Wolk, David A.

Abstract The link between regional tau load and clinical manifestation of Alzheimer’s disease (AD) highlights the importance of characterizing spatial tau distribution across disease variants. In typical (memory-predominant) AD, the spatial progression of tau pathology mirrors the functional connections from temporal lobe epicentres. However, given the limited spatial heterogeneity of tau in typical AD, atypical (non-amnestic-predominant) AD variants with distinct tau patterns provide a key opportunity to investigate the universality of connectivity as a scaffold for tau progression. In this large-scale, multicentre study across 14 international sites, we included cross-sectional tau-PET data from 320 individuals with atypical AD (n = 139 posterior cortical atrophy/PCA-AD; n = 103 logopenic variant primary progressive aphasia/lvPPA-AD; n = 35 behavioural variant AD/bvAD; n = 43 corticobasal syndrome/CBS-AD), with a subset of individuals (n = 78) having longitudinal tau-PET data. Additionally, as an independent sample, we included regional post-mortem tau stainings from 93 atypical AD patients from two sites (n = 19 PCA-AD, n = 32 lvPPA-AD, n = 23 bvAD, n = 19 CBS-AD). Gaussian mixture modelling was used to harmonize different tau-PET tracers by transforming tau-PET standardized uptake value ratios to tau positivity probabilities (a uniform scale ranging from 0% to 100%). Using linear regression, we assessed whether brain regions with stronger resting-state functional MRI-based functional connectivity, derived from healthy elderly controls in the Alzheimer’s Disease Neuroimaging Initiative (ADNI), showed greater covariance in cross-sectional and longitudinal tau-PET and post-mortem tau pathology. Furthermore, we examined whether functional connectivity of tau-PET epicentres (i.e. the top 5% of regions with the highest baseline tau load) and tau-PET accumulation epicentres (i.e. the top 5% of regions with the highest tau accumulation rates) was associated with cross-sectional and longitudinal tau patterns. Our findings show that tau-PET epicentres aligned with clinical variants, e.g. a visual network predominant pattern in PCA-AD (‘visual AD’) and left-hemispheric temporal predominance, particularly within the language network, in lvPPA-AD (‘language AD’). Moreover, more strongly functionally connected regions showed correlated concurrent tau-PET levels (confirmed with post-mortem data) and tau-PET accumulation rates. The functional connectivity profile of tau-PET epicentres and accumulation epicentres corresponded to tau-PET progression patterns, with higher tau-PET levels and accumulation rates in functionally close regions, and lower tau-PET levels and accumulation rates in functionally distant regions. Our data are consistent with the hypothesis that tau propagation occurs along functional connections originating from local epicentres, across all AD clinical variants. Since tau proteinopathy is a major driver of neurodegeneration and cognitive decline, this finding may advance personalized medicine and participant-specific end points in clinical trials.

Objective Early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) differ in many respects. Here, we address the issue of possible differences in fibrillar amyloid pathology as measured by positron emission tomography (PET), which remains unresolved due to the lack of large‐scale comparative studies. Methods Three hundred ninety‐nine cognitively impaired participants younger than 65 years of age from the multicenter Longitudinal Early‐onset Alzheimer's Disease Study (LEADS) and 450 cognitively impaired participants older than 65 years from the Alzheimer's Disease Neuroimaging Initiative (ADNI) underwent clinical assessment, brain magnetic resonance imaging (MRI), and amyloid PET and were included in this study. We compared amyloid PET outcomes (positivity rate based on visual read and quantified tracer uptake expressed as Centiloids [CLs]) between the 2 cohorts and studied their association with age, sex, APOE genotype, and cognition. Results The amyloid positivity rate was higher in LEADS (78%, 95% confidence interval [CI] = 74–82) than in ADNI (71%, 95% CI = 67–75, p = 0.02). Lower Mini‐Mental State Examination (MMSE) and APOE4 genotype increased the odds of amyloid positivity in both cohorts. Visually positive scans had higher CLs in LEADS (EOAD, mean = 95.3 ± 26.1) than in ADNI (LOAD, mean = 80.9 ± 36.8, p < 0.0001), predominantly in parietal cortex/precuneus, superior temporal, and frontal cortices. In amyloid‐positive patients, (1) CLs were higher in female patients in both cohorts; (2) APOE4 carriership was associated with lower CLs in EOAD, which was not observed in LOAD; and (3) correlations between CLs and MMSE scores were significantly stronger in EOAD than in LOAD. Interpretation Differences in the burden of amyloid pathology may contribute to differences in clinical and anatomic patterns in sporadic EOAD and LOAD, and have implications for optimizing therapeutic strategies in each group. ANN NEUROL 2025;98:236–248

Abstract INTRODUCTION Early‐onset and late‐onset Alzheimer's disease (EOAD and LOAD, respectively) have distinct clinical manifestations, with prior work based on small samples suggesting unique patterns of neurodegeneration. The current study performed a head‐to‐head comparison of cortical atrophy in EOAD and LOAD, using two large and well‐characterized cohorts (LEADS and ADNI). METHODS We analyzed brain structural magnetic resonance imaging (MRI) data acquired from 377 sporadic EOAD patients and 317 sporadicLOAD patients who were amyloid positive and had mild cognitive impairment (MCI) or mild dementia (i.e., early‐stage AD), along with cognitively unimpaired participants. RESULTS After controlling for the level of cognitive impairment, we found a double dissociation between AD clinical phenotype and localization/magnitude of atrophy, characterized by predominant neocortical involvement in EOAD and more focal anterior medial temporal involvement in LOAD. DISCUSSION Our findings point to the clinical utility of MRI‐based biomarkers of atrophy in differentiating between EOAD and LOAD, which may be useful for diagnosis, prognostication, and treatment. Highlights Early‐onset Alzheimer's disease (EOAD) and late‐onset AD (LOAD) patients showed distinct and overlapping cortical atrophy patterns. EOAD patients showed prominent atrophy in widespread neocortical regions. LOAD patients showed prominent atrophy in the anterior medial temporal lobe. Regional atrophy was correlated with the severity of global cognitive impairment. Results were comparable when the sample was stratified for mild cognitive impairment (MCI) and dementia.

Abstract Limbic predominant age‐related TDP‐43 encephalopathy neuropathologic change (LATE‐NC) is highly prevalent in late life and a common co‐pathology with Alzheimer's disease neuropathologic change (ADNC). LATE‐NC is a slowly progressive, amnestic clinical syndrome. Alternatively, when present with ADNC, LATE‐NC is associated with a more rapid course. With the emergence of anti‐amyloid therapeutics, discrimination of LATE‐NC from ADNC is critical and will lead to greater clinical recognition of amnestic patients without ADNC. Furthermore, co‐pathology with LATE‐NC may influence outcomes of these therapeutics. Thus there is a need to identify patients during life with likely LATE‐NC. We propose criteria for clinical diagnosis of LATE as an initial framework for further validation. In the context of progressive memory loss and substantial hippocampal atrophy, criteria are laid out for probable (amyloid negative) or possible LATE (amyloid biomarkers are unavailable or when amyloid is present, but hippocampal neurodegeneration is out of proportion to expected pure ADNC). Highlights Limbic‐predominant age‐related TDP‐43 encephalopathy (LATE) is a highly prevalent driver of neuropathologic memory loss in late life. LATE neuropathologic change (LATE‐NC) is a common co‐pathology with Alzheimer's disease neuropathologic change (ADNC) and may influence outcomes with emerging disease‐modifying medicines. We provide initial clinical criteria for diagnosing LATE during life either when LATE‐NC is the likely primary driver of symptoms or when observed in conjunction with AD. Definitions of possible and probable LATE are provided.

AbstractBackgroundThere is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity‐based tau spreading model in AD variants with distinct tau deposition patterns is crucial.MethodWe included 269 amyloid‐β‐positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA‐AD; 83 logopenic variant primary progressive aphasia, lvPPA‐AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS‐AD) and 68 with typical AD from 12 international cohorts, who underwent tau‐PET (54% [18F]AV1451/[18F]flortaucipir/Tauvid, 27% [18F]MK6240, 19% [18F]PI2620). Using Gaussian mixture modeling including amyloid‐β‐negative controls, cross‐sectional tau‐PET standardized uptake value ratios within Schaefer‐200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity‐based distance (using the 30% strongest positive region‐to‐region connections of a group‐average connectivity matrix from ADNI elderly controls) and tau‐PET covariance (group‐average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1‐4) and tau positivity probabilities were assessed accordingly.ResultTau positivity probabilities matched clinical variants, with a posterior pattern in PCA‐AD, left‐hemispheric dominant pattern in lvPPA‐AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS‐AD, and temporo‐parietal predominance in typical AD (Figure 1). In line with this, tau epicenters were highly heterogeneous across variants (Figure 1). In all variants, greater tau‐PET covariance was associated with shorter functional connectivity‐based distance (Figure 2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Figure 3).ConclusionThis multi‐center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant‐specific endpoints in clinical trials.

AbstractBackgroundThere is a strong link between tau and progression of Alzheimer’s disease (AD), necessitating an understanding of tau spreading mechanisms. Prior research, predominantly in typical AD, suggested that tau propagates from epicenters (regions with earliest tau) to functionally connected regions. However, given the constrained spatial heterogeneity of tau in typical AD, validating this connectivity‐based tau spreading model in AD variants with distinct tau deposition patterns is crucial.MethodWe included 269 amyloid‐ß‐positive (PET/CSF) individuals with clinically diagnosed atypical AD (113 posterior cortical atrophy, PCA‐AD; 83 logopenic variant primary progressive aphasia, lvPPA‐AD; 33 behavioural variant AD, bvAD; 40 corticobasal syndrome, CBS‐AD) and 68 with typical AD from 12 international cohorts, who underwent tau‐PET (54% [18F]AV1451/[18F]flortaucipir/Tauvid, 27% [18F]MK6240, 19% [18F]PI2620). Using Gaussian mixture modeling including amyloid‐ß‐negative controls, cross‐sectional tau‐PET standardized uptake value ratios within Schaefer‐200 atlas regions were transformed to tau positivity probabilities. Tau epicenters were defined as the 5% regions with highest tau positivity probabilities. For each variant, the association between functional connectivity‐based distance (using the 30% strongest positive region‐to‐region connections of a group‐average connectivity matrix from ADNI elderly controls) and tau‐PET covariance (group‐average correlation per region pair) was assessed through linear regression, adjusting for age, sex, site, and Euclidean distance. Regions were categorized based on functional proximity to the epicenter (quartiles 1‐4) and tau positivity probabilities were assessed accordingly.ResultTau positivity probabilities matched clinical variants, with a posterior pattern in PCA‐AD, left‐hemispheric dominant pattern in lvPPA‐AD, widespread pattern in bvAD, sensorimotor cortex involvement in CBS‐AD, and temporo‐parietal predominance in typical AD (Fig.1). In line with this, tau epicenters were highly heterogeneous across variants (Fig.1). In all variants, greater tau‐PET covariance was associated with shorter functional connectivity‐based distance (Fig.2). We observed that regions in closer functional proximity to the epicenter exhibited higher tau positivity probabilities than regions functionally further away (p<0.05, Fig.3).ConclusionThis multi‐center study shows that the brain’s functional architecture serves as a universal predictor of tau spreading in AD. Since tau is a key driver of neurodegeneration and cognitive decline in AD, this finding holds potential for personalized medicine and defining participant‐specific endpoints in clinical trials.

Abstract INTRODUCTION Early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) share similar amyloid etiology, but evidence from smaller‐scale studies suggests that they manifest differently clinically. Current analyses sought to contrast the cognitive profiles of EOAD and LOAD. METHODS Z‐ score cognitive‐domain composites for 311 amyloid‐positive sporadic EOAD and 314 amyloid‐positive LOAD participants were calculated from baseline data from age‐appropriate control cohorts. Z‐ score composites were compared between AD groups for each domain. RESULTS After controlling for cognitive status, EOAD displayed worse visuospatial, executive functioning, and processing speed/attention skills relative to LOAD, and LOAD displayed worse language, episodic immediate memory, and episodic delayed memory. DISCUSSION Sporadic EOAD possesses distinct cognitive profiles relative to LOAD. Clinicians should be alert for non‐amnestic impairments in younger patients to ensure proper identification and intervention using disease‐modifying treatments. Highlights Both early‐onset Alzheimer's disease (EOAD) and late‐onset Alzheimer's disease (LOAD) participants displayed widespread cognitive impairments relative to their same‐aged peers. Cognitive impairments were more severe for EOAD than for LOAD participants in visuospatial and executive domains. Memory and language impairments were more severe for LOAD than for EOAD participants Results were comparable after removing clinical phenotypes of posterior cortical atrophy (PCA), primary progressive aphasia (lv‐PPA), and frontal‐variant AD.

AbstractBackgroundWe examined neuropsychiatric symptoms (NPS) and psychotropic medication use at the midway point of data collection of the Longitudinal Early‐onset Alzheimer’s Disease Study (LEADS). We compared amyloid‐positive early‐onset Alzheimer’s disease (EOAD) participants and amyloid‐negative early‐onset non‐Alzheimer’s disease [EOnonAD]) participants across NPS domains and psychotropic medication categories.Method282 participants enrolled in LEADS were compared across diagnostic groups – amyloid‐positive EOAD (n = 212) and amyloid‐negative EOnonAD (n = 70). All participants were between ages 40 and 64 at baseline. NPS were measured with the Neuropsychiatric Inventory – Questionnaire (NPI‐Q) and Geriatric Depression Scale – Short Form (GDS‐SF). Medications were categorized into psychotropic category (e.g., antidepressants, antipsychotics, benzodiazepines). Groups were compared on NPI‐Q using binary logistic regression adjusting for sex and disease severity, on GDS‐SF using linear regression adjusting for sex and disease severity, and on psychotropic medication use using Chi square or Fisher’s exact test.ResultDemographic data are presented in Table 1. Average total NPI‐Q score was higher in EOnonAD than EOAD (p = .002, ŋp 2 = .03). Regardless of diagnostic group, informants endorsed Affective behaviors most frequently (76.2%). EOAD informants endorsed fewer Distress‐Tension behaviors (p = .02, OR = .498) and Impulse Control behaviors (p = .01, OR = .438). EOAD informants also endorsed apathy (p < .001, OR = .316), irritability (p = .02, OR = .498), and disinhibition (p = .04, OR = .476) at lower frequency than EOnonAD. GDS‐SF showed that the EOnonAD participants self‐reported more symptoms of depression than the EOAD group (p = .002, ŋp 2 = .03). (Table 2A). A minority of participants, regardless of diagnostic group, reported psychotropic medication use (35%). There was higher use in EOnonAD (38%) than EOAD (24%) (p = .03, Cohen W = .14) (Table 2B).ConclusionNPS burden and psychotropic medication use were higher in EOnonAD than EOAD participants, despite EOnonAD being less severely impaired. LEADS continues to collect data. Future studies will examine etiologies that may be larger drivers of NPS than AD neuropathology, mediators (e.g., race, ethnicity) in the association between NPS and diagnostic group classication, compare NPS in EOAD vs. LOAD, and examine longitudinal changes in neuropsychiatric profiles.

AbstractBackgroundApproximately 10‐25% of amnestic early‐onset dementia are negative for amyloid (Early‐onset non‐Alzheimer’s Disease, EOnonAD). EOnonAD is a heterogenous group with various etiologies. In order to better understand the traits of EOnonAD, we clustered amnestic EOnonAD individuals according to cognitive patterns.MethodCognitive data of 62 EOnonAD individuals from the Longitudinal Early‐onset AD study (LEADS) were Z‐normalized to 91 intact individuals after adjusting for age, sex, and education. This led to domain‐averaged composite Z‐scores for memory, language, speed/attention, visuospatial, and executive function. Cluster analysis using the Ward method on the residuals for the five composites was performed. We used Scree plot using the pseudo T‐squared to determine the optimal number of clusters. We compared demographics, imaging biomarkers (amyloid and tau PET SUVR, hippocampal volume), plasma (pTau231, Aß40, Aß42, glial fibrillary acidic protein, and neurofilament light chain (NfL)), and cerebrospinal fluid (CSF) biomarkers (Aß42/40, NfL, Neurogranin, Visinin‐like protein, YKL‐40, and Synaptosome Associated Protein 25) across the clusters after adjusting for age, sex, and education. We compared gray matter density of each EOnonAD cluster to controls (n = 84) using voxel‐wise multiple linear regressions in SPM12, adjusting for age, sex, education, and intracranial volume.ResultScree plot using the pseudo‐T‐squared suggested three clusters. Cluster 1 (38/62, 61.3%) was characterized by having the mildest cognitive impairment in all domains. Cluster 2 (19/62, 30.6%) was characterized by more severe memory and language impairment. Cluster 3 (5/62, 8.1%) was characterized by more severe impairment in speed/attention, visuospatial, and executive function (Table 1). Cluster 3 showed significantly higher NfL for both plasma (Cluster 1 vs 3, p<0.001; Cluster 2 vs 3, p < 0.004) and CSF (Cluster 1 vs 3, p = 0.001). Other biomarkers did not show significant difference (Table 1). Only Cluster 3 showed significant atrophy in the bilateral fronto‐parietal areas at p<0.01, uncorrected (Figure 1).ConclusionWe identified heterogeneity in cognitive patterns among amnestic EOnonAD individuals. Cluster 1 had mildest cognitive impairment; Cluster 2 had predominant impairment in memory and language; and Cluster 3 had predominant impairment in speed/attention, visuospatial, and executive function, with significant fronto‐parietal atrophy and high NfL indicating active neurodegeneration.

Abstract INTRODUCTION Magnetic resonance imaging (MRI) research has advanced our understanding of neurodegeneration in sporadic early‐onset Alzheimer's disease (EOAD) but studies include small samples, mostly amnestic EOAD, and have not focused on developing an MRI biomarker. METHODS We analyzed MRI scans to define the sporadic EOAD‐signature atrophy in a small sample (n = 25) of Massachusetts General Hospital (MGH) EOAD patients, investigated its reproducibility in the large longitudinal early‐onset Alzheimer's disease study (LEADS) sample (n = 211), and investigated the relationship of the magnitude of atrophy with cognitive impairment. RESULTS The EOAD‐signature atrophy was replicated across the two cohorts, with prominent atrophy in the caudal lateral temporal cortex, inferior parietal lobule, and posterior cingulate and precuneus cortices, and with relative sparing of the medial temporal lobe. The magnitude of EOAD‐signature atrophy was associated with the severity of cognitive impairment. DISCUSSION The EOAD‐signature atrophy is a reliable and clinically valid biomarker of AD‐related neurodegeneration that could be used in clinical trials for EOAD. Highlights We developed an early‐onset Alzheimer's disease (EOAD)–signature of atrophy based on magnetic resonance imaging (MRI) scans. EOAD signature was robustly reproducible across two independent patient cohorts. EOAD signature included prominent atrophy in parietal and posterior temporal cortex. The EOAD‐signature atrophy was associated with the severity of cognitive impairment. EOAD signature is a reliable and clinically valid biomarker of neurodegeneration.