CSF Biomarkers in Longitudinal Alzheimer Disease Cohorts: Pre-Analytic Challenges.

BACKGROUND: The sensitivity of amyloid to pre-analytic factors complicates cerebrospinal fluid (CSF) diagnostics for Alzheimer disease. We report reliability and validity evidence for automated immunoassays from frozen and fresh CSF samples in an ongoing, single-site research program. METHODS: CSF samples were obtained from 2 Wisconsin cohorts (1256 measurements; 727 participants). Levels of amyloid beta 1-42 (Aß42), phosphorylated tau 181 (pTau181), and total tau (tTau) were obtained using an Elecsys cobas e 601 platform. Repeatability and fixed effects of storage tube type, extraction method, and freezing were assessed via mixed models. Concordance with amyloid positron emission tomography (PET) was investigated with 238 participants having a temporally proximal PET scan. RESULTS: Repeatability was high with intraclass correlation (ICC) ≥0.9, but tube type strongly affected measurements. Discriminative accuracy for PET amyloid positivity was strong across tube types (area under the curve [AUC]: Aß42, 0.87; pTau181Aß42 , 0.96), although optimal thresholds differed. CONCLUSIONS: Under real-world conditions, the Elecsys platform had high repeatability. However, strong effects of pre-analytic factors suggest caution in drawing longitudinal inferences.

Alzheimer's disease biomarker burden in primary motor cortices is associated with poorer dexterity performance.

INTRODUCTION: Motor function has correlated with longevity and functionality; however, there is limited research on those with Alzheimer's disease (AD). We studied the association between motor functionality and AD pathology in primary motor and medial temporal cortices. METHODS: A total of 206 participants with a clinical diagnosis of cognitively healthy, AD, or mild cognitive impairment (MCI) underwent imaging and motor assessment. Linear regressions and analyses of variance were applied to test the prediction from AD imaging biomarkers to motor performance and the diagnosis group differences in motor performance. RESULTS: Increased neurodegeneration was associated with worsening dexterity and lower walking speed, and increased amyloid and tau were associated with worsening dexterity. AD and MCI participants had lower motor performance than the cognitively healthy participants. DISCUSSION: Increased AD pathology is associated with worsening dexterity performance. The decline in dexterity in those with AD pathology may offer an opportunity for non-pharmacological therapy intervention. HIGHLIGHTS: Noted worsening dexterity performance was associated with greater Alzheimer's disease (AD) pathology (tau, amyloid beta, and neurodegeneration) in primary motor cortices. Similarly, increased neurodegeneration and tau pathology in parahippocampal, hippocampal, and entorhinal cortices is associated with worsening dexterity performance. Motor performance declined in those with clinical and preclinical AD among an array of motor assessments.

Revised criteria for diagnosis and staging of Alzheimer's disease: Alzheimer's Association Workgroup.

The National Institute on Aging and the Alzheimer's Association convened three separate work groups in 2011 and single work groups in 2012 and 2018 to create recommendations for the diagnosis and characterization of Alzheimer's disease (AD). The present document updates the 2018 research framework in response to several recent developments. Defining diseases biologically, rather than based on syndromic presentation, has long been standard in many areas of medicine (e.g., oncology), and is becoming a unifying concept common to all neurodegenerative diseases, not just AD. The present document is consistent with this principle. Our intent is to present objective criteria for diagnosis and staging AD, incorporating recent advances in biomarkers, to serve as a bridge between research and clinical care. These criteria are not intended to provide step-by-step clinical practice guidelines for clinical workflow or specific treatment protocols, but rather serve as general principles to inform diagnosis and staging of AD that reflect current science. HIGHLIGHTS: We define Alzheimer's disease (AD) to be a biological process that begins with the appearance of AD neuropathologic change (ADNPC) while people are asymptomatic. Progression of the neuropathologic burden leads to the later appearance and progression of clinical symptoms. Early-changing Core 1 biomarkers (amyloid positron emission tomography [PET], approved cerebrospinal fluid biomarkers, and accurate plasma biomarkers [especially phosphorylated tau 217]) map onto either the amyloid beta or AD tauopathy pathway; however, these reflect the presence of ADNPC more generally (i.e., both neuritic plaques and tangles). An abnormal Core 1 biomarker result is sufficient to establish a diagnosis of AD and to inform clinical decision making throughout the disease continuum. Later-changing Core 2 biomarkers (biofluid and tau PET) can provide prognostic information, and when abnormal, will increase confidence that AD is contributing to symptoms. An integrated biological and clinical staging scheme is described that accommodates the fact that common copathologies, cognitive reserve, and resistance may modify relationships between clinical and biological AD stages.

KLOTHO KL-VS heterozygosity is associated with diminished age-related neuroinflammation, neurodegeneration, and synaptic dysfunction in older cognitively unimpaired adults.

INTRODUCTION: We examined whether the aging suppressor KLOTHO gene's functionally advantageous KL-VS variant (KL-VS heterozygosity [KL-VSHET]) confers resilience against deleterious effects of aging indexed by cerebrospinal fluid (CSF) biomarkers of neuroinflammation (interleukin-6 [IL-6], S100 calcium-binding protein B [S100B], triggering receptor expressed on myeloid cells [sTREM2], chitinase-3-like protein 1 [YKL-40], glial fibrillary acidic protein [GFAP]), neurodegeneration (total α-synuclein [α-Syn], neurofilament light chain protein), and synaptic dysfunction (neurogranin [Ng]). METHODS: This Alzheimer disease risk-enriched cohort consisted of 454 cognitively unimpaired adults (Mage = 61.5 ± 7.75). Covariate-adjusted multivariate regression examined relationships between age (mean-split[age ≥ 62]) and CSF biomarkers (Roche/NeuroToolKit), and whether they differed between KL-VSHET (N = 122) and non-carriers (KL-VSNC; N = 332). RESULTS: Older age was associated with a poorer biomarker profile across all analytes (Ps ≤ 0.03). In age-stratified analyses, KL-VSNC exhibited this same pattern (Ps ≤ 0.05) which was not significant for IL-6, S100B, Ng, and α-Syn (Ps ≥ 0.13) in KL-VSHET. Although age-related differences in GFAP, sTREM2, and YKL-40 were evident for both groups (Ps ≤ 0.01), the effect magnitude was markedly stronger for KL-VSNC. DISCUSSION: Higher levels of neuroinflammation, neurodegeneration, and synaptic dysfunction in older adults were attenuated in KL-VSHET. HIGHLIGHTS: Older age was associated with poorer profiles across all cerebrospinal fluid biomarkers of neuroinflammation, neurodegeneration, and synaptic dysfunction. KLOTHO KL-VS non-carriers exhibit this same pattern, which is does not significantly differ between younger and older KL-VS heterozygotes for interleukin-6, S100 calcium-binding protein B, neurogranin, and total α-synuclein. Although age-related differences in glial fibrillary acidic protein, triggering receptor expressed on myeloid cells, and chitinase-3-like protein 1 are evident for both KL-VS groups, the magnitude of the effect is markedly stronger for KL-VS non-carriers. Higher levels of neuroinflammation, neurodegeneration, and synaptic dysfunction in older adults are attenuated in KL-VS heterozygotes.

The ADNI4 Digital Study: A novel approach to recruitment, screening, and assessment of participants for AD clinical research.

INTRODUCTION: We evaluated preliminary feasibility of a digital, culturally-informed approach to recruit and screen participants for the Alzheimer's Disease Neuroimaging Initiative (ADNI4). METHODS: Participants were recruited using digital advertising and completed digital surveys (e.g., demographics, medical exclusion criteria, 12-item Everyday Cognition Scale [ECog-12]), Novoic Storyteller speech-based cognitive test). Completion rates and assessment performance were compared between underrepresented populations (URPs: individuals from ethnoculturally minoritized or low education backgrounds) and non-URPs. RESULTS: Of 3099 participants who provided contact information, 654 enrolled in the cohort, and 595 completed at least one assessment. Two hundred forty-seven participants were from URPs. Of those enrolled, 465 met ADNI4 inclusion criteria and 237 evidenced possible cognitive impairment from ECog-12 or Storyteller performance. URPs had lower ECog and Storyteller completion rates. Scores varied by ethnocultural group and educational level. DISCUSSION: Preliminary results demonstrate digital recruitment and screening assessment of an older diverse cohort, including those with possible cognitive impairment, are feasible. Improving engagement and achieving educational diversity are key challenges. HIGHLIGHTS: A total of 654 participants enrolled in a digital cohort to facilitate ADNI4 recruitment. Culturally-informed digital ads aided enrollment of underrepresented populations. From those enrolled, 42% were from underrepresented ethnocultural and educational groups. Digital screening tools indicate > 50% of participants likely cognitively impaired. Completion rates and assessment performance vary by ethnocultural group and education.

The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core: A culturally informed, community-engaged research (CI-CER) model to advance brain health equity.

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core was launched to advance Alzheimer's disease (AD) and AD-related dementia (ADRD) health equity research in underrepresented populations (URPs). We describe our evidence-based, scalable culturally informed, community-engaged research (CI-CER) model and demonstrate its preliminary success in increasing URP enrollment. METHODS: URPs include ethnoculturally minoritized, lower education (≤ 12 years), and rural populations. The CI-CER model includes: (1) culturally informed methodology (e.g., less restrictive inclusion/exclusion criteria, sociocultural measures, financial compensation, results disclosure, Spanish Language Capacity Workgroup) and (2) inclusive engagement methods (e.g., the Engagement Core team; Hub Sites; Community-Science Partnership Board). RESULTS: As of April 2024, 60% of ADNI-4 new in-clinic enrollees were from ethnoculturally or educationally URPs. This exceeds ADNI-4's ≥ 50% URP representation goal for new enrollees but may not represent final enrollment. DISCUSSION: Findings show a CI-CER model increases URP enrollment in AD/ADRD clinical research and has important implications for clinical trials to advance health equity. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) uses a culturally informed, community-engaged research (CI-CER) approach. The CI-CER approach is scalable and sustainable for broad, multisite implementation. ADNI-4 is currently exceeding its inclusion goals for underrepresented populations.

Increasing participant diversity in AD research: Plans for digital screening, blood testing, and a community-engaged approach in the Alzheimer's Disease Neuroimaging Initiative 4.

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to validate biomarkers for Alzheimer's disease (AD) clinical trials. To improve generalizability, ADNI4 aims to enroll 50-60% of its new participants from underrepresented populations (URPs) using new biofluid and digital technologies. ADNI4 has received funding from the National Institute on Aging beginning September 2022. METHODS: ADNI4 will recruit URPs using community-engaged approaches. An online portal will screen 20,000 participants, 4000 of whom (50-60% URPs) will be tested for plasma biomarkers and APOE. From this, 500 new participants will undergo in-clinic assessment joining 500 ADNI3 rollover participants. Remaining participants (∼3500) will undergo longitudinal plasma and digital cognitive testing. ADNI4 will add MRI sequences and new PET tracers. Project 1 will optimize biomarkers in AD clinical trials. RESULTS AND DISCUSSION: ADNI4 will improve generalizability of results, use remote digital and blood screening, and continue providing longitudinal clinical, biomarker, and autopsy data to investigators.

Screening and enrollment of underrepresented ethnocultural and educational populations in the Alzheimer's Disease Neuroimaging Initiative (ADNI).

INTRODUCTION: An analysis of the ethnocultural and socioeconomic composition of Alzheimer's Disease Neuroimaging Initiative (ADNI) participants is needed to assess the generalizability of ADNI data to diverse populations. METHODS: ADNI data collected between October 2004 and November 2020 were used to determine ethnocultural and educational composition of the sample and differences in the following metrics: screening, screen fails, enrollment, biomarkers. RESULTS: Of 3739 screened individuals, 11% identified as being from ethnoculturally underrepresented populations (e.g., Black, Latinx) and 16% had <12 years of education. Of 2286 enrolled participants, 11% identified as ethnoculturally underrepresented individuals and 15% had <12 years of education. This participation is considerably lower than US Census data for adults 60+ (ethnoculturally underrepresented populations: 25%; <12 years of education: 4%). Individuals with <12 years of education failed screening at a higher rate. DISCUSSION: Our findings suggest that ADNI results may not be entirely generalizable to ethnoculturally diverse and low education populations.

Truly Cross-fit: The Association of Exercise and Clinical Outcomes: Introduction to a JINS Special Section.

We introduce a JINS special section inspired by a symposium presented at INS 2020 in Denver. The symposium was entitled Truly Cross-fit: The Association of Exercise and Cognitive Reserve. The collection of papers herein spans diverse methods, a range of developmental and clinical conditions, and a variety of outcomes all reflecting on the association of exercise and cognition-related outcomes. Taken together, the studies in this Special Section direct us to the variety of dimensions to be considered in understanding this association including what mode, intensity, duration, and timing of physical activity and aspects of age, sex, genetics, baseline characteristics, and disease status moderate these findings. We hope this Special Section will not only provide a framing for important future research on exercise and clinical outcomes but also inspiration to pursue them.

Recruitment of a multi-site randomized controlled trial of aerobic exercise for older adults with amnestic mild cognitive impairment: The EXERT trial.

INTRODUCTION: Effective strategies to recruit older adults with mild cognitive impairment (MCI) into nonpharmacological intervention trials are lacking. METHODS: Recruitment for EXERT, a multisite randomized controlled 18-month trial examining the effects of aerobic exercise on cognitive trajectory in adults with amnestic MCI, involved a diverse portfolio of strategies to enroll 296 participants. RESULTS: Recruitment occurred September 2016 through March 2020 and was initially slow. After mass mailings of 490,323 age- and geo-targeted infographic postcards and brochures, recruitment rates increased substantially, peaking at 16 randomizations/month in early 2020. Mass mailings accounted for 52% of randomized participants, whereas 25% were recruited from memory clinic rosters, electronic health records, and national and local registries. Other sources included news broadcasts, public service announcements (PSA), local advertising, and community presentations. DISCUSSION: Age- and geo-targeted mass mailing of infographic materials was the most effective approach in recruiting older adults with amnestic MCI into an 18-month exercise trial.

Biomarker clusters are differentially associated with longitudinal cognitive decline in late midlife.

The ability to detect preclinical Alzheimer's disease is of great importance, as this stage of the Alzheimer's continuum is believed to provide a key window for intervention and prevention. As Alzheimer's disease is characterized by multiple pathological changes, a biomarker panel reflecting co-occurring pathology will likely be most useful for early detection. Towards this end, 175 late middle-aged participants (mean age 55.9 ± 5.7 years at first cognitive assessment, 70% female) were recruited from two longitudinally followed cohorts to undergo magnetic resonance imaging and lumbar puncture. Cluster analysis was used to group individuals based on biomarkers of amyloid pathology (cerebrospinal fluid amyloid-ß42/amyloid-ß40 assay levels), magnetic resonance imaging-derived measures of neurodegeneration/atrophy (cerebrospinal fluid-to-brain volume ratio, and hippocampal volume), neurofibrillary tangles (cerebrospinal fluid phosphorylated tau181 assay levels), and a brain-based marker of vascular risk (total white matter hyperintensity lesion volume). Four biomarker clusters emerged consistent with preclinical features of (i) Alzheimer's disease; (ii) mixed Alzheimer's disease and vascular aetiology; (iii) suspected non-Alzheimer's disease aetiology; and (iv) healthy ageing. Cognitive decline was then analysed between clusters using longitudinal assessments of episodic memory, semantic memory, executive function, and global cognitive function with linear mixed effects modelling. Cluster 1 exhibited a higher intercept and greater rates of decline on tests of episodic memory. Cluster 2 had a lower intercept on a test of semantic memory and both Cluster 2 and Cluster 3 had steeper rates of decline on a test of global cognition. Additional analyses on Cluster 3, which had the smallest hippocampal volume, suggest that its biomarker profile is more likely due to hippocampal vulnerability and not to detectable specific volume loss exceeding the rate of normal ageing. Our results demonstrate that pathology, as indicated by biomarkers, in a preclinical timeframe is related to patterns of longitudinal cognitive decline. Such biomarker patterns may be useful for identifying at-risk populations to recruit for clinical trials.

Intraindividual Cognitive Variability in Middle Age Predicts Cognitive Impairment 8-10 Years Later: Results from the Wisconsin Registry for Alzheimer's Prevention.

OBJECTIVES: Intraindividual cognitive variability (IICV) has been shown to differentiate between groups with normal cognition, mild cognitive impairment (MCI), and dementia. This study examined whether baseline IICV predicted subsequent mild to moderate cognitive impairment in a cognitively normal baseline sample. METHODS: Participants with 4 waves of cognitive assessment were drawn from the Wisconsin Registry for Alzheimer's Prevention (WRAP; n=684; 53.6(6.6) baseline age; 9.1(1.0) years follow-up; 70% female; 74.6% parental history of Alzheimer's disease). The primary outcome was Wave 4 cognitive status ("cognitively normal" vs. "impaired") determined by consensus conference; "impaired" included early MCI (n=109), clinical MCI (n=11), or dementia (n=1). Primary predictors included two IICV variables, each based on the standard deviation of a set of scores: "6 Factor IICV" and "4 Test IICV". Each IICV variable was tested in a series of logistic regression models to determine whether IICV predicted cognitive status. In exploratory analyses, distribution-based cutoffs incorporating memory, executive function, and IICV patterns were used to create and test an MCI risk variable. RESULTS: Results were similar for the IICV variables: higher IICV was associated with greater risk of subsequent impairment after covariate adjustment. After adjusting for memory and executive functioning scores contributing to IICV, IICV was not significant. The MCI risk variable also predicted risk of impairment. CONCLUSIONS: While IICV in middle-age predicts subsequent impairment, it is a weaker risk indicator than the memory and executive function scores contributing to its calculation. Exploratory analyses suggest potential to incorporate IICV patterns into risk assessment in clinical settings. (JINS, 2016, 22, 1016-1025).

Fornix Microstructure and Memory Performance Is Associated with Altered Neural Connectivity during Episodic Recognition.

OBJECTIVES: The purpose of this study was to assess whether age-related differences in white matter microstructure are associated with altered task-related connectivity during episodic recognition. METHODS: Using functional magnetic resonance imaging and diffusion tensor imaging from 282 cognitively healthy middle-to-late aged adults enrolled in the Wisconsin Registry for Alzheimer's Prevention, we investigated whether fractional anisotropy (FA) within white matter regions known to decline with age was associated with task-related connectivity within the recognition network. RESULTS: There was a positive relationship between fornix FA and memory performance, both of which negatively correlated with age. Psychophysiological interaction analyses revealed that higher fornix FA was associated with increased task-related connectivity amongst the hippocampus, caudate, precuneus, middle occipital gyrus, and middle frontal gyrus. In addition, better task performance was associated with increased task-related connectivity between the posterior cingulate gyrus, middle frontal gyrus, cuneus, and hippocampus. CONCLUSIONS: The findings indicate that age has a negative effect on white matter microstructure, which in turn has a negative impact on memory performance. However, fornix microstructure did not significantly mediate the effect of age on performance. Of interest, dynamic functional connectivity was associated with better memory performance. The results of the psychophysiological interaction analysis further revealed that alterations in fornix microstructure explain-at least in part-connectivity among cortical regions in the recognition memory network. Our results may further elucidate the relationship between structural connectivity, neural function, and cognition.

Beta-amyloid and cognitive decline in late middle age: Findings from the Wisconsin Registry for Alzheimer's Prevention study.

INTRODUCTION: The present study investigated the relationship between beta-amyloid (Aß) and cognition in a late middle-aged cohort at risk for Alzheimer's disease (AD). METHODS: One eighty-four participants (mean age = 60; 72% parental history of AD) completed a [C-11]Pittsburgh compound B positron emission tomography scan and serial cognitive evaluations. A global measure of Aß burden was calculated, and composite scores assessing learning, delayed memory, and executive functioning were computed. RESULTS: Higher Aß was associated with classification of psychometric mild cognitive impairment (MCI) at follow-up (P < .01). Linear mixed effects regression results indicated higher Aß was associated with greater rates of decline in delayed memory (P < .01) and executive functioning (P < .05). Apolipoprotein E (APOE) ε4 status moderated the relationship between Aß and cognitive trajectories (P values <.01). DISCUSSION: In individuals at risk for AD, greater Aß in late middle age is associated with increased likelihood of MCI at follow-up and steeper rates of cognitive decline.

Multi-resolution statistical analysis of brain connectivity graphs in preclinical Alzheimer's disease.

There is significant interest, both from basic and applied research perspectives, in understanding how structural/functional connectivity changes can explain behavioral symptoms and predict decline in neurodegenerative diseases such as Alzheimer's disease (AD). The first step in most such analyses is to encode the connectivity information as a graph; then, one may perform statistical inference on various 'global' graph theoretic summary measures (e.g., modularity, graph diameter) and/or at the level of individual edges (or connections). For AD in particular, clear differences in connectivity at the dementia stage of the disease (relative to healthy controls) have been identified. Despite such findings, AD-related connectivity changes in preclinical disease remain poorly characterized. Such preclinical datasets are typically smaller and group differences are weaker. In this paper, we propose a new multi-resolution method for performing statistical analysis of connectivity networks/graphs derived from neuroimaging data. At the high level, the method occupies the middle ground between the two contrasts - that is, to analyze global graph summary measures (global) or connectivity strengths or correlations for individual edges similar to voxel based analysis (local). Instead, our strategy derives a Wavelet representation at each primitive (connection edge) which captures the graph context at multiple resolutions. We provide extensive empirical evidence of how this framework offers improved statistical power by analyzing two distinct AD datasets. Here, connectivity is derived from diffusion tensor magnetic resonance images by running a tractography routine. We first present results showing significant connectivity differences between AD patients and controls that were not evident using standard approaches. Later, we show results on populations that are not diagnosed with AD but have a positive family history risk of AD where our algorithm helps in identifying potentially subtle differences between patient groups. We also give an easy to deploy open source implementation of the algorithm for use within studies of connectivity in AD and other neurodegenerative disorders.

Cardiorespiratory Fitness Attenuates the Influence of Amyloid on Cognition.

The aim of this study was to examine cross-sectionally whether higher cardiorespiratory fitness (CRF) might favorably modify amyloid-ß (Aß)-related decrements in cognition in a cohort of late-middle-aged adults at risk for Alzheimer's disease (AD). Sixty-nine enrollees in the Wisconsin Registry for Alzheimer's Prevention participated in this study. They completed a comprehensive neuropsychological exam, underwent 11C Pittsburgh Compound B (PiB)-PET imaging, and performed a graded treadmill exercise test to volitional exhaustion. Peak oxygen consumption (VO2peak) during the exercise test was used as the index of CRF. Forty-five participants also underwent lumbar puncture for collection of cerebrospinal fluid (CSF) samples, from which Aß42 was immunoassayed. Covariate-adjusted regression analyses were used to test whether the association between Aß and cognition was modified by CRF. There were significant VO2peak*PiB-PET interactions for Immediate Memory (p=.041) and Verbal Learning & Memory (p=.025). There were also significant VO2peak*CSF Aß42 interactions for Immediate Memory (p<.001) and Verbal Learning & Memory (p<.001). Specifically, in the context of high Aß burden, that is, increased PiB-PET binding or reduced CSF Aß42, individuals with higher CRF exhibited significantly better cognition compared with individuals with lower CRF. In a late-middle-aged, at-risk cohort, higher CRF is associated with a diminution of Aß-related effects on cognition. These findings suggest that exercise might play an important role in the prevention of AD.

Cerebral blood flow is diminished in asymptomatic middle-aged adults with maternal history of Alzheimer's disease.

Cerebral blood flow (CBF) provides an indication of the metabolic status of the cortex and may have utility in elucidating preclinical brain changes in persons at risk for Alzheimer's disease (AD) and related diseases. In this study, we investigated CBF in 327 well-characterized adults including patients with AD (n = 28), patients with amnestic mild cognitive impairment (aMCI, n = 23), older cognitively normal (OCN, n = 24) adults, and asymptomatic middle-aged adults (n = 252) with and without a family history (FH) of AD. Compared with the asymptomatic cohort, AD patients displayed significant hypoperfusion in the precuneus, posterior cingulate, lateral parietal cortex, and the hippocampal region. Patients with aMCI exhibited a similar but less marked pattern of hypoperfusion. Perfusion deficits within the OCN adults were primarily localized to the inferior parietal lobules. Asymptomatic participants with a maternal FH of AD showed hypoperfusion in hippocampal and parietofrontal regions compared with those without a FH of AD or those with only a paternal FH of AD. These observations persisted when gray matter volume was included as a voxel-wise covariate. Our findings suggest that having a mother with AD might confer a particular risk for AD-related cerebral hypoperfusion in midlife. In addition, they provide further support for the potential utility of arterial spin labeling for the measurement of AD-related neurometabolic dysfunction, particularly in situations where [18F]fluorodeoxyglucose imaging is infeasible or clinically contraindicated.

Imaging-based enrichment criteria using deep learning algorithms for efficient clinical trials in mild cognitive impairment.

The mild cognitive impairment (MCI) stage of Alzheimer's disease (AD) may be optimal for clinical trials to test potential treatments for preventing or delaying decline to dementia. However, MCI is heterogeneous in that not all cases progress to dementia within the time frame of a trial and some may not have underlying AD pathology. Identifying those MCIs who are most likely to decline during a trial and thus most likely to benefit from treatment will improve trial efficiency and power to detect treatment effects. To this end, using multimodal, imaging-derived, inclusion criteria may be especially beneficial. Here, we present a novel multimodal imaging marker that predicts future cognitive and neural decline from [F-18]fluorodeoxyglucose positron emission tomography (PET), amyloid florbetapir PET, and structural magnetic resonance imaging, based on a new deep learning algorithm (randomized denoising autoencoder marker, rDAm). Using ADNI2 MCI data, we show that using rDAm as a trial enrichment criterion reduces the required sample estimates by at least five times compared with the no-enrichment regime and leads to smaller trials with high statistical power, compared with existing methods.

Multi-resolutional shape features via non-Euclidean wavelets: applications to statistical analysis of cortical thickness.

Statistical analysis on arbitrary surface meshes such as the cortical surface is an important approach to understanding brain diseases such as Alzheimer's disease (AD). Surface analysis may be able to identify specific cortical patterns that relate to certain disease characteristics or exhibit differences between groups. Our goal in this paper is to make group analysis of signals on surfaces more sensitive. To do this, we derive multi-scale shape descriptors that characterize the signal around each mesh vertex, i.e., its local context, at varying levels of resolution. In order to define such a shape descriptor, we make use of recent results from harmonic analysis that extend traditional continuous wavelet theory from the Euclidean to a non-Euclidean setting (i.e., a graph, mesh or network). Using this descriptor, we conduct experiments on two different datasets, the Alzheimer's Disease NeuroImaging Initiative (ADNI) data and images acquired at the Wisconsin Alzheimer's Disease Research Center (W-ADRC), focusing on individuals labeled as having Alzheimer's disease (AD), mild cognitive impairment (MCI) and healthy controls. In particular, we contrast traditional univariate methods with our multi-resolution approach which show increased sensitivity and improved statistical power to detect a group-level effects. We also provide an open source implementation.

Emergence of mild cognitive impairment in late middle-aged adults in the wisconsin registry for Alzheimer's prevention.

AIM: It is difficult to reliably detect the earliest signs of Alzheimer's disease (AD)-associated cognitive impairment. Our aim was to compare 3 psychometric methods of identifying amnestic mild cognitive impairment (aMCI) in a middle-aged longitudinal cohort enriched for AD risk. METHODS: Wisconsin Registry for Alzheimer's Prevention (WRAP) participants with 3 waves of cognitive assessment over approximately 6 years were coded as meeting each of 3 psychometric aMCI definitions: (a) 'aMCI standard-baseline' used published norms to establish cutoffs for baseline performance; (b) 'aMCI robust-baseline' applied WRAP-specific robust norms to baseline, and (c) 'aMCI robust-multiwave' applied these robust norms across 3 waves of assessment. Each group was compared to a cognitively healthy subset. RESULTS: Half the aMCI standard-baseline and one third of the aMCI robust-baseline group reverted to normal ranges at follow-up. Only the aMCI robust-multiwave method had an aMCI × age interaction showing significantly worse age-related memory declines in the aMCI group compared to the cognitively healthy group over 6 years of follow-up. CONCLUSION: Both cross-sectional methods showed instability over time, with many reverting to normal performance after baseline. The multiwave approach identified a group who showed progressive memory declines over 3 visits. Being able to detect progressive decline in late middle age is a critical step in improving prevention efforts.