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Alzheimers disease typically progresses in stages, which have been defined by the presence of disease-specific biomarkers: amyloid (A), tau (T) and neurodegeneration (N). This progression of biomarkers has been condensed into the ATN framework, in which each of the biomarkers can be either positive (+) or negative (-). Over the past decades, genome-wide association studies have implicated ∼90 different loci involved with the development of late-onset Alzheimers disease. Here, we investigate whether genetic risk for Alzheimers disease contributes equally to the progression in different disease stages or whether it exhibits a stage-dependent effect. Amyloid (A) and tau (T) status was defined using a combination of available PET and CSF biomarkers in the Alzheimers Disease Neuroimaging Initiative cohort. In 312 participants with biomarker-confirmed A-T- status, we used Cox proportional hazards models to estimate the contribution of APOE and polygenic risk scores (beyond APOE) to convert to A+T- status (65 conversions). Furthermore, we repeated the analysis in 290 participants with A+T- status and investigated the genetic contribution to conversion to A+T+ (45 conversions). Both survival analyses were adjusted for age, sex and years of education. For progression from A-T- to A+T-, APOE-e4 burden showed a significant effect [hazard ratio (HR) = 2.88; 95% confidence interval (CI): 1.70-4.89; P < 0.001], whereas polygenic risk did not (HR = 1.09; 95% CI: 0.84-1.42; P = 0.53). Conversely, for the transition from A+T- to A+T+, the contribution of APOE-e4 burden was reduced (HR = 1.62; 95% CI: 1.05-2.51; P = 0.031), whereas the polygenic risk showed an increased contribution (HR = 1.73; 95% CI: 1.27-2.36; P < 0.001). The marginal APOE effect was driven by e4 homozygotes (HR = 2.58; 95% CI: 1.05-6.35; P = 0.039) as opposed to e4 heterozygotes (HR = 1.74; 95% CI: 0.87-3.49; P = 0.12). The genetic risk for late-onset Alzheimers disease unfolds in a disease stage-dependent fashion. A better understanding of the interplay between disease stage and genetic risk can lead to a more mechanistic understanding of the transition between ATN stages and a better understanding of the molecular processes leading to Alzheimers disease, in addition to opening therapeutic windows for targeted interventions.

ObjectiveRecent work has shown that people with common epilepsies have characteristic patterns of cortical thinning, and that these changes may be progressive over time. Leveraging a large multicenter cross-sectional cohort, we investigated whether regional morphometric changes occur in a sequential manner, and whether these changes in people with mesial temporal lobe epilepsy and hippocampal sclerosis (MTLE-HS) correlate with clinical features.MethodsWe extracted regional measures of cortical thickness, surface area, and subcortical brain volumes from T1-weighted (T1W) magnetic resonance imaging (MRI) scans collected by the ENIGMA-Epilepsy consortium, comprising 804 people with MTLE-HS and 1625 healthy controls from 25 centers. Features with a moderate case-control effect size (Cohen d ≥ .5) were used to train an event-based model (EBM), which estimates a sequence of disease-specific biomarker changes from cross-sectional data and assigns a biomarker-based fine-grained disease stage to individual patients. We tested for associations between EBM disease stage and duration of epilepsy, age at onset, and antiseizure medicine (ASM) resistance.ResultsIn MTLE-HS, decrease in ipsilateral hippocampal volume along with increased asymmetry in hippocampal volume was followed by reduced thickness in neocortical regions, reduction in ipsilateral thalamus volume, and finally, increase in ipsilateral lateral ventricle volume. EBM stage was correlated with duration of illness (Spearman ρ = .293, p = 7.03 × 10-16 ), age at onset (ρ = -.18, p = 9.82 × 10-7 ), and ASM resistance (area under the curve = .59, p = .043, Mann-Whitney U test). However, associations were driven by cases assigned to EBM Stage 0, which represents MTLE-HS with mild or nondetectable abnormality on T1W MRI.SignificanceFrom cross-sectional MRI, we reconstructed a disease progression model that highlights a sequence of MRI changes that aligns with previous longitudinal studies. This model could be used to stage MTLE-HS subjects in other cohorts and help establish connections between imaging-based progression staging and clinical features.

Epilepsy is a common and serious neurological disorder, with many different constituent conditions characterized by their electro clinical, imaging, and genetic features. MRI has been fundamental in advancing our understanding of brain processes in the epilepsies. Smaller-scale studies have identified many interesting imaging phenomena, with implications both for understanding pathophysiology and improving clinical care. Through the infrastructure and concepts now well-established by the ENIGMA Consortium, ENIGMA-Epilepsy was established to strengthen epilepsy neuroscience by greatly increasing sample sizes, leveraging ideas and methods established in other ENIGMA projects, and generating a body of collaborating scientists and clinicians to drive forward robust research. Here we review published, current, and future projects, that include structural MRI, diffusion tensor imaging (DTI), and resting state functional MRI (rsfMRI), and that employ advanced methods including structural covariance, and event-based modeling analysis. We explore age of onset- and duration-related features, as well as phenomena-specific work focusing on particular epilepsy syndromes or phenotypes, multimodal analyses focused on understanding the biology of disease progression, and deep learning approaches. We encourage groups who may be interested in participating to make contact to further grow and develop ENIGMA-Epilepsy.

Artificial intelligence has recently gained popularity across different medical fields to aid in the detection of diseases based on pathology samples or medical imaging findings. Brain magnetic resonance imaging (MRI) is a key assessment tool for patients with temporal lobe epilepsy (TLE). The role of machine learning and artificial intelligence to increase detection of brain abnormalities in TLE remains inconclusive. We used support vector machine (SV) and deep learning (DL) models based on region of interest (ROI-based) structural (n = 336) and diffusion (n = 863) brain MRI data from patients with TLE with ("lesional") and without ("non-lesional") radiographic features suggestive of underlying hippocampal sclerosis from the multinational (multi-center) ENIGMA-Epilepsy consortium. Our data showed that models to identify TLE performed better or similar (68-75%) compared to models to lateralize the side of TLE (56-73%, except structural-based) based on diffusion data with the opposite pattern seen for structural data (67-75% to diagnose vs. 83% to lateralize). In other aspects, structural and diffusion-based models showed similar classification accuracies. Our classification models for patients with hippocampal sclerosis were more accurate (68-76%) than models that stratified non-lesional patients (53-62%). Overall, SV and DL models performed similarly with several instances in which SV mildly outperformed DL. We discuss the relative performance of these models with ROI-level data and the implications for future applications of machine learning and artificial intelligence in epilepsy care.

The epilepsies are commonly accompanied by widespread abnormalities in cerebral white matter. ENIGMA-Epilepsy is a large quantitative brain imaging consortium, aggregating data to investigate patterns of neuroimaging abnormalities in common epilepsy syndromes, including temporal lobe epilepsy, extratemporal epilepsy, and genetic generalized epilepsy. Our goal was to rank the most robust white matter microstructural differences across and within syndromes in a multicentre sample of adult epilepsy patients. Diffusion-weighted MRI data were analysed from 1069 healthy controls and 1249 patients: temporal lobe epilepsy with hippocampal sclerosis (n = 599), temporal lobe epilepsy with normal MRI (n = 275), genetic generalized epilepsy (n = 182) and non-lesional extratemporal epilepsy (n = 193). A harmonized protocol using tract-based spatial statistics was used to derive skeletonized maps of fractional anisotropy and mean diffusivity for each participant, and fibre tracts were segmented using a diffusion MRI atlas. Data were harmonized to correct for scanner-specific variations in diffusion measures using a batch-effect correction tool (ComBat). Analyses of covariance, adjusting for age and sex, examined differences between each epilepsy syndrome and controls for each white matter tract (Bonferroni corrected at P

This review summarizes the last decade of work by the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) Consortium, a global alliance of over 1400 scientists across 43 countries, studying the human brain in health and disease. Building on large-scale genetic studies that discovered the first robustly replicated genetic loci associated with brain metrics, ENIGMA has diversified into over 50 working groups (WGs), pooling worldwide data and expertise to answer fundamental questions in neuroscience, psychiatry, neurology, and genetics. Most ENIGMA WGs focus on specific psychiatric and neurological conditions, other WGs study normal variation due to sex and gender differences, or development and aging; still other WGs develop methodological pipelines and tools to facilitate harmonized analyses of "big data" (i.e., genetic and epigenetic data, multimodal MRI, and electroencephalography data). These international efforts have yielded the largest neuroimaging studies to date in schizophrenia, bipolar disorder, major depressive disorder, post-traumatic stress disorder, substance use disorders, obsessive-compulsive disorder, attention-deficit/hyperactivity disorder, autism spectrum disorders, epilepsy, and 22q11.2 deletion syndrome. More recent ENIGMA WGs have formed to study anxiety disorders, suicidal thoughts and behavior, sleep and insomnia, eating disorders, irritability, brain injury, antisocial personality and conduct disorder, and dissociative identity disorder. Here, we summarize the first decade of ENIGMA's activities and ongoing projects, and describe the successes and challenges encountered along the way. We highlight the advantages of collaborative large-scale coordinated data analyses for testing reproducibility and robustness of findings, offering the opportunity to identify brain systems involved in clinical syndromes across diverse samples and associated genetic, environmental, demographic, cognitive, and psychosocial factors.

INTRODUCTION: Neuroanatomical normative modeling captures individual variability in Alzheimer's disease (AD). Here we used normative modeling to track individuals' disease progression in people with mild cognitive impairment (MCI) and patients with AD. METHODS: Cortical and subcortical normative models were generated using healthy controls (n ≈ 58k). These models were used to calculate regional z scores in 3233 T1‐weighted magnetic resonance imaging time‐series scans from 1181 participants. Regions with z scores < –1.96 were classified as outliers mapped on the brain and summarized by total outlier count (tOC). RESULTS: tOC increased in AD and in people with MCI who converted to AD and also correlated with multiple non‐imaging markers. Moreover, a higher annual rate of change in tOC increased the risk of progression from MCI to AD. Brain outlier maps identified the hippocampus as having the highest rate of change. DISCUSSION: Individual patients' atrophy rates can be tracked by using regional outlier maps and tOC. Highlights: Neuroanatomical normative modeling was applied to serial Alzheimer's disease (AD) magnetic resonance imaging (MRI) data for the first time.Deviation from the norm (outliers) of cortical thickness or brain volume was computed in 3233 scans.The number of brain‐structure outliers increased over time in people with AD.Patterns of change in outliers varied markedly between individual patients with AD.People with mild cognitive impairment whose outliers increased over time had a higher risk of progression from AD. [ABSTRACT FROM AUTHOR]

In its original form, the amyloid cascade hypothesis of Alzheimer's disease holds that fibrillar deposits of amyloid are an early, driving force in pathological events leading ultimately to neuronal death. Early clinicopathological investigations highlighted a number of inconsistencies leading to an updated hypothesis in which amyloid plaques give way to amyloid oligomers as the driving force in pathogenesis. Rather than focusing on the inconsistencies, amyloid imaging studies have tended to highlight the overlap between regions that show early amyloid plaque signal on positron emission tomography and that also happen to be affected early in Alzheimer's disease. Recent imaging studies investigating the regional dependency between metabolism and amyloid plaque deposition have arrived at conflicting results, with some showing regional associations and other not. We extracted multimodal neuroimaging data from the Alzheimer's disease neuroimaging database for 227 healthy controls and 434 subjects with mild cognitive impairment. We analysed regional patterns of amyloid deposition, regional glucose metabolism and regional atrophy using florbetapir ((18)F) positron emission tomography, (18)F-fluordeoxyglucose positron emission tomography and T1-weighted magnetic resonance imaging, respectively. Specifically, we derived grey matter density and standardized uptake value ratios for both positron emission tomography tracers in 404 functionally defined regions of interest. We examined the relation between regional glucose metabolism and amyloid plaques using linear models. For each region of interest, correcting for regional grey matter density, age, education and disease status, we tested the association of regional glucose metabolism with (i) cortex-wide florbetapir uptake; (ii) regional (i.e. in the same region of interest) florbetapir uptake; and (iii) regional florbetapir uptake while correcting in addition for cortex-wide florbetapir uptake. P-values for each setting were Bonferroni corrected for 404 tests. Regions showing significant hypometabolism with increasing cortex-wide amyloid burden were classic Alzheimer's disease-related regions: the medial and lateral parietal cortices. The associations between regional amyloid burden and regional metabolism were more heterogeneous: there were significant hypometabolic effects in posterior cingulate, precuneus, and parietal regions but also significant positive associations in bilateral hippocampus and entorhinal cortex. However, after correcting for global amyloid burden, few of the negative associations remained and the number of positive associations increased. Given the wide-spread distribution of amyloid plaques, if the canonical cascade hypothesis were true, we would expect wide-spread, cortical hypometabolism. Instead, cortical hypometabolism appears to be linked to global amyloid burden. Thus we conclude that regional fibrillar amyloid deposition has little to no association with regional hypometabolism.

Background: APOE*4 status and sex (and their interaction) are among the strongest risk factors for late‐onset Alzheimer's disease (AD). We thus sought to perform large‐scale, stratified genome‐wide association study (GWAS) of AD to chart the role of common genetic variation in AD sexual dimorphism and heterogeneity of APOE*4‐related AD risk. Method: The study overview and GWAS models are shown in Figure.1A. SNP‐array AD GWAS datasets primarily composing the ADGC, together with whole‐genome sequencing (WGS) from ADSP (NG00067.v7), provided case‐control diagnoses for phase‐1. The UK Biobank provided subjects with ICD codes and family history of AD status for phase‐2. Subjects were of European ancestry. Linear mixed model regressions were performed (LMM‐BOLT v.2.3.4) on AD outcome measures, adjusting for sex, APOE*4/APOE*2 dosage, genetic principal components, and array/batch/center. GWAS hits (P<5e‐*8) were evaluated for interactions effects (meta‐regression for phase1+2 meta‐analyses) and assessed in functional follow‐up analyses. The effect of stratification on genetic risk scores (GRS) predictive performance on AD risk was also evaluated. Result: Stratified AD GWAS identified 28 loci/variants, including 5 known lead variants in known AD loci, 6 novel independent variants in known AD loci, and 17 novel loci (Figure.1B). 13 out of 17 novel loci showed varying degrees of functional support, with 4 also showing evidence for QTL colocalization (Figure.2). Many implicated genes showed prior support or effects relevant to AD: MARCHF10 was recently implicated as a risk gene for all‐cause dementia; EMP2 is a regulator of cell membrane composition, cell‐matrix adhesion, and blood vessel endothelial cell migration; SLIT2 binds APP, and EYA4 and TBC1D9 have respectively been implicated in brain tau deposition and MAPT haplotype‐stratified GWAS. Nonetheless, most novel hits were low/uncommon frequency variants and QTL colocalization evidence was limited. Further, there appeared to be no predictive benefit for stratified GRS analyses (Figure.3). Conclusion: We identified novel loci/variants differentially associated with AD risk across APOE*4 and/or sex. Yet, we inferred relatively limited evidence for stratified effects (propensity for low frequency hits and no benefit for stratified GRS). Overall, our findings contribute to our understanding of the genetic etiology of AD, which in turn contributes to advance personalized genetic medicine. [ABSTRACT FROM AUTHOR]

Amyloid-β is thought to facilitate the spread of tau throughout the neocortex in Alzheimer's disease, though how this occurs is not well understood. This is because of the spatial discordance between amyloid-β, which accumulates in the neocortex, and tau, which accumulates in the medial temporal lobe during ageing. There is evidence that in some cases amyloid-β-independent tau spreads beyond the medial temporal lobe where it may interact with neocortical amyloid-β. This suggests that there may be multiple distinct spatiotemporal subtypes of Alzheimer's-related protein aggregation, with potentially different demographic and genetic risk profiles. We investigated this hypothesis, applying data-driven disease progression subtyping models to post-mortem neuropathology and in vivo PET-based measures from two large observational studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We consistently identified 'amyloid-first' and 'tau-first' subtypes using cross-sectional information from both studies. In the amyloid-first subtype, extensive neocortical amyloid-β precedes the spread of tau beyond the medial temporal lobe, while in the tau-first subtype, mild tau accumulates in medial temporal and neocortical areas prior to interacting with amyloid-β. As expected, we found a higher prevalence of the amyloid-first subtype among apolipoprotein E (APOE) ε4 allele carriers while the tau-first subtype was more common among APOE ε4 non-carriers. Within tau-first APOE ε4 carriers, we found an increased rate of amyloid-β accumulation (via longitudinal amyloid PET), suggesting that this rare group may belong within the Alzheimer's disease continuum. We also found that tau-first APOE ε4 carriers had several fewer years of education than other groups, suggesting a role for modifiable risk factors in facilitating amyloid-β-independent tau. Tau-first APOE ε4 non-carriers, in contrast, recapitulated many of the features of primary age-related tauopathy. The rate of longitudinal amyloid-β and tau accumulation (both measured via PET) within this group did not differ from normal ageing, supporting the distinction of primary age-related tauopathy from Alzheimer's disease. We also found reduced longitudinal subtype consistency within tau-first APOE ε4 non-carriers, suggesting additional heterogeneity within this group. Our findings support the idea that amyloid-β and tau may begin as independent processes in spatially disconnected regions, with widespread neocortical tau resulting from the local interaction of amyloid-β and tau. The site of this interaction may be subtype-dependent: medial temporal lobe in amyloid-first, neocortex in tau-first. These insights into the dynamics of amyloid-β and tau may inform research and clinical trials that target these pathologies. [ABSTRACT FROM AUTHOR]

Background: Genetic risk scores (GRS), be it polygenic risk scores (PRS) or polygenic hazard scores (PHS), have shown promise to support genetic risk prediction and clinical trial recruitment for Alzheimer's disease (AD). A recent study further suggested that sex‐matched AD PHS (e.g., male‐to‐male) outperform sex‐mismatched PHS (e.g., male‐to‐female), while no benefit was observed for PRS. Similar benefits may be expected for matching by APOE*4 positivity status, but this question has remained unexplored. The prior sex‐stratified work neither evaluated whether full sample GRS (males+females) outperform sex‐matched GRS, nor compared polygenic (including many variants regardless of significance) to oligogenic (including fewer more significant variants) effects. Here, we performed the largest‐to‐date sex‐stratified, and first APOE*4‐stratified, evaluation of AD GRS. Methods: The study design is detailed in Figure‐1: genome‐wide association studies (GWAS) were performed in a Discovery cohort (LMM‐BOLT v.2.3.5) and GRS were constructed and evaluated in an independent Replication cohort (PRSice‐2 & R v3.6). All analyses performed multiple linear regression on an AD‐age score that models resilience to age‐related risk for AD (Figure‐2). Models adjusted for sex, APOE*4/APOE*2 dosage (rendering GRS estimates/effects independent of APOE), the first five genetic principal components (PC‐AiR; GENESIS; R v3.6), and array/sequencing center. GRS were evaluated at different P‐value thresholds to elucidate oligogenic versus polygenic effects, while all GRS with other covariates were combined to evaluate predictive performance in the Replication. Results: Stratified discovery GWAS are shown in Figure‐3. GRS tended to perform best at more stringent P‐value thresholds (i.e., oligogenic) and GRS derived from the full‐sample discovery virtually always outperformed stratified GRS (Figure‐4‐5). Predictive performance was similarly the highest for GRS derived from the full‐sample discovery (Table‐1). Conclusions: Our results were consistent with emerging literature supporting that AD is oligogenic and showed that the performance of sex‐ or APOE*4‐stratificatied GRS was outweighed by using full‐sample derived GRS. While not shown here, we made consistent observations when using a case‐control model (thus equivalent to PRS) without age adjustment. These findings have important implications to guide further efforts at translating AD GRS into clinical practice. aging after age 90. [ABSTRACT FROM AUTHOR]