Genetic and clinical correlates of two neuroanatomical AI dimensions in the Alzheimer's disease continuum.

Alzheimer's disease (AD) is associated with heterogeneous atrophy patterns. We employed a semi-supervised representation learning technique known as Surreal-GAN, through which we identified two latent dimensional representations of brain atrophy in symptomatic mild cognitive impairment (MCI) and AD patients: the "diffuse-AD" (R1) dimension shows widespread brain atrophy, and the "MTL-AD" (R2) dimension displays focal medial temporal lobe (MTL) atrophy. Critically, only R2 was associated with widely known sporadic AD genetic risk factors (e.g., APOE ε4) in MCI and AD patients at baseline. We then independently detected the presence of the two dimensions in the early stages by deploying the trained model in the general population and two cognitively unimpaired cohorts of asymptomatic participants. In the general population, genome-wide association studies found 77 genes unrelated to APOE differentially associated with R1 and R2. Functional analyses revealed that these genes were overrepresented in differentially expressed gene sets in organs beyond the brain (R1 and R2), including the heart (R1) and the pituitary gland, muscle, and kidney (R2). These genes were enriched in biological pathways implicated in dendritic cells (R2), macrophage functions (R1), and cancer (R1 and R2). Several of them were "druggable genes" for cancer (R1), inflammation (R1), cardiovascular diseases (R1), and diseases of the nervous system (R2). The longitudinal progression showed that APOE ε4, amyloid, and tau were associated with R2 at early asymptomatic stages, but this longitudinal association occurs only at late symptomatic stages in R1. Our findings deepen our understanding of the multifaceted pathogenesis of AD beyond the brain. In early asymptomatic stages, the two dimensions are associated with diverse pathological mechanisms, including cardiovascular diseases, inflammation, and hormonal dysfunction-driven by genes different from APOE-which may collectively contribute to the early pathogenesis of AD. All results are publicly available at https://labs-laboratory.com/medicine/ .

Alzheimer's disease genetic risk and changes in brain atrophy and white matter hyperintensities in cognitively unimpaired adults.

Reduced brain volumes and more prominent white matter hyperintensities on MRI scans are commonly observed among older adults without cognitive impairment. However, it remains unclear whether rates of change in these measures among cognitively normal adults differ as a function of genetic risk for late-onset Alzheimer's disease, including APOE-ɛ4, APOE-ɛ2 and Alzheimer's disease polygenic risk scores (AD-PRS), and whether these relationships are influenced by other variables. This longitudinal study examined the trajectories of regional brain volumes and white matter hyperintensities in relationship to APOE genotypes (N = 1541) and AD-PRS (N = 1093) in a harmonized dataset of middle-aged and older individuals with normal cognition at baseline (mean baseline age = 66 years, SD = 9.6) and an average of 5.3 years of MRI follow-up (max = 24 years). Atrophy on volumetric MRI scans was quantified in three ways: (i) a composite score of regions vulnerable to Alzheimer's disease (SPARE-AD); (ii) hippocampal volume; and (iii) a composite score of regions indexing advanced non-Alzheimer's disease-related brain aging (SPARE-BA). Global white matter hyperintensity volumes were derived from fluid attenuated inversion recovery (FLAIR) MRI. Using linear mixed effects models, there was an APOE-ɛ4 gene-dose effect on atrophy in the SPARE-AD composite and hippocampus, with greatest atrophy among ɛ4/ɛ4 carriers, followed by ɛ4 heterozygouts, and lowest among ɛ3 homozygouts and ɛ2/ɛ2 and ɛ2/ɛ3 carriers, who did not differ from one another. The negative associations of APOE-ɛ4 with atrophy were reduced among those with higher education (P < 0.04) and younger baseline ages (P < 0.03). Higher AD-PRS were also associated with greater atrophy in SPARE-AD (P = 0.035) and the hippocampus (P = 0.014), independent of APOE-ɛ4 status. APOE-ɛ2 status (ɛ2/ɛ2 and ɛ2/ɛ3 combined) was not related to baseline levels or atrophy in SPARE-AD, SPARE-BA or the hippocampus, but was related to greater increases in white matter hyperintensities (P = 0.014). Additionally, there was an APOE-ɛ4 × AD-PRS interaction in relation to white matter hyperintensities (P = 0.038), with greater increases in white matter hyperintensities among APOE-ɛ4 carriers with higher AD-PRS. APOE and AD-PRS associations with MRI measures did not differ by sex. These results suggest that APOE-ɛ4 and AD-PRS independently and additively influence longitudinal declines in brain volumes sensitive to Alzheimer's disease and synergistically increase white matter hyperintensity accumulation among cognitively normal individuals. Conversely, APOE-ɛ2 primarily influences white matter hyperintensity accumulation, not brain atrophy. Results are consistent with the view that genetic factors for Alzheimer's disease influence atrophy in a regionally specific manner, likely reflecting preclinical neurodegeneration, and that Alzheimer's disease risk genes contribute to white matter hyperintensity formation.

Brain aging patterns in a large and diverse cohort of 49,482 individuals.

Brain aging process is influenced by various lifestyle, environmental and genetic factors, as well as by age-related and often coexisting pathologies. Magnetic resonance imaging and artificial intelligence methods have been instrumental in understanding neuroanatomical changes that occur during aging. Large, diverse population studies enable identifying comprehensive and representative brain change patterns resulting from distinct but overlapping pathological and biological factors, revealing intersections and heterogeneity in affected brain regions and clinical phenotypes. Herein, we leverage a state-of-the-art deep-representation learning method, Surreal-GAN, and present methodological advances and extensive experimental results elucidating brain aging heterogeneity in a cohort of 49,482 individuals from 11 studies. Five dominant patterns of brain atrophy were identified and quantified for each individual by respective measures, R-indices. Their associations with biomedical, lifestyle and genetic factors provide insights into the etiology of observed variances, suggesting their potential as brain endophenotypes for genetic and lifestyle risks. Furthermore, baseline R-indices predict disease progression and mortality, capturing early changes as supplementary prognostic markers. These R-indices establish a dimensional approach to measuring aging trajectories and related brain changes. They hold promise for precise diagnostics, especially at preclinical stages, facilitating personalized patient management and targeted clinical trial recruitment based on specific brain endophenotypic expression and prognosis.

Patterns of cognitive domain abnormalities enhance discrimination of dementia risk prediction: The ARIC study.

INTRODUCTION: The contribution of neuropsychological assessments to risk assessment for incident dementia is underappreciated. METHODS: We analyzed neuropsychological testing results in dementia-free participants in the Atherosclerosis Risk in Communities (ARIC) study. We examined associations of index domain-specific neuropsychological test performance with incident dementia using cumulative incidence curves and Cox proportional hazards models. RESULTS: Among 5296 initially dementia-free participants (mean [standard deviation] age of 75.8 [5.1] years; 60.1% women, 22.2% Black) over a median follow-up of 7.9 years, the covariate-adjusted hazard ratio varied substantially depending on the pattern of domain-specific performance and age, in an orderly manner from single domain language abnormalities (lowest risk) to single domain executive or memory abnormalities, to multidomain abnormalities including memory (highest risk). DISCUSSION: By identifying normatively defined cognitive abnormalities by domains based on neuropsychological test performance, there is a conceptually orderly and age-sensitive spectrum of risk for incident dementia that provides valuable information about the likelihood of progression. HIGHLIGHTS: Domain-specific cognitive profiles carry enhanced prognostic value compared to mild cognitive impairment. Single-domain non-amnestic cognitive abnormalities have the most favorable prognosis. Multidomain amnestic abnormalities have the greatest risk for incident dementia. Patterns of domain-specific risks are similar by sex and race.

Cross-Sectional Associations of Peripheral Hearing, Brain Imaging, and Cognitive Performance With Speech-in-Noise Performance: The Aging and Cognitive Health Evaluation in Elders Brain Magnetic Resonance Imaging Ancillary Study.

PURPOSE: Population-based evidence in the interrelationships among hearing, brain structure, and cognition is limited. This study aims to investigate the cross-sectional associations of peripheral hearing, brain imaging measures, and cognitive function with speech-in-noise performance among older adults. METHOD: We studied 602 participants in the Aging and Cognitive Health Evaluation in Elders (ACHIEVE) brain magnetic resonance imaging (MRI) ancillary study, including 427 ACHIEVE baseline (2018-2020) participants with hearing loss and 175 Atherosclerosis Risk in Communities Neurocognitive Study Visit 6/7 (2016-2017/2018-2019) participants with normal hearing. Speech-in-noise performance, as outcome of interest, was assessed by the Quick Speech-in-Noise (QuickSIN) test (range: 0-30; higher = better). Predictors of interest included (a) peripheral hearing assessed by pure-tone audiometry; (b) brain imaging measures: structural MRI measures, white matter hyperintensities, and diffusion tensor imaging measures; and (c) cognitive performance assessed by a battery of 10 cognitive tests. All predictors were standardized to z scores. We estimated the differences in QuickSIN associated with every standard deviation (SD) worse in each predictor (peripheral hearing, brain imaging, and cognition) using multivariable-adjusted linear regression, adjusting for demographic variables, lifestyle, and disease factors (Model 1), and, additionally, for other predictors to assess independent associations (Model 2). RESULTS: Participants were aged 70-84 years, 56% female, and 17% Black. Every SD worse in better-ear 4-frequency pure-tone average was associated with worse QuickSIN (-4.89, 95% confidence interval, CI [-5.57, -4.21]) when participants had peripheral hearing loss, independent of other predictors. Smaller temporal lobe volume was associated with worse QuickSIN, but the association was not independent of other predictors (-0.30, 95% CI [-0.86, 0.26]). Every SD worse in global cognitive performance was independently associated with worse QuickSIN (-0.90, 95% CI [-1.30, -0.50]). CONCLUSIONS: Peripheral hearing and cognitive performance are independently associated with speech-in-noise performance among dementia-free older adults. The ongoing ACHIEVE trial will elucidate the effect of a hearing intervention that includes amplification and auditory rehabilitation on speech-in-noise understanding in older adults. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.25733679.

The HOPE4MCI study: A randomized double-blind assessment of AGB101 for the treatment of MCI due to AD.

INTRODUCTION: In addition to the accumulation of amyloid plaques and neurofibrillary tangles, the presence of excess neural activity is a pathological hallmark of Alzheimer's disease (AD) and a prognostic indicator for progression of AD pathology and clinical/cognitive worsening in mild cognitive impairment due to Alzheimer's disease (MCI due to AD). The HOPE4MCI clinical study tested the efficacy of a therapeutic with demonstrated ability to normalize heightened neural activity in the hippocampus in a randomized controlled trial of 78 weeks duration in patients with MCI due to AD. METHODS: One hundred and sixty-four participants were randomized to placebo (n = 83) or AGB101 (n = 81), an extended-release formulation of low dose (220 mg) levetiracetam. The primary endpoint was the change in Clinical Dementia Rating Scale Sum of Boxes score (CDR-SB) comparing follow up at 18 months to baseline. The goal of the primary efficacy analysis was to estimate the difference between the AGB101 and placebo arms in the mean change of the primary endpoint. RESULTS: The mean change in CDR-SB was estimated to be 1.12 (95% confidence interval [CI]: 0.66, 1.69) for the AGB101 arm and 1.22 (95% CI: 0.75, 1.78) for the placebo arm. The estimated difference between arms is -0.10 (95% CI: -0.85, 0.58), which was not statistically significant. In a prespecified analysis, the difference was -0.45 (95% CI: -1.43, 0.53) for ApoE-4 noncarriers and -0.10 (95% CI: -0.92, 0.72) for apolipoprotein E (ApoE)-4 carriers. DISCUSSION: The possibility that ApoE-4 carriers and noncarriers will respond differently to therapeutic intervention is consistent with recently reported findings from biologics and the present results show further testing of AGB101 in patients with MCI due to AD who are noncarriers of the ApoeE-4 allele is warranted. Conclusions from the HOPE4MCI study are limited primarily due to the small sample size and results can only be regarded as a guide to future research.

Genetic and Clinical Correlates of AI-Based Brain Aging Patterns in Cognitively Unimpaired Individuals.

Importance: Brain aging elicits complex neuroanatomical changes influenced by multiple age-related pathologies. Understanding the heterogeneity of structural brain changes in aging may provide insights into preclinical stages of neurodegenerative diseases. Objective: To derive subgroups with common patterns of variation in participants without diagnosed cognitive impairment (WODCI) in a data-driven manner and relate them to genetics, biomedical measures, and cognitive decline trajectories. Design, Setting, and Participants: Data acquisition for this cohort study was performed from 1999 to 2020. Data consolidation and harmonization were conducted from July 2017 to July 2021. Age-specific subgroups of structural brain measures were modeled in 4 decade-long intervals spanning ages 45 to 85 years using a deep learning, semisupervised clustering method leveraging generative adversarial networks. Data were analyzed from July 2021 to February 2023 and were drawn from the Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) international consortium. Individuals WODCI at baseline spanning ages 45 to 85 years were included, with greater than 50 000 data time points. Exposures: Individuals WODCI at baseline scan. Main Outcomes and Measures: Three subgroups, consistent across decades, were identified within the WODCI population. Associations with genetics, cardiovascular risk factors (CVRFs), amyloid ß (Aß), and future cognitive decline were assessed. Results: In a sample of 27 402 individuals (mean [SD] age, 63.0 [8.3] years; 15 146 female [55%]) WODCI, 3 subgroups were identified in contrast with the reference group: a typical aging subgroup, A1, with a specific pattern of modest atrophy and white matter hyperintensity (WMH) load, and 2 accelerated aging subgroups, A2 and A3, with characteristics that were more distinct at age 65 years and older. A2 was associated with hypertension, WMH, and vascular disease-related genetic variants and was enriched for Aß positivity (ages ≥65 years) and apolipoprotein E (APOE) ε4 carriers. A3 showed severe, widespread atrophy, moderate presence of CVRFs, and greater cognitive decline. Genetic variants associated with A1 were protective for WMH (rs7209235: mean [SD] B = -0.07 [0.01]; P value = 2.31 × 10-9) and Alzheimer disease (rs72932727: mean [SD] B = 0.1 [0.02]; P value = 6.49 × 10-9), whereas the converse was observed for A2 (rs7209235: mean [SD] B = 0.1 [0.01]; P value = 1.73 × 10-15 and rs72932727: mean [SD] B = -0.09 [0.02]; P value = 4.05 × 10-7, respectively); variants in A3 were associated with regional atrophy (rs167684: mean [SD] B = 0.08 [0.01]; P value = 7.22 × 10-12) and white matter integrity measures (rs1636250: mean [SD] B = 0.06 [0.01]; P value = 4.90 × 10-7). Conclusions and Relevance: The 3 subgroups showed distinct associations with CVRFs, genetics, and subsequent cognitive decline. These subgroups likely reflect multiple underlying neuropathologic processes and affect susceptibility to Alzheimer disease, paving pathways toward patient stratification at early asymptomatic stages and promoting precision medicine in clinical trials and health care.

Differential methylation analysis in neuropathologically confirmed dementia with Lewy bodies.

Dementia with Lewy bodies (DLB) is a common form of dementia in the elderly population. We performed genome-wide DNA methylation mapping of cerebellar tissue from pathologically confirmed DLB cases and controls to study the epigenetic profile of this understudied disease. After quality control filtering, 728,197 CpG-sites in 278 cases and 172 controls were available for the analysis. We undertook an epigenome-wide association study, which found a differential methylation signature in DLB cases. Our analysis identified seven differentially methylated probes and three regions associated with DLB. The most significant CpGs were located in ARSB (cg16086807), LINC00173 (cg18800161), and MGRN1 (cg16250093). Functional enrichment evaluations found widespread epigenetic dysregulation in genes associated with neuron-to-neuron synapse, postsynaptic specialization, postsynaptic density, and CTCF-mediated synaptic plasticity. In conclusion, our study highlights the potential importance of epigenetic alterations in the pathogenesis of DLB and provides insights into the modified genes, regions and pathways that may guide therapeutic developments.

Gene-SGAN: discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and single nucleotide polymorphism data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-associated neuroimaging phenotypes.

Walking energetics and white matter hyperintensities in mid-to-late adulthood.

INTRODUCTION: White matter hyperintensities (WMHs) increase with age and contribute to cognitive and motor function decline. Energy costs for mobility worsen with age, as the energetic cost of walking increases and energetic capacity declines. We examined the cross-sectional associations of multiple measures of walking energetics with WMHs in mid- to late-aged adults. METHODS: A total of 601 cognitively unimpaired adults (mean age 66.9 ± 15.3 years, 54% women) underwent brain magnetic resonance imaging scans and completed standardized slow- and peak-paced walking assessments with metabolic measurement (V̇O2). T1-weighted scans and fluid-attenuated inversion recovery images were used to quantify WMHs. Separate multivariable linear regression models examined associations adjusted for covariates. RESULTS: Lower slow-paced V̇O2 (B = 0.07; P = 0.030), higher peak-paced V̇O2 (B = -0.10; P = 0.007), and lower cost-to-capacity ratio (B = .12; P < 0.0001) were all associated with lower WMH volumes. DISCUSSION: The cost-to-capacity ratio, which describes the percentage of capacity required for ambulation, was the walking energetic measure most strongly associated with WMHs.

Multi-Site Cross-Site Inter-Rater and Test-Retest Reliability and Construct Validity of the MarkVCID White Matter Hyperintensity Growth and Regression Protocol.

BACKGROUND: White matter hyperintensities (WMH) that occur in the setting of vascular cognitive impairment and dementia (VCID) may be dynamic increasing or decreasing volumes or stable over time. Quantifying such changes may prove useful as a biomarker for clinical trials designed to address vascular cognitive-impairment and dementia and Alzheimer's Disease. OBJECTIVE: Conducting multi-site cross-site inter-rater and test-retest reliability of the MarkVCID white matter hyperintensity growth and regression protocol. METHODS: The NINDS-supported MarkVCID Consortium evaluated a neuroimaging biomarker developed to track WMH change. Test-retest and cross-site inter-rater reliability of the protocol were assessed. Cognitive test scores were analyzed in relation to WMH changes to explore its construct validity. RESULTS: ICC values for test-retest reliability of WMH growth and regression were 0.969 and 0.937 respectively, while for cross-site inter-rater ICC values for WMH growth and regression were 0.995 and 0.990 respectively. Word list long-delay free-recall was negatively associated with WMH growth (p < 0.028) but was not associated with WMH regression. CONCLUSIONS: The present data demonstrate robust ICC validity of a WMH growth/regression protocol over a one-year period as measured by cross-site inter-rater and test-retest reliability. These data suggest that this approach may serve an important role in clinical trials of disease-modifying agents for VCID that may preferentially affect WMH growth, stability, or regression.

Multi-ancestry genome-wide meta-analysis of 56,241 individuals identifies LRRC4C, LHX5-AS1 and nominates ancestry-specific loci PTPRK , GRB14 , and KIAA0825 as novel risk loci for Alzheimer's disease: the Alzheimer's Disease Genetics Consortium.

Limited ancestral diversity has impaired our ability to detect risk variants more prevalent in non-European ancestry groups in genome-wide association studies (GWAS). We constructed and analyzed a multi-ancestry GWAS dataset in the Alzheimer's Disease (AD) Genetics Consortium (ADGC) to test for novel shared and ancestry-specific AD susceptibility loci and evaluate underlying genetic architecture in 37,382 non-Hispanic White (NHW), 6,728 African American, 8,899 Hispanic (HIS), and 3,232 East Asian individuals, performing within-ancestry fixed-effects meta-analysis followed by a cross-ancestry random-effects meta-analysis. We identified 13 loci with cross-ancestry associations including known loci at/near CR1 , BIN1 , TREM2 , CD2AP , PTK2B , CLU , SHARPIN , MS4A6A , PICALM , ABCA7 , APOE and two novel loci not previously reported at 11p12 ( LRRC4C ) and 12q24.13 ( LHX5-AS1 ). Reflecting the power of diverse ancestry in GWAS, we observed the SHARPIN locus using 7.1% the sample size of the original discovering single-ancestry GWAS (n=788,989). We additionally identified three GWS ancestry-specific loci at/near ( PTPRK ( P =2.4×10 -8 ) and GRB14 ( P =1.7×10 -8 ) in HIS), and KIAA0825 ( P =2.9×10 -8 in NHW). Pathway analysis implicated multiple amyloid regulation pathways (strongest with P adjusted =1.6×10 -4 ) and the classical complement pathway ( P adjusted =1.3×10 -3 ). Genes at/near our novel loci have known roles in neuronal development ( LRRC4C, LHX5-AS1 , and PTPRK ) and insulin receptor activity regulation ( GRB14 ). These findings provide compelling support for using traditionally-underrepresented populations for gene discovery, even with smaller sample sizes.

Hearing intervention versus health education control to reduce cognitive decline in older adults with hearing loss in the USA (ACHIEVE): a multicentre, randomised controlled trial.

BACKGROUND: Hearing loss is associated with increased cognitive decline and incident dementia in older adults. We aimed to investigate whether a hearing intervention could reduce cognitive decline in cognitively healthy older adults with hearing loss. METHODS: The ACHIEVE study is a multicentre, parallel-group, unmasked, randomised controlled trial of adults aged 70-84 years with untreated hearing loss and without substantial cognitive impairment that took place at four community study sites across the USA. Participants were recruited from two study populations at each site: (1) older adults participating in a long-standing observational study of cardiovascular health (Atherosclerosis Risk in Communities [ARIC] study), and (2) healthy de novo community volunteers. Participants were randomly assigned (1:1) to a hearing intervention (audiological counselling and provision of hearing aids) or a control intervention of health education (individual sessions with a health educator covering topics on chronic disease prevention) and followed up every 6 months. The primary endpoint was 3-year change in a global cognition standardised factor score from a comprehensive neurocognitive battery. Analysis was by intention to treat. This trial was registered at ClinicalTrials.gov, NCT03243422. FINDINGS: From Nov 9, 2017, to Oct 25, 2019, we screened 3004 participants for eligibility and randomly assigned 977 (32·5%; 238 [24%] from ARIC and 739 [76%] de novo). We randomly assigned 490 (50%) to the hearing intervention and 487 (50%) to the health education control. The cohort had a mean age of 76·8 years (SD 4·0), 523 (54%) were female, 454 (46%) were male, and most were White (n=858 [88%]). Participants from ARIC were older, had more risk factors for cognitive decline, and had lower baseline cognitive scores than those in the de novo cohort. In the primary analysis combining the ARIC and de novo cohorts, 3-year cognitive change (in SD units) was not significantly different between the hearing intervention and health education control groups (-0·200 [95% CI -0·256 to -0·144] in the hearing intervention group and -0·202 [-0·258 to -0·145] in the control group; difference 0·002 [-0·077 to 0·081]; p=0·96). However, a prespecified sensitivity analysis showed a significant difference in the effect of the hearing intervention on 3-year cognitive change between the ARIC and de novo cohorts (pinteraction=0·010). Other prespecified sensitivity analyses that varied analytical parameters used in the total cohort did not change the observed results. No significant adverse events attributed to the study were reported with either the hearing intervention or health education control. INTERPRETATION: The hearing intervention did not reduce 3-year cognitive decline in the primary analysis of the total cohort. However, a prespecified sensitivity analysis showed that the effect differed between the two study populations that comprised the cohort. These findings suggest that a hearing intervention might reduce cognitive change over 3 years in populations of older adults at increased risk for cognitive decline but not in populations at decreased risk for cognitive decline. FUNDING: US National Institutes of Health.

Genome-wide structural variant analysis identifies risk loci for non-Alzheimer's dementias.

We characterized the role of structural variants, a largely unexplored type of genetic variation, in two non-Alzheimer's dementias, namely Lewy body dementia (LBD) and frontotemporal dementia (FTD)/amyotrophic lateral sclerosis (ALS). To do this, we applied an advanced structural variant calling pipeline (GATK-SV) to short-read whole-genome sequence data from 5,213 European-ancestry cases and 4,132 controls. We discovered, replicated, and validated a deletion in TPCN1 as a novel risk locus for LBD and detected the known structural variants at the C9orf72 and MAPT loci as associated with FTD/ALS. We also identified rare pathogenic structural variants in both LBD and FTD/ALS. Finally, we assembled a catalog of structural variants that can be mined for new insights into the pathogenesis of these understudied forms of dementia.

Test-retest reliability of 3D velocity-selective arterial spin labeling for detecting normal variations of cerebral blood flow.

Velocity-selective inversion (VSI) based velocity-selective arterial spin labeling (VSASL) has been developed to measure cerebral blood flow (CBF) with low susceptibility to the prolonged arterial transit time and high sensitivity to brain perfusion signal. The purpose of this magnetic resonance imaging study is to evaluate the test-retest reliability of a VSI-prepared 3D VSASL protocol with whole-brain coverage to detect baseline CBF variations among cognitively normal participants in different brain regions. Coefficients of variation (CoV) of both absolute and relative CBF across scans or sessions, subjects, and gray matter regions were calculated, and corresponding intraclass correlation coefficients (ICC) were computed. The higher between-subject CoV of absolute CBF (13.4 ± 2.0%) over within-subject CoV (within-session: 3.8 ± 1.1%; between-session: 4.9 ± 0.9%) yielded moderate to excellent ICC (within-session: 0.88±0.08; between-session: 0.77±0.14) to detect normal variations of individual CBF. The higher between-region CoV of relative CBF (11.4 ± 3.0%) over within-region CoV (within-session: 2.3 ± 0.9%; between-session: 3.3 ± 1.0%) yielded excellent ICC (within-session: 0.92±0.06; between-session: 0.85±0.12) to detect normal variations of regional CBF. Age, blood pressure, end-tidal CO2, and hematocrit partially explained the variability of CBF across subjects. Together these results show excellent test-retest reliability of VSASL to detect both between-subject and between-region variations supporting its clinical utility.

Early amygdala and ERC atrophy linked to 3D reconstruction of rostral neurofibrillary tau tangle pathology in Alzheimer's disease.

Previous research has emphasized the unique impact of Alzheimer's Disease (AD) pathology on the medial temporal lobe (MTL), a reflection that tau pathology is particularly striking in the entorhinal and transentorhinal cortex (ERC, TEC) early in the course of disease. However, other brain regions are affected by AD pathology during its early phases. Here, we use longitudinal diffeomorphometry to measure the atrophy rate from MRI of the amygdala compared with that in the ERC and TEC in cognitively unimpaired (CU) controls, CU individuals who progressed to mild cognitive impairment (MCI), and individuals with MCI who progressed to dementia of the AD type (DAT), using a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Our results show significantly higher atrophy rates of the amygdala in both groups of 'converters' (CU→MCI, MCI→DAT) compared to controls, with rates of volume loss comparable to rates of thickness loss in the ERC and TEC. We localize atrophy within the amygdala within each of these groups using fixed effects modeling. Controlling for the familywise error rate highlights the medial regions of the amygdala as those with significantly higher atrophy in both groups of converters than in controls. Using our recently developed method, referred to as Projective LDDMM, we map measures of neurofibrillary tau tangles (NFTs) from digital pathology to MRI atlases and reconstruct dense 3D spatial distributions of NFT density within regions of the MTL. The distribution of NFTs is consistent with the spatial distribution of MR measured atrophy rates, revealing high densities (and atrophy) in the amygdala (particularly medial), ERC, and rostral third of the MTL. The similarity of the location of NFTs in AD and shape changes in a well-defined clinical population suggests that amygdalar atrophy rate, as measured through MRI may be a viable biomarker for AD.

Gene-SGAN: a method for discovering disease subtypes with imaging and genetic signatures via multi-view weakly-supervised deep clustering.

Disease heterogeneity has been a critical challenge for precision diagnosis and treatment, especially in neurologic and neuropsychiatric diseases. Many diseases can display multiple distinct brain phenotypes across individuals, potentially reflecting disease subtypes that can be captured using MRI and machine learning methods. However, biological interpretability and treatment relevance are limited if the derived subtypes are not associated with genetic drivers or susceptibility factors. Herein, we describe Gene-SGAN - a multi-view, weakly-supervised deep clustering method - which dissects disease heterogeneity by jointly considering phenotypic and genetic data, thereby conferring genetic correlations to the disease subtypes and associated endophenotypic signatures. We first validate the generalizability, interpretability, and robustness of Gene-SGAN in semi-synthetic experiments. We then demonstrate its application to real multi-site datasets from 28,858 individuals, deriving subtypes of Alzheimer's disease and brain endophenotypes associated with hypertension, from MRI and SNP data. Derived brain phenotypes displayed significant differences in neuroanatomical patterns, genetic determinants, biological and clinical biomarkers, indicating potentially distinct underlying neuropathologic processes, genetic drivers, and susceptibility factors. Overall, Gene-SGAN is broadly applicable to disease subtyping and endophenotype discovery, and is herein tested on disease-related, genetically-driven neuroimaging phenotypes.

Five dominant dimensions of brain aging are identified via deep learning: associations with clinical, lifestyle, and genetic measures.

Brain aging is a complex process influenced by various lifestyle, environmental, and genetic factors, as well as by age-related and often co-existing pathologies. MRI and, more recently, AI methods have been instrumental in understanding the neuroanatomical changes that occur during aging in large and diverse populations. However, the multiplicity and mutual overlap of both pathologic processes and affected brain regions make it difficult to precisely characterize the underlying neurodegenerative profile of an individual from an MRI scan. Herein, we leverage a state-of-the art deep representation learning method, Surreal-GAN, and present both methodological advances and extensive experimental results that allow us to elucidate the heterogeneity of brain aging in a large and diverse cohort of 49,482 individuals from 11 studies. Five dominant patterns of neurodegeneration were identified and quantified for each individual by their respective (herein referred to as) R-indices. Significant associations between R-indices and distinct biomedical, lifestyle, and genetic factors provide insights into the etiology of observed variances. Furthermore, baseline R-indices showed predictive value for disease progression and mortality. These five R-indices contribute to MRI-based precision diagnostics, prognostication, and may inform stratification into clinical trials.

CSF Alzheimer Disease Biomarkers: Time-Varying Relationships With MCI Symptom Onset and Associations With Age, Sex, and ApoE4.

BACKGROUND AND OBJECTIVES: This study aimed to examine whether baseline CSF measures of Alzheimer disease (AD)-related pathology are associated with the time to onset of mild cognitive impairment (MCI) and whether these associations differ by age, sex, Apolipoprotein E (ApoE4) status, and proximal (≤7 years) vs distal (>7 years) time to symptom onset. METHODS: Measures of amyloid (Aß1-42 and Aß1-40), phospho-tau (ptau181), and total tau (t-tau) were determined from CSF samples obtained at baseline from participants in an ongoing longitudinal project, known as the Biomarkers for Older Controls at Risk for Alzheimer Disease study (BIOCARD) study. The fully automated, Lumipulse G immunoassay was used to analyze the specimens. Cox regression models were used to examine the relationship of baseline biomarker levels with time to symptom onset of MCI and interactions with age, sex, and ApoE allelic status in subjects who progressed from normal cognition to MCI. RESULTS: Analyses included 273 participants from the BIOCARD cohort, who were cognitively normal and predominantly middle-aged at baseline, and have been followed for an average of 16 years (max = 23.6). During follow-up, 94 progressed to MCI (median time to symptom onset = 6.9 years). In Cox regression models, elevated ptau181 and t-tau levels were associated with time to MCI symptom onset if it occurred within 7 years of baseline (HR 1.386 and 1.329; p = 0.009 and 0.017, respectively), while a lower Aß42/Aß40 ratio was associated with symptom onset if it occurred >7 years from baseline (HR 0.596, p = 0.003). There were also significant 3-way CSF × age × sex interactions for ptau181 and Aß42/Aß40, with follow-up analyses indicating that associations between these biomarkers and progression to MCI were stronger among men than among women, but this difference between sexes diminished with increasing age. DISCUSSION: The lengthy follow-up of BIOCARD participants permitted an examination of time-varying associations between CSF AD biomarkers with MCI symptom onset and the influence of sex, baseline age, and ApoE4 genotype on these associations. These factors may inform clinical trial enrollment strategies, or trial duration and outcomes, which may use these measures as surrogate markers of treatment response.

Accelerated longitudinal changes and ordering of Alzheimer disease biomarkers across the adult lifespan.

The temporal evolutions and relative orderings of Alzheimer disease biomarkers, including CSF amyloid-ß42 (Aß42), Aß40, total tau (Tau) and phosphorylated tau181 (pTau181), standardized uptake value ratio (SUVR) from the molecular imaging of cerebral fibrillar amyloid-ß with PET using the 11C-Pittsburgh Compound-B (PiB), MRI-based hippocampal volume and cortical thickness and cognition have been hypothesized but not yet fully tested with longitudinal data for all major biomarker modalities among cognitively normal individuals across the adult lifespan starting from 18 years. By leveraging a large harmonized database from 8 biomarker studies with longitudinal data from 2609 participants in cognition, 873 in MRI biomarkers, 519 in PET PiB imaging and 475 in CSF biomarkers for a median follow-up of 5-6 years, we estimated the longitudinal trajectories of all major Alzheimer disease biomarkers as functions of baseline age that spanned from 18 to 103 years, located the baseline age window at which the longitudinal rates of change accelerated and further examined possible modifying effects of apolipoprotein E (APOE) genotype. We observed that participants 18-45 years at baseline exhibited learning effects on cognition and unexpected directions of change on CSF and PiB biomarkers. The earliest acceleration of longitudinal change occurred for CSF Aß42 and Aß42/Aß40 ratio (with an increase) and for Tau, and pTau181 (with a decrease) at the next baseline age interval of 45-50 years, followed by an accelerated increase for PiB SUVR at the baseline age of 50-55 years and an accelerated decrease for hippocampal volume at the baseline age of 55-60 years and finally by an accelerated decline for cortical thickness and cognition at the baseline age of 65-70 years. Another acceleration in the rate of change occurred at the baseline age of 65-70 years for Aß42/Aß40 ratio, Tau, pTau181, PiB SUVR and hippocampal volume. Accelerated declines in hippocampal volume and cognition continued after 70 years. For participants 18-45 years at baseline, significant increases in Aß42 and Aß42/Aß40 ratio and decreases in PiB SUVR occurred in APOE ɛ4 non-carriers but not carriers. After age 45 years, APOE ɛ4 carriers had greater magnitudes than non-carriers in the rates of change for all CSF biomarkers, PiB SUVR and cognition. Our results characterize the temporal evolutions and relative orderings of Alzheimer disease biomarkers across the adult lifespan and the modification effect of APOE ɛ4. These findings may better inform the design of prevention trials on Alzheimer disease.