bluuebunny/crossref_metadata_2025 huggingface dataset:
The Association between measures of Sleepiness and Subjective Cognitive Decline symptoms in a diverse population of cognitively normal older Adults
Authors: Briggs Anthony Q , Betensky Rebecca A , Bernard Mark A , Masurkar Arjun V.
Published: January 2025

Background

Subjective cognitive decline (SCD) is a preclinical stage of Alzheimer’s disease (AD), with correlations to cerebral amyloid and tau and accelerated cognitive decline. Studies have also revealed an association between sleep fragmentation and such AD biomarkers and cognitive decline, suggesting that cognitive function should be monitored in individuals experiencing excessive sleepiness. It is unclear if and how sleep dysfunction relates to SCD apart from AD biomarkers, as well as symptoms related to SCD and sleep dysfunction, such as depression. Furthermore, studies of SCD and sleep dysfunction have primarily focused on non‐Hispanic Whites. Hence, a better understanding of how sleep impacts preclinical AD risk in SCD is needed in more diverse cohorts. We investigated the cross‐sectional relationship between excessive sleepiness in cognitively normal older adults and SCD symptoms linked to cognitive domains in a cohort consisting of NHWs and Black/African Americans (B/AA).

Method

This retrospective cross‐sectional study included cognitively normal older participants (n = 147, age> 60 years, 61 B/AA, 86 NHW) evaluated at the NYU Alzheimer’s Disease Research Center. Participants underwent Uniform Data Set 3.0 psychometric testing to confirm normal cognition, Epworth Sleepiness Scale (ESS), Geriatric Depression Scale (GDS), and the Cognitive Change Index (CCI) as a measure of SCD. The CCI was further divided into subscores of non‐amnestic (i.e. executive) and amnestic domains (i.e. memory). Participants underwent amyloid (florbetaben) and tau (PI‐2620 or MK‐6240) PET MRI, with composite SUVR calculated by averaging key cortical regions. Multivariable linear regression models tested the association of ESS and covariates (GDS, amyloid SUVR, tau SUVR, race) to the three CCI outcome measures (total, non‐amnestic, amnestic).

Result

ESS (excessive sleepiness) was significantly positively associated with total CCI (p = 0.01042) as well as non‐amnestic (0.00771) and amnestic CCI subgroups (0.02583), after full covariate adjustment, as was GDS. Race did not show an independent statistically significant association.

Conclusion

These results suggest that excessive sleepiness, independent of depression and amyloid and tau burdens, may be a significant contributor to SCD severity. Furthermore, these contributions impact both amnestic and non‐amnestic domains. Future studies are needed to investigate the longitudinal impact of excessive sleepiness on SCD outcomes.
Characterizing White Matter Hyperintensity (WMH) Patterns with Texture‐Based Analysis
Authors: Alshikho Mohamad J. , Morales Clarissa , Pyne Jeffrey D. , Amarante Erica , Lao Patrick J. , Turney Indira C. , Edwards Natalie C. , Gu Yian , Manly Jennifer J. , Mayeux Richard , Brickman Adam M.
Published: January 2025

Background

White matter hyperintensities (WMHs) are areas of increased signal on T2‐weighted MRI scans. They vary in size, location, and intensity, suggesting different underlying conditions like small vessel disease and inflammation. This variation potentially links WMH to outcomes ranging from normal aging to severe neurological disorders. We used a texture analytic approach, Gray‐Level Co‐occurrence Matrix (GLCM), to characterize the intensity heterogeneity of WMHs. This method classifies WMHs into distinct zones based on homogeneity scores that represent uniformity in signal intensity. We explored associations among white matter microstructure, cerebral blood flow, and WMH volume within these zones.

Method

One hundred sixty‐one participants from the Washington Heights‐Inwood Columbia Aging Project (WHICAP) were included. White matter hyperintensities were labeled using in‐house software, which also created normalized homogeneity maps through GLCM. These maps were used to define three WMH zones based on their degree of homogeneity (i.e., homogeneous, intermediate, and heterogeneous) (Figure 1). We examined the relationship among fractional anisotropy (FA), cerebral blood flow (CBF), and WMH volume within these WMH zones and compared CBF and FA values across the zones.

Result

In homogeneous and intermediate WMH zones, there was no association between FA and CBF (rhomo. = 0.022, phomo. = 0.787; rinter. = ‐0.12, pinter. = 0.132). However, both FA and CBF negatively correlated with WMH volume in these zones (rFA‐homo. = ‐0.348, pFA‐homo.< 0.0001; rFA‐inter. = ‐0.538, pFA‐inter.< 0.0001; rCBF‐homo. = ‐0.36, pCBF‐homo.< 0.0001; rCBF‐inter. = ‐0.40, pCBF‐inter.< 0.0001). In the heterogeneous zone (Figure 2), FA and CBF were negatively correlated (rhetero. = ‐0.422, phetero.< 0.0001). CBF, but not FA, was associated with WMH volume (rCBF‐hetero. = ‐0.471, pCBF‐hetero.< 0.0001; rFA‐hetero. = 0.085, pFA‐hetero. = 0.300) In homogenous WMH regions, both [FARatio (0.776±0.009) and CBFRatio (0.96±0.014)] values were lower than in intermediate [FARatio (0.84±0.009) and CBFRatio (0.97±0.014)] and heterogeneous zones [FARatio: (1.029±0.009), CBFRatio: (1.002±0.014)].(Figure 3)

Conclusion

Distinct correlation patterns exist between CBF and FA in WMH clusters defined by intensity heterogeneity. Homogeneous zones, likely reflect older lesions, show low microstructural integrity and minimal relationship with CBF, suggesting high gliosis. Heterogeneous zones, possibly reflecting recent white matter damage, show associations between CBF and microstructure, potentially indicating active inflammatory processes contributing to vascular injury.
In vivo data‐driven patterns of Amyloid‐ and Tau accumulation associated with AD progression using 18F‐MK6240 and 18F‐NAV4694 PET
Authors: Fonov Vladimir S , Rosa‐Neto Pedro , Collins D Louis
Published: January 2025

Background

Alzheimer’s disease is a neurodegenerative disease associated with the accumulation of Amyloid‐ß and Tau neurofibrillary tangles following a pattern known as Thal and Braak stages, respectively (Thal 2002; Braak 1995,2011). Recent research (Pascoal 2020) showed the possibility of recapitulating Braak’s histopathological stages in vivo using PET tracer [18F]‐MK‐6240 with manually defined regions of interest. This study analyzes the joint patterns of Amyloid‐ß and Tau accumulation associated with AD in a completely data‐driven fashion.

Method

We used T1w MRI, [18F]‐NAV4694, and [18F]MK6240 PET scans from 36 young individuals (47 scans), 192 healthy elderly individuals (280 scans) in the community or outpatients at the McGill University Research Centre for Studies in Aging (97 individuals with mild cognitive impairment (140 scans) and 75 patients with AD (98 scans). T1w MRIs were pre‐processed (non‐uniformity correction, intensity normalization, stereotaxic registration, brain masking, tissue classification) and non‐linearly registered to the ADNI template using ANTs (Avants et al 2011). PET scans were linearly registered to the T1w MRI, and voxel‐wise standardized uptake value ratios (SUVR) were calculated using whole cerebellum grey matter as the reference, and then non‐linearly warped into the space of the ADNI template and downsampled to 2mm3 resolution. All PET scans were concatenated to create a matrix 518×345846. This matrix was factorized into two low‐rank components (Krichene 2018) with the constraint that all matrix elements should be positive, spatial loadings constrained to be between 0 and 1, latent vectors ordered monotonously to capture the additive nature of disease progression, and the loadings aligned with MOCA scores, by minimizing cosine distance. We used spatial components to create pseudo‐probabilistic ROIs associated with the different stages of disease progression.

Result

Our method revealed regional patterns of joint tau and amyloid accumulation associated with AD progression, shown in Figure 1. Associated weighted SUVR average values are shown on Figure 2. The first two components, as visible on Figure 2, have almost no relationship with MOCA score and are most likely associated with off‐target bindings for both tracers.

Conclusion

This work is a stepping stone towards creation of the Tau & Amyloid AD disease staging system.
Alzheimer's disease staging with [18F]MK6240 and [18F]Flortaucipir ‐ the HEAD study
Authors: Soares Carolina , Povala Guilherme , Bauer‐Negrini Guilherme , Lussier Firoza Z , Ferreira Pamela C.L. , Bellaver Bruna , Amaral Livia , Zalzale Hussein , Madeiros Marina Scop , Cehula Juli , Bloomquist Madeleine , Oliveira Markley , Felix Cynthia , Rodrigues Matheus Scarpatto , Ruppert Emma Patrice , Abbas Sarah , Zimmer Eduardo R. , Pascual Belen , Gordon Brian A. , Lowe Val J. , Oh Hwamee , Soleimani‐Meigooni David N. , Rosa‐Neto Pedro , Tudorascu Dana , Jagust William J. , Klunk William E , Baker Suzanne L. , Pascoal Tharick A.
Published: January 2025

Background

Utilizing PET amyloid‐beta (Aß) and tau for staging Alzheimer’s Disease (AD) has demonstrated potential in identifying individuals with varying degrees of disease severity, applicable to both clinical trials and practice. However, the diverse binding characteristics of tau tracers pose challenges to the application of this staging across different ligands. In this study, we evaluate a novel staging framework proposed by the AA working group, employing Aß PET and either [18F]MK6240 or [18F]Flortaucipir in individuals participating in a head‐to‐head study of tau PET tracers. This will provide insights into the robustness of this framework when applied across different tau ligands.

Method

We evaluated 154 people (89 CU and 65 CI), with MK6240, Flortaucipir, Aß‐PET, MRI. SUVRs used inferior cerebellar gray as reference. Participants were categorized in Aß positivity(A), using visual read, and tau positivity (T) in medial temporal lobe(MTL) and neocortex(NEO). Tau MTL(TMTL) and NEO positivity were defined as average +3 SD of youngs. NEO moderate(TMOD) and high(THIGH) was defined as below and above median SUVR of NEO‐positive individuals. Individuals were grouped as: A‐T‐, A+T‐, A+TMTL+(early), A+TMOD+(intermediary), and A+THIGH+(advanced).

Results

We found that the distribution of the number of individuals across the five AD stages was comparable when using both MK6240 and Flortaucipir tracers (Figure 1). However, upon examining the concordance within each group, we found a low concordance of 30% (3/10) between tracers in the early tau stage (A+TMTL+). Concordance increased in the intermediary stage A+TMOD+, with a rate of 65% (9/14), and further increased to 79% (11/14) in the advanced stage (A+THIGH+). The A+T‐ group exhibited the highest concordance between tracer results, at 95% (21/22).

Conclusion

Our findings indicate that the application of this framework across different populations using different tau tracers can lead to a compatible distribution among groups. However, in a direct comparison, we noted that this did not necessarily result in high concordance between groups, as group shifts were observed that depended on the cutoffs used (data not shown). These results emphasize the necessity for harmonization of tau PET tracers prior to their utilization for stage determination.
Theta‐ and alpha‐band functional connectivity are associated with longitudinal tau accumulation in asymptomatic individuals expressing Aβ pathology
Authors: Rudolf Jonathan Gallego , Wiesman Alex I , Baillet Sylvain , Villeneuve Sylvia
Published: January 2025

Background

Recent studies suggest that tau pathology spreads between functionally connected (FC) brain regions. The accumulation of amyloid‐beta (Aß) promotes neural hyper‐activity in asymptomatic older adults, which might enhance tau spreading. We assessed the relationship between band‐specific neurophysiological FC and the rate of tau accumulation over time and tested whether this association is dependent on the presence of Aß pathology.

Method

We measured cortical Aß deposition using positron emission tomography (PET) and resting‐state neurophysiological activity using magnetoencephalography (MEG) at baseline, as well as longitudinal cortical tau PET deposition (mean time between scans = 4.3 years), in a group of cognitively unimpaired older adults with family history of Alzheimer’s Disease (PREVENT‐AD cohort; N = 84). Region‐specific measures (i.e. Aß, tau, MEG timeseries) were extracted from 68 cortical parcels of the Desikan‐Killiany atlas. Neurophysiological FC between cortical regions was estimated across delta, theta, alpha and beta frequency bands using the corrected amplitude envelope correlation (AEC‐c). A global Aß standardized uptake value ratio (SUVR) was calculated by taking the average across a set of early Aß accumulating regions. Individual‐specific tau epicenters were defined as the six cortical regions with the highest levels of baseline tau‐PET binding. The annual rate of change in tau SUVR between the first and second scan was calculated for each cortical region (Figure 1). Linear mixed‐effects models were designed to test if the neurophysiological FC connectivity of each cortical parcel with tau epicenters was related to increased longitudinal tau accumulation in that region, and whether this association was dependent on baseline levels of Aß pathology.

Result

Tau epicenters identified across individuals were mainly localized in temporal and medial posterior brain regions. Regional increases in theta‐ and alpha‐band FC with tau epicenters at baseline related to greater longitudinal tau accumulation in individuals expressing higher levels of global Aß pathology (Figure 2).

Conclusion

Our results suggest that tau spreads across cortical regions that are functionally connected in the theta‐ and alpha‐bands, but only in individuals with high levels of cortical Aß. This finding supports the notion that Aß‐related neural hyper‐activity promotes tau spreading across the cortex following individual‐specific patterns of FC in asymptomatic older adults.
Brain Functional Flexibility and its Relationship to the Preclinical Stage of Alzheimer’s Disease
Authors: Javanray Mohammadali , St‐Onge Frederic , Bellec Pierre , Misic Bratislav , Soucy Jean‐Paul , Villeneuve Sylvia
Published: January 2025

Background

Characterizing pathological and functional features of the preclinical stage of Alzheimer’s Disease (AD) is essential as Amyloid beta (Aß) and tau, the pathological hallmarks of AD, start to accumulate years prior to the onset of clinical symptoms. Whether Aß and/or tau are related to the brain’s ability to functionally reconfigure in time (functional flexibility) remains unclear despite its important role in behavior and cognition.

Method

We included 233 cognitively unimpaired individuals with family history of AD from the PREVENT‐AD cohort who underwent both Positron Emission Tomography (PET) and functional Magnetic Resonance Imaging (fMRI). We computed the element‐wise product of the BOLD fMRI timeseries of the 400 Schaefer atlas nodes as co‐fluctuation matrices for the scan duration (all TRs). We then calculated the variabilities of the obtained co‐fluctuations at the whole brain and network level (DMN and limbic as early susceptible networks in AD) and used these measures of variability as a proxy of functional flexibility. We also computed the average of the obtained co‐fluctuation matrices, which mathematically corresponds to the Pearson correlation of the nodal timeseries pairs and used this as our marker of static functional connectivity (sFC). We then assessed the relationship between functional flexibility and sFC with the level of Aß (global index) and tau (temporal meta‐ROIs), using those, both as continuous and dichotomized variables (Figure.1). Two thresholds were used for Aß, one associated with low Aß accumulation (centiloid of 18) and the other with significant Aß burden (centiloid of 40).

Result

No association was found between AD pathology and functional flexibility or sFC using AD pathology as continuous or dichotomized variables (Figure 2 and 3). However, the maximum range of functional flexibility values in individuals with significant Aß burden and high tau was about half the one found in individuals with low or no pathology, a result that was particularly striking with tau.

Conclusion

The absence of group difference suggests that functional flexibility cannot be used as a proxy of AD. While individuals with AD pathology have a low range of functional flexibility values, low values are also frequent in individuals with no pathology.
Distinguishing Frontotemporal Lobar Degeneration Tau from TDP‐43 using white matter microstructure
Authors: Radhakrishnan Hamsanandini , Cook Philip A. , Phillips Jeffrey S , Olm Christopher A , Brown Christopher D. , Gee James C. , Lee Eddie B , Deerlin Vivianna M Van , Irwin David J , Massimo Lauren , McMillan Corey T
Published: January 2025

Background

Frontotemporal lobar degeneration (FTLD) is defined neuropathologically by misfolded tau (FTLD‐tau) or TAR DNA‐binding protein of 43 kDa (FTLD‐TDP). However, we lack biomarkers that can distinguish them in vivo which is a major barrier to effective disease‐modifying treatment trials. Based on neuropathological evidence of distinct patterns of cellular degeneration, more prominent in white matter (WM) for FTLD‐tau relative to FTLD‐TDP, we hypothesized that diffusion MRI (dMRI) measures of white matter microstructure would help dissociate FTLD‐tau and FTLD‐TDP during life.

Method

We evaluated dMRI for 157 individuals with FTLD‐Tau (n=66, autopsy‐confirmed sporadic=51, MAPT=15) and FTLD‐TDP (n=91, autopsy‐confirmed sporadic=25, GRN=19, C9orf72=47) defined by a pathogenic genetic mutation or autopsy confirmation. Images were preprocessed using QSIprep and reconstructed using Generalized Q‐sampling Imaging. Tensor‐based scalar maps of fractional anisotropy (FA) and mean diffusivity (MD) were then calculated. Deterministic tractography was run and white matter bundles of pathogenic interest based on literature were segmented with DSIstudio using template‐based tracking.

Result

Overall, FTLD‐Tau individuals showed worse white matter integrity compared to those with FTLD‐TDP with lower FA in the superior longitudinal fasciculus (SLF), uncinate fasciculus and fronto‐parietal cingulum; and higher MD in the uncinate fasciculus. Even within sporadic autopsy‐confirmed individuals, FTLD‐Tau subtypes had significantly lower FA in the SLF, fronto‐parietal cingulum, and left corticostriatal tract relative to FTLD‐TDP.

Conclusion

Our results demonstrate that white matter microstructure can distinguish between FTLD‐Tau and FTLD‐TDP pathologies in a tract‐specific manner with greater evidence of white matter degeneration in disease‐associated regions in FTLD‐Tau. Future work examining longitudinal associations with disease progression and white matter microstructure could aid in non‐invasive differentiation of these subtypes enabling more sensitive treatment monitoring.
Goldilocks Framework: semi‐automated model configuration for democratising data‐driven analyses of Alzheimer's disease progression
Authors: Oxtoby Neil P
Published: January 2025

Background

Data‐driven models are at the mercy of the data upon which they are built. Poorly configured models can lead to misleading conclusions. Here we introduce a framework called Goldilocks for carefully configuring data‐driven modelling analyses to remain in the Goldilocks Zone — features that are not too weak (in terms of disease signal), and model (hyper)parameters that are not too strong (with respect to available training data).

Method

Goldilocks has three key steps: 1. Normalise data. Transform data from cases (e.g., patients) into “disease signal”, e.g., normative z‐scores relative to controls. 2. Feature selection. Identify candidate input features having the necessary minimum signal. 3. Model configuration (hyperparameters). Remain within the available maximum signal. Experiments: we synthesize ADNI‐like biomarker data, define the Goldilocks Zone to lie between two event horizons (minimum and maximum disease signal), and demonstrate Goldilocks for configuring a Subtype and Stage Inference (SuStaIn) analysis. Minimum disease signal zmin is determined by the tipping point between case/control data, where the half‐normal cumulative density function HN‐CDF(zmin)==0.5. Maximum disease signal zmax is defined where the empirical distribution function EDF(zmax)==0.9, such that 10% of the biomarker’s data (from cases, not controls) exceeds zmax.

Result

We synthesized z‐scored ADNI‐like synthetic CSF biomarkers (ABETA, TAU), cognitive test scores (ADAS13, MMSE), and a brain volume (Ventricles normalised by intracranial volume). Figure 1 shows biomarker empirical distributions as a function of z‐score, and the Goldilocks Zone between event horizons at zmin==0.67 and EDF(zmax)==0.9. Since EDF(zmin) < 0.5 for all biomarkers, they each contain sufficient disease signal to be included in a data‐driven modelling analysis like SuStaIn. The maximum disease signal for each biomarker ranged between approximately 2 to 6. Figure 2 compares default parameters in a Z‐Score SuStaIn analysis (using the official pySuStaIn software) to Goldilocks‐informed parameters.

Conclusion

Goldilocks has the potential to improve validity and interpretability of data‐driven discoveries in general while also making data‐driven modelling accessible to non‐experts. This promotes democratization of data‐driven analyses, unlocking previously untapped data responsibly. This is particularly important in fields such as computational medicine, where data‐driven decisions can relate to life‐or‐death outcomes.
CSF sTREM2 is associated with neuroprotective microglial states early in Alzheimer's disease and deleterious effects later in the disease trajectory
Authors: Crook Harry , Wahdan Malak , Tuil Marie Emilie , Livingston Nicholas R , Raza Sanara , Nowell Joseph , Edison Paul
Published: January 2025

Background

Neuroinflammation is a key component of Alzheimer’s Disease (AD) pathology. Triggering receptor expressed on myeloid cells 2 (TREM2) is crucial to microglial involvement in AD, mediating trem2‐dependent activation and Disease‐Associated Microglia (DAM) polarization. However, GWAS revealed that loss‐of‐function mutations of its encoding gene are an important risk factor for AD. There is mounting evidence supporting a biphasic microglial response in AD trajectory, and such contrasting effects would support this. We evaluated the influence of cerebrospinal fluid soluble TREM2 (CSF sTREM2) on AD pathology in healthy, Mild Cognitive Impairment (MCI), and AD patients.

Method

Patients were grouped by diagnosis and each group segmented according to amyloid and tau, into amyloid‐positive (Aß+) and negative (Aß‐), and tau‐positive (tau+) and negative (tau‐). We used a combination of CSF biomarkers and positron emission tomography (PET) scans to assess neuropathological substrates of AD: CSF p‐tau181 for tau pathology, CSF Amyloid‐ß 42 (Aß42) and 18F‐florbetapir (18F‐AV45) PET scans for amyloid deposition, and magnetic resonance imaging (MRI) scans for neurodegeneration.

Result

We observed higher sTREM2 levels in Aß+/tau+ AD compared with Aß+/tau‐ AD and Aß‐/tau‐ MCI patients. Likewise, Aß+/tau+ MCI patients showed higher sTREM2 than Aß‐/tau‐MCI patients. Furthermore, sTREM2 levels were negatively associated with cortical volume in MCI and AD, and with MMSE scores in AD. sTREM2 was positively associated with CSF p‐tau181 in all groups except Aß+ controls. However, a positive association with CSF Aß42 was only observed in the Aß‐ control and the Aß+ and Aß‐ MCI groups. Similarly, sTREM2 was positively correlated with amyloid load (18F‐AV45 uptake) in controls and MCI patients yet negatively correlated in the AD group.

Conclusion

The MCI and AD stages of the disease showed contrasting associations between sTREM2 and neuroimaging markers for AD pathology. These suggest diverging effects of the microglial response along AD trajectory, with a neuroprotective phase in early AD and a neurotoxic one later on. This could indicate that microglia polarize first towards an anti‐inflammatory phenotype before switching to a pro‐inflammatory one as neuropathology progresses.
Longitudinal neurodegeneration in relation to amyloid pathology among cognitively unimpaired individuals
Authors: Stephenson Henry Gilreath , Betthauser Tobey J. , Hulle Carol A. Van , Du Lianlian , Langhough Rebecca E. , Jonaitis Erin M. , Kollmorgen Gwendlyn , Quijano‐Rubio Clara , Chin Nathaniel A. , Okonkwo Ozioma C , Carlsson Cynthia M. , Asthana Sanjay , Christian Bradley T. , Johnson Sterling C. , Blennow Kaj , Zetterberg Henrik , Bendlin Barbara B.
Published: January 2025

Background

The timing of neurodegeneration in relation to the onset of Alzheimer’s disease pathology is not fully known. This study examined the association of longitudinal atrophy derived from T1‐weighted MRI with 1) cerebrospinal fluid (CSF) amyloid‐tau (AT) groupings and 2) Pittsburgh compound B (PiB) PET‐derived estimates of amyloid duration among cognitively unimpaired (CU) individuals.

Method

CU participants in the Wisconsin Registry for Alzheimer’s Prevention and Wisconsin Alzheimer’s Disease Research Center (N = 297) underwent longitudinal MRI, APOE genotyping, and lumbar puncture to determine CSF Aß42/40 (A) and pTau181 (T) concentration at baseline using in‐house cutoffs. CSF measurements were performed using the NeuroToolKit, a panel of robust prototype biomarker assays (Roche Diagnostics International Ltd). Image segmentation was performed using a longitudinal pipeline in SPM12. Grey matter volumes from AD‐associated regions (adjusted for intracranial volume) were z‐scored according to age‐associated predictions from a robust longitudinal A‐T‐/APOE4‐/CU control group. Differences in longitudinal atrophy relative to controls were tested in linear mixed‐effects models using contrasts between A+T‐ and A+T+ groups and A‐T‐ controls and interactions with time (years from baseline). In the PiB PET subset, sampled iterative local approximation was used to estimate time‐to‐positivity at baseline relative to a PiB DVR threshold of 1.16. CSF A+ individuals were grouped as CSF A+ only, early PiB+ (PiB+ <= 5 years), and established PiB+ (PiB+ > 5 years). Differences in longitudinal atrophy were tested with A+ group/A‐T‐ contrasts and interactions with time. All analyses were FDR‐corrected. See Table 1 and Figure 1 for participant characteristics and model details.

Result

Based on CSF assays, both A+ groups showed longitudinal atrophy relative to controls in a majority of ROIs, suggesting that A+, regardless of pTau+, is associated with neurodegeneration. Further analysis incorporating PET showed that CSF A+ in the absence of PiB+ was not associated with atrophy, but early PiB+ was associated with atrophy in medial temporal ROIs and precuneus. The largest and most widespread atrophy was observed in the established PiB+ group (Figure 2).

Conclusion

Neurodegeneration appears to begin soon after the onset of PET‐measurable amyloid‐positivity. Clinical intervention at the earliest signs of amyloid deposition may be needed to delay neurodegeneration.
Renal Atrophy Relates to Brain Volume Loss on 7,149 MRI Scans
Authors: Meysami Somayeh , Raji Cyrus A. , Hashemi Sam , Garg Saurabh , Akbari Nasrin , Gouda Ahmed , Chodakiewitz Yosef Gavriel , Nguyen Thanh Duc , Niotis Kellyann , Merrill David A. , Attariwala Rajpaul
Published: January 2025

Background

Renal atrophy may reflect an end organ consequence of chronic vascular disease. Renal volume loss may therefore provide a window into brain aging and Alzheimer disease risk.

Method

We obtained whole‐body 1.5T MRI scans on 7,149 healthy individuals across four sites. The participants had a mean age of 53.06 ± 12.95 years (age range: 18‐97 years), with a gender distribution of 48% women and 52% men; 38% were non‐white. Deep learning with FastSurfer on 3D T1 volumetric MPRAGE trained on 134 participants aged 27‐66 and segmented 96 brain regions. Whole body sequences included coronal T1 that allowed for separate segmentation and quantification of renal volumes. Partial correlation analysis controlling for sex and total intracranial volume was done on kidney volumes and the following brain regions: tissue types, lobar structures and Alzheimer disease risk regions – hippocampus, precuneus, and posterior cingulate gyrus. Renal volumes were normalized to the aggregate abdominal organ volumes and tissues such as the liver, spleen and abdominal fat. Multiple comparisons were corrected for with the Bonferroni method.

Result

Lower renal volumes were correlated with lower gray matter volumes (rp = ‐0.229, p = 5.18×10‐85) as well as total white matter volume (rp = ‐0.204, p = 2.51×10‐67). The cerebral ventricles, demonstrated a moderate positive correlation with lower renal volumes (rp = 0.099, p = 5.28×10‐16) reflective of a generalized parenchymal volume loss. Lower renal volumes also predicted lower lobar volumes in: frontal lobe (rp = ‐0.204, p = 3.34×10‐67), temporal lobe (rp = ‐0.236, p = 9.76×10‐90), parietal lobe (rp = ‐0.215, p = 2.69×10‐74), and occipital lobe (rp = ‐0.207, p = 8.29×10‐69), all showed significant negative correlations with lower renal volumes. Lower renal volumes also correlated to lower volumes in AD risk regions: the Hippocampus (rp = ‐0.203, p = 2.89×10‐66) and posterior cingulate (rp = ‐0.18, p = 3.06×10‐52), precuneus (rp = ‐0.171, p = 8.00×10‐47).

Conclusion

This study demonstrates a strong relationship in a large sample between lower renal volumes and reduced volumes in various brain regions, including gray matter, white matter, and specific lobar structures, as well as Alzheimer's disease risk regions.
Impact of anchoring cutoff point of tau positivity on CU young or older adults using Flortaucipir and MK‐6240 – Head Study
Authors: Ruppert Emma Patrice , Povala Guilherme , Bauer‐Negrini Guilherme , Lussier Firoza Z , Ferreira Pamela C.L. , Bellaver Bruna , Amaral Livia , Zalzale Hussein , Oliveira Markley , Saha Pampa , Rodrigues Matheus Scarpatto , Abbas Sarah , Soares Carolina , Felix Cynthia , Madeiros Marina Scop , de Paula Elisa , Resende França , Caramelli Paulo , Klunk William E , Tudorascu Dana , Jagust William J. , Pascual Belen , Gordon Brian A. , Lowe Val J. , Oh Hwamee , Soleimani‐Meigooni David N. , Rosa‐Neto Pedro , Baker Suzanne L. , Pascoal Tharick Ali
Published: January 2025

Background

Tau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head.

Methods

We studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [18F]Flortaucipir and [18F]MK‐6240, and amyloid‐ß (Aß) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAß‐.

Results

Demographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [18F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [18F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAß‐, [18F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [18F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1).

Conclusions

Our findings indicate variations in tau positivity when employing different methods based on either the young or CUAß‐. [18F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [18F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.
Single nucleotide polimorphisms of the main glutamate transporter in the brain are associated with lower regional brain glucose metabolism in cognitively impaired individuals
Authors: de Oliveira Roberta dos Santos , Limberger Christian , Machado Luiza Santos , de Bastiani Marco Antônio , Schlickmann Thomas Hugentobler , Carello‐Collar Giovanna , Povala Guilherme , Pascoal Tharick A. , Zimmer Eduardo R.
Published: January 2025

Background

Astrocytes play a main role in brain energy metabolism, primarily through the metabolic cooperation with neurons. The use of [18F]fluorodeoxyglucose(FDG)‐PET has become a valuable indicator of neurodegeneration in Alzheimer’s disease (AD), revealing a brain hypometabolic signature, but it is sensitive to changes in astrocyte metabolism. It is postulated that the activation of the excitatory amino acid transporter 2 (EAAT2) is the main trigger of FDG‐PET uptake in astrocytes. However, the potential relationship between single nucleotide polymorphisms (SNPs) in the gene that encodes for EAAT2 (SLC1A2) and the AD hypometabolic signature remains unknown. Here, we investigated whether SNPs in SLC1A2 affect brain glucose metabolism in cognitively impaired (CI) individuals.

Method

We assessed 331 CI individuals from the ADNI cohort with available [18F]FDG‐PET imaging data, CSF Aß1‐42 and p‐tau181 measures, and genotyping data of SLC1A2 (Table 1). After the genetic data processing, 100 SNPs from the SLC1A2 gene were identified. In this study, we focused on the 10 SNPs more associated with [18F]FDG SUVR in an exploratory ROI‐wise analysis. Then, we performed voxel‐wise correlation analysis with RMINC, testing the association between the SLC1A2 SNP carriership and [18F]FDG‐PET, adjusting for age, sex, APOE4 status, and CSF Aß42 and p‐tau181 levels (corrected p‐value < 0.05).

Result

Our findings revealed a negative association between four SLC1A2 SNPs and [18F]FDG‐PET in specific brain regions. This indicates that SLC1A2 SNP carriership is associated with lower regional glucose metabolism. Notably, rs57339940 carriers presented a significant cluster in the cingulate region (tmax = 3.04, p < 0.01, Figure 1A). Carriers of rs11033098 and rs11033099 exhibited bilateral clusters in areas of the lingual gyrus (tmax = 2.4806 and tmax = 2.8736, respectively, p < 0.01, Figure 1B‐C). Additionally, individuals with rs10768132 showed a substantial bilateral impact on the posterior areas of the cuneus (tmax = 2.9821, p < 0.01, Figure 1D).

Conclusion

These preliminary results show that SLC1A2 SNP carriership had a significant influence on regional glucose hypometabolism, specifically in areas comprising the occipital lobe and the cingulate region. Metabolic alterations may influence the susceptibility of those regions to accumulate amyloid in later stages of AD.
Characterization of plasma biomarkers of individuals with amyloid‐independent increased tau positron‐emission tomography uptake
Authors: Chan Tevy , Therriault Joseph , Wang Yi‐Ting , Aumont Etienne , Arias Jaime Fernandez , Macedo Arthur C. , Rahmouni Nesrine , Servaes Stijn , Trudel Lydia , Hosseini Seyyed Ali , Zheng Yansheng , Socualaya Kely Monica Quispialaya , Mathotaarachchi Sulantha , Stevenson Jenna , Jia Wan Lu , Matsudaira Takashi , Hall Brandon J , Lussier Firoza Z , Gauthier Serge , Triana‐Baltzer Gallen , Kolb Hartmuth C. , Karikari Thomas K , Ashton Nicholas J. , Benedet Andrea L. , Zetterberg Henrik , Blennow Kaj , Pascoal Tharick Ali , Rosa‐Neto Pedro
Published: January 2025

Background

Increased uptake on Tau positron‐emission tomography (PET) is sometimes observed in the absence of amyloid ß accumulation. This A‐T+ PET profile might represent primary age‐related tauopathy (PART), an amyloid ß‐independent 3R/4R tauopathy observed in aging brains. Although A‐T+ individuals have been shown to follow a different cognitive trajectory compared to A‐T‐ and A+T+ individuals, it remains unknown how they differ in terms of plasma biomarkers. Here, we aim to characterize the plasma biomarkers of A‐T+ individuals.

Method

This is a cross‐sectional study with data from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort. Participants were classified into four categories based on their amyloid ß ([18F]AZD4694) and Tau ([18F]MK6240) PET status (A‐T‐, A+T‐, A+T+ and A‐T+). Plasma biomarkers phosphorylated tau (p‐tau)181, p‐tau217, p‐tau231, total tau (t‐tau), neurofilament light (NfL) and GFAP were compared using the Kruskal‐Wallis test with post hoc Benjamini‐Hochberg (BH) correction. Discriminative performance was assessed using the area under the receiver operating characteristic curve (AUROC).

Result

Among the total 468 participants, 64 (13.7%) had A‐T+ PET status. The A‐T+ PET participants had a mean age 66.9 years (SD: 12.8), with 62.5% women and 39.1% showing cognitive impairment. Plasma p‐tau181, p‐tau217, p‐tau231 and GFAP were significantly higher in A+T+ individuals compared to A‐T+ (all p < 0.001). On the other hand, plasma p‐tau181 and 217 were significantly different between A‐T‐ vs A‐T+ (p<0.05), but not p‐tau231 and plasma GFAP. No significant differences were found in t‐tau or NfL regarding A‐T‐. Plasma p‐tau217 and plasma GFAP had the highest discriminative accuracies for A‐T+ vs A+T+ (AUROC: 0.860 and 0.836 respectively).

Conclusion

Our data suggest that individuals with an A‐T+ PET status exhibit a different plasma biomarker profile than those with A+T+ and A‐T‐ status. Further characterization of fluid biomarkers could help identify this group of individuals and facilitate the differential diagnosis of adults with cognitive impairment. Furthermore, our results lend support to the use of plasma biomarkers for identifying amyloid ß PET positivity.
Evaluating quality of routine vs. tape‐transfer histology sections for 3D reconstruction of paraffin embedded brain tissue and correlation with 7T MRI
Authors: Ho William Hai Dang , Wang Yixin , Taghavi Hossein Moein , DiGiacomo Phillip , Nirschl Jeffrey , Chu Pauline , Georgiadis Marios , Zeineh Michael
Published: January 2025

Background

Specimen analysis is crucial for identifying imaging and neuropathological signatures. Histology is the gold‐standard, but sample preparation and sectioning induce tissue deformations which hinder quantitative analysis or registration of histology to 3D MRI providing a challenge to the development of MRI biomarkers. Overall, we aim to develop a workflow to correlate histology with high‐resolution MRI at a microscopic level (Fig. 1), Here, we evaluate a critical step in this process ‐ the section quality from tissue mounting techniques, comparing: A) traditional water bath (Fig. 1F), and B) tape transfer (Fig. 1G), for the purpose of image segmentation and correlation with high‐resolution MRI.

Methods

Fixed human brain specimens were MRI scanned, dehydrated with graded ethanol, xylene‐cleared, and paraffin‐embedded. Before sectioning, the specimen surface was captured by a brightfield microscope (Fig. 2, top left, “Blockface image”). Embedded tissue was sectioned at 7 µm using a Leica 100 microtome and mounted on slides using either A) traditional water bath capture onto standard charged slides (Fig. 1F), or B) tape‐transfer capture onto adhesive‐coated slides (Alphametrix) (Fig. 1G). Slides were hematoxylin and eosin stained followed by imaging using the same microscope settings as the blockface image, and total stained tissue area was calculated. Area of expansion was determined by change from the blockface image (Fig. 2, 3rd column). Internal tissue distortions were calculated by subtracting the tissue section mask from the original blockface mask (Fig. 2, 2nd column).

Results

The tissue area measurements show that traditional capture resulted in average expansion of 16.7% relative to the blockface image, whereas tape transfer had 0.9% expansion (Fig. 3). Both methods had similar amounts of average total tissue damage (approximately 3%), representing regions with tissue in the blockface image where no tissue was present on the slide. Subjectively, tissue distortion was concentrated at the periphery on traditional capture, whereas there were linear striations within internal structures on tape transfer (red arrows). Tissue yield for tape transfer was subjectively near 100% compared to traditional at approximately 60‐70%.

Conclusion

Tape transfer had a more consistent yield and better edge preservation with blockface image but had more internal microfractures.
Machine‐learning derived proxy phenotypes for Alzheimer’s disease genome‐wide association study by proxy
Authors: Goudey Benjamin , Saint‐Jalmes Martin , Liu Shu , Laws Simon M. , Masters Colin L
Published: January 2025

Background

For large genomic studies of middle‐aged individuals, the prevalence of Alzheimer’s disease (AD) is extremely low, making it difficult to conduct genomic analysis of the condition. To enable genome‐wide association studies of AD in such datasets, an approach called Genome‐wide association by proxy (GWAX) uses family history of disease as a proxy for disease status. Borrowing from the machine learning (ML) literature, we treat the development of proxy phenotypes as a pseudo‐labelling task, where an ideal proxy label accurately predicts the lifetime risk of AD. Given this, this work develops ML‐derived proxy phenotypes of AD for downstream GWAX based on survival models of conversion to AD and demonstrates their predictive performance and impact on downstream GWAX.

Method

Using cognitively normal (CN) and mild cognitive impaired (MCI) individuals with at least 2 timepoints in AIBL (n = 426), ADNI (n = 283) and UK Biobank (n = 56,105), we derived two proxy phenotypes of AD: i) a ML‐derived boosting model with age, sex, years of education and AD family history, i.e. number of recorded parents with a history of Alzheimer’s disease and ii) family history alone. The prognostic ability for future AD conversion was measured using concordance index (C‐index) in 10‐fold cross‐validation. The two models were applied to the subset of UK Biobank with white British ancestry (n = 408,165) and a GWAX was conducted on the predicted labels. From this GWAX, PRS was derived and evaluated using area under the ROC curve (AUC).

Results

In 10‐fold cross‐validation, the ML‐derived phenotypes were more predictive of conversion to AD than family history alone (median C‐index: 0.7 vs 0.5, Mann Whitney p‐value <0.001). GWAX of UKB from these proxy phenotypes indicates that the multivariate model detects a higher number of significant regions compared to family history alone. PRS constructed from these ML‐derived phenotypes GWAX show greater power to separate true AD case/control labels when externally validated on ADNI.

Conclusion

Preliminary analysis of ML‐derived proxy phenotypes of AD indicates may be a promising approach to improve AD genomic studies in middle age cohorts. Further analysis of the results and potential biases of this approach are needed to fully characterise this method.
A multiple cohort study exploring the influence of partial volume correction on age and sex effects on tau PET imaging
Authors: Hobbs Diana A , Flores Shaney , Keefe Sarah J. , Morris John C. , Benzinger Tammie L.S. , Gordon Brian A.
Published: January 2025

Background

Tau PET imaging has become pivotal in understanding the pathophysiological processes underlying Alzheimer disease (AD). In individuals without amyloid pathology, there is evidence tau levels are elevated with increase age and that females show greater levels of binding. An unknown question is how consistent these effects are, and whether they are susceptible to methodological choices impacting PET quantification. We explored the impact of partial volume correction (PVC) on tau regional values in data from the Charles F. and Joanne Knight Alzheimer Disease Research Center (ADRC) and the Alzheimer Disease Neuroimaging Initiative (ADNI).

Method

Data came from 323 participants (age: 67.89±8.57) at the Knight ADRC and 274 individuals (age: 70.14±6.61) from ADNI. Forty‐two regional values of tau PET binding were measured using flortaucipir with and without partial‐volume correction as implemented using study specific geometric transfer matrix approaches. Participants were selected to be amyloid‐negative. Similarity in regional estimates was determined using spatial permutation testing.

Result

Within the ADRC cohort the value of the regression coefficients changed with PVC by the spatial pattern of the effects remained highly similar for both age (Fig. 1A: r = 0.66, p<0.001) and sex (Fig. 1C: r = 0.85, p<0.001). In ADNI there were moderate association between the maps of age‐related effects (Fig. 1B: r = 0.36, p<0.05) and sex (Fig. 1D: r = 0.31, p<0.05). When comparing between the ADRC and ADNI cohorts using non‐PVC data there were highly significant associations for both age (Fig. 2A: r = 0.83, p<0.001) and sex (Fig. 2C: r = 0.95, p<0.001). When using PVC, data no significant associations were found for age (Fig. 2B: r = 0.32, p = 0.06) or sex (Fig. 2D: r = 0.02, p = 0.92).

Conclusion

The implementation of, and choice of PVC approach can drastically impact the detection of PET signals. This is a particular concern in the context of cohorts where there may be low to moderate levels of underlying pathology and where non‐specific binding may dominate the signal. These results emphasize the importance of considering PVC in tau PET analyses and highlight the reliability of observed age‐ and sex‐related effects across different cohorts.
Brain Age Gap Correlates with 18F‐NAV‐4694 and 18F‐MK‐6240 Standardized Uptake Value Ratio
Authors: Rajabli Reza , Soltaninejad Mahdie , Shafiee Neda , Fonov Vladimir S , Rahmouni Nesrine , Servaes Stijn , Therriault Joseph , Gauthier Serge , Rosa‐Neto Pedro , Collins D Louis
Published: January 2025

Background

It is feasible to train a model on a healthy cohort to estimate the chronological age from a T1‐weighted (T1w) MRI. This model can be used to estimate the apparent brain age of subjects with Alzheimer's Disease (AD). The difference between the true chronological age and the apparent brain age, called Brain Age Gap (BAG), is a potential feature to estimate the level of pathology and neurodegeneration of an individual patient with AD. To further study this, here we examined the linear relationship between BAG and the standardized uptake value ratio (SUVR) of the amyloid‐beta tracer (18F‐NAV‐4694) and tau tangle tracer (18F‐MK‐6240).

Method

We used ∼40K T1w MRIs from the UK Biobank dataset, with improved preprocessing and more extensive data augmentation, to train an SFCN‐reg model (Leonardsen 2022). Our implementation achieved a generalization gap error of less than 1 year, surpassing the performance reported in the original study. Then, using the trained model, we estimated BAG for 245 T1w images from the Translational Biomarkers in Aging and Dementia (TRIAD) dataset (Therriault 2022) consisting of 146 normal controls (NC), 46 with Mild Cognitive Impairment (MCI) due to AD, and 53 with AD dementia. Subsequently, We compared the estimated BAG values with the SUVR values for neocortical amyloid and tau PET meta‐ROI (Jack Jr. 2016).

Result

Figure 1 illustrates the linear relationship between the 18F‐NAV‐4694 SUVR for all subjects (at baseline scan) vs age (no correlation), apparent brain age (r = 0.31, p << 0.001), and BAG (r = 0.43, p << 0.001). Figure 2 illustrates the linear relationship between the 18F‐MK‐6240 SUVR vs age (r = ‐0.27, p << 0.001), apparent brain age (r = 0.11, p < 0.08), and BAG for the same subjects (r = 0.58, p << 0.001).

Conclusion

We demonstrated a meaningful relationship between 18F‐NAV‐4694 and 18F‐MK‐6240 SUVR values and BAG, stronger than with age or apparent age, reinforcing the notion that BAG can serve as a feature to estimate the amount of neurodegeneration due to accumulation of amyloid and tau when more precise data, such as PET scans, are not available.