Disease Neuroimaging Initiative Study Research Articles

Metabolic phenotyping reveals an emerging role of ammonia abnormality in Alzheimer’s disease

The metabolic implications in Alzheimer’s disease (AD) remain poorly understood. Here, we conducted a metabolomics study on a moderately aging Chinese Han cohort (n = 1397; mean age 66 years). Conjugated bile acids, branch-chain amino acids (BCAAs), and glutamate-related features exhibited strong correlations with cognitive impairment, clinical stage, and brain amyloid-β deposition (n = 421). These features demonstrated synergistic performances across clinical stages and subpopulations and enhanced the differentiation of AD stages beyond demographics and Apolipoprotein E ε4 allele (APOE-ε4). We validated their performances in eight data sets (total n = 7685) obtained from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Religious Orders Study and Memory and Aging Project (ROSMAP). Importantly, identified features are linked to blood ammonia homeostasis. We further confirmed the elevated ammonia level through AD development (n = 1060). Our findings highlight AD as a metabolic disease and emphasize the metabolite-mediated ammonia disturbance in AD and its potential as a signature and therapeutic target for AD.

Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC v1.0 BeadChip microarrays

ABSTRACT DNA methylation (DNAm) plays a crucial role in a number of complex diseases. However, the reliability of DNAm levels measured using Illumina arrays varies across different probes. Previous research primarily assessed probe reliability by comparing duplicate samples between the 450k-450k or 450k-EPIC platforms, with limited investigations on Illumina EPIC v1.0 arrays. We conducted a comprehensive assessment of the EPIC v1.0 array probe reliability using 69 blood DNA samples, each measured twice, generated by the Alzheimer’s Disease Neuroimaging Initiative study. We observed higher reliability in probes with average methylation beta values of 0.2 to 0.8, and lower reliability in type I probes or those within the promoter and CpG island regions. Importantly, we found that probe reliability has significant implications in the analyses of Epigenome-wide Association Studies (EWAS). Higher reliability is associated with more consistent effect sizes in different studies, the identification of differentially methylated regions (DMRs) and methylation quantitative trait locus (mQTLs), and significant correlations with downstream gene expression. Moreover, blood DNAm measurements obtained from probes with higher reliability are more likely to show concordance with brain DNAm measurements. Our findings, which provide crucial reliability information for probes on the EPIC v1.0 array, will serve as a valuable resource for future DNAm studies.

Imputation-Based Variable Selection Method for Block-Wise Missing Data When Integrating Multiple Longitudinal Studies

When integrating data from multiple sources, a common challenge is block-wise missing. Most existing methods address this issue only in cross-sectional studies. In this paper, we propose a method for variable selection when combining datasets from multiple sources in longitudinal studies. To account for block-wise missing in covariates, we impute the missing values multiple times based on combinations of samples from different missing pattern and predictors from different data sources. We then use these imputed data to construct estimating equations, and aggregate the information across subjects and sources with the generalized method of moments. We employ the smoothly clipped absolute deviation penalty in variable selection and use the extended Bayesian Information Criterion criteria for tuning parameter selection. We establish the asymptotic properties of the proposed estimator, and demonstrate the superior performance of the proposed method through numerical experiments. Furthermore, we apply the proposed method in the Alzheimer’s Disease Neuroimaging Initiative study to identify sensitive early-stage biomarkers of Alzheimer’s Disease, which is crucial for early disease detection and personalized treatment.

Disentangling Accelerated Cognitive Decline from the Normal Aging Process and Unraveling Its Genetic Components: A Neuroimaging-Based Deep Learning Approach.

The progressive cognitive decline, an integral component of Alzheimer's disease (AD), unfolds in tandem with the natural aging process. Neuroimaging features have demonstrated the capacity to distinguish cognitive decline changes stemming from typical brain aging and AD between different chronological points. To disentangle the normal aging effect from the AD-related accelerated cognitive decline and unravel its genetic components using a neuroimaging-based deep learning approach. We developed a deep-learning framework based on a dual-loss Siamese ResNet network to extract fine-grained information from the longitudinal structural magnetic resonance imaging (MRI) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. We then conducted genome-wide association studies (GWAS) and post-GWAS analyses to reveal the genetic basis of AD-related accelerated cognitive decline. We used our model to process data from 1,313 individuals, training it on 414 cognitively normal people and predicting cognitive assessment for all participants. In our analysis of accelerated cognitive decline GWAS, we identified two genome-wide significant loci: APOE locus (chromosome 19 p13.32) and rs144614292 (chromosome 11 p15.1). Variant rs144614292 (G > T) has not been reported in previous AD GWA studies. It is within the intronic region of NELL1, which is expressed in neurons and plays a role in controlling cell growth and differentiation. The cell-type-specific enrichment analysis and functional enrichment of GWAS signals highlighted the microglia and immune-response pathways. Our deep learning model effectively extracted relevant neuroimaging features and predicted individual cognitive decline. We reported a novel variant (rs144614292) within the NELL1 gene.

Association of latent factors of neuroinflammation with Alzheimer's disease pathology and longitudinal cognitive decline.

We investigated the association of inflammatory mechanisms with markers of Alzheimer's disease (AD) pathology and rates of cognitive decline in the AD spectrum. We studied 296 cases from the Deutsches Zentrum für Neurodegenerative Erkrankungen Longitudinal Cognitive Impairment and Dementia Study (DELCODE) cohort, and an extension cohort of 276 cases of the Alzheimer's Disease Neuroimaging Initiative study. Using Bayesian confirmatory factor analysis, we constructed latent factors for synaptic integrity, microglia, cerebrovascular endothelial function, cytokine/chemokine, and complement components of the inflammatory response using a set of inflammatory markers in cerebrospinal fluid. We found strong evidence for an association of synaptic integrity, microglia response, and cerebrovascular endothelial function with a latent factor of AD pathology and with rates of cognitive decline. We found evidence against an association of complement and cytokine/chemokine factors with AD pathology and rates of cognitive decline. Latent factors provided access to directly unobservable components of the neuroinflammatory response and their association with AD pathology and cognitive decline.

Regression analysis of longitudinal data with mixed synchronous and asynchronous longitudinal covariates

In linear models, omitting a covariate that is orthogonal to covariates in the model does not result in biased coefficient estimation. This generally does not hold for longitudinal data, where additional assumptions are needed to get an unbiased coefficient estimation in addition to the orthogonality between omitted longitudinal covariates and longitudinal covariates in the model. We propose methods to mitigate the omitted variable bias under weaker assumptions. A two-step estimation procedure is proposed to infer the asynchronous longitudinal covariates when such covariates are observed. For mixed synchronous and asynchronous longitudinal covariates, we get a parametric convergence rate for the coefficient estimation of the synchronous longitudinal covariates by the two-step method. Extensive simulation studies provide numerical support for the theoretical findings. We illustrate the performance of our method on a dataset from the Alzheimer’s Disease Neuroimaging Initiative study.

OP142 Progression Analysis Versus Traditional Methods To Quantify Slowing Of Disease Progression In Alzheimer’s Disease

IntroductionNew statistical methodology, known as progression models for repeated measures (PMRM), can estimate the slowing of progression (percentage slowing or time delay) of Alzheimer’s disease from trial data on disease-modifying therapies. We compared the PMRM methodology with mixed models for repeated measures (MMRM) and Cox time-to-event analysis on simulated trial data with respect to their power and interpretability of estimates.MethodsTwo novel models were included: PMRM (estimating slowing of progression and allowing different rates across visits) and proportional-slowing PMRM. Clinical Dementia Rating (CDR) Sum of Boxes score and progression to dementia as assessed by CDR global score were the primary outcomes for MMRM/PMRM and the Cox model, respectively. Subject-level placebo arm trajectories were jointly simulated based on estimated CDR mean trajectories and joint temporal correlation structure of 538 amyloid-positive patients with mild cognitive impairment who met typical disease-modifying trial inclusion criteria from the Alzheimer’s Disease Neuroimaging Initiative study. Active arm trajectories were simulated to show an average 20 percent slowing of disease progression, compared with placebo, at each visit. We conducted 1,000 simulations across multiple scenarios, varying the number of patients per arm (200 to 700) and clinical trial duration (18 to 36 months).ResultsThe power of PMRM models was greater than that of MMRM, and much greater than that of the Cox model whose power never exceeded 45 percent. PMRM models accurately estimated the underlying treatment effect (median 20% slowed progression, which translated to a delay in progression of 5 and 7 months at trial durations of 24 and 36 months, respectively), unlike quantifications of the MMRM (median estimated 25% reduction in decline), and the Cox model (median estimated hazard ratio of 0.9).ConclusionsFor disease-modifying therapies, PMRM estimates may have a more intuitive clinical interpretation in terms of delayed progression than MMRM or Cox models and enable a description of the amount of time spent in less severe disease stages. Among all the methods studied, PMRM offered the best combination of interpretability and power.

Blood co‐expression network analysis identifies molecular pathways involved in cognitive resilience

AbstractBackgroundTwo major risk factors of Alzheimer’s disease (AD) are advanced age and the APOE‐ε4 risk allele. However, despite having these risk factors, some individuals escape from developing AD or have slower cognitive decline. In this study we hypothesized that “resilient” individuals, defined as those who remain cognitively normal despite being >80 years‐old and carrying at least one copy of the APOE‐ε4 allele, have concerted changes in their blood transcriptomes that are associated with resilience to AD.MethodWe generated blood RNA‐sequence data from 105 APOE‐ε4 carriers from the Mayo Clinic Study of Aging (MCSA) who remained cognitively normal after age 80 (resilient), or who developed mild cognitive impairment (MCI) or AD (non‐resilient). Weighted gene co‐expression network analysis (WGCNA) was performed to evaluate the association of the blood co‐expression network with primary phenotypes of resilience and memory scores. To validate our findings, we constructed a consensus co‐expression network using both MCSA and a replication cohort of blood microarray data from 91 Alzheimer’s Disease Neuroimaging Initiative (ADNI) study participants who had a similar demographic profile. We annotated the consensus modules for their enrichment of cell types, biological processes, genes within previously published genome‐wide significant (GWS) AD‐associated loci, and genes that reside in consensus modules built from seven brain regions across three different autopsy cohorts. To identify genetic variants regulating blood consensus module co‐expression, we performed module quantitative trait locus (modQTL) analysis.ResultWe found co‐expression modules significantly (q<0.05) correlated with the memory phenotype, logical memory delayed recall, across both MCSA and ADNI cohorts. Some of these modules were enriched with blood cell types and biological processes, including telomere maintenance, mitochondrial functions, and immune response. We found 1 module significantly (q<0.05) enriched with genes near GWS AD‐associated loci and 4 modules with genes in brain consensus modules. Lastly, we found 8 modQTLs significantly (Bonferroni‐p<0.05) associated with the co‐expression of 5 different modules.ConclusionWe identified blood co‐expression modules, biological pathways and specific genetic variants associated with cognitive resilience in the presence of AD risk factors. This study provides novel insights into the potential role of peripheral blood in the development of and resilience to AD.

X chromosome-wide association study of quantitative biomarkers from the Alzheimer's Disease Neuroimaging Initiative study.

Alzheimer's disease (AD) is a complex neurodegenerative disease with high heritability. Compared to autosomes, a higher proportion of disorder-associated genes on X chromosome are expressed in the brain. However, only a few studies focused on the identification of the susceptibility loci for AD on X chromosome. Using the data from the Alzheimer's Disease Neuroimaging Initiative Study, we conducted an X chromosome-wide association study between 16 AD quantitative biomarkers and 19,692 single nucleotide polymorphisms (SNPs) based on both the cross-sectional and longitudinal studies. We identified 15 SNPs statistically significantly associated with different quantitative biomarkers of the AD. For the cross-sectional study, six SNPs (rs5927116, rs4596772, rs5929538, rs2213488, rs5920524, and rs5945306) are located in or near to six genes DMD, TBX22, LOC101928437, TENM1, SPANXN1, and ZFP92, which have been reported to be associated with schizophrenia or neuropsychiatric diseases in literature. For the longitudinal study, four SNPs (rs4829868, rs5931111, rs6540385, and rs763320) are included in or near to two genes RAC1P4 and AFF2, which have been demonstrated to be associated with brain development or intellectual disability in literature, while the functional annotations of other five novel SNPs (rs12157031, rs428303, rs5953487, rs10284107, and rs5955016) have not been found. 15 SNPs were found statistically significantly associated with the quantitative biomarkers of the AD. Follow-up study in molecular genetics is needed to verify whether they are indeed related to AD. The findings in this article expand our understanding of the role of the X chromosome in exploring disease susceptibility, introduce new insights into the molecular genetics behind the AD, and may provide a mechanistic clue to further AD-related studies.

66 An Examination of Racial Disparities on Dementia Types in the Black Community

Objective:There is limited and inconsistent research exploring diagnosis, treatment, and prevention of dementias amongst Black Indigenous People of Color (BIPOC). By 2050, it is suspected that the White population will significantly decrease and BIPOC groups will comprise the majority of the US population yet BIPOC are historically underrepresented in dementia research. Prior research indicates apolipoprotein 4 allele (APOE-4) status is associated with a greater risk of developing Alzheimer’s disease in Black individuals when compared to non-Hispanic Whites. Investigating the racial disparities in dementia will expand our knowledgebase of risk for dementia types in the Black community to better meet the evergrowing population needs. The current study explored the impact of racial identity on global cognitive functioning, independent of age, education, and APOE e4 status.Participants and Methods:Participants were drawn from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study and consisted of five pairs of Black and White individuals (n = 10) matched based on age, education, and APOE status. Global cognitive performance was measured by the total Mini Mental Status Examination (MMSE) score. Notably, only five Black individuals in phase 1 of ADNI met inclusion criteria. It was hypothesized Black individuals would be more cognitively impaired than their White counterparts. A matched pairs t-test was utilized to examine the relationship between global cognitive performance and race.Results:Black and White individuals’ MMSE scores did not significantly differ (p > .05). The mean MMSE performance of White participants (26.40) was less robust than Black participants (27.80). Findings are inconsistent with current research, indicating that BIPOC individuals are disproportionately impacted by AD, with increased severity of cognitive impairment. There is a profound need for more research in preventative interventions and recruitment of BIPOC individuals who have been historically marginalized in cognitive research trials to help better understand diagnosis, treatment, and prevention of AD in BIPOC.Conclusions:The observed commensurate global cognitive functioning performance between matched Black and White individuals is not consistent with prior research findings demonstrating increased risk of developing dementia amongst BIPOC. This study’s small sample size reflects a significant barrier to detecting clinically meaningful differences. Efforts to address the recruitment crisis, underreporting, cultural influences, and overall mistrust of research among BIPOC is warranted. Inclusive research is critical to dismantling health disparities.

18 Vascular Risk, Cerebral White Matter, and Executive Functioning in Vietnam-Era Veterans with Traumatic Brain Injury and/or Post-Traumatic Stress Disorder: A Department of Defense Alzheimer’s Disease Initiative (DoD-ADNI) Study

Objective:Research indicates that Veterans with a history of traumatic brain injury (TBI) are at increased risk for dementia. Although the precise mechanisms underlying this relationship are poorly understood, remote TBI may exacerbate normal age-related changes to cerebral white matter (WM) and result in cognitive decline. However, Veterans commonly experience a constellation of mental (e.g., post-traumatic stress disorder [PTSD] and depression) and vascular (e.g., diabetes, hypertension, obesity) health conditions that have also been implicated in pathologic cerebral WM and cognitive aging trajectories. Therefore, the present study sought to (1) clarify the effects of remote TBI within the context of PTSD, depression, and vascular risk on WM micro- and macrostructure, and (2) explore if WM integrity is associated with cognition in a sample of Vietnam-Era Veterans.Participants and Methods:The sample consisted of 195 male Veterans ages 60-80 (mean age=69.3) enrolled in the Department of Defense-Alzheimer’s Disease Neuroimaging Initiative (DoD-ADNI) study. 102 Veterans met criteria for TBI by sustaining a head-injury that resulted in a loss of consciousness, alteration of consciousness, or post-traumatic amnesia. Current and/or lifetime PTSD was designated by scores >30 on the Clinician-Administered PTSD Scale. The Geriatric Depression Scale was used as a continuous measure of depression. A vascular risk score (0-3) was calculated based on diabetes, hypertension, and obesity (BMI >30 kg/m2). An executive functioning composite was created by averaging sample-specific z-scores for Trail Making Tests (A and B), with higher scores indicating worse performance. Voxelwise analysis of WM microstructure (fractional anisotropy [FA]) was conducted with Tract-Based Spatial Statistics (TBSS), using non-parametric permutation testing with threshold-free cluster enhancement. SPM’s Lesion Segmentation Tool was used to investigate WM macrostructure (WM hyperintensity [WMH] volume). Lesion probability maps were masked to restrict WMH volume calculations to WM. Robust regression using M-estimation and predictive R2 calculated using 10-fold cross-validation examined WMH volume, predictor, and cognitive associations. Age was a covariate in all WM analyses, and education was a covariate in all cognitive analyses.Results:TBSS analysis revealed widespread, significant negative relationships between vascular risk and FA across numerous WM tracts (p’s<0.05). These associations remained significant after adjusting for TBI history, PTSD, and depression. TBSS identified significant positive relationships between executive functioning performance and FA across similar brain regions (p’s<0.05). Robust regressions revealed that vascular risk significantly predicted WMH volume (p=0.006; ß=0.161; R2=0.093), whereas TBI history, PTSD, and depression did not (p’s=0.107-0.166; ß's=-0.089-0.101). WMH volume significantly predicted executive functioning (p=0.002; ß=0.216; R2=0.105), whereas TBI history, PTSD, depression, and vascular risk did not (p’s=0.123-0.888; ß's=-0.012-0.125).Conclusions:Our results suggest that vascular health, relative to remote TBI, PTSD, and depression, may be more robustly associated with cerebral WM micro- and macrostructure in older Veterans. Furthermore, poorer WM integrity is associated with poorer cognitive performance. These findings underscore the importance of vascular health interventions in preventing negative brain and cognitive aging outcomes in Veterans, independent of TBI history. Future studies might leverage other neuroimaging modalities (e.g., functional MRI) to further investigate the effects of vascular health on aging in Veterans with a history of TBI.

3 Type 2 Diabetes Moderates the Association between Amyloid PET and Attention/Executive Functioning in Older Veterans

Objective:Type 2 diabetes (T2D) is a risk factor for cognitive impairment/dementia and has been shown to modify the impact of Alzheimer’s disease (AD) biomarkers on cognition and everyday functioning. Studies examining amyloid-ß (Aß), one of the hallmark AD pathologies, have shown mixed results regarding associations of Aß biomarkers with cross-sectional cognition as well as T2D, though Aß is generally associated with future cognitive declines. The purpose of the present study is to examine whether T2D impacts the associations between amyloid positron emission tomography (PET) and cognition in older Veterans.Participants and Methods:The current study included 202 mostly male Vietnam-Era Veterans from the Department of Defense-Alzheimer’s Disease Neuroimaging Initiative (DOD ADNI) study (age M=69.38 years, SD=4.37; 40% with self-reported T2D) who completed neuropsychological testing and florbetapir PET imaging. The Aß PET standardized uptake variable ratio (SUVR) was measured using a previously-validated summary SUVR calculated by dividing the mean uptake across 4 AD-vulnerable cortical regions by whole cerebellar uptake. General linear models examined whether T2D moderated the relationship of Aß PET with memory, attention/executive functioning, and language composite scores. Models adjusted for age, education, apolipoprotein E e4 carrier status, vascular risk burden, depressive symptoms, post-traumatic stress disorder (PTSD) symptom severity, and history of traumatic brain injury (TBI).Results:There was no main effect of diabetes on memory, attention/executive functioning, or language performance, and higher Aß PET SUVR was only associated with worse attention/executive functioning performance (ß=-.146, 95% CI [-.261, -.031], p=.013). The Aß PET x T2D interaction was significant for attention/executive functioning such that higher Aß PET SUVR was associated with lower attention/executive functioning scores, but only in those with T2D (ß=-.116, [-.225, -.006], p=.038). This interaction was not significant for language or memory.Conclusions:The results show that Aß may negatively impact attention/executive functioning, but this effect was only found in Veterans with T2D. Prior work has suggested that T2D may be more associated with tau biomarkers than markers of Aß, so it is possible that the current results are due to a compounding effect of Aß pathology plus microvascular and/or tau pathology. Notably, the sample was relatively young, a relatively large proportion had elevated PTSD symptoms and/or a TBI history (which have both been shown to relate to attention/executive function), and the measures that made up the attention/executive composite (Trail Making Test A and B) have been shown to be particularly sensitive - all of which may have contributed to the domain-specific effects. Future research is needed to investigate the role that tau and vascular pathology may play in cognition among individuals with T2D. Longitudinal studies are also needed to better understand the timing and progression of these relationships.

Bayesian nonparametric method for genetic dissection of brain activation region.

Biological evidence indicewates that the brain atrophy can be involved at the onset of neuropathological pathways of Alzheimer's disease. However, there is lack of formal statistical methods to perform genetic dissection of brain activation phenotypes such as shape and intensity. To this end, we propose a Bayesian hierarchical model which consists of two levels of hierarchy. At level 1, we develop a Bayesian nonparametric level set (BNLS) model for studying the brain activation region shape. At level 2, we construct a regression model to select genetic variants that are strongly associated with the brain activation intensity, where a spike-and-slab prior and a Gaussian prior are chosen for feature selection. We develop efficient posterior computation algorithms based on the Markov chain Monte Carlo (MCMC) method. We demonstrate the advantages of the proposed method via extensive simulation studies and analyses of imaging genetics data in the Alzheimer's disease neuroimaging initiative (ADNI) study.

A new accuracy metric under three classes when subclasses are involved and its confidence interval estimation.

"Compound multi-class classification" refers to the setting where three or more main classes are involved and at least one of the main classes have multiple subclasses. A common practice in evaluating biomarker performance under "compound multi-class classification" is "subclasses pooling." In this article, we first explore the downsides of accuracy metrics based on pooled data. Then we propose a new accuracy measure proper for "compound multi-class classification" with three ordinal main classes, namely "volume under compound surface ( )." The proposed evaluates the accuracy of a biomarker appropriately by identifying main classes without requiring specification of an ordering for marker values of subclasses within each main class. For confidence interval estimation of , both parametric and nonparametric methods are studied, and simulation studies are carried out to assess coverage probabilities. A subset of Alzheimer's Disease Neuroimaging Initiative study dataset is analyzed.

Identification of an APOE ε4-specific blood-based molecular pathway for Alzheimer's disease risk.

The precise apolipoprotein E (APOE) ε4-specific molecular pathway(s) for Alzheimer's disease (AD) risk are unclear. Plasma protein modules/cascades were analyzed using weighted gene co-expression network analysis (WGCNA) in the Alzheimer's Disease Neuroimaging Initiative study. Multivariable regression analyses were used to examine the associations among protein modules, AD diagnoses, cerebrospinal fluid (CSF) phosphorylated tau (p-tau), and brain glucose metabolism, stratified by APOE genotype. The Green Module was associated with AD diagnosis in APOE ε4 homozygotes. Three proteins from this module, C-reactive protein (CRP), complement C3, and complement factor H (CFH), had dose-dependent associations with CSF p-tau and cognitive impairment only in APOE ε4 homozygotes. The link among these three proteins and glucose hypometabolism was observed in brain regions of the default mode network (DMN) in APOE ε4 homozygotes. A Framingham Heart Study validation study supported the findings for AD. The study identifies the APOE ε4-specific CRP-C3-CFH inflammation pathway for AD, suggesting potential drug targets for the disease.Highlights: Identification of an APOE ε4 specific molecular pathway involving blood CRP, C3, and CFH for the risk of AD.CRP, C3, and CFH had dose-dependent associations with CSF p-Tau and brain glucose hypometabolism as well as with cognitive impairment only in APOE ε4 homozygotes.Targeting CRP, C3, and CFH may be protective and therapeutic for AD onset in APOE ε4 carriers.

Research Letter: TBI Severity Moderates the Association Between Subjective and Objective Attention in Older Veterans.

This study examined the moderating effect of traumatic brain injury (TBI) history on subjective and objective cognition across multiple cognitive domains. Participants included 242 Vietnam-era veterans with a history of no TBI (n = 86), mild TBI (n = 74), or moderate-to-severe TBI (n = 82) from the observational Department of Defense-Alzheimer's Disease Neuroimaging Initiative (DoD-ADNI) study. Objective cognition was the outcome and was measured using neuropsychological measures in the domains of memory, attention/executive functioning, and language. Subjective cognition was measured using the memory, divided attention, and language subscales from the Everyday Cognition (ECog) measure. TBI severity status was the moderating variable. Veterans with a history of moderate-to-severe TBI had a stronger negative association between subjective and objective attention relative to participants without a TBI ( P = .002). Although this association did not differ between mild TBI and no TBI history groups ( P = .100), the association between subjective and objective attention for the mild TBI group was intermediate to the no TBI and moderate-to-severe TBI history groups. TBI status did not moderate associations between subjective and objective memory or language. Results highlight the importance of assessing subjective and objective cognition in older veterans and the relevance of attention in the context of TBI history. More work is needed to better understand the intersection of TBI and aging and how these factors may be used to guide individualized assessment and treatment approaches for older veterans.

Bayesian Heterogeneous Hidden Markov Models with an Unknown Number of States

Hidden Markov models (HMMs) are valuable tools for analyzing longitudinal data due to their capability to describe dynamic heterogeneity. Conventional HMMs typically assume that the number of hidden states (i.e., the order of HMMs) is known or predetermined through criterion-based methods. However, prior knowledge about the order is often unavailable, and a pairwise comparison using criterion-based methods becomes increasingly tedious and computationally demanding when the model space enlarges. A few studies have considered simultaneously performing order selection and parameter estimation under the frequentist framework. Still, they focused only on homogeneous HMMs and thus cannot accommodate situations where potential covariates affect the between-state transition. This study proposes a Bayesian double-penalized (BDP) procedure to conduct a simultaneous order selection and parameter estimation for heterogeneous HMMs. We develop a novel Markov chain Monte Carlo algorithm coupled with an efficient adjust-bound reversible jump scheme to address the challenges in updating the order. Simulation studies show that the proposed BDP procedure considerably outperforms the commonly used criterion-based methods. An application to the Alzheimer’s Disease Neuroimaging Initiative study further confirms the utility of the proposed method. Supplementary materials for this article are available online.

Higher longitudinal brain white matter atrophy rate in aquaporin-4 IgG-positive NMOSD compared with healthy controls

We aimed to compare longitudinal brain atrophy in patients with neuromyelitis optica spectrum disorder (NMOSD) with healthy controls (HCs). The atrophy rate in patients with anti-aquaporin-4 antibody-positive NMOSD (AQP4 + NMOSD) was compared with age-sex-matched HCs recruited from the Japanese Alzheimer’s Disease Neuroimaging Initiative study and another study performed at Chiba University. Twenty-nine patients with AQP4 + NMOSD and 29 HCs were enrolled in the study. The time between magnetic resonance imaging (MRI) scans was longer in the AQP4 + NMOSD group compared with the HCs (median; 3.2 vs. 2.9 years, P = 0.009). The annualized normalized white matter volume (NWV) atrophy rate was higher in the AQP4 + NMOSD group compared with the HCs (median; 0.37 vs. − 0.14, P = 0.018). The maximum spinal cord lesion length negatively correlated with NWV at baseline MRI in patients with AQP4 + NMOSD (Spearman’s rho = − 0.41, P = 0.027). The annualized NWV atrophy rate negatively correlated with the time between initiation of persistent prednisolone usage and baseline MRI in patients with AQP4 + NMOSD (Spearman’s rho = − 0.43, P = 0.019). Patients with AQP4 + NMOSD had a greater annualized NWV atrophy rate than HCs. Suppressing disease activity may prevent brain atrophy in patients with AQP4 + NMOSD.

Prediction of Cognitive Test Scores from Variable Length Multimodal Data in Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurogenerative condition characterized by sharp cognitive decline with no confirmed effective treatment or cure. This makes it critically important to identify the symptoms of Alzheimer’s disease in its early stages before significant cognitive deterioration has taken hold and even before any brain morphology and neuropathology are noticeable. In this study, five different multimodal deep neural networks (MDNN), with different architectures, in search of an optimal model for predicting the cognitive test scores for the Mini-Mental State Examination (MMSE) and the modified Alzheimer’s Disease Assessment Scale (ADAS-CoG13) over a span of 60 months (5 years). The multimodal data utilized to train and test the proposed models were obtained from the Alzheimer’s Disease Neuroimaging Initiative study and includes cerebrospinal fluid (CSF) levels of tau and beta-amyloid, structural measures from magnetic resonance imaging (MRI), functional and metabolic measures from positron emission tomography (PET), and cognitive scores from the neuropsychological tests (Cog). The models developed herein delve into two main issues: (1) application merits of single-task vs. multitask for predicting future cognitive scores and (2) whether time-varying input data are better suited than specific timepoints for optimizing prediction results. This model yields a high of 90.27% (SD = 1.36) prediction accuracy (correlation) at 6 months after the initial visit to a lower 79.91% (SD = 8.84) prediction accuracy at 60 months. The analysis provided is comprehensive as it determines the predictions at all other timepoints and all MDNN models include converters in the CN and MCI groups (CNc, MCIc) and all the unstable groups in the CN and MCI groups (CNun and MCIun) that reverted to CN from MCI and to MCI from AD, so as not to bias the results. The results show that the best performance is achieved by a multimodal combined single-task long short-term memory (LSTM) regressor with an input sequence length of 2 data points (2 visits, 6 months apart) augmented with a pretrained Neural Network Estimator to fill in for the missing values.

Profiling and predicting distinct tau progression patterns: An unsupervised data-driven approach to flortaucipir positron emission tomography.

How to detect patterns of greater tau burden and accumulation is still an open question. An unsupervised data-driven whole-brain pattern analysis of longitudinal tau positron emission tomography (PET) was used first to identify distinct tau accumulation profiles and then to build baseline models predictive of tau-accumulation type. The data-driven analysis of longitudinal flortaucipir PET from studies done by the Alzheimer's Disease Neuroimaging Initiative, Avid Pharmaceuticals, and Harvard Aging Brain Study (N=348 cognitively unimpaired, N=188 mild cognitive impairment, N=77 dementia), yielded three distinct flortaucipir-progression profiles: stable, moderate accumulator, and fast accumulator. Baseline flortaucipir levels, amyloid beta (Aβ) positivity, and clinical variables, identified moderate and fast accumulators with 81% and 95% positive predictive values, respectively. Screening for fast tau accumulation and Aβ positivity in early Alzheimer's disease, compared to Aβ positivity with variable tau progression profiles, required 46% to 77% lower sample size to achieve 80% power for 30% slowing of clinical decline. Predicting tau progression with baseline imaging and clinical markers could allow screening of high-risk individuals most likely to benefit from a specific treatment regimen.