solution and found that deep frontal WMHs were\nassociated with hypertension and diabetes, while parietal WMHs were associated with\namyloid SUVR.12 Habes et al. (2018) used non-negative matrix factorization to uncover\nclusters of covarying WMH volumes and found that high blood pressure and female sex were\nassociated with higher frontal WMH load, while AD genetic risk was associated with higher\ndorsal WMH load.21 Our pathophysiology-based approach to parsing WMH spatial\nheterogeneity is thus consistent with this emerging data-driven literature.”\nComment 3: The central construct—“pathophysiology” defined as the z scored deviation\nfrom normative NAWM—relies on the assumption that NAWM is sufficiently disease free to\nserve as a reference. This is unlikely to hold, as cerebral small vessel disease is diffuse and\nnot confined to WMHs. Failing to account for this risks biasing estimates toward zero,\nparticularly in participants with high WMH burden, and the magnitude of this bias is difficult\nto predict given the unknown contribution of each metric. A sensitivity analysis excluding\nindividuals with high WMH burden could strengthen confidence in the approach and this\nshould issue should be ackowledged in the discussion and/or the limitations.\nResponse: This issue was also pointed out by Reviewer 2 (comment 5). We now removed\nwording related to “healthy” white matter, replacing it with “normal appearing”, and\nacknowledge the limitation in the Results (p. 8, lines 174-177):\n“We note that NAWM may still be pathological to a certain extent, as has been previously\ndemonstrated.16 However, pathological NAWM remains ill-defined, and there is currently no\nestablished framework to identify it. We thus focus on contrasting WMH from non-WMH\ntissue, considering that some tissue alterations are part of the NAWM variations.”.\nWe argue that even excluding individuals with high WMH burden does not guarantee that\nNAWM is healthy, hence would only partly mitigate the issue raised. Furthermore, we argue\nthat the framework implemented here does not rely on the assumption of healthy white matter\nto be efficient. Given the sample size of the dataset (n=32,000), the presence of some\npathological NAWM in some subjects included in the normative modeling should have a\nrelatively small effect. This could still result in the z-score metrics being a little bit higher\nor lower relative to the hypothetical scenario of excluding these pathological voxels.\nHowever, the effect of age and sex would still be accurately removed. Furthermore, the most\nimportant feature of this voxel-wise normative modeling step is removing the spatial contrast\ninduced by the anatomy, leaving spatial patterns that are caused by the presence of WMHs\nand not by the anatomy. This crucial aspect should not be significantly affected by the\npresence of some pathological values in the normative modeling, particularly given that even\nwhen present, they would be at mild (sub-threshold) severity levels.\nComment 4: Clusters 2 (posterior) and 3 (anterior) show very similar pathophysiological\nprofiles, differing mainly in the magnitude of abnormality rather than in distinct marker\npatterns. This similarity is further reinforced by the SuStaIn modeling, which shows nearly\nidentical pseudo-time progression of biomarkers for these two clusters. This raises concerns\nabout whether the clustering truly captures biologically meaningful heterogeneity or simply\npartitions WMHs by spatial location.\nResponse: We added clarifications regarding our interpretation of the spatial clustering\nresults in the Results section (p. 11, lines 230-233):\n“The posterior and anterior clusters mostly differed in the severity of pathophysiological\nmarkers, but still revealed distinct spatial signatures. Further characterization of specific\npathophysiological alterations is needed to assess potential etiological signatures.”\nand our hypotheses as to why SuStaIn did not find any subtrajectories in the Discussion\nsection (p. 23, lines 483-491):\n“Furthermore, we hypothesize that SuStaIn did not uncover subtrajectories because the\nsubgroup of individuals with neurodegeneration-related WMHs could be very small and\nvastly over-weighted by the subgroup with vascular-related WMHs (e.g., a few hundred\nversus 10s of thousands). Another limitation of SuStaIn is that it depends on the sensitivity of\nthe markers. It is possible that small deviations in fiber-sensitive metrics like OD are\nbiologically meaningful (as evidenced by the differential OD effects in vascular and\nneurodegenerative diseases) but were overshadowed by less disease-specific markers that\nshow very high abnormality (like the MD marker). Further analyses are needed to establish if\nour results are due to modeling limitations.”.\nDespite these more negative SuStaIn results, we still demonstrate the usefulness of the spatial\nclusters to capture biologically-meaningful heterogeneity with our observations of spatially\ndistinct sex effects (Figure 3B-C), spatially distinct and reproducible effects across\nneurodegenerative and cerebrovascular disorders (Figure 4), and differential connectivity\nprofiles with grey matter regions and their associated vulnerability to Alzheimer’s pathology\n(Figure 5). We also now clarify our claim of differential etiology by spatial region in the\nDiscussion section (p. 24, lines 523-530):\n“This is supported by our finding that WMH volume was significantly elevated relative to\ncontrols across all three spatial regions in both cerebrovascular disorders and dementia,\nhighlighting the need for a deeper phenotyping of WMH pathophysiology to parse etiologies.\nWe still encourage other groups to use our publicly available parcellation scheme\n(https://github.com/CoBrALab/WMH_patho_UKB), but note that this is informative but not\nsufficient to disentangle vascular from neurodegenerative contributions. Based on our\nobservations, metrics derived from multi-shell diffusion MRI acquisitions in particular show\npromise for identifying WMH pathogenesis.”\nComment 5: The clustering approach is based on a population-averaged pathophysiology\nmap restricted to voxels with high WMH prevalence. While this ensures stability, it assumes\na single canonical pattern of WMH pathophysiology across individuals and does not allow\nfor subject-level heterogeneity or co-pathology. This design choice may also explain why the\nposterior and anterior clusters exhibit very similar pathophysiological signatures, differing\nmainly in magnitude and location rather than in distinct biological profiles.\nResponse: This is true and is a limitation of our approach. We explored a few other strategies\nfor including subject variability in the spatial clustering (i.e., directly clustering 7\nvoxel*subject matrices) but encountered the problem of sparsity because most of these\nmatrices were empty since, for each subject, WMHs cover a small amount of the total white\nmatter that is highly variable across subjects. In each of these matrices, the non-WMH voxels\nhad zero value and WMH voxels had non-zero values (the pathophysiology z-score). This\nresulted in clusters that were simply driven by the presence or absence of WMHs and\nneglected the pathophysiology variance (i.e., driven by the patterns of non-zero voxels, not by\nthe variance within the non-zero voxels). Since our goal was to explore the spatial patterning\nbased on pathophysiological estimates, we instead opted to make a dense matrix by\ncalculating the average pathophysiology maps across subjects, which solved this issue. Our\nstrategy was then to investigate spatially-overlapping subtypes using SuStaIn; however, this\nexperiment did not reveal any subtrajectories. We added hypotheses as to why SuStaIn did\nnot find any subtypes in the Discussion section (p. 23, lines 483-491):\n“Furthermore, we hypothesize that SuStaIn did not uncover subtrajectories because the\nsubgroup of individuals with neurodegeneration-related WMHs could be very small and\nvastly over-weighted by the subgroup with vascular-related WMHs (e.g., a few hundred\nversus 10s of thousands). Another limitation of SuStaIn is that it depends on the sensitivity of\nthe markers. It is possible that small deviations in fiber-sensitive metrics like OD are\nbiologically meaningful (as evidenced by the differential OD effects in vascular and\nneurodegenerative diseases) but were overshadowed by less disease-specific markers that\nshow very high abnormality (like the MD marker). Further analyses are needed to establish if\nour results are due to modeling limitations.”\nComment 6: For both UK Biobank and ADNI, neurodegenerative dementia diagnoses appear\nto be based on clinical criteria rather than biomarker-confirmed status (e.g., amyloid or tau\nPET, CSF). This distinction is important because clinical diagnosis alone can misclassify\ncases, especially in mixed or atypical presentations, and may dilute disease-specific WMH\nsignatures. The potential for misclassification is particularly relevant when interpreting the\nposterior WMH pattern as Alzheimer’s-related. Please clarify.\nResponse: We now added a supplementary analysis comparing WMH pathophysiology\nbetween amyloid-positive MCI and AD subjects (combined) and amyloid-negative CN\nsubjects. Details are available in the Results section (p. 19, lines 406-411):\n“We additionally compared amyloid-positive MCI and dementia subjects (n=42) to amyloid-\nnegative cognitively normal subjects from ADNI (Supplementary Figure 15A) in order to\nisolate biomarker-confirmed cases. Effects were similar to the other ADNI patterns and\nshowed higher correlations with effect size patterns in the neurodegenerative grouping\n(average r = 0.63) compared to the vascular grouping (average r = 0.32; Supplementary\nFigure 15B).”\nand the Methods section (p. 34, lines 752-756):\n“However, we compared amyloid-positive MCI and dementia subjects combined (n=42) to\namyloid-negative cognitively normal subjects from ADNI (n=73; Supplementary Figure 15).\nAmyloid positivity was determined from PET data ([18F] florbetaben (FBB) or [18F]\nflorbetapir (FBP)) using tracer-specific cerebellum-normalized cortical uptake SUVR\nthresholds.85”\nComment 7: The reported correlation between the posterior WMH cluster's cortical\nconnectivity profile and tau PET distribution is intriguing, but its biological specificity\nwarrants caution. Occipital and posterior association cortices-regions contributing the\nlargest number of fibres to posterior white matter-are also heavily connected hubs in the brain.\nIf these regions naturally exhibit high streamline density and are simultaneously among the\nearliest or most prominent sites of tau deposition in Alzheimer's disease, the observed\nassociation could reflect network topology rather than a mechanistic link between WMHs and\ntau pathology. Please clarify: how did the analysis account for baseline connectivity density\nor hubness? Could the correlation be driven by the fact that posterior WMH clusters\ninherently connect to high-degree nodes (e.g., visual association cortices)? Would the\nassociation remain significant after normalising for streamline count or using a null model\nthat preserves degree distribution (beyond the spin test)? Consider tempering the\ninterpretation: the result may indicate spatial co-localization of two processes in highly\nconnected regions, rather than direct evidence of Wallerian degeneration from tau-positive\ncortex. Including these caveats or performing a sensitivity analysis would strengthen the\nclaim and avoid overinterpretation.\nResponse: The connectivity maps are indeed normalized by dividing by the total streamline\ncounts across all clusters. This results in the relative streamline density for each spatial\nclusters relative to the other clusters, thus normalizing for hubness. We added this\nclarification in the main text in the Results section (p. 20, lines 422-424):\n“Since larger ROIs will have more streamlines, and thus higher raw connectivity metrics, we\nfurther normalized the values by dividing by the number of streamlines of each ROI across\nall three clusters.”.\nWe also now temper our interpretation of this result in the Discussion section (p. 23, lines\n498-504):\n“This interpretation is further supported by our finding that posterior WMH regions\nstructurally connect cortical regions prone to early tau accumulation. We speculate that our\nobservations point to a link between tau pathology in inferior temporal regions, degeneration\nof the associated fiber populations, and the appearance of posterior FLAIR hyperintensities.\nCortical regions connecting through WMHs have been shown to be disproportionately\naffected relative to unconnected regions.54,55 Further research is needed to test the causality of\nthis hypothesis. ”\nComment 8: The statement on lines 415-417 claims that the data-driven approach \"revealed\nthat deep-anterior WMHs had more severe pathophysiology than periventricular-posterior\nWMHs, which does not seem to be caused by spreading patterns.\" While this is technically\naccurate in terms of severity, the interpretation appears to overstate novelty. Based on the\npresented results: a) the most salient axis of heterogeneity still aligns with the traditional\nperiventricular vs deep WMH distinction, as the periventricular cluster clearly differs from\nthe other two in both magnitude and pattern, b) the additional anterior/posterior split adds\nspatial nuance but does not fundamentally overturn the established classification; rather, it\nrefines it, c) furthermore, the posterior and anterior clusters share highly similar\npathophysiological signatures and nearly identical SuStaIn trajectories, suggesting that the\nmain biological contrast remains between periventricular and deep WMHs. We recommend\ntempering the claim and clarifying that the findings largely confirm the periventricular vs\ndeep dichotomy, while providing additional granularity within the deep WMH category. A\nbrief discussion of how this refinement compares to prior heuristic classifications would\nstrengthen the interpretation.\nResponse: We agree with the reviewer that the new clustering methods does not contradict\nthe periventricular/deep parcellation and instead refines it. Wording was adapted as such in\nthe Discussion section (p. 22, line 456):\n“Our data-driven approach refined the often-used periventricular/deep parcellation (...)”.\nComparison with prior heuristic parcelations is now discussed in the Discussion section (p.\n22, lines 458-461):\n“These spatial patterns were mostly influenced by the MD, ICVF, and ISOVF markers, and\nwere a mix of different heuristic parcellations since they incorporate both the medial-to-\nlateral axis (similar to periventricular/deep) and the anterior-to-posterior axis (represented in\nthe lobar parcellation). ”.\nWe also clarify that the data-driven “periventricular” cluster is much more narrow in\ncoverage than the a priori region, as now discussed in the Results section (p. 12, lines 250-\n252):\n“This limited overlap between cluster 1 and the “periventricular” a priori region is due to the\nmuch broader coverage of the a priori region (<8mm from the ventricle, as is typically\ndone),19 as compared to the data-driven cluster.”\nComment 9: Lines 438-442: while the authors interpret the correlation between posterior\nWMHs and tau-prone cortical regions as evidence of a mechanistic link, this association\ncould equally reflect the inherent topology of white matter pathways (see previous\ncomments). Without tract-specific degeneration evidence or longitudinal data, the claim of a\ncausal chain from tau accumulation to fibre loss and WMH formation remains speculative.\nResponse: We agree with the reviewer that the evidence shown remains correlational and not\ncausal, and that further research is needed to test the hypothesis that we proposed based on\nour observational results. We now specify that this study was observational/correlational and\nnot experimental/causal in various parts of the manuscript, including the Introduction section\n(p. 4, lines 118-119):\n“Our observational study aimed to parse the pathophysiological, spatial, temporal, and\nultimately etiological heterogeneity of WMHs.”,\nthe Results section (p. 19, lines 413-414):\n“However, these results remain correlational and require validation using gold-standard ex\nvivo methods.”,\nand Discussion, specifically addressing the issue raised by the reviewer regarding the lack of\ntract-specific degeneration evidence (p. 23, lines 500-504):\n“We speculate that our observations point to a link between tau pathology in inferior\ntemporal regions, degeneration of the associated fiber populations, and the appearance of\nposterior FLAIR hyperintensities. Cortical regions connecting through WMHs have been\nshown to be disproportionately affected relative to unconnected regions.54,55 Further research\nis needed to test the causality of this hypothesis. ”\nComment 10: Although the authors argue that spatiotemporal pathophysiological signatures\ncan disentangle vascular and neurodegenerative WMHs, they also acknowledge that anterior\nWMHs are associated with both small vessel disease and neurodegeneration (lines 450–454),\nwhich “cannot purely be distinguished by neuroanatomical location.” This admission appears\nto contrast with their spatial clustering approach, as their primary evidence for differentiation\nrelies heavily on location (posterior vs anterior) combined with microstructural profiles that\nare largely similar in pattern and differ mainly in magnitude.\nResponse: This issue raised is similar to Comment 8 by Reviewer 2. We now clarify the\nlimitations of a purely spatial approach in differentiating etiologies and make more specific\ncomments on how our work should be used by others in the Discussion section (p. 24, lines\n523-530):\n“This is supported by our finding that WMH volume was significantly elevated relative to\ncontrols across all three spatial regions in both cerebrovascular disorders and dementia,\nhighlighting the need for a deeper phenotyping of WMH pathophysiology to parse etiologies.\nWe still encourage other groups to use our publicly available parcellation scheme\n(https://github.com/CoBrALab/WMH_patho_UKB), but note that this is informative but not\nsufficient to disentangle vascular from neurodegenerative contributions. Based on our\nobservations, metrics derived from multi-shell diffusion MRI acquisitions in particular show\npromise for identifying WMH pathogenesis.”\nComment 11: The discussion section ventures into clinical implications (lines 455-464; e.g.,\nantihypertensive treatment decisions and eligibility for amyloid-clearing drugs) that are not\ndirectly supported by the presented data. Given the cross-sectional design and the modest,\npartially overlapping pathophysiological patterns, these statements risk overstating the\nimmediate clinical applicability of the findings. While we concur that a more nuanced\napproach may have potential clinical relevance, it should be framed more cautiously as a\nfuture perspective rather than as a practical implication.\nResponse: We have now nuanced our paragraph on potential use cases in the Discussion (p.\n25, lines 548-551):\n“While our findings will require further validation before being integrated into clinical\nsettings, using in vivo MRI-based pathophysiological markers to disentangle\nneurodegenerative and vascular contributions to WMHs could have broad implications for\nthe clinical management of patients and the interpretation of research findings and clinical\ntrials.”\nComment 12: Voxel inclusion thresholds (Methods, Spatial\nclustering section, lines 710, 724-725): please justify the fixed\ncut‑ offs used for voxel inclusions (WMH prevalence < 30, NAWM <\n5,000). In a sample of 32,526, these map to 0.09% and 15.4% of\nparticipants, respectively, which appears arbitrary. We recommend\nadopting either (a) percentage‑ based thresholds (e.g., WMH ≥ 0.5%,\nNAWM ≥ 15%) or, preferably, (b) precision/estimate-stability based\nthresholds (e.g. thresholds that bind the SEM of voxelwise mean\nz‑ scores).\nResponse: Since we used these thresholds only to chose which voxels will be included in the\naveraged pathophysiology maps for clustering, our choice of a fixed-value cutoff (as opposed\nto percentage-based) was based in the rationale that we only needed enough values for what\nwe considered to be a stable and robust estimate of WMH pathophysiology. We chose a\nWMH prevalence value of 30 to balance achieving both a stable estimate of the average\nvalue while still including enough voxels to achieve a broad coverage of the white matter. We\nalso chose a NAWM prevalence threshold of 5000 in order to have enough observations so\nthat our Bayesian linear regression normative model was stable and because voxels where\nalmost everybody has a WMH are less interesting. We now added two analyses: 1) plotting\nthe SEM relative to NAWM and WMH prevalence in Supplementary Figure 2, detailed in the\nMethods section (p. 30, lines 657-661):\n“This was necessary to 1) accurately model the NAWM microstructure trends using our\nBayesian linear regression approach, and 2) obtain robust estimates of the WMH average\npathophysiology. These thresholds were informed by prevalence by standardized mean error\n(SEM) plots (Supplementary Figure 2), since we observed SEM plateaus starting at these\ncutoff values.”\nand 2) calculating clusters with WMH prevalence > 1 and NAWM prevalence > 1000 shown\nin Supplementary Figure 5B, detailed in the Results section (p. 13, lines 274-265):\n“This yielded a parcellation that was comparable but more spatially contiguous relative to a\nclustering solution where voxels were not excluded based on WMH prevalence\n(Supplementary Fig. 7B).”\nComment 13: We suggest rephrasing lines 323–324 for clarity. The current sentence reads\n“Control subjects had no endocrine, circulatory, behavioural, nervous, or metabolic-disease\nrelated lifetime diagnoses, with some exceptions (n = 10,629).” The phrase “with some\nexceptions” is ambiguous and does not align well with the explicit sample size. Instead, we\nrecommend specifying the rationale for these exceptions, for example: “Control subjects had\nno endocrine, circulatory, behavioural, nervous, or metabolic disease-related lifetime\ndiagnoses, except for common conditions considered unlikely to impact WMH burden (e.g.,\nmigraine, depressive episode).” Additionally, please note that the assumption that migraine\nhas no influence on WMH burden is debatable and should be acknowledged or justified with\nsupporting evidence.\nResponse: In order to remove this ambiguity, we have rerun the analysis without making any\nexceptions for specific diagnoses to include in the control group, which reduced the number\nof control participants from 10,629 to 8,528. The results are virtually identical and all\ninterpretations remained unchanged. To satisfy the reviewer’s curiosity, we note that\nindividuals with a Migraine diagnosis did not have more WMHs relative to individuals\nwithout a diagnosis (average beta = -0.008), while the depression diagnosis (average beta =\n0.04) and anxiety diagnosis (average beta = 0.04) had significantly more WMHs relative to\nindividuals without a diagnosis, but with very low effect sizes.\nComment 14: The analysis reports standardised betas for volume differences by sex (age\nadjusted), and for pathophysiology (additionally volume adjusted). These are small to modest\neffects but consistent. Consider adding absolute volume differences (mL) alongside betas for\ninterpretability.\nResponse: While we understand that effect sizes expressed as absolute volume differences are\nuseful and easily interpretable to clinicians, we note that it was necessary to log-transform\nWMH volumes to respect the linear model assumptions of normality (since distributions very\nheavily left-skewed, as is usually the case for WMH volume distributions). This is standard\nwhen analyzing WMHs and is recommended (Roseborough et al., 2022, Alzheimer’s and\nDementia). Thus, the unstandardized betas would still not be expressed in mL. On the other\nhand, not log-transforming WMH volumes would render the fit of linear models less\nrepresentative of the true trends present in the data.\nReviewer 4\nSummary: The manuscript by Parent et al. called ‘Characterizing spatiotemporal white matter\nhyperintensity pathophysiology in vivo to disentangle vascular and neurodegenerative\ncontributions” describes a study in which based on multi-modal brain MRI the authors have\nidentified different spatial patterns of white matter hyperintensity pathology using a data\ndriven and voxel-wise approach on a large population-based dataset (UKBB). To assess the\ntissue properties of white matter hyperintensity pathology, the following microstructural\nmarkers were used: MD, FA, ISOVF, ICVF, OD, T2* and QSM. The extensive analyses\nperformed in this study broadly consist of the following steps: 1) Using voxel-based\nnormative modelling of each of these markers based on only the NAWM voxels, resulted in\nage- and sex- specific maps of expected healthy microstructure. Using these maps for the\nnormalization, individual-level pathophysiology maps are created. 2) To assess patterns of\nshared pathophysiological properties, spectral clustering was performed. Averaging the\nindividual-level pathophysiology maps, only using the WMH voxels, creates seven between-subject averaged WMH pathophysiology maps, where the value of the voxel in a map\nrepresents the average deviation from healthy NAWM (expressed in SD) at that voxel\nlocation. These between subject maps are the input of the spectral clustering, which\nessentially leads to clustering the voxels based on the values of the 7 microstructural markers\nin those voxels. This resulted in the following spatial patterns: Periventricular (Cluster 1),\nPosterior (Cluster 2) and Anterior (Cluster 3). There was no consistent agreement of these\nspatial patterns and often-used WMH parcellations (periventricular vs deep, lobar regions,\ncerebral artery territories). 3) temporal pathological cascades per spatial cluster were\ndetermined using SuStaIn (disease progression model which performs subtyping as well as\nstaging) and provides information on the order at which changes per microstructural marker\noccurs within each cluster. In general the progression patterns was similar across the clusters,\nexcept for the amount of ‘events’ that take place (corresponds with the amount of stages)\nin each cluster. The authors conclude that these spatial patterns show that periventricular-\nanterior WMHs have more severe pathophysiology than periventricular-posterior WMHs.\nTemporal progression pattern of WMHs were similar across the different clusters and is in\ngeneral in agreement with the hypothesis that water infiltration and inflammation are the\nearly events in the progression pattern of WMH. Based on the dominance of WMHs in the\nposterior cluster in patients with dementia/AD and differences in OD, they conclude that the\nWMHs in individuals with dementia/AD are neurodegeneration-related. This paper is an\nimportant contribution to the field, where the advanced data-driven approach provides\nrelevant new insights in the pathophysiology behind WMH. The authors did not stop when\nspatial clusters with similar pathophysiological signatures were identified, but also assessed\ntemporal progression of the microstructural markers and evaluated in which WMH clusters\nthe WMH volume occurred in vascular and neurodegenerative disorders and which\nmicrostructural markers deviated most compared to controls. Putting all this information\ntogether the authors suggest that WMH in patients with AD/dementia is the result of selective\ndegeneration of fibers. I believe it is an excellent paper and it was a pleasure to read it. I only\nhave a few minor concerns/questions for the authors:\nComment 1: Regarding the exclusion of voxels for the between-subject averaged\npathophysiology maps, used as input for the spectral clustering, how are the cut off values of\n30 (WMH) and 5000 (NAWM) chosen? A prevalence of only 30 individuals with WMH at a\ncertain voxel in the large UKBB dataset seems still very low to me.\nResponse: We thank the reviewer for their time and thorough and comprehensive review of\nour paper. The issue raised is similar to Comment 12 by Reviewer 3. While these thresholds\nwere chosen subjectively, we chose a WMH prevalence value of 30 to balance achieving both\na stable estimate of the average value while still including enough voxels to achieve a broad\ncoverage of the white matter. We also chose a NAWM prevalence threshold of 5000 in order\nto have enough observations so that our Bayesian linear regression normative model was\nstable and because voxels where almost everybody has a WMH are less interesting. While we\nagree that a prevalence of 30 in the context of UKBB is low, this speaks to the sparsity of\nvoxel-wise WMH data.. We now added two analyses: 1) plotting the SEM relative to NAWM\nand WMH prevalence in Supplementary Figure 2, detailed in the Methods section (p. 30,\nlines 657-661):\n“This was necessary to 1) accurately model the NAWM microstructure trends using our\nBayesian linear regression approach, and 2) obtain robust estimates of the WMH average\npathophysiology. These thresholds were informed by prevalence by standardized mean error\n(SEM) plots (Supplementary Figure 2), since we observed SEM plateaus starting at these\ncutoff values.”\nand 2) calculating clusters with WMH prevalence > 1 and NAWM prevalence > 1000 shown\nin Supplementary Figure 5B, detailed in the Results section (p. 13, lines 274-265):\n“This yielded a parcellation that was comparable but more spatially contiguous relative to a\nclustering solution where voxels were not excluded based on WMH prevalence\n(Supplementary Fig. 7B).”\nComment 2: Did the authors explore the relationship between stage and WMH volume?\nSince there is not a very strong relationship between WMH volume and Z-score values of the\n7 markers within the periventricular cluster (as shown in Extended Figure 4), perhaps\ncomparing the staging with the volumes provides a clearer picture of the meaning of WMH\nvolume in this location.\nResponse: We now investigated associations between SuStaIn stages and WMH volumes in\nSupplementary Fig. 11B, and interpret these new results in the Results section (p. 14, lines\n309-312):\n“SuStaIn stages were correlated with regional WMH volumes (Spearman ρ: Periventricular =\n0.41, Posterior = 0.66, Anterior = 0.85; all p<0.001; Supplementary Fig. 11B) but still\nshowed substantial variation not explained by WMH volumes.”.\nThese results indeed showed that individuals with low WMH volumes tend to be at earlier\npathophysiological stages. Regarding individuals with no visible WMHs, this data is not\nincluded in our analyses, as our experiment is centered around WMHs that were detected by\nour segmentation algorithm, so by definition visible on FLAIR or T1w images. When there is\nno WMH voxels in the spatial regions defined in our clustering step, the SuStaIn stage is\ndefined as 0. In future work, we aim to explore pathophysiological alterations to the tissue\nbefore any WMHs are visible using UK Biobank longitudinal data.\nComment 3: In the results section where spatial clustering is described, I’d suggest to change\nthe sentence: “Subject-level maps of WMH pathophysiology were first collapsed into an\naverage for each of the seven markers, ……” to something like: “Between-subject averaged\nWMH pathophysiology maps were retrieved by averaging all subject-level maps, resulting in\none between-subject map per marker”. I found the description in the methods much clearer\nand the term ‘collapsed’ and then only stating ‘average of each of the seven markers’ in the\nresults section are confusing in my opinion.\nResponse: We agree that the proposed wording is clearer and have adopted it in the Results\nsection (p. 10, lines 206-208):\n“Mean WMH pathophysiology maps were calculated by averaging all subject-level maps,\nresulting in one between-subject map per marker.”\nComment 4: I struggle a bit with the interpretation or the way the authors describe the level\nof pathophysiology, especially in the results section of the temporal pathophysiological\ncascades. For example It is stated that: ‘Periventricular WMHs showed low pathophysiology\nacross temporal stages’. I am not sure whether ‘low pathophysiology’ makes sense here, I\ndon’t understand what the authors meant by this. The pathological events are defined as Z-\nscores, or in this case steps of 0.5, 1, 1.5 SD. Using SuStaIn you could also choose to label\n0.25 SD as an event, so the number of events does not perse reflect severity or level of\npathology, in case that is wat the authors meant with low pathophysiology. Furthermore,\nAlthough in this region it only goes until 1.5 SD, it could in terms of ‘pathology’ perhaps be\nas severe as reaching 5SD in another region? Later on in the paragraph the authors see the Z-\nscore as level of abnormality, however assigning the biological meaning of a certain\ndeviation from the mean is not something SuStaIn does, as user of the algorithm you have to\nchoose what you define as an event. To me it makes sense here to use the Z-scores as the\nauthors did (with the addition of 0.5 and 1.5 SD), but in my opinion the authors should be\ncareful with the interpretation and formulation.\nResponse: We appreciate the need for a more nuanced interpretation of the SuStaIn z-score\nthresholds and interpretations. To clarify, we meant to interpret the absolute z-score\nthresholds reached and not the number of events. In the text, we have added a clarification to\nhow the z-score thresholds are determined empirically (p. 14, lines 291-293):\n“Pathophysiological events were determined empirically on a per-marker basis (at least 1% of\nparticipants needed to reach the threshold; Methods; Supplementary Fig. 8).”\nand we changed our interpretation to not infer biological meaning from SuStain stages (p.\n14, lines 300-302):\n“In periventricular WMHs, we observed low abnormality in most markers, with the\nmaximum z-score thresholds being set to 1.5 SD (for MD and ICVF) according to our\nheuristic. ICVF was the earliest marker to be altered.”.\nOur interpretation of the z-score levels as an indication of abnormality does not stem from\nSuStaIn modeling, but from our normative modeling step. We thus keep the wording\n“abnormality” instead of “pathology” in the context of SuStaIn. We also discuss the\ndependence of SuStaIn on the sensitivity of the underlying metrics in the Discussion section\n(p. 23, lines 486-491):\n“Another limitation of SuStaIn is that it depends on the sensitivity of the markers. It is\npossible that small deviations in fiber-sensitive metrics like OD are biologically meaningful\n(as evidenced by the differential OD effects in vascular and neurodegenerative diseases) but\nwere overshadowed by less disease-specific markers that show very high abnormality (like\nthe MD marker). Further analyses are needed to establish if our results are due to modeling\nlimitations.”\nComment 5: Did the authors assess whether the staging within each of the spatial clusters\ncorrelated with WMH volume in that region? You’d expect that individuals with low WMH\nin a spatial cluster would be staged early in the temporal cascade. It would also be interesting\nto see whether individuals with no visible WMH yet are always ending up in stage=0, or that\nthey already show signs of deviations in some of the markers. In that case the staging could\nbe an interesting predictive marker of WMH in the future potentially?\nResponse: In line with the reviewer’s Comment 2, we now investigated associations between\nSuStaIn stages and WMH volumes in the Extended Figure 4B, and interpret these new results\nin the Results section (p. 14, lines 309-312):\n“SuStaIn stages were correlated with regional WMH volumes (Spearman ρ: Periventricular =\n0.41, Posterior = 0.66, Anterior = 0.85; all p<0.001; Supplementary Fig. 11B) but still\nshowed substantial variation not explained by WMH volumes.”.\nThese results indeed showed that individuals with low WMH volumes tend to be at earlier\npathophysiological stages. Regarding individuals with no visible WMHs, this data is not\nincluded in our analyses, as our experiment is centered around WMHs that were detected by\nour segmentation algorithm, so by definition visible on FLAIR or T1w images. When there is\nno WMH voxels in the spatial regions defined in our clustering step, the SuStaIn stage is\ndefined as 0. In future work, we aim to explore pathophysiological alterations to the tissue\nbefore any WMHs are visible using UK Biobank longitudinal data.\nOther changes\n- Grammar\n- Put Extended Data figures into the Supplementary Data\n- Abbreviated Alzheimer’s disease with AD throughout the manuscript\n- Adapted figure numbers\nSummary of the main changes\nWe were encouraged that two of the four reviewers indicated that the manuscript was ready\nfor publication. Both Reviewers 1 and 4 remarked that they found we had “adequately” and\n“convincingly” addressed “all” previous comments. Reviewer 3 provided similarly positive\nfeedback, and we found that their new comments and suggestions were very helpful and\nimproved the clarity and completeness of our manuscript. We have also carefully considered\nthe remaining comments from Reviewer 2. Some of these concerns revisited issues\npreviously raised by other reviewers that had already been addressed to their satisfaction,\nwhile others were broader in nature and did not always lend themselves to direct\nmethodological or analytic resolution. In response, we focused on clarifying the scope of our\naims, sharpening the distinction between empirically supported findings and biological\ninterpretation, and adding new quantitative analyses that directly assess the extent to which\nWMH spatial and microstructural features differentiate cerebrovascular from\nneurodegenerative disease contexts.\nIn this revision, we made both analytical additions and language clarifications to improve the\nclarity, robustness, and impact of the manuscript. Most importantly, we added an analysis\ndemonstrating that MRI-based WMH pathophysiological data and our customized data-\ndriven parcellation lead to significant improvements in the separation of cerebrovascular and\nneurodegenerative disease contexts compared to traditional approaches based on\nperiventricular/deep WMH volumes.\nThe Discussion was also revised to more clearly distinguish empirically supported findings\nfrom biological interpretation, reducing speculative tone while preserving statements that\nwere added in response to previous reviewer requests. To further clarify the manuscript’s\ncontribution, we added a concluding paragraph summarizing the main findings and their\nrelevance relative to the existing WMH literature.\nA sensitivity analysis was added, where we reproduced our disease-specific patterns (Figure\n4) using a parcellation that did not include rare WMH voxels using our region-growing\napproach. We also added a figure showing the NAWM and WMH prevalence maps, overlaps\nbetween our clusters and an atlas of fiber types, and more details in the README of our\ncode repository to facilitate reproducibility and replication that could be performed in the UK\nBiobank or other cohorts.\nReviewer 1\nSummary: The authors have adequately addressed all comments.\nResponse: We thank the reviewer for their efforts in reviewing our manuscript.\nReviewer 2\nSummary: Apologies if I am stating the obvious, but part of the revision process is to\nimprove the quality of the paper through feedback from other researchers and by thoughtfully\naccepting or rebutting the points raised during review. The keyword here is “improve”. While\nsome aspects are indeed clearer in this second version of the manuscript, some of the authors’\nresponses honestly diminish the significance of the work.\nComment 1: Reviewer #1 (Comment 3), Reviewer #2 (Comment 8), and Reviewer #3\n(Comment 4 and 10): Since clusters 2 and 3 differ primarily in overall severity, an important\nquestion is whether they actually represent distinct aetiologies or underlying\npathophysiological mechanisms. The response provided—namely, that aetiological\ndifferences cannot be inferred solely from this finding and that further characterisation of\nspecific pathophysiological alterations is required—raises two concerns.\nResponse: We thank the reviewer for their careful reading and the opportunity to clarify the\nscope, aims, and empirical contributions of this work. Several concerns appear to stem from\ndiffering expectations regarding the level of mechanistic inference that can be supported by\nin vivo MRI-based analyses, which we address explicitly below. Regarding the question of\nwhether etiological differences could be distinguished purely from spatial patterns, we wish\nto point out that post-mortem studies tend to agree with our assessment and findings (e.g.,\nMcAleese et al., 2021: “To conclude, this pilot data suggest that frontal WML in AD may\nresult from both increased arteriolosclerosis and AD-associated degenerative changes”). We\ntherefore believe that this is not a failure of our approach, but a general feature of the\nproblem at hand, and that expecting spatial patterns alone to yield discrete aetiologies is not\nrealistic. This is also clearer in the new analysis that we describe in our response to\nComment 2.\nComment 2: Given that the stated aim of the manuscript was to “disentangle vascular and\nneurodegenerative contributions” and to “parse the pathophysiological, spatial, temporal, and\nultimately aetiological heterogeneity of WMHs”, I find it difficult to conclude that the current\nversion fully meets these objectives. The authors appear to address this discrepancy primarily\nby noting in the limitations that the study’s aims were not entirely achieved. While this is an\nimportant acknowledgement, it also suggests that key elements of the intended contribution\nremain underdeveloped, leaving the work feeling incomplete in its present form. This is not\nmerely a matter of wording choices, but rather a concern that relates to the conceptualisation,\nstudy design, and interpretation of the findings. The inability to disentangle aetiology also\nimplies that the discussion requires reconsideration. For example, the authors state that\n“While our findings will require further validation before being integrated into clinical\nsettings, using in vivo MRI-based pathophysiological markers to disentangle\nneurodegenerative and vascular contributions to WMHs could have broad implications for\nclinical management and for the interpretation of research studies and clinical trials”. This\nmay be true in principle, but it does not sound fitting given the earlier acknowledgement that\nsuch disentanglement was not actually achieved in the study.\nResponse: We have added an analysis to answer the reviewer’s current issues with our\nmanuscript, where we quantify the extent to which our approach is able to disentangle\ncerebrovascular from neurodegenerative pathologies by training a machine learning model\nto classify stroke from cognitive impairment (i.e., MCI/dementia) purely based on different\ncombinations of WMH predictors. This analysis demonstrates that 1) WMH microstructure\nprovides substantial independent discriminatory information beyond volume alone, and 2)\nour custom spatial parcellation consistently outperforms the conventional\nperiventricular/deep scheme on this task. This is described in the Results section (p. 20-21,\nlines 418-437):\n“As a proof of concept, we investigated whether a machine learning model could\ndifferentiate vascular from neurodegenerative pathologies based on our derived spatial and\npathophysiological WMH data. We trained Lasso logistic regression models to classify\nindividuals with stroke diagnoses (n=642) from individuals with AD-related dementia or\nMCI diagnoses (n=127), combining data from UKB and ADNI. WMH variables were\nadditionally harmonized across datasets using ComBat (see Methods).46 Models were trained\nusing 5-fold cross-validation repeated 100 times. We used 6 sets of predictors: WMH\nvolumes only or WMH volumes and microstructure, either unparcellated, following the\nperiventricular/deep parcellation, or using our custom clusters (distributions of area under the\nreceiver operating characteristic curve (AUROC), balanced accuracy, and F1 score are\navailable in Supplementary Fig. 18). Performance was compared using paired Wilcoxon tests.\nUnparcellated WMH volumes performed poorly in differentiating stroke from cognitive\nimpairment (AUROC mean[SD]: 0.562[0.001]) and improved using clustered volumes\n(periventricular/deep: 0.630[0.005]; custom clusters: 0.666[0.006]). Performance was\nnoticeably elevated when adding microstructural data (unparcellated: 0.653[0.007];\nperiventricular/deep: 0.697[0.009]; custom clusters: 0.706[0.011]). All performance\ndifferences were significant at p(Bonferroni) < 0.001. Results were similar for balanced\naccuracy and F1 score. Thus, we observed that 1) WMH microstructural information adds\nmeaningful predictive value for disentangling pathologies and 2) our custom clusters\nperformed better than the traditional periventricular/deep parcellation on this task.”\nthe Discussion section (p. 25-26, lines 531-545):\n“Our prediction framework, where we classified individuals with MCI and dementia from\nindividuals with stroke, demonstrated that our approach using customized spatial regions and\nmicrostructural WMH information meaningfully outperformed the traditional approach of\nonly using WMH volumes following the periventricular/deep parcellation (mean AUROC of\n0.706 for the former and 0.630 for the latter). Still, the highest accuracy remained moderate,\nhighlighting the inherent difficulty of the task, driven in part by the challenge of obtaining\n“pure” groups (i.e., a group with only AD pathology and no cerebrovascular pathology, and\nvice-versa) given the high degree of co-presence of the two disorders.57 However, this\nanalysis should be considered as a proof-of-concept since its primary goal was to demonstrate\nthat WMH characteristics between the two groups systematically differ to the degree that this\ninformation can be used to classify the two disorders. In other words, we wanted to\ninvestigate this classification purely based on WMH characteristics, and therefore did not use\nmore disease-specific markers (e.g., blood pressure, amyloid PET, whole brain features) that\nwould undoubtedly improve the accuracy for practical applications.”\nas well as the Methods section (p. 37-38, lines 829-848):\n“We then used a machine learning classification framework to classify individuals with\nstroke from individuals with MCI or dementia purely based on their WMH characteristics\n(Supplementary Fig. 18). The input data was region-of-interest measures following three\nparcellations (unparcellated, periventricular/deep, custom clusters) derived as specified in\nsection Spatial clustering, with either volumes only or volumes and microstructural variables.\nFrom UKB, we selected individuals with stroke or dementia diagnoses, excluding vascular\ndementia as in the previous analyses, and removed overlapping participants. From ADNI, we\nselected participants with a clinical diagnosis of MCI or dementia, excluding participants\nwhere the cause was determined to be due to non-AD factors as in the previous analysis. The\nWMH data (volumes and microstructure) were harmonized across ADNI and UKB using the\nComBat method with the neuroHarmonize python package.89 Specifically, controls from\nUKB and ADNI were used to calculate site effects with the covariates of age and sex, and\ncorrections were applied to the diseased groups. Lasso logistic regression models were fit\nusing the LogisticRegression function from sklearn in python (parameters: L1 penalty, C = 1,\nsaga solver, balanced class weights (to address class imbalances), and max_iter = 1000). We\nperformed 5-fold cross-validation, calculated performance metrics using all out-of-fold\npredictions, and repeated the process 100 times. Input data was normalized using a scaler fit\non the train set and predicted on the test set at each fold. We did six runs across six sets of\npredictors as specified above and compared their performance using paired Wilcoxon tests\nusing the rstatix R package, adjusting p-values with Bonferroni.”\nComment 3: If the authors—who have access to what is likely the largest multimodal dataset\nfor analysing WMH patterns—are unable to characterise these clusters deeply, disentangle\nvascular from neurodegenerative contributions, or describe (not speculate) aetiologies, then it\nis difficult to see who would be able to do so. The literature is already full of papers offering\nspeculative interpretations of WMH and their patterns, so producing yet another one—even if\nit uses a larger sample—would not be sufficient. The authors need to move beyond\nspeculation and clearly articulate the concrete findings that their analyses have revealed.\nWhat do these massive datasets allow us to learn that has not already been demonstrated in\nsmaller studies, many of which have reported similar patterns? In other words, what is\ngenuinely new here—clinically, medically, or neuroscientifically?\nResponse: We understand the reviewer’s request for greater clarity regarding what our\nanalyses demonstrate, as distinct from how these findings may be interpreted biologically.\nFirst, our new analysis demonstrates that we can disentangle vascular from\nneurodegenerative contributions significantly better than the traditional approach of using\nperiventricular/deep WMH volumes. Second, our work demonstrates that microstructural\nMRI metrics can be used to characterise within-lesion WMH properties across diseases. The\nprimary advancement enabled by large multimodal datasets, therefore, is not merely\nreplication at scale, but the ability to link spatial WMH patterns to distinct tissue-level\nprocesses and disorders. We acknowledge that, in responding to requests for nuance and\ncaution, some of the Discussion language may have obscured these concrete advances. We\nhave therefore revised the Discussion to more clearly separate empirically supported\nfindings from hypothesis-generating biological interpretations. Specifically, we updated the\nwording in several key areas to emphasise what is directly supported by the data:\np. 23, lines 479-480: “Our data-driven approach adds important nuance to the often-used\nperiventricular/deep parcellation (...)”\np. 23-24, lines 492-496: “Our novel approach to parsing WMH spatial heterogeneity extends\nthis emerging data-driven literature by anchoring these spatial patterns in the underlying\nWMH pathophysiology.”\np. 24, lines 499-502: “While the early reduction in ICVF could point to axonal loss,28,32 other\nobservations are most consistent with inflammatory swelling of cells which could increase\nthe extracellular signal fraction and thus decrease the intracellular signal fraction, resulting in\nthe observed effect.”\np. 24, lines 508-509: “As with all in vivo MRI studies, biological interpretations remain\ntentative and will need to be confirmed with gold standard ex vivo methods such as\nhistology.”\np. 25, lines 522-523: “The pathophysiological signature suggests selective degeneration of\nfibers (...)”\np. 26, lines 546-548: “The question remains as to why neurodegeneration would cause\nhyperintense signals on FLAIR images, which are mostly sensitive to increased fluid content.\nOur observations allow for a more informed hypothesis.”\nWe added a conclusion paragraph where we summarize the main contributions in the\nDiscussion section (p. 39, lines 621-630):\n“In conclusion, this work demonstrates that moving beyond volumetric summaries of WMHs\nand incorporating microstructural MRI markers enables more specific separation of\ncerebrovascular and neurodegenerative pathologies. By modeling spatial WMH\nheterogeneity in a data-driven manner based on pathophysiology, we identified a posterior\nWMH signature in individuals on the AD spectrum, characterized by selective fiber\nalterations and anatomical connectivity to cortical regions vulnerable to early tau\naccumulation. While in vivo MRI cannot provide a definitive biological ground truth, the\npresent results establish concrete, reproducible pathophysiological distinctions that extend\nbeyond existing periventricular/deep classifications. This framework for measuring WMH\npathophysiology using clinically translatable imaging tools provides a principled foundation\nfor future mechanistic and clinical studies.”\nComment 4: Reviewer # 3 (Comment 8): Reviewer 3 argues that the anterior-posterior split\nadds nuance but does not fundamentally alter the established classification; it merely refines\nit. The authors’ response appears to concur with this viewpoint, which, from my perspective,\nimplies that undertaking such a large and complex study may not have been necessary if\nexisting classifications were already adequate. This can happen in scientific research, of\ncourse, but in my view simply agreeing with this point substantially diminishes the impact of\nthe work. A closer inspection of the parcellation shows that the anterior–posterior wording is\nnot entirely accurate. Several juxtacortical regions appear to align with cluster 2 (posterior),\nand, conversely, describing cluster 3 as “deep-anterior” is not particularly precise, as most\nwhite-matter regions—aside from those in the occipital lobe—are assigned to this cluster.\nEven if one assumes that the main contribution of the work is to add nuance to the anterior–\nposterior distinction (which, in my view, is not the case), the subsequent question is whether\nthis added nuance actually holds any benefits. To address this, the authors need to compare\nthe performance of their parcellation with the established anterior–posterior split, as well as\nwith the commonly used periventricular versus deep white-matter distinction, across the\nrelevant tasks (those already conducted in their work, for instance).\nResponse: First, we want to clarify that we believe our custom parcellation is very valuable\nand the refinements it adds to the PV/deep parcellation are crucial for distinguishing\netiologies. We have modified the wording used in the Discussion section (p. 23, lines 479-\n481):\n“Our data-driven approach adds important nuance to the often-used periventricular/deep\nparcellation and revealed that deep-anterior WMHs had more severe pathophysiology than\nperiventricular-posterior WMHs, which does not seem to be caused by spreading patterns.”\nSecond, qualitative descriptions of complex spatial information will always struggle to\ncapture this complexity. We present quantitative overlaps of our parcellation with different\nwhite matter regions (Figure 2C) and show that the “deep-anterior” cluster mostly overlaps\nwith the frontal white matter (DSC = 0.66) and not with the occipital or temporal white\nmatter (DSC of 0.01 and 0.06, respectively), which does not support that the deep anterior\ncluster encompasses most white matter regions. Third, our new analysis now demonstrates\nthat our customized parcellation outperforms the PV/deep scheme on the most crucial test\n(distinguishing pathologies), both when only using volumes (mean AUROC of 0.630 for\nPV/deep and 0.666 for custom parcellation) and when integrating microstructural\ninformation (mean AUROC of 0.697 for PV/deep and 0.706 for custom parcellation).\nHowever, we could not find an established parcellation of anterior-posterior in the literature\nwith specific criteria (i.e., where to put the boundary between anterior and posterior).\nTherefore, we compared only with PV/deep, which is by far the most common parcellation\nscheme in the field.\nComment 5: One of the points I was not able to articulate clearly during the first round of\nrevisions concerns the region-growing strategy used by the authors to incorporate voxels with\ngreater sparsity (that is, voxels observed in relatively few participants). I am uncertain about\nthe implications of this step for the clustering results. Specifically, I wonder whether the\ninclusion of very rare voxels may be diluting—or even obscuring—the genuine differences\nbetween clusters, thereby making the separation of clusters appear less biologically\nmeaningful than it might otherwise be. Given this possibility, it would be valuable to assess\nthe sensitivity of the findings to the region-growing approach. Presenting the clustering\nresults both with and without region growth would allow readers to evaluate whether this\nmethodological choice meaningfully alters the spatial patterns, the cluster characteristics, or\nthe downstream associations.\nResponse: To clarify, all clustering analyses were performed before the region growing\napproach, including the described overlaps (i.e., all of Figure 2, Supplementary Figures 4 to\n8). It is only when calculating ROI medians that the region-growing approach is used to\ninclude all segmented WMH voxels. In order to assess the sensitivity of our results to this\napproach, we replicated our central result of comparing WMH features across disorders\n(Figure 4) without the region-growing approach (now available in Supplementary Figure\n17), and we find very few differences between the results, which do not alter any of the\nconclusions. This indicates that our results and conclusions are robust to our methodological\nchoices. This is described in the Results section (p. 20, lines 412-414):\n“These findings were also reproduced using a parcellation that was made without including\nrare WMH voxels using our search area strategy (Supplementary Fig. 17), with minimal\ndifferences that do not alter our conclusions.”\nComment 6: Remarks on code availability. I commend the authors for sharing both the\npatterns and the accompanying code. This is valuable, and I can imagine such an analysis\nbeing useful to replicate in the future. However, the current version of the code does not\nappear to be designed or documented to the extent that an external user could readily run it.\nFor instance, the README describes the data included in the repository but does not explain\nhow to execute the code. To make the code genuinely usable, additional documentation is\nneeded. There is Python code in the repository, but it is unclear which environment or\nsoftware versions are required to run the application.\nResponse: We clarified how the code should be run and the required dependencies in the\nREADME:\n“To run the code, the user needs to assign the working directory to the directory of the script\n(e.g., analyses/1_norm_mod), load the required dependencies (see below), and then run each\nscript in order.”\n“# Dependencies\n- R/4.1.2\n- python/3.9.8\n- PCNtoolkit/0.35 (python package; https://github.com/amarquand/PCNtoolkit)\n- Spectrum/v1.1 (R package; https://cran.r-project.org/package=Spectrum)\n- pySuStaIn (python package; https://github.com/ucl-pond/pySuStaIn)\n- neuromaps (python package; https://github.com/netneurolab/neuromaps)”\nReviewer 3\nSummary: I thank the authors for their work, I have no further comment.\nResponse: We thank the reviewer for their efforts in reviewing our manuscript.\nReviewer 4\nSummary: I have carefully reviewed the authors’ rebuttal and the revised manuscript. I am\nfully satisfied with the responses provided to my previous comments. The authors have\naddressed all concerns in a thorough and convincing manner. I have no further comments or\nrequests.\nResponse: We thank the reviewer for their efforts in reviewing our manuscript."}, {"round": 2, "reviewer_comments": {"reviewer_2": "This revised version highlights the importance of the work much more clearly, and I am happy with it. I again thank the authors for their substantial efforts in addressing my concerns, their patience throughout the review process, and for this excellent contribution.", "reviewer_3": "I thank the authors for their work, I have no further comment."}, "author_response": ""}]

Decision letter