Divergent white matter metabolic signature patterns indicate impending cognitive decline in aging and dementia

Domain: biology
Published: 2026-03-19
Editorial outcome: minor_revision

Abstract
White matter (WM) is a key substrate for interregional neural communication and cognitive function but the role of WM glucose metabolism in cognitive aging has been understudied. Using multimodal neuroimaging (MRI, FDG-PET, amyloid-PET) from 3,142 participants (15,287 visits) across two studies, we examined the contribution of WM to cognition and identified divergent WM signatures. Higher glucose metabolism in expected WM (EWM; corpus callosum and cingulum) was associated with better cognition, whereas increased metabolism in atypical WM (AWM; corona radiata) was linked to worse cognition, indicating a compensatory mechanism. EWM metabolism declined with aging, Alzheimer’s disease (AD) progression (amyloid-β and APOE-ε4 carrier), and white matter hyperintensities, while AWM metabolism increased with aging and vascular risk but was partially weakened by AD neuropathology. Longitudinally, higher EWM and lower AWM metabolism predicted slower cognitive decline. Divergent WM metabolic patterns shed light on the dynamic role of WM in maintaining cognitive function. This study emphasizes the complementary information provided by WM metabolism for predicting future cognitive decline and identifying cognitive resilience.

Introduction
Ongoing research into the biological processes of Alzheimer's disease (AD) has driven substantial advancements in its conceptual framework and the therapeutic development of treatments targeting core pathologies[1]. However, the dissociation between AD neuropathology and clinical cognitive manifestations remains an unresolved challenge, as nearly 30% of older adults exhibit hallmark AD pathologies (amyloid-β (Aβ) plaques and neurofibrillary tangles) without concomitant cognitive impairment[2, 3]. This phenomenon, termed cognitive resilience, refers to the preservation of cognitive abilities despite age-related brain changes or AD-associated pathologies[4-6]. In recent years, cognitive resilience has emerged as a critical focus in AD research. Investigating its underlying mechanisms may not only inform prevention strategies to delay cognitive decline but also refine the identification of high-risk populations for early intervention.

The brain mechanisms underlying cognitive resilience involve preserved brain structures, normal glucose metabolism, efficient network connectivity, and compensatory processes that buffer against age- and disease-related pathology[6, 7]. Gray matter (GM) supports local processing, synaptic plasticity, and high metabolic activity, with preserved cortical thickness, volume, and dendritic spine density in key regions (such as medial temporal and prefrontal cortices) linked to better cognition despite neurodegeneration[8-10]. White matter (WM) ensures rapid communication between GM regions, with high microstructural integrity and connectivity enabling compensation for focal damage[8, 11]. Neuroglia (including astrocytes, microglia, and oligodendrocytes) maintain homeostasis, modulate injury responses, and preserve axonal health and myelination, thereby sustaining network efficiency and resilience[12, 13]. Metabolically, GM’s high glucose utilization supports synaptic activity, whereas WM’s lower but efficient energy metabolism maintains myelin and axons[14, 15].

Fluorodeoxyglucose positron emission tomography (FDG-PET), which measures regional glucose metabolism and reflects neuronal/synaptic activity, astrocytic activity, and neuroinflammation[16], is a valuable tool for investigating and quantifying brain regions crucial to cognitive resilience. Several studies have reported age- and dementia-related declines in brain glucose metabolism, particularly in certain GM regions, which are well-established features of AD and cerebrovascular disease[17, 18]. Our recent work suggests that FDG-PET uptake in the cingulate cortex and temporal pole could serve as a marker of cognitive resilience[19]. In contrast to these GM-centric findings, WM, despite its crucial role in cortical connectivity and cognitive integration[20, 21], remains underexplored in metabolic studies. To date, relatively few studies have examined WM metabolism using FDG-PET, and most have focused on selected regions such as the corpus callosum, cingulum, or white matter hyperintensities (WMH) rather than the whole WM[15, 22-29]. A systematic characterization of WM metabolic patterns across the brain has not yet been established.

In this study, we used FDG-PET imaging with a specific focus on WM to characterize brain glucose metabolic patterns associated with aging, dementia, and cognitive performance. Our primary hypotheses were that inter-individual variation in WM metabolism is relevant for cognitive resilience, is associated with modifiable protective and risk factors, and may improve prediction of future cognitive decline beyond established imaging biomarkers.

Results
Summary of major analytic steps
To address our hypotheses, we conducted a structured series of analyses across the Mayo Clinic Study of Aging (MCSA) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Fig. 1 provides an overview of the study framework. First, distinct WM metabolic signatures associated with baseline cognitive performance were identified in cognitively unimpaired (CU) participants from the MCSA cohort using voxel-wise FDG-PET analyses. These signatures were then validated in participants with mild cognitive impairment (MCI) from MCSA and in the full ADNI cohort, and we further tested whether WM metabolism provides unique contributions to cognition beyond traditional GM metabolism and amyloid pathology. Second, we explored the associations between WM metabolic signatures and a comprehensive set of protective and risk factors in the MCSA cohort, including demographics, genetics, systemic vascular health, brain pathologies, and WM integrity. Further, a structural equation model (SEM) was constructed to evaluate the potential driving mechanisms underlying WM metabolic signatures. Finally, we assessed the predictive value of baseline WM metabolic signatures for future cognitive decline using longitudinal data, incorporating over 15,000 follow-up observations from MCSA and ADNI combined.

Study participants
A total of 3,142 participants were included, comprising 1,989 individuals from the MCSA cohort (mean age 71.4 ± 10.1 years, 52.8% males) and 1,153 from the ADNI cohort (mean age 72.8 ± 7.5 years, 52.3% males). Of these, 2,777 had longitudinal follow-up with 15,287 total observations. The MCSA cohort contributed 1,722 participants with 9,948 visits (mean ± SD follow-up: 6.3 ± 3.7 years; median 6 visits, IQR: 3–8), while the ADNI cohort included 1,055 participants with 5,339 visits (mean ± SD follow-up: 4.3 ± 3.1 years; median 5 visits, IQR: 3–7). In MCSA, the median interval from clinical visit to magnetic resonance imaging (MRI) was 63 days (IQR: 44–85), with FDG-PET acquired a median of 12 days after MRI (IQRS: 4–28), Aβ-PET 10 days (IQR: 2–27), and Tau-PET 3 days (IQR: 0–9). In ADNI, the corresponding MRI interval was 17 days (IQR: 11–27), with FDG-PET acquired 24 days after MRI (IQR: 15–41), Aβ-PET 27 days (IQR: 17–44), and Tau-PET 40 days (IQR: 24–72). Overall, multimodal data was collected within closely aligned time windows across cohorts. Patterns of missing data across key variables in the MCSA and ADNI cohorts are summarized in Supplementary Table 1.

The baseline characteristics of the participants are summarized in Table 1. Across both cohorts, CU had a lower prevalence of APOE-ε4 carriers, fewer AD- and vascular-related pathological biomarkers, and higher educational attainment compared with those with MCI and dementia. A major distinction between the two cohorts reflects their design and resulting profiles. The MCSA is a population-based study that represents typical community older adults who exhibit both vascular comorbidities and AD pathology. Participants in MCSA had higher prevalence of hypertension, diabetes, and dyslipidemia, captured by a composite cardiovascular metabolic conditions (CMC) score. In contrast, ADNI was specifically designed to track AD progression for clinical trials, and participants have lower systemic vascular risk due to the recruitment mechanism.

Expected positive and atypical negative associations between cognition and WM-FDG uptake: EWM and AWM regions
In this study, “cognitive resilience” referred to individuals who maintained normal cognition despite aging, pathology, or other risk factors for cognitive decline.[6, 7] We conducted voxel-wise multiple regression analysis on FDG-PET data from cross-sectional MCSA-CU participants (n = 1,754) to identify brain regions where glucose metabolism is associated with global cognition, adjusting for age, sex and APOE-ε4. Multiple comparison corrections were applied using both a conservative threshold (false discovery rate [FDR], voxel-wise p < 0.05, cluster size k > 1000) and a liberal threshold (uncorrected voxel-wise p < 0.05, cluster size k > 1000) to refine statistical maps.

Under the conservative threshold, as expected, global cognition was positively associated with FDG-PET standardized uptake value ratio (SUVR) in nearly all GM areas (Fig. 2a). In WM, significant positive associations were observed mainly in the splenium and body of the corpus callosum and bilateral cingulum adjoining the cingulate gyrus, defined as expected WM (EWM; Fig. 2b). Unexpected negative associations, primarily in the anterior and superior corona radiata, were defined as atypical WM (AWM; Fig. 2c). At the liberal threshold, additional positively associated appeared in the posterior thalamic radiation and juxtacortical area, while negative associations extended to deep and subcortical WM, including the anterior and superior corona radiata, precentral WM, and superior longitudinal fasciculus. Peak coordinates and cluster details are provided in Supplementary Tables 2–3. To further explore these divergent FDG-cognition associations in WM, we extracted median SUVR values from FDR-corrected EWM and AWM region masks.

Considering both EWM and AWM metabolism jointly contribute to cognition, we further ranked participants in each subgroup according to the EWM-AWM difference (EWM-FDG minus AWM-FDG) to examine whether this metabolic pattern can be reproduced in participants not used for WM signature discovery (MCSA-MCI and the three ADNI subgroups). Across these groups, cognition declined along the EWM-AWM gradient, although the slopes varied by clinical stage. The decline was most significant in MCSA-MCI and ADNI-dementia groups, while the association was relatively flat in ADNI-CU group. These findings suggest that the relative higher EWM and lower AWM metabolism reliably correlates with cognitive performance across cohorts and disease stages, although with variations in effect strength. (Fig. 2d, e).

WM metabolism provides information comparable to GM-FDG and Aβ-PET for cognition
We investigated the association of WM-FDG, GM-FDG (both EWM and AWM), and Aβ-centiloid to global cognition through multiple regression models that included age, sex, APOE-ε4, and education as covariates (Fig. 3). In the MCSA cohort, GM-FDG (adjusted R²= 43%), WM-FDG (44%), and Aβ-centiloid (43%) explained similar variance in cognition, and the combined model increased explanatory power to 46%. Shapley value (LMG) decomposition of R2 assessed the relative importance of each predictor in regression[30], which confirmed that WM-FDG, GM-FDG, and Aβ-centiloid each contributed substantial, partly nonoverlapping information to global cognition, with WM-FDG and GM-FDG accounting for the largest share. In the ADNI cohort, GM-FDG, WM-FDG, and Aβ-centiloid explained 28%, 28%, and 23% of variance, respectively, and the combined model reached 37%. Here in ADNI, Shapley value again showed complementary contributions, although the effect of AWM-FDG on cognition was small despite being significant. Overall, across both cohorts, WM metabolism provides information about cognition comparable to GM-FDG and Aβ-PET, and its contribution is complementary rather than redundant.

Factors associated with EWM and AWM metabolism
Given that demographic characteristics, genetics, vascular health, and mixed brain pathologies are known to be associated with GM metabolism in aging[31-36], we examined whether these same factors also contribute to inter-individual variation in WM FDG metabolism across cohorts. Age showed opposite associations with WM metabolism, being negatively correlated with EWM uptake (MCSA: r = -0.51, p = 5.3 × 10⁻132; ADNI: r = -0.34, p = 5.1 × 10⁻37) but positively with AWM (MCSA: r = 0.39, p = 8.7 × 10⁻73; ADNI: r = 0.12, p = 1.9 × 10⁻5). Females consistently exhibited higher EWM uptake than males in both cohorts (MCSA p = 5.1 × 10⁻35; ADNI p = 4.4 × 10⁻16), while no significant sex difference was observed for AWM (MCSA p = 0.45; ADNI p = 0.06). Vascular risk showed cohort-specific patterns: in MCSA, high vascular risk was associated with lower EWM and higher AWM uptake (EWM p = 5.4 × 10⁻29; AWM p = 4 × 10⁻39), whereas in ADNI the association was restricted to higher AWM uptake (p = 1.8 × 10⁻4) with no corresponding difference in EWM uptake (p = 0.89). ApoE-ε4 carriers consistently showed lower FDG uptake than non-carriers in both regions across cohorts. Education effects were modest and cohort-specific, observed only in MCSA, where higher education was linked to lower AWM uptake (r = -0.12, p = 3.7 × 10⁻8), with no significant associations for EWM or in ADNI. These results are illustrated in Fig. 4 (age, sex, and vascular risk) and Supplementary Fig. 1 (education and ApoE-ε4 status).

To examine associations with brain pathology, we included five biomarkers corresponding to the revised AD biomarker framework[1]: Aβ (Aβ-PET), tau (Tau-PET), axonal injury (plasma neurofilament light chain, NfL), neuroinflammation (plasma glial fibrillary acidic protein, GFAP), and vascular injury (WMH). After adjusting for age, sex, and ApoE-ε4 status, higher Aβ, Tau, and WMH burden were consistently associated with lower FDG uptake in EWM across cohorts (MCSA: Aβ r = -0.13, p = 7.4 × 10⁻9; WMH r = -0.22, p = 1.6 × 10⁻22; Tau r = -0.1, p = 0.029; ADNI: Aβ r = -0.28, p = 3.7 × 10⁻25; WMH r = -0.15, p = 2.7 × 10⁻8; Tau r = -0.28, p = 1 × 10⁻5; Fig. 3d–f). In contrast, AWM showed weaker and more selective associations, with significant associations observed only for Aβ (MCSA: r = -0.05, p = 0.032; ADNI r = -0.07, p = 0.015), whereas WMH and tau were not significantly associated after covariate adjustment (Fig. 3j–l). Plasma biomarkers showed broader associations, with both EWM and AWM uptake negatively associated with GFAP and NfL in MCSA and ADNI (Supplementary Fig. 1). These findings suggest that EWM metabolism is broadly associated with aging, vascular burden, and multiple neuropathological markers, while AWM metabolism shows weaker and more selective pathological associations.

Contrasting correlations of EWM and AWM metabolism
To further characterize the framework of associations linking demographic, vascular, genetic, and pathological factors to WM metabolism and cognition, we fitted SEM separately in the MCSA and ADNI cohorts (Fig. 5). Both models showed good fit, and full pathways and effect estimates are provided in Supplementary Tables 4–7. Across cohorts, SEMs revealed that demographic, genetic, vascular, and pathological factors jointly shaped EWM and AWM metabolism, which in turn contributed to cognition in opposite directions. Specifically: (i) Age and vascular risk showed bidirectional associations with WM metabolism, with older age associated with lower EWM but higher AWM uptake (β total: EWM -0.48/-0.31; AWM 0.32/0.1 in MCSA/ADNI), and a similar pattern observed for vascular risk (β total: EWM -0.05/-0.01; AWM 0.21/0.1); (ii) Male sex consistently associated with lower EWM metabolism (β total: -0.22/-0.2), while only minor indirect effects on AWM; (iii) APOE-ε4 exerted indirect effects on WM metabolism through its association with amyloid burden; and (iv) pathological associations differed by WM regions, with amyloid burden negatively associated with both EWM and AWM across cohorts (EWM β total: -0.12/-0.30; AWM β total: -0.06/-0.09), whereas WMH showed consistently negative associations with EWM (β total: -0.18/-0.13) but minimal associations with AWM. Decomposition of total effects suggested that aging and vascular risk were the dominant positive contributors to AWM metabolism, whereas amyloid burden demonstrated an opposing association. In contrast, EWM metabolism was associated with multiple demographic and pathological factors, including age, sex, APOE-ε4, amyloid, and WMH, with relatively weaker contributions from vascular risk (Fig. 5b, d).

The contribution and predictive role of EWM and AWM metabolism in global cognition across cohorts
We conducted longitudinal analyses using linear mixed-effects model to examine whether baseline WM metabolism predicted subsequent cognitive decline. Models included baseline age, sex, education, APOE-ε4, WM metabolism, Aβ, WMH burden, follow-up time, visit cycle, and their interactions with time. Across both MCSA and ADNI, significant WM metabolism × time interactions indicated that baseline WM-FDG levels were associated with differences in the rate of cognitive decline (Table 2). Specifically, higher EWM-FDG and lower AWM-FDG were consistently associated with a slower rate of cognitive decline, with a stronger effect for EWM than AWM across cohorts (Fig. 6b). Among other variables, ApoE-ε4 and Aβ also interacted with time, with ApoE-ε4 carriers and those with higher baseline Aβ deposition showing faster cognitive decline. Increased WMH burden was associated with accelerated cognitive decline in MCSA p = 7.2 × 10-9) but not in ADNI (p = 0.06).

Considering the interaction between Aβ pathology and time, we examined cognitive trajectories stratified by Aβ status and observed a graded pattern of cognitive decline. Fig. 6b depicts longitudinal cognitive trajectories with slopes reflecting the rate of cognitive decline as a function of baseline FDG levels. When stratified by Aβ status, the protective effect of high EWM metabolism and the harmful effect of high AWM metabolism persisted in both Aβ– and Aβ+ groups. Notably, the effect of EWM was markedly amplified in the Aβ+ group compared with AWM. Baseline intercept differences reflect baseline cognitive performance and were accompanied by differences in age, vascular risk, and pathological burden (Supplementary Tables 8–9). All these results indicate that WM metabolism provides information on longitudinal cognitive change beyond established risk factors. Higher EWM and lower AWM metabolism at baseline were associated with more favorable cognitive trajectories, whereas lower EWM and higher AWM metabolism were associated with faster decline.

Preserved WM integrity supports healthy WM metabolism
We further analyzed regional neurite density index (NDI) in a subset of MCSA-CU participants (n = 235) who had neurite orientation dispersion and density imaging (NODDI) data (Fig. 6c). Significant positive correlations were observed between regional NDI in the splenium of the corpus callosum (SCC: r = 0.31, p = 1.1 × 10-6) and cingulum adjacent to the cingulate gyrus (CGC: r = 0.21, p = 0.001) with FDG-PET uptake in EWM. In contrast, regional NDI in the anterior and superior corona radiata (ACR: r = -0.16, p = 0.012; SCR: r = -0.18, p = 0.006) showed negative correlations with FDG-PET uptake in AWM. All these associations are adjusted for age, sex and ApoE-ε4 status. Because higher NDI values reflect healthier WM microstructural integrity, these results suggest that the link between higher EWM metabolism and lower AWM metabolism with better cognitive performance may be supported by preserved WM integrity.

Robustness of WM metabolic signatures
To evaluate the robustness of WM metabolic signatures identified in the MCSA-CU voxel-wise regressions, we conducted a series of sensitivity analyses. At the image-processing level, we compared partial volume corrected versus uncorrected images to evaluate partial volume effects and applied different Gaussian smoothing kernels (4, 6, and 8 mm) to examine potential gray matter spillover. At the modeling-level, we tested adding covariates (age, sex, APOE-ε4), further adjusting for brain parenchymal fraction [total tissue volume (TTV)/total intracranial volume (TIV)], and excluding participants with severe atrophy (TTV/TIV < mean-1.5 SD by age- and sex-stratified distributions). These variations did not materially alter the spatial distribution of EWM and AWM clusters or their associations with cognition (see Supplementary Figure 2–4), indicating that the observed associations were not attributable to image-processing noises or confounding from demographics or brain atrophy. In addition, we repeated the cross-sectional regressions and longitudinal mixed-effect models using Mini-Mental State Examination (MMSE) as the outcome variable, with results provided in the Supplementary Table 10–11, which were consistent with the main analyses.

Methods
Selection of participants
This study included participants from two independent cohorts: MCSA, a population-based prospective study with a stratified random sampling design, aimed to investigate cognitive aging among residents of Olmsted County, Minnesota, USA[57]. ADNI, a multicenter longitudinal study designed to investigate clinical, imaging, genetic, and biological markers for the early detection and tracking of MCI and AD[58]. Study details are available at: MCSA: https://www.mayo.edu/research/centers-programs/alzheimers-disease-research-center/research-activities/mayo-clinic-study-aging/overview ; ADNI: https://adni.loni.usc.edu/.

For cross-sectional analyses, eligible participants were ≥50 years old, had concurrent amyloid PET, FDG-PET, structural MRI scans, and completed neuropsychological assessments. Those with incomplete clinical data or uncertain cognitive diagnoses were excluded. In ADNI, we further reviewed available diagnostic and neuropathological information and excluded cases with dementia with Lewy bodies (DLB), frontotemporal lobar degeneration with TDP-43 (FTLD-TDP), or limbic-predominant age-related TDP-43 encephalopathy (LATE). For longitudinal analyses, participants were required to have ≥2 cognitive assessments. Given differences in cohort composition and diagnostic criteria, MCSA and ADNI were analyzed separately.

Sex was defined based on biological sex as recorded in the medical record at enrollment. Sex was included as a covariate in all regression, linear mixed-effects, and SEM to account for potential sex-related differences in brain metabolism and cognition. In addition, group comparisons of WM metabolic signatures were performed between males and females. All analyses were conducted at the individual participant level (biological replicates only). No technical replicates were used.

Standard protocol approvals, registrations, and patient consents
The MCSA was approved by the Mayo Clinic and Olmsted Medical Center Institutional Review Boards, and informed consent was obtained from all participants or their legally authorized representatives. ADNI was approved by the institutional review boards of all participating sites, with written informed consent from all participants.

Cognitive evaluation
In MCSA, cognition was assessed by a neuropsychological battery covering executive function, language, memory, and visuospatial performance; test scores were standardized and averaged into a global cognitive z-scores (higher scores indicate better cognition) [57]. In ADNI, global cognition was measured by the Alzheimer’s Disease Assessment Scale-Cognitive Subscale (ADAS-Cog-11), with scores inverted (multiplied by -1) so that higher values reflected better performance, consistent with MCSA scaling. Because of differences in assessment protocols, cognitive-related analyses were conducted separately within each cohort. MMSE scores were also available in both cohorts and are reported but were not used in the primary analyses due to limited sensitivity among CU participants. Results using MMSE as the cognitive outcome are provided as sensitivity analyses in the Supplementary Table 8–9.

MR acquisition and processing
For MCSA, all MRI images were acquired on 3T scanners. Each participant underwent 3D T1-weighted MPRAGE and 3D FLAIR imaging. T1 was used for PVC[59], normalization, and tissue volumetry[60]. WMH was segmented from FLAIR using a validated in-house pipeline and normalized to TIV (WMH/TIV%)[61]. In ADNI, MPRAGE and FLAIR images were processed with the same pipeline as MCSA. A subset of MCSA participants had multi-shell diffusion MRI enabling NODDI, with acquisition and processing details that can be found in previous publications[62]. The JHU 'Eve' WM atlas was warped to participant native space to derive regional NODDI measures for certain WM tracts involved in the significant FDG-cognition results. NDI was used as a marker of WM integrity in this study[20]. NODDI data were not available in the ADNI. Full details are provided in the Supplementary Methods.

FDG-PET acquisition and processing
The acquisition and processing procedures for FDG-PET in the MCSA[63] and ADNI have been detailed provided in the Supplementary Methods. We analyzed FDG-PET images with an automated in-house pipeline using SPM12[64]. Individual PET frames were visually inspected for motion and averaged. Each PET image was rigidly co-registered (6 DOF) to the participant’s T1 images. T1 images were segmented into GM, WM, and cerebrospinal fluid probability maps, and the MRI-derived deformation fields were applied to spatially normalize PET images to the Mayo Clinic Adult Lifespan Template (MCALT; https://www.nitrc.org/projects/mcalt/) space. For voxel-wise analyses, explicit GM and WM masks were constructed in template space from mean tissue probability maps across participants. GM voxels were defined using a probability threshold > 0.20 and WM voxels using > 0.50. To prevent overlapping between compartments, voxels were assigned to GM when GM > WM and WM when WM > GM. Mask distributions were visually inspected to ensure anatomical plausibility. PVC was performed using a two-compartment model. Images were smoothed with a 6 mm full-width at half-maximum Gaussian kernel (with sensitivity analyses at 4 and 8 mm) and normalized to the pons to generate SUVR maps. Regional SUVRs for GM, EWM, and AWM were calculated as the median uptake within masks defined from FDR-corrected voxel-wise regression results.

Amyloid and tau PET acquisition and processing
Amyloid-PET used 11C-PiB in MCSA and 18F-florbetapir or 18F-florbetaben in ADNI. A subset of participants from both cohorts underwent tau-PET with 18F-flortaucipir (MCSA: n = 499 [25%], ADNI: n = 235 [20%]). Acquisition details are provided in the Supplementary Methods. Amyloid and tau PET images were motion-checked, frame-averaged, rigidly co-registered (6 DOF) to the participant’s T1-weighted MRI and spatially normalized to the Mayo MCALT atlas using MRI-derived deformation fields. Global amyloid-PET SUVR was calculated as the voxel-count–weighted median across predefined cortical regions (prefrontal, orbitofrontal, parietal, temporal, anterior cingulate, and posterior cingulate/precuneus), scaled to cerebellar crus GM, and converted to centiloid units. For ADNI, centiloid values processed by UC Berkeley were downloaded from the LONI database. Global tau-PET SUVR was calculated as the median uptake across entorhinal, amygdala, parahippocampal, fusiform, inferior temporal, and middle temporal regions, also scaled to cerebellar crus GM.

Plasma biomarkers
Both MCSA and ADNI participants had previously collected fasting blood samples, which were centrifuged and stored at -80°C until testing. In MCSA, plasma GFAP and NfL were measured using the Simoa® Neurology 4-Plex Advantage kit (N4PE) on a Quanterix Simoa HD-X analyzer (Quanterix, Lexington, MA) [66]. In ADNI, plasma biomarkers was measured using the same platform and protocol. Availability was limited to a subset of participants in each cohort, as summarized in Supplementary Table 1.

Revised AD biomarker categorization
According to the latest modified AD biomarker categories [1], we evaluated four pathogenic pathways using imaging measures and plasma analytes: amyloid (A) pathology via amyloid-PET, tau (T) pathology via tau-PET, neurodegeneration (N) via NfL, inflammatory (I) activity via plasma GFAP, and vascular injury (V) via WMH. Specifically, A+ was classified by an established cut-off (centiloid ≥ 22) [67], T+ was defined as SUVR ≥ 1.29, and V+ was defined as WMH/TIV% > 1.3%[68, 69]. Although inflammation is recognized as an important mechanism in AD pathogenesis[70] and plasma GFAP is emerging as a sensitive marker of astrocytic reactivity associated with early amyloid deposition, a widely accepted cut-off value for GFAP is not yet available.

APOE-ε4, education, and vascular risk
APOE-ε4 positivity was defined as carrying one or more ε4 alleles. Intellectual enrichment was assessed by years of education. In MCSA, systemic vascular health was measured by a composite CMC score based on the presence or absence of seven cardiovascular metabolic conditions: hypertension, hyperlipidemia, cardiac arrhythmias, coronary artery disease, congestive heart failure, diabetes mellitus, and stroke[71, 72]. As the ADNI cohort does not capture all seven conditions, we also constructed a harmonized vascular risk factor (VRF) score based on the four vascular conditions available in both cohorts (hypertension, diabetes, hyperlipidemia, and stroke). Creating this harmonized VRF score enabled consistent cross-cohort interpretation while maintaining conceptual equivalence with existing vascular burden measures. In the final analytic sample, the median VRF score was 2 in MCSA and 1 in ADNI.

Statistical analysis
All statistical analyses except for voxel-wise statistics were conducted with R 4.4.0.

Cognition-associated WM glucose metabolism regions
Voxel-wise multiple linear regression was performed on MCSA-CU participants (n = 1754) to examine associations between z-global cognition scores and FDG SUVR, with age, sex, and ApoE-ε4 genotype as covariates. GM and WM masks were applied as explicit masks in MCALT space, derived from the mean segmented and normalized probability maps across all participants. Probability thresholds of GM > 0.20 and WM > 0.50 were used. A higher threshold was chosen for WM to account for its proximity to GM and less distinct boundaries, whereas a lower threshold was applied for GM due to its clearer cortical definition and sharper tissue contrast. To avoid overlapping, voxels were assigned to GM when GM > WM and to WM when WM > GM. The resulting masks were visually inspected to ensure anatomical plausibility. Voxel-wise results were corrected for multiple comparisons using both a conservative threshold (voxel-level FDR-corrected, p < 0.05, cluster size k> 1000 mm3) and a liberal threshold (uncorrected voxel-level p< 0.001, cluster size k> 1000 mm3). Specific WM anatomical labels inside the significant clusters were reported based on the JHU “Eve” WM atlas[73]. In addition, we extracted the FDG-PET SUVR in the “EWM and AWM regions” of each participant using ROIs mask derived from the FDR-corrected voxel-wise multiple regression maps for further analysis.

Cross-sectional contribution of WM metabolism to global cognition variance
We evaluated the contribution of WM metabolism to baseline global cognitive performance compared to the established imaging markers (GM-FDG and Aβ). Multiple linear regression models were constructed within each cohort to assess the associations between imaging measures and global cognition. Each model included demographic covariates (age, sex, and education) and APOE-ε4 status. Imaging variables (FDG SUVR in EWM, AWM, and GM, and Aβ centiloid) were first included separately in the regression model and then combined into one model. To ensure model stability, multicollinearity among predictors was evaluated using variance inflation factor (VIF), tolerance, and condition index. All predictors met conventional thresholds (VIF < 5, tolerance > 0.2). Although the maximum condition index exceeded 30, no joint variance concentration was detected (i.e., no two predictors shared >50% of variance at the same index), confirming that multicollinearity did not bias model estimation. We reported the adjusted R2 to evaluate the overall fit of the regression models. In addition, we calculated the Shapley value (LMG) decomposition of R2 to assess the relative importance of each predictor[30].

Risk/protective factors and their impact on regional WM metabolism
The effects of sex, ApoE-ε4 genotype (carrier vs. non-carrier), and vascular risk (high vs. low) on WM metabolism were examined by independent samples t-test. Associations with continuous demographic variables (age and education) were tested using Pearson correlation analysis. To investigate relationships between WM metabolism and various pathological markers, we performed partial correlation analyses adjusting for age, sex, and APOE-ε4 status. In these models, WM metabolic measures (EWM and AWM FDG SUVR) and biomarker values (Aβ centiloid, tau SUVR, WMH/TIV%, plasma GFAP, and plasma NfL) were entered directly, and covariates were controlled simultaneously within the correlation computation; thus, the reported correlation coefficients represent partial correlations. No prior revisualization of FDG or biomarker values was performed. To improve normality, WMH/TIV%, GFAP, and NfL values were log10-transformed prior to analysis. All analyses were conducted separately in MCSA and ADNI using all available data for each biomarker; sample sizes varied by biomarker and are reported in the figure legends.

Structural equation modeling
SEM analysis was conducted to explore the direct and indirect pathways linking WM metabolic signatures and its potential modifiers in both cohorts, using the R package lavaan. This framework combines multiple regression equations into a unified model, enabling the simultaneous estimation of direct, indirect, and total effects. Model variables were selected to maximize comparability between cohorts while retaining the largest possible sample size. These included demographic characteristics (age, sex, APOE-ε4 status, and vascular risk), brain pathological biomarkers (amyloid PET and WMH), WM metabolic signatures (EWM and AWM SUVR), and global cognition. Standardized path coefficients were estimated, and statistical significance was assessed using robust t-tests. Model fit was evaluated by χ² test, comparative fit index (CFI), Tucker–Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Good model fit was defined as non-significant χ², CFI/TLI > 0.95, and RMSEA/SRMR < 0.05. In addition, we computed the direct, indirect, and total effects of all predictors on EWM and AWM metabolism. This allowed us to compare the relative contributions of age, sex, APOE-ε4, vascular risk, amyloid, and WMH to each WM metabolic signature, and to highlight both consistent and divergent patterns across the MCSA and ADNI cohorts.

Microstructural basis underlying WM metabolism
To clarify whether there is a biological basis for the EWM and AWM metabolic signatures, we utilized available NODDI data in MCSA-CU participants (n = 235) to extract regional NDI values for the major WM fibers within two predefined regions of interest, with higher NDI values indicating greater microstructural integrity. Associations between regional WM NDIs and FDG-PET SUVR were assessed using partial correlation analyses, with the latter adjusted for age, sex and APOE-ε4 status.

Prognostic value of WM metabolism in predicting cognitive decline beyond established imaging biomarkers
We investigated whether FDG-PET uptake in the WM metabolic signatures predicted longitudinal global cognitive change in both the MCSA and ADNI cohorts, while accounting for amyloidosis and vascular pathology. We fit a linear mixed effect model with global cognition as the dependent variable. The model included baseline age, follow-up time, sex, years of education, APOE-ε4 status, WM metabolism (both EWM and AWM regions), amyloid deposition, WMH burden, and the cycle number of the visit as fixed effects. Additionally, all main effects and their interactions with time were incorporated. Random effects for intercept and slopes were also included to account for initial differences and varying trajectories among participants.

Robustness analysis
To evaluate the robustness of the voxel-wise WM metabolic signatures, several processing- and modeling-level variations were performed. First, to assess the effect of image processing methods and potential GM spillover, voxel-wise regressions were repeated using PVC-uncorrected images and varying Gaussian smoothing kernel size (4, 6, and 8 mm). Second, to examine the influence of covariate adjustment, analyses were repeated with and without inclusion of age, sex, and APOE-ε4 status. Third, to address potential confounding by brain atrophy, total tissue volume to intracranial volume ratio (TTV/TIV) was included as an additional covariate, and analyses were repeated after excluding individuals with severe atrophy (TTV/TIV < mean–1.5 SD within age- and sex-stratified MCSA-CU distributions). In addition, cross-sectional regressions and longitudinal mixed-effects models were repeated using the MMSE score as the cognitive outcome.

Data Availability
The MRI, PET, and associated clinical data from the MCSA are available under restricted access due to institutional review board (IRB) requirements and participant consent limitations. Access is limited to qualified academic and industry researchers for research purposes. Requests for access must be submitted to the MCSA Executive Committee via the Global Alzheimer’s Association Interactive Network (GAAIN) portal (https://www.gaaindata.org/partner/MCSA), where data use agreements and IRB documentation are reviewed prior to approval. Approved users are granted access under a formal data use agreement. The imaging and demographic data used in this study from the ADNI used in this study are publicly available in the ADNI database (https://adni.loni.usc.edu/) to qualified researchers upon registration. Source data generated in this study are provided with this paper.

Code Availability
All statistical analyses were conducted using R (version 4.4.0). No custom algorithms were developed. Image preprocessing was performed using an automated in-house pipeline built on SPM12. The analysis scripts used for image preprocessing are available from the corresponding author for research purposes.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The authors have extensively revised this study, resulting in greater readability and rigor. The revisions to the Discussion on lines 287-336 are particularly welcome and provide much improved contextualization of the current findings within the wider literature. I have some additional comments, both conceptual and more minor technical issues:\n1. The introduction frames the study in terms of cognitive resilience and the role of WM metabolism is preserving myelination and axonal integrity. However, one unclear point is whether the authors are principally aiming to describe inter-individual, cross-sectional differences in the relative metabolic activity of expected and abnormal white matter (EWM and AWM, respectively) or are trying to characterize a dynamic process within individuals, whereby AWM metabolism rises in the face of aging and disease-related challenges, even as EWM metabolism declines. Indeed, the authors frequently report results of cross-sectional analyses using language that is more appropriate for within-subject, longitudinal observations--see for example lines 273-276: \"EWM metabolism declined with aging, Aβ, and WMH, whereas the increase in AWM metabolism was driven primarily by aging and vascular risk, but this rise was partially weakened by AD progression (Aβ and APOE-ε4).\" The pattern of longitudinal analyses depicted in Fig. 6B seems to indicate that the overall trend for both EWM and AWM metabolism is to decrease over time. This pattern would seem to contradict the authors claim that \"WM metabolism undergoes distinct and, in some regions, inverse changes compared with GM\" (line 277-278). It is important that the authors clarify both their hypotheses and whether the evidence and study design better link global cognition to a) within-subject, opposing dynamics for EWM vs. AWM metabolism; or b) inter-individual differences in the ratio/difference between EWM and AWM metabolism.\n2. I ask that the authors please clarify whether they believe that the corona radiata tracts are directly/causally involved in domain-specific cognitive performance as analyzed here, or are meant to be an anatomical proxy for other white matter components that may physically connect functional networks involved in performance of the measured tasks. In either case, I ask the authors to temper the causal language used throughout in describing SEM results: for example, instead of \"low EWM and high AWM metabolism accelerate decline\", it would be more rigorous to say something like \"low EWM and high AWM metabolism were associated with faster decline.\" This causal language pervades other parts of the paper as well, e.g., line 339 (\"Aging drives widespread hypometabolism\") and should be revised to acknowledge the correlational nature of the methods used throughout this study.\n3. The sensitivity analyses on lines 255-268 are helpful but fall short of demonstrating reliable subject-by-subject localization of the corona radiata using template-based masks. I would ask the authors to acknowledge the uncertainty around precise localization of this structure as a limitation of the study. Indeed, given the maps of EWM and AWM components presented in Figs. 2B & 2C, it would seem possible to describe these two components are principally representing periventricular vs. deep WM, respectively, without reference to particular fiber tracts.\n4. The NODDI analysis reported on lines 245-254 is interesting. Overall, did EWM and AWM differ in terms of NDI? This information would be helpful in interpreting the differential associations with metabolism.\n5. Has the VRF score been previously validated?\n6. Line 173: \"WM metabolism is affected by demographic, genetics, vascular health, and brain pathology.\" Please provide citations.\n7. Lines 186-200: please specify how many participants from each dataset were included in the AD biomarker analysis. Additionally, a section explaining the statistical analysis approach for this section would be good to include--it appears to be absent from the Statistical Analysis section. In particular, the authors list covariates of age, sex, and APOE e4 carrier status; however, it's not clear whether these covariates were regressed out of the FDG PET data, the biomarker data (A-beta, tau, WMH burden, NfL, and GFAP), or both--i.e., are the r values reported partial or semi-partial correlations?", "reviewer_3": "I really appreciate the hard work by the authors. The manuscript has significantly improved in terms of rigor and replicability. I have a few remaining comments:\n1. The corona radiata (CR) are not only supplied by small penetrating arteries, but also drained by deep medullary veins. As such, CR could be vulnerable to small vessel disease pathology in the form of WMH visible on MRI. Authors however report that no association was found between AWM (=CR) FDG uptake and WMH. Instead, higher AWM FDG uptake was associated with vascular risk “but this effect can be partly offset by increased amyloid, which has an opposite influence”. Additionally, higher AWM FDG was associated with lower NDI, suggesting microstructural disintegration (I infer this from (1) negative associations between AWM FDG and NDI, and (2) “higher NDI values reflect healthier WM microstructural integrity”). I have several comments:\n- Given these findings, it remains unclear to me why AWM FDG is interpreted as a disease-related compensatory mechanism.\n- Please explain what is meant by “partial off-set by increased amyloid” and indicate where these results are reported. Fig 5A and C display the total effect of VRF on AWM FDG but no indirect effects via AD pathology are shown?\n- If AWM FDG is particularly driven by (aging and) VRF, then how does microstructure (measured by NDI) fit into this framework?\n2. In the new figure 6, lower EWM FDG uptake was associated with steeper cognitive decline in CU A+. On the other hand, for AWM FDG, the intercept of the FDG-high group is lower than that of the FDG-low group, but their slopes are similar. This suggests that higher AWM FDG does not accelerate cognitive decline in the preclinical stage compared to low FDG. Instead, CN A+ individuals with higher AWM FDG appear to have overall lower cognition compared to low FDG (how is it compensatory then?), although it is unclear whether this difference in intercept is statistically significant. Overall, this points to EWM and not AWM FDG accelerating cognitive decline in the preclinical stage, which warrants more in-depth discussion in the manuscript. While the study was not designed to determine whether AWM or EWM FDG changes occur first, their relative effects on early-stage cognition could be further discussed.\n3. In response to R2 #13 (and throughout the manuscript), the results are described as “EWM indicating neurodegenerative vulnerability and AWM indexing age- and vascular-related metabolic adaptation”. However, the new analysis presented in Supplementary Table 10 indicates that EWM FDG is also associated with vascular risk (p=0.008) and age (p<0.001). There is a known strong bidirectional influence between vascular and neurodegenerative processes as well as aging, which makes it hard to disentangle these processes in the way that it is concluded there."}, "author_response": "Dear Editor:\nWe sincerely thank you for the opportunity to revise our manuscript and for the constructive feedback\nprovided by the reviewers. We greatly appreciate their thoughtful and insightful comments, which have\nhelped us substantially improve the clarity and interpretation of our work. We have carefully addressed\nall comments with corresponding revisions in the manuscript (highlighted in yellow). In the point-by-point\nresponse below, the reviewers’ original comments are presented in blue, our responses are provided in\nblack, and all associated changes made within the manuscript are indicated in red for clarity. We believe\nthese revisions have strengthened the manuscript, and we hope that it is now suitable for publication.\nReviewer #1\nThe authors have extensively revised this study, resulting in greater readability and rigor. The revisions to\nthe Discussion on lines 287-336 are particularly welcome and provide much improved contextualization\nof the current findings within the wider literature. I have some additional comments, both conceptual and\nmore minor technical issues:\nWe appreciate the reviewers’ positive evaluation of our revisions.\nIn this round, we revised wording that could imply causality or longitudinal interpretations in cross-\nsectional analyses, improved some methodological descriptions, and expanded the limitations to\nacknowledge current design draws and data constraints, thereby strengthening the clarity and rigor of the\nmanuscript.\n1. The introduction frames the study in terms of cognitive resilience and the role of WM metabolism is\npreserving myelination and axonal integrity. However, one unclear point is whether the authors are\nprincipally aiming to describe inter-individual, cross-sectional differences in the relative metabolic activity\nof expected and abnormal white matter (EWM and AWM, respectively) or are trying to characterize a\ndynamic process within individuals, whereby AWM metabolism rises in the face of aging and disease-\nrelated challenges, even as EWM metabolism declines. Indeed, the authors frequently report results of\ncross-sectional analyses using language that is more appropriate for within-subject, longitudinal\nobservations--see for example lines 273-276: \"EWM metabolism declined with aging, Aβ, and WMH,\nwhereas the increase in AWM metabolism was driven primarily by aging and vascular risk, but this rise\nwas partially weakened by AD progression (Aβ and APOE-ε4).\" The pattern of longitudinal analyses\ndepicted in Fig. 6B seems to indicate that the overall trend for both EWM and AWM metabolism is to\ndecrease over time. This pattern would seem to contradict the authors claim that \"WM metabolism\nundergoes distinct and, in some regions, inverse changes compared with GM\" (line 277-278). It is\nimportant that the authors clarify both their hypotheses and whether the evidence and study design\nbetter link global cognition to a) within-subject, opposing dynamics for EWM vs. AWM metabolism; or b)\ninter-individual differences in the ratio/difference between EWM and AWM metabolism.\nResponse: We thank the reviewer for this insightful comment. We clarify that the primary objective of our\nstudy is to characterize inter-individual variation in EWM and AWM metabolism and to examine how these\ndifferences relate to demographic, vascular, pathologies, and cognitive performance. Because longitudinal\nFDG-PET data were not available now, our analyses cannot address within-subject temporal changes in\nWM metabolism. We recognize that some phrasing in the original manuscript could have been interpreted\nas implying longitudinal metabolic change. We have revised these sentences (see below) to make clear\nthat all reported associations with age, vascular risk, and pathologies represent cross-sectional inter-\nindividual relationships, not dynamic metabolic trajectories.\nWe also clarify that Fig. 6B presents longitudinal cognitive trajectories, not longitudinal FDG changes. The\ndownward trends in this figure can only reflect cognitive decline over time and should not be interpreted\nas evidence that both EWM and AWM metabolism decrease longitudinally.\nRevised text:\nIntroduction (Line 85-89): “In this study, we used FDG-PET imaging with a specific focus on WM to\ncharacterize brain glucose metabolic patterns associated with aging, dementia, and cognitive performance.\nOur primary hypotheses were that inter-individual variation in WM metabolism is relevant for cognitive\nresilience, is associated with modifiable protective and risk factors, and may improve prediction of future\ncognitive decline beyond established imaging biomarkers.”\nDiscussion (Line 282-289): “Cross-sectionally, EWM metabolism was lower in individuals with greater age,\nAβ burden, and WMH, whereas higher AWM metabolism was observed in people with older age and\nvascular risk, with reduced levels in the presence of greater AD pathology (Aβ and APOE-ε4).” …… “These\nfindings suggest that WM metabolism undergoes distinct and, in some regions, opposite patterns of\nmetabolic associations compared with GM, and that both EWM and AWM provide important and\ncomplementary information for understanding cognitive aging and dementia-related vulnerability.”\n2. I ask that the authors please clarify whether they believe that the corona radiata tracts are\ndirectly/causally involved in domain-specific cognitive performance as analyzed here, or are meant to be\nan anatomical proxy for other white matter components that may physically connect functional networks\ninvolved in performance of the measured tasks. In either case, I ask the authors to temper the causal\nlanguage used throughout in describing SEM results: for example, instead of \"low EWM and high AWM\nmetabolism accelerate decline\", it would be more rigorous to say something like \"low EWM and high AWM\nmetabolism were associated with faster decline.\" This causal language pervades other parts of the paper\nas well, e.g., line 339 (\"Aging drives widespread hypometabolism\") and should be revised to acknowledge\nthe correlational nature of the methods used throughout this study.\nResponse: We agree that the corona radiata (CR) should not be interpreted as directly generating domain-\nspecific cognitive functions. Instead, its metabolic characteristics likely reflect the integrity and efficiency\nof broader frontoparietal and limbic–executive networks that support cognitive domains. To clarify this\npoint, we conducted additional analyses examining whether EWM and AWM metabolism, and specifically\nthe regional components comprising them (EWM: SCC, BCC, and CGC; AWM: ACR and SCR), which showed\ndifferential associations with specific cognitive domains. Using multivariable regression models including\nmemory, attention, and language scores simultaneously, we found that:\n• EWM metabolism was most strongly associated with memory and attention (below Table 1).\n• AWM metabolism was selectively associated with attention (below Table 1).\n• Regional analyses showed consistent association patterns, with SCC, BCC, and CGC linked to both\nmemory and attention, while ACR and SCR were associated with attention (below Table 2-3).\nThese findings indicate that the CR is likely to serve as anatomical proxies for large-scale cognitive networks,\nespecially frontoparietal attention systems, rather than acting as direct causal substrate of cognition. The\nresults, therefore, support a network-based interpretation of WM metabolic differences.\nTable 1. Associations between EWM and AWM FDG metabolism and cognitive domain z-scores in MCSA.\nEWM-FDG SUVR AWM-FDG SUVR\nstd β P std β P\nMemory (z-score) 0.194 <0.001 -0.029 0.301\nAttention (z-score) 0.315 <0.001 -0.28 <0.001\nLanguage (z-score) -0.01 0.718 -0.045 0.111\nStandardized regression coefficients (β) and p-values are shown.\nTable 2. Associations between regional EWM-FDG metabolism and cognitive domain z-scores in MCSA.\nSCC SUVR BCC SUVR Left CGC SUVR Right CGC SUVR\nstd β P std β P std β P std β P\nMemory (z-score) 0.148 <0.001 0.152 <0.001 0.182 <0.001 0.146 <0.001\nAttention (z-score) 0.341 <0.001 0.299 <0.001 0.225 <0.001 0.138 <0.001\nLanguage (z-score) -0.038 0.159 -0.018 0.509 0.02 0.462 -0.021 0.478\nStandardized regression coefficients (β) and p-values are shown. SCC, splenium of the corpus callosum; BCC, body of\nthe corpus callosum; CGC, cingulum adjoining the cingulate gyrus.\nTable 3. Associations between regional AWM-FDG metabolism and cognitive domain z-scores in MCSA.\nLeft ACR SUVR Left SCR SUVR Right ACR SUVR Right SCR SUVR\nstd β P std β P std β P std β P\nMemory (z-score) 0.029 0.327 0.025 0.4 -0.008 0.787 -0.001 0.969\nAttention (z-score) -0.108 <0.001 -0.099 <0.001 -0.174 <0.001 -0.147 <0.001\nLanguage (z-score) 0.003 0.91 -0.019 0.519 -0.02 0.503 -0.031 0.293\nStandardized regression coefficients (β) and p-values are shown. ACR, anterior corona radiata; SCR, superior corona\nradiata.\nAdditionally, we fully agree that our results and findings are correlational. In response to the reviewer’s\nsuggestion, we have carefully revised all causal language to more neutral descriptions throughout the text,\nsuch as “associated with”, “related to”, “linked to”, “statistically consistent with”, etc.\n3. The sensitivity analyses on lines 255-268 are helpful but fall short of demonstrating reliable subject-by-\nsubject localization of the corona radiata using template-based masks. I would ask the authors to\nacknowledge the uncertainty around precise localization of this structure as a limitation of the study.\nIndeed, given the maps of EWM and AWM components presented in Figs. 2B & 2C, it would seem possible\nto describe these two components are principally representing periventricular vs. deep WM, respectively,\nwithout reference to particular fiber tracts.\nResponse: We thank the reviewer for raising this point. We agree that template-based ROIs cannot provide\nprecise subject-by-subject localization of specific WM tracts, particularly in regions where multiple fiber\npathways converge. In the revised Discussion section, we now explicitly acknowledge this limitation.\nWe also clarify that the aim of our primary FDG analyses was not to delineate individual fiber tracts but to\ncharacterize component-level patterns of WM metabolism and their associations with demographic,\nvascular, and pathological factors. Accordingly, references to specific WM tracts are intended as\nanatomically informed contextual interpretations of the spatial components rather than definitive tract-\nlevel identification. Importantly, the divergent metabolic–cognitive associations observed for EWM and\nAWM generate hypotheses that may guide future work using individualized diffusion tractography to\ndetermine the specific fiber pathways underlying these metabolic patterns.\nRevised text (Line 439):\n“…Fifth, the spatial localization of EWM and AWM in this study was derived from a WM template instead\nof subject-specific tractography. Given the resolution of FDG-PET and the convergence of multiple fiber\npathways in these regions, tract references in our interpretation should be considered anatomically\napproximations rather than precise tract-level identification. Future work using individualized diffusion\ntractography will be required to refine the specific tract pathways underlying these metabolic patterns.”\n4. The NODDI analysis reported on lines 245-254 is interesting. Overall, did EWM and AWM differ in terms\nof NDI? This information would be helpful in interpreting the differential associations with metabolism.\nResponse: We thank the reviewer for this suggestion. To determine whether EWM and AWM differed in\nmicrostructural integrity, we compared NDI between the EWM & AWM using a linear mixed-effects model.\nWe modeled ROI-level NDI values (SCC, CGC for EWM; ACR, SCR for AWM) as repeated measures with WM\ntype (EWM vs. AWM) as a fixed effect and both ROI name and participant id as random intercepts. This\nanalysis showed no significant difference in overall NDI between EWM and AWM (β = 0.025, SE = 0.056,\np = 0.7), and their estimated marginal mean values were similar (EWM = 0.576; AWM = 0.551).\nIn our consideration, the absence of an overall NDI difference does not imply that microstructure is\nunrelated to the metabolic distinction between the components. Microstructural integrity provides a\nstructural context for metabolic activities and their association with cognition. In our data, EWM and AWM\nshow opposite regional NDI-FDG association patterns (Fig. 6C), and their FDG-cognition associations are\nalso in opposite directions (Fig. 2 & Table 2). Taken together, these findings suggest that healthier WM\nmicrostructure is generally associated with better cognitive performance. This view can also be supported\nby establishing a microstructure-metabolism-cognition mediation model in subjects with NDI value\navailable (figure below).\n5. Has the VRF score been previously validated?\nResponse: The vascular risk factors (VRF) score used in this study is not a new concept but follows a widely\nused approach for quantifying vascular risk burden in MCSA. In the series of MCSA publications (paper\nbelow), a validated composite cardiovascular metabolic conditions (CMC) score is routinely used to\nsummarize seven such conditions: hypertension, hyperlipidemia, cardiac arrhythmia, coronary artery\ndisease, congestive heart failure, diabetes, and stroke. However, since the ADNI does not cover all seven\nvariables, we constructed a simplified, but conceptually equivalent VRF score based on four diseases\nrecorded in both cohorts (hypertension, diabetes, hyperlipidemia, and stroke). This allows us to derive a\nunified vascular risk index for interpreting cross-cohort results while maintaining consistency with\nestablished practices in the field. We have further revised the Methods section to describe the purpose,\nrationale, and construction of the VRF score to ensure clarity for readers.\nRevised text (Line 550):\n“As the ADNI cohort does not capture all seven conditions, we also constructed a harmonized vascular risk\nfactor (VRF) score based on the four vascular conditions available in both cohorts (hypertension, diabetes,\nhyperlipidemia, and stroke). Creating this harmonized VRF score enabled consistent cross-cohort\ninterpretation while maintaining conceptual equivalence with existing vascular burden measures…”\nReferences:\nVemuri P, Lesnick TG, Przybelski SA, et al. Age, vascular health, and Alzheimer disease biomarkers in an elderly sample.\nAnn Neurol. 2017;82(5):706-718.\n6. Line 173: \"WM metabolism is affected by demographic, genetics, vascular health, and brain pathology.\"\nPlease provide citations.\nResponse: The original sentence was intended to convey that demographic factors, genetic risk, vascular\nhealth, and brain pathology are widely known to influence cortical (gray matter) metabolism in aging and\nAD. However, their effects on white matter metabolism have been far less studied. Our goal in this section\nwas to systematically examine whether these well-established related factors of gray matter metabolism\ncontribute to regional variations in WM FDG uptake. To avoid confusion, we have revised the sentence and\nadded citations supporting the influence of these factors on GM metabolism (e.g., Willis MW, 2002; Verger\nA, 2017; Goyal MS, 2019; Kuczynski B, 2009; Jiaerken Y, 2019; Hanseeuw BJ, 2017). The revised text now\nexplicitly states that this background motivates our investigation into influence factors of WM metabolism.\nRevised text (Line 174):\n“Given that demographic, genetics, vascular health, and mixed pathologies are known to influence GM\nmetabolism in brain aging [31-36], we examined whether these same factors also contribute to inter-\nindividual variation in WM FDG metabolism across cohorts.”\nReferences:\nWillis MW, Ketter TA, Kimbrell TA, et al. Age, sex and laterality effects on cerebral glucose metabolism in healthy\nadults. Psychiatry Res. 2002;114(1):23-37.\nVerger A, Stegmayr C, Galldiks N, et al. Evaluation of factors influencing 18F-FET uptake in the brain. Neuroimage Clin.\n2017;17:491-497.\nGoyal MS, Blazey TM, Su Y, et al. Persistent metabolic youth in the aging female brain. Proc Natl Acad Sci U S A.\n2019;116(8):3251-3255.\nKuczynski B, Jagust W, Chui HC, Reed B. An inverse association of cardiovascular risk and frontal lobe glucose\nmetabolism. Neurology. 2009;72(8):738-743.\nJiaerken Y, Luo X, Yu X, et al. Microstructural and metabolic changes in the longitudinal progression of white matter\nhyperintensities. J Cereb Blood Flow Metab. 2019;39(8):1613-1622.\nHanseeuw BJ, Betensky RA, Schultz AP, et al. Fluorodeoxyglucose metabolism associated with tau-amyloid interaction\npredicts memory decline. Ann Neurol. 2017;81(4):583-596.\n7. Lines 186-200: please specify how many participants from each dataset were included in the AD\nbiomarker analysis. Additionally, a section explaining the statistical analysis approach for this section\nwould be good to include--it appears to be absent from the Statistical Analysis section. In particular, the\nauthors list covariates of age, sex, and APOE e4 carrier status; however, it's not clear whether these\ncovariates were regressed out of the FDG PET data, the biomarker data (A-beta, tau, WMH burden, NfL,\nand GFAP), or both--i.e., are the r values reported partial or semi-partial correlations?\nResponse: Previously, we noted the exact sample size from each cohort included in the AD biomarker\nanalysis in the legend of Fig. 3, and Supplementary Table 1 also provides information on missing data for\neach variable across different datasets.\n[Sample sizes]: Each partial correlation analysis includes all participants with available data for the\ncorresponding biomarker: A-beta: MCSA n = 1,989; ADNI n = 1,153, WMH: MCSA n = 1,947; ADNI n = 1,150,\nTau: MCSA n = 499; ADNI n = 235, Plasma GFAP: MCSA n = 1,696; ADNI n = 419, Plasma NfL: MCSA n =\n1,696; ADNI n = 215.\nIn the revised manuscript, we have revised the Methods section to clearly describe the statistical methods.\nNo revisualization was performed beforehand; rather, the covariates were included jointly in the partial\ncorrelation computation. The reported r* values represent partial correlations controlling simultaneously\nfor the listed covariates.\nRevised text (Line 592):\n“To investigate relationships between WM metabolism and various pathological markers, we performed\npartial correlation analyses adjusting for age, sex, and APOE-ε4 status. In these models, WM metabolic\nmeasures (EWM and AWM FDG SUVR) and biomarker values (Aβ centiloid, tau SUVR, WMH/TIV%, plasma\nGFAP, and plasma NfL) were entered directly, and covariates were controlled simultaneously within the\ncorrelation computation; thus, the reported correlation coefficients represent partial correlations. No\nprior revisualization of FDG or biomarker values was performed. To improve normality, WMH/TIV%, GFAP,\nand NfL values were log10-transformed prior to analysis. All analyses were conducted separately in MCSA\nand ADNI using all available data for each biomarker; sample sizes varied by biomarker and are reported\nin the figure legends.”\nReviewer #3\nI really appreciate the hard work by the authors. The manuscript has significantly improved in terms of\nrigor and replicability. I have a few remaining comments:\nWe appreciate the reviewers’ positive evaluation of our revisions.\nIn this round, we clarified unclear sentences and revised language that could imply causality to improve\nthe rigor of the manuscript, and we also added supplementary information about the longitudinal analyses\nto help interpretation.\n1. The corona radiata (CR) are not only supplied by small penetrating arteries, but also drained by deep\nmedullary veins. As such, CR could be vulnerable to small vessel disease pathology in the form of WMH\nvisible on MRI. Authors however report that no association was found between AWM (=CR) FDG uptake\nand WMH. Instead, higher AWM FDG uptake was associated with vascular risk “but this effect can be partly\noffset by increased amyloid, which has an opposite influence”. Additionally, higher AWM FDG was\nassociated with lower NDI, suggesting microstructural disintegration (I infer this from (1) negative\nassociations between AWM FDG and NDI, and (2) “higher NDI values reflect healthier WM microstructural\nintegrity”). I have several comments:\n- Given these findings, it remains unclear to me why AWM FDG is interpreted as a disease-related\ncompensatory mechanism.\n- Please explain what is meant by “partial off-set by increased amyloid” and indicate where these results\nare reported. Fig 5A and C display the total effect of VRF on AWM FDG but no indirect effects via AD\npathology are shown?\n- If AWM FDG is particularly driven by (aging and) VRF, then how does microstructure (measured by NDI)\nfit into this framework?\nResponse: We thank the reviewer for this insightful and detailed comment. We address the points you\nraised below:\n(1) Correlational mismatch: AWM vs WMH null but VRF positive\nWe agree that the corona radiata is anatomically vulnerable to small vessel disease, and our current\nfindings need careful interpretation. In unadjusted Pearson correlation analyses, AWM FDG showed a\nsignificant positive correlation with WMH in both cohorts (MCSA r = 0.26; ADNI r = 0.15; both p < 0.001),\nconsistent with shared age-related variance. However, after adjusting for age, sex, and APOE-ε4, this\nassociation was no longer significant (MCSA r = 0.02, p = 0.35; ADNI r = 0.01, p = 0.81), with age accounting\nfor the largest attenuation. We therefore reported covariate-adjusted partial correlations in the\nmanuscript (Fig. 4) to reflect the association independent of these strong confounders. In contrast, EWM\nFDG showed a robust negative association with WMH both before and after adjustment (unadjusted:\nMCSA r = −0.43; ADNI r = −0.29; adjusted: MCSA r = −0.22; ADNI r = −0.15; all p < 0.001), indicating a more\ndirect relationship with MRI-visible WM injury. These results suggest that AWM metabolism captures\nvascular-related variation not fully represented by WMH burden.\n(2) Whether elevated AWM FDG represents compensation?\nWe do not interpret elevated AWM FDG as a successful compensatory mechanism. Instead, elevated AWM\nmetabolism appears more likely to present a maladaptive metabolic state, potentially reflecting gliosis-\nrelated activity, altered glycolytic processes, or reduced energetic efficiency in the context of aging and\nvascular stress. This interpretation is supported by observations, including a) elevated AWM FDG\nassociated with current worse cognition and future faster cognitive decline. b) higher AWM FDG is\nassociated with lower NDI, indicating compromised microstructural integrity. c) individuals with higher\nbaseline AWM metabolism are typically older, predominantly male, have higher vascular risk, and exhibit\nhigher Aβ burden, more severe white matter lesions, and higher plasma GFAPOE-ε4, and higher plasma GFAP levels. (Note: corrected to include APOE-ε4 as per context)\n(3) Clarification of “partial offset by increased amyloid”\nBy this phrase, we did not claim a mediation or indirect pathway between vascular and AWM metabolism\nthrough AD pathology. This refers to opposing total effects observed in the SEM models (Supplementary\nTable 5 and 7). Specifically, in both cohorts, vascular risk showed a positive total effect on AWM FDG (MCSA\nβ = 0.21; ADNI β = 0.099), whereas amyloid burden showed a negative total effect on AWM FDG (MCSA β\n= -0.056; ADNI β = -0.087). Therefore, in the ADNI cohort, which is AD-enriched and has lower vascular\nrisk, a higher amyloid burden reduces the net increase in AWM metabolism associated with vascular risk.\nWe have revised the manuscript to avoid implying unmodeled indirect effects.\nRevised text (Discussion, line 282):\n“Cross-sectionally, EWM metabolism was lower in individuals with greater age, Aβ burden, and WMH,\nwhereas higher AWM metabolism was observed in people with older age and vascular risk, with reduced\nlevels in the presence of greater AD pathology (Aβ and APOE-ε4).”\n(4) Role of microstructure (NDI) in the AWM framework\nIn our consideration, microstructural integrity provides a structural context for metabolic activities and\ntheir association with cognition. In our results, EWM and AWM show opposite regional NDI-FDG\nassociation patterns (Fig. 6C), and their FDG-cognition associations are also in opposite directions (Fig. 2\n& Table 2). Taken together, these findings indicate that healthier WM microstructure is generally\nassociated with better cognitive performance. This view can also be supported by establishing a\nmicrostructure-metabolism-cognition mediation model in subjects with NDI value available (figure below).\n2. In the new figure 6, lower EWM FDG uptake was associated with steeper cognitive decline in CU A+. On\nthe other hand, for AWM FDG, the intercept of the FDG-high group is lower than that of the FDG-low\ngroup, but their slopes are similar. This suggests that higher AWM FDG does not accelerate cognitive\ndecline in the preclinical stage compared to low FDG. Instead, CN A+ individuals with higher AWM FDG\nappear to have overall lower cognition compared to low FDG (how is it compensatory then?), although it\nis unclear whether this difference in intercept is statistically significant. Overall, this points to EWM and\nnot AWM FDG accelerating cognitive decline in the preclinical stage, which warrants more in-depth\ndiscussion in the manuscript. While the study was not designed to determine whether AWM or EWM FDG\nchanges occur first, their relative effects on early-stage cognition could be further discussed.\nResponse: We would like to clarify the interpretation of Fig. 6B.\n(1) Fig. 6B depicts longitudinal cognitive trajectories, not longitudinal changes in FDG metabolism.\nAccordingly, the slopes represent the rate of cognitive decline, reflecting how baseline FDG levels relate\nto subsequent cognitive change over time. To help interpretation, we additionally annotated the slopes of\neach predicted trajectory. Across both cohorts, and in both A- and A+ groups, individuals with lower EWM\nFDG and higher AWM FDG consistently exhibited steeper cognitive decline, indicating faster deterioration.\nThis pattern is visually most pronounced in the A+ group, likely because baseline cognitive differences and\nindividual overall risk profiles between FDG-defined groups become amplified in the presence of AD\npathology.\nFig.6B Longitudinal trajectories of cognition stratified by baseline WM metabolism. Shaded colored bands indicate 95% CI of the\nestimated mean. Outcomes are predicted global z-score (MCSA) and sign-reversed ADAS-Cog11 (-ADAS-Cog11; higher values\nindicate better cognition) in ADNI. Higher EWM-FDG (>75th percentile) was consistently associated with slower cognitive decline,\nwhereas higher AWM-FDG (<25th percentile) predicted faster decline in both MCSA and ADNI. Stratification by Aβ status further\nshowed that these effects persisted in both Aβ– and Aβ+ groups, with the impact of EWM being amplified in Aβ+ individuals.\n(2) The intercepts represent baseline cognitive performance. To address the reviewer’s concern regarding\nintercept differences, we provide supplementary tables (below) summarizing baseline cognition and\nparticipant characteristics across FDG groups. These data show that individuals with lower EWM FDG or\nhigher AWM FDG at baseline were generally older, more often male, with higher vascular risks, and had\nhigher Aβ burden, greater WMH, and higher plasma GFAP levels, which is also consistent with our cross-\nsectional analyses. Thus, baseline intercept differences are likely to reflect differences in overall risk\nprofiles at that time point.\nSupplementary Tables 8. Baseline characteristics across FDG groups of MCSA cohort\nMCSA cohort\nA- A+ A- A+\nEWM-high EWM-low EWM-high EWM-low AWM-high AWM-low AWM-high AWM-low\n(n=407) (n=260) (n=79) (n=213) (n=305) (n=320) (n=168) (n=93)\nEWM-FDG SUVR 1.07 (0.04) 0.88 (0.04) 1.06 (0.03) 0.87 (0.05)\nAWM-FDG SUVR 0.79 (0.03) 0.68 (0.02) 0.79 (0.03) 0.68 (0.02)\nAge, years 64.0 (8.6) 75.7 (8.6) 71.1 (8.0) 79.9 (6.5) 73.9 (8.8) 63.6 (8.4) 79.1 (7.0) 72.7 (7.7)\nMale, no. (%) 148 (36.4) 184 (70.8) 27 (34.2) 136 (63.8) 173 (56.7) 171 (53.4) 90 (53.6) 42 (45.2)\nCMC score, median (IQR) 1 (0–2) 2 (1–3) 2 (1–3) 3 (2–4) 2 (1–3) 1 (0–2) 3 (2–4) 2 (1–3)\nGlobal cognition (z-score) 0.62 (0.92) -0.28 (1.09) 0.12 (1.03) -0.73 (1.06) -0.11 (1.06) 0.61 (0.96) -0.68 (1.14) 0.16 (0.91)\nAβ SUVR 1.34 (0.07) 1.36 (0.07) 1.77 (0.30) 2.03 (0.47) 1.36 (0.07) 1.34 (0.08) 1.97 (0.44) 1.91 (0.40)\nWMH/TIV (%) 0.45 (0.44) 1.14 (1.08) 0.67 (0.54) 1.45 (1.05) 0.91 (0.87) 0.51 (0.74) 1.43 (1.15) 0.74 (0.64)\nPlasma GFAP, pg/ml 84.3 (43.4) 115.0 (54.8) 113.4 (50.0) 163.8 (72.5) 109.3 (55.9) 79.1 (38.3) 147.4 (72.6) 133.2 (58.9)\nSupplementary Tables 9. Baseline characteristics across FDG groups of ADNI cohort\nADNI cohort\nA- A+ A- A+\nEWM-high EWM-low EWM-high EWM-low AWM-high AWM-low AWM-high AWM-low\n(n=126) (n=62) (n=68) (n=166) (n=122) (n=135) (n=109) (n=163)\nEWM-FDG SUVR 1.10 (0.05) 0.79 (0.06) 1.10 (0.06) 0.78 (0.06)\nAWM-FDG SUVR 0.89 (0.04) 0.73 (0.02) 0.88 (0.04) 0.73 (0.03)\nAge, years 67.6 (5.7) 77.1 (7.2) 71.9 (6.9) 75.3 (7.5) 73.5 (8.0) 70.1 (7.2) 75.6 (7.3) 73.5 (7.3)\nMale, no. (%) 47 (37.3) 44 (71.0) 22 (32.4) 101 (60.8) 75 (61.5) 79 (58.5) 54 (49.5) 82 (50.3)\nVRF score, median (IQR) 1 (0–1) 1 (0–1) 1 (0–1) 1 (0–1) 1 (0–1) 1 (0–1) 1 (0–1) 1 (0–1)\nADAScog-11 6.1 (3.2) 11.4 (6.0) 7.4 (3.9) 16.9 (8.2) 7.9 (4.8) 7.2 (4.2) 12.7 (6.9) 11.9 (7.5)\nAβ centiloid 0.7 (9.3) -4.6 (11.9) 66.3 (40.0) 87.4 (34.2) -2.1 (11.2) -1.5 (10.0) 79.4 (33.5) 76.2 (37.5)\nWMH/TIV (%) 0.44 (0.50) 0.98(0.76) 0.72 (0.82) 1.08 (1.02) 0.71 (0.79) 0.58 (0.64) 0.94 (1.24) 0.88 (0.79)\nPlasma GFAP, pg/ml# 93.1 (30.5) 148.9 (80.0) 190.4 (85.9) 259.8 (101.6) 119.2 (62.8) 106.9 (36.9) 238.7 (119.2) 193.7 (101.6)\nTo summarize, our findings indicate that both baseline EWM and AWM metabolic levels are related to\nearly cognitive function and provide prognostic information about future cognitive decline. Regarding the\nreviewer’s question about compensation, we do not interpret higher AWM FDG as a compensatory sign.\nIntegrating evidence from the SEM analyses (Fig. 5), NDI results (Fig. 6C), and baseline group\ncharacteristics (above Table 1-2), elevated AWM metabolism is more consistent with maladaptive\nmetabolic processes, potentially related to gliosis or inefficient energy utilization, reflecting failure rather\nthan successful compensation. We have revised the Results and Discussion accordingly to clarify these\npoints.\nRevised text:\n(Results: line 240-251) “Considering the interaction between Aβ pathology and time, we examined\ncognitive trajectories stratified by Aβ status and observed a graded pattern of cognitive decline. Fig. 6B\ndepicts longitudinal cognitive trajectories with slopes reflecting the rate of cognitive decline as a function\nof baseline FDG levels. When stratified by Aβ status, the protective effect of high EWM metabolism and\nthe harmful effect of high AWM metabolism persisted in both Aβ– and Aβ+ groups. Notably, the effect of\nEWM was markedly amplified in the Aβ+ group compared with AWM. Baseline intercept differences reflect\nbaseline cognitive performance and were accompanied by differences in age, vascular risk, and\npathological burden (Supplementary Tables 8–9). All these results indicate that WM metabolism provides\ninformation on longitudinal cognitive change beyond established risk factors. Higher EWM and lower\nAWM metabolism at baseline were associated with more favorable cognitive trajectories, whereas lower\nEWM and higher AWM metabolism were associated with faster decline.”\n(Discussion: line 392-397) “… In this context, elevated AWM metabolism is more consistent with\nmaladaptive metabolic states, potentially reflecting gliosis-related activity or reduced energetic efficiency\nrather than successful compensation. These findings help contextualize why higher EWM and lower AWM\nmetabolism at baseline are associated with flatter cognitive decline trajectories, consistent with\ndifferences in WM microstructural integrity and network efficiency.”\n3. In response to R2 #13 (and throughout the manuscript), the results are described as “EWM indicating\nneurodegenerative vulnerability and AWM indexing age- and vascular-related metabolic adaptation”.\nHowever, the new analysis presented in Supplementary Table 10 indicates that EWM FDG is also associated\nwith vascular risk (p=0.008) and age (p<0.001). There is a known strong bidirectional influence between\nvascular and neurodegenerative processes as well as aging, which makes it hard to disentangle these\nprocesses in the way that it is concluded there.\nResponse: We agree that vascular, neurodegeneration, and aging exert strong bidirectional and\ninterdependent influences on brain metabolism, making strict separation challenging. Our intention was\nnot to imply that EWM and AWM metabolism exclusively reflect neurodegenerative or vascular processes.\nOur interpretation is based on relative effect patterns observed in the SEM analyses, particularly\ncomparison of the absolute values of total effects across pathways. In both cohorts, amyloid burden and\nAPOE-ε4 showed stronger total effects on EWM-FDG, whereas vascular risk exerted a larger total effect on\nAWM-FDG (Supplementary Table 5 and 7, also can see below). These findings suggest that EWM\nmetabolism is more strongly influenced by AD neurodegenerative pathology, while AWM metabolism\nshows greater sensitivity to vascular-related metabolic alterations, rather than indicating mutually\nexclusive mechanisms. We have revised the manuscript to emphasize that EWM and AWM capture\npartially overlapping but differentially weighted contributions from aging, vascular, and\nneurodegenerative processes.\nSupplementary Table 5. Direct, indirect and total effect size measured from SEM in the MCSA cohort.\nPredictor Outcome Direct effect Indirect effect Total effect\nAPOE-ε4 EWM-FDG SUVR 0.000 -0.032 -0.032\nAPOE-ε4 AWM-FDG SUVR 0.000 -0.014 -0.014\nVascular risk score EWM-FDG SUVR -0.054 0.000 -0.054\nVascular risk score AWM-FDG SUVR 0.210 0.000 0.210\nAmyloid EWM-FDG SUVR -0.112 -0.012 -0.124\nAmyloid AWM-FDG SUVR -0.056 0.000 -0.056\nSupplementary Table 7. Direct, indirect and total effect size measured from SEM in the ADNI cohort.\nPredictor Outcome Direct effect Indirect effect Total effect\nAPOE-ε4 EWM-FDG SUVR 0.000 -0.133 -0.133\nAPOE-ε4 AWM-FDG SUVR 0.000 -0.039 -0.039\nVascular risk score EWM-FDG SUVR 0.000 -0.012 -0.012\nVascular risk score AWM-FDG SUVR 0.099 0.000 0.099\nAmyloid EWM-FDG SUVR -0.282 -0.013 -0.296\nAmyloid AWM-FDG SUVR -0.087 0.000 -0.087\nRevised text (line 450):\n“In conclusion, our study demonstrates that EWM and AWM represent complementary metabolic features\ncapturing differentially weighted aspects of aging-related WM vulnerability. EWM shows greater\nsusceptibility to AD-related degeneration, whereas AWM appears more strongly influenced by vascular-\nrelated alterations. Together, these patterns shed light on the heterogeneity of WM metabolic processes\nand their differential associations with cognitive decline. Elevated AWM metabolism may reflect\nmaladaptive metabolic states, potentially related to inflammatory activity or reduced energetic efficiency,\nrather than successful compensation.”"}, {"round": 2, "reviewer_comments": {"reviewer_1": "I appreciate the authors' responsiveness to the prior round of reviews, which have greatly improved the manuscript. I have only a couple of minor requests:\nIn Figure 2, the AWM factor is much less prominent in the ADNI models than the MCSA models. Can the authors comment on this? Do they still feel the AWM metabolism has useful explanatory value in ADNI?\nIn the Discussion, the authors provide some speculation (lines 357-362) about factors that might lead to hypermetabolism in the AWM. Do the authors similarly have some speculation or interpretation about why EWM and AWM components have opposite patterns of association with age, cognition, and other factors? In other words, what factors might account for the existence of WM components with opposing metabolic trends, rather than a single, common trend across all WM?", "reviewer_3": "I thank the authors for their great response and I have no further comments."}, "author_response": "Dear Editor:\nWe sincerely thank you for accepting our manuscript. We are also deeply grateful to you and the reviewers\nfor your time, thoughtful evaluation, and constructive feedback, which have substantially strengthened\nour work. We have carefully addressed the remaining comments and provided a detailed, point-by-point\nresponse below. In the response that follows, the reviewers’ original comments are presented in blue, our\nresponses are provided in black, and all corresponding revisions in the manuscript are indicated in red for\nclarity. We appreciate the opportunity to revise our manuscript and look forward to its publication.\nReviewer #1\nI appreciate the authors' responsiveness to the prior round of reviews, which have greatly improved the\nmanuscript. I have only a couple of minor requests:\nIn Figure 2, the AWM factor is much less prominent in the ADNI models than the MCSA models. Can the\nauthors comment on this? Do they still feel the AWM metabolism has useful explanatory value in ADNI?\nResponse: The reduced prominence of the AWM factor in ADNI likely reflects cohort differences rather\nthan diminished biological relevance (as stated in Discussion, page 15, lines 421–426). Notably, AWM-FDG\nremained significantly associated with cognition in ADNI and explained non-overlapping variance in\nShapley decomposition. Thus, although reduced in magnitude, its contribution remains complementary\nrather than redundant.\nDiscussion (page 15, lines 414–419): “MCSA is a population-based aging cohort with broader age\nvariability and higher vascular burden, whereas ADNI is clinic-based and enriched for Alzheimer’s disease,\nwith narrower age dispersion and minimized vascular comorbidities. In our SEM analyses, age and vascular\nrisk were key drivers of increased AWM metabolism in both cohorts, but the total effect was smaller in\nADNI, consistent with its lower vascular burden. Because AWM appears to capture an aging- and vascular-\nrelated metabolic axis, its relative explanatory weight is weakened in ADNI, where cognitive variance is\nmore strongly driven by AD pathology.”\nIn the Discussion, the authors provide some speculation (lines 357-362) about factors that might lead to\nhypermetabolism in the AWM. Do the authors similarly have some speculation or interpretation about\nwhy EWM and AWM components have opposite patterns of association with age, cognition, and other\nfactors? In other words, what factors might account for the existence of WM components with opposing\nmetabolic trends, rather than a single, common trend across all WM?\nResponse: We speculate that the opposing metabolic patterns of EWM and AWM reflect biologically\ndistinct white matter axes rather than uniform processes across all tracts. Through direct molecular\nvalidation is currently lacking, this interpretation is grounded in our findings, the known anatomical\norganization of white matter, and prior literature on neurodegenerative and vascular mechanisms.\nEWM regions (corpus callosum and cingulum) are long-range commissural and association fibers that are\nparticularly vulnerable to age- and AD-related neurodegeneration. Decreased EWM-FDG may therefore\nreflect reduced axonal integrity, synaptic disconnection, or demyelination accompanying\nneurodegenerative processes. In contrast, AWM regions (corona radiata) are projection fibers located in\ndeep white matter supplied by small penetrating arteries and are especially susceptible to microvascular\nstress. Chronic hypoperfusion and vascular burden may trigger reactive glial activation and metabolic shifts\ntoward glycolysis, which could manifest as relatively increased AWM-FDG uptake.\nThus, the WM components with opposing metabolic trends may reflect the coexistence of two partially\ndissociable biological processes: neurodegenerative hypometabolism and vascular/inflammatory-related\nhypermetabolism. These mechanisms are not mutually exclusive and may operate concurrently within the\naging brain, but further multimodal and molecular studies will be required to directly adjudicate these\nhypotheses. This clarification has been added to the revised Discussion (page 13, lines 357-362)."}]

Decision letter