Barkhof, Frederik

Abstract INTRODUCTION Although frontotemporal dementia (FTD) with right anterior temporal lobe (RATL) predominance has been recognized, a uniform description of the syndrome is still missing. This multicenter study aims to establish a cohesive clinical phenotype. METHODS Retrospective clinical data from 18 centers across 12 countries yielded 360 FTD patients with predominant RATL atrophy through initial neuroimaging assessments. RESULTS Common symptoms included mental rigidity/preoccupations (78%), disinhibition/socially inappropriate behavior (74%), naming/word‐finding difficulties (70%), memory deficits (67%), apathy (65%), loss of empathy (65%), and face‐recognition deficits (60%). Real‐life examples unveiled impairments regarding landmarks, smells, sounds, tastes, and bodily sensations (74%). Cognitive test scores indicated deficits in emotion, people, social interactions, and visual semantics however, lacked objective assessments for mental rigidity and preoccupations. DISCUSSION This study cumulates the largest RATL cohort unveiling unique RATL symptoms subdued in prior diagnostic guidelines. Our novel approach, combining real‐life examples with cognitive tests, offers clinicians a comprehensive toolkit for managing these patients. Highlights This project is the first international collaboration and largest reported cohort. Further efforts are warranted for precise nomenclature reflecting neural mechanisms. Our results will serve as a clinical guideline for early and accurate diagnoses.

AbstractBackgroundEthnoculturally diverse individuals from underrepresented populations (URPs), including but not limited to Black/African American and Hispanic/Latinx adults, are disproportionally affected by Alzheimer’s disease (AD) compared to non‐Hispanic/Latinx white (NLW) adults. However, URPs have historically been under‐included in AD studies. The objective of this study is to describe efforts, and the results of those efforts, to increase enrollment and engagement of ethnoculturally diverse individuals from URPs in the Alzheimer’s Disease Neuroimaging Initiative 3 (ADNI3).MethodsEfforts undertaken by ADNI to increase enrollment and engagement of URPs included (1) launching of a Diversity Taskforce (DVTF) lead by two health disparities experts in July 2020, charged with overseeing ADNI’s mission in this space. Specific DVTF activities included: (2) the establishment, funding, and instrumental support (e.g., training, consultation, resources) of diversity at 13 ADNI3 sites; (3) changing ADNI protocol to facilitate research participation of URPs (e.g., optional lumbar puncture and sharing of amyloid PET results); (4) hiring a marketing agency to launch culturally‐informed digital/social media campaigns for the 13 DVTF Sites, with local websites; (5) development of a database to capture performance metrics of inclusion efforts at the 13 DVTF Sites; and (6) formation of a six‐member External Advisory Board, comprised of leaders in health disparities research, to guide DVTF efforts.ResultURP enrollment at these 13 DVTF sites improved from 1.1 URPs per month previously to >4 URPs per month, a 264% increase. Across all 59 ADNI sites, overall enrollment of ethnoculturally diverse individuals from URPs went from 89 (16.8%) to 126 (21.1%), representing a 25% increase in enrollment between May 2020 and September 2021. Moreover, since February 1st, 2021, ADNI enrolled 43 new participants of which 33 (77%) were from URPs.ConclusionIt is possible to improve the enrollment and retention of URPs in AD research using a multi‐pronged community engaged approach that also provides human capital, infrastructure, and financial resources. The success of the DVTF has led to the formation of a new Engagement Core in ADNI4, tasked with implementing a scaled‐up culturally‐informed, community‐engaged research approach to URP inclusion and retention across all 59 ADNI sites.

AbstractBackgroundThe Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a longitudinal observational study of individuals across the dementia spectrum that validates biomarkers for clinical trials. The newest phase of the study, ADNI4, aims to substantially increase the inclusion of underrepresented populations to improve generalizability of ADNI results. Novel biomarkers, including a speech‐based cognitive assessment and blood‐based plasma biomarkers, are integrated into the study design as key participant screening measures.MethodTo help achieve recruitment goals, ADNI4 uses a new, multi‐phase approach to remotely enroll and screen participants. A large study cohort (aiming for 20,000+ participants) provide initial screening data via an online study platform including demographics, medical history related to study exclusionary criteria, and results from a speech‐based cognitive memory assessment (Novoic Ltd. Storyteller test) and a measure of subjective cognitive/functional decline (Everyday Cognition (ECog) 12‐item). Based on screening results, a subset of online study participants is referred to a Quest Diagnostics location to provide a blood sample for plasma biomarker analysis (aiming for 4,000+ participants). Finally, select participants are referred to ADNI clinical sites for in‐depth characterization (goal of up to 1,500 in‐clinic participants).ResultWe report ADNI’s initial approach for selecting participants from the online screening cohort to undergo plasma biomarker phenotyping, and subsequent selection to a clinical site. Participants from diverse demographic backgrounds (especially for race, ethnicity, and/or education) and those with a higher estimated likelihood of cognitive impairment are given priority in the referral selection process. Likelihood of cognitive impairment is assessed using results from subjective memory complaint questions, responses to the ECog 12‐item questionnaire, and the Novoic Storyteller results. Individuals with results showing high concordance across these measures are prioritized, as well as those who have subsequent plasma results indicating biomarker positivity.ConclusionWeighing the various study requirements and inclusivity goals increases the complexity of participant selection decision making, and is a time‐intensive process. As the ADNI study progresses we aim to automate the participant selection/referral process by developing algorithms that account and encode for changing study prioritizations and dynamics across numerous dimensions. Learnings from ADNI4’s new remote processes will help inform future clinical trial design.

AbstractBackgroundSmartphone‐based cognitive and motor tests enable frequent remote assessments in observational research studies and clinical trials, limiting travel burden and increasing access to research for underrepresented groups. One such smartphone tool is the ALLFTD Mobile App, which was designed to capture the clinical features of neurodegenerative diseases via a battery of cognitive, speech, and motor tasks. In this study, we focus on recruiting a demographically diverse normative sample. We have partnered with the UCSF Brain Health Registry (BHR) to recruit 1,000 cognitively healthy participants across the lifespan. Here we describe the study design and initial feasibility results.MethodsWe designed an automated workflow in which participants never interact with the study team except for troubleshooting. BHR participants without reported cognitive impairment were contacted by email to participate in this study (n = 668). Interested participants downloaded the app onto their smartphone and completed in‐app electronic consent. They were asked to complete three ∼30‐minute sessions of cognitive, motor and speech measures over two weeks. Adherence was determined by evaluating the completion rate of app tasks. In a subset who completed a user experience questionnaire (n = 116), responses were quantified using descriptive statistics.ResultsThe current enrollment rate is 23.20% (155/668) of contacted participants. Average age is 62 (SD = 8.2, range = 25‐84). Average adherence is 76%. Participants reported a range of perceived task difficulty: 38% indicated tasks were “very easy” or “somewhat easy”, 26% indicated they were “neutral,” 36% found the tasks “somewhat difficult,” and none reported them to be “very difficult.” The time commitment was reported to be acceptable by 97% of participants, and 20% were willing to complete additional tasks. Over half (59%) of participants indicated that they preferred using a smartphone to complete cognitive assessments over traditional pen‐and‐paper measures.ConclusionsOur preliminary results demonstrate the feasibility and acceptability of recruiting participants, including older adults, through a registry and conducting remote smartphone testing via a fully automated workflow that is highly scalable. Future analysis will study the effects of demographic factors on feasibility and investigate the reliability and validity of the app measures in this sample.

Abstract INTRODUCTION Sex influences neurodegeneration, but it has been poorly investigated in dementia with Lewy bodies (DLB). We investigated sex differences in brain atrophy in DLB using magnetic resonance imaging (MRI). METHODS We included 436 patients from the European‐DLB consortium and the Mayo Clinic. Sex differences and sex‐by‐age interactions were assessed through visual atrophy rating scales ( n = 327; 73 ± 8 years, 62% males) and automated estimations of regional gray matter volume and cortical thickness ( n = 165; 69 ± 9 years, 72% males). RESULTS We found a higher likelihood of frontal atrophy and smaller volumes in six cortical regions in males and thinner olfactory cortices in females. There were significant sex‐by‐age interactions in volume (six regions) and cortical thickness (seven regions) across the entire cortex. DISCUSSION We demonstrate that males have more widespread cortical atrophy at younger ages, but differences tend to disappear with increasing age, with males and females converging around the age of 75. Highlights Male DLB patients had higher odds for frontal atrophy on radiological visual rating scales. Male DLB patients displayed a widespread pattern of cortical gray matter alterations on automated methods. Sex differences in gray matter measures in DLB tended to disappear with increasing age.

Accurate prediction of progression in subjects at risk of Alzheimer's disease is crucial for enrolling the right subjects in clinical trials. However, a prospective comparison of state-of-the-art algorithms for predicting disease onset and progression is currently lacking. We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international teams at predicting the future trajectory of 219 individuals at risk of Alzheimer's disease. Challenge participants were required to make a prediction, for each month of a 5-year future time period, of three key outcomes: clinical diagnosis, Alzheimer's Disease Assessment Scale Cognitive Subdomain (ADAS-Cog13), and total volume of the ventricles. The methods used by challenge participants included multivariate linear regression, machine learning methods such as support vector machines and deep neural networks, as well as disease progression models. No single submission was best at predicting all three outcomes. For clinical diagnosis and ventricle volume prediction, the best algorithms strongly outperform simple baselines in predictive ability. However, for ADAS-Cog13 no single submitted prediction method was significantly better than random guesswork. Two ensemble methods based on taking the mean and median over all predictions, obtained top scores on almost all tasks. Better than average performance at diagnosis prediction was generally associated with the additional inclusion of features from cerebrospinal fluid (CSF) samples and diffusion tensor imaging (DTI). On the other hand, better performance at ventricle volume prediction was associated with inclusion of summary statistics, such as the slope or maxima/minima of patient-specific biomarkers. On a limited, cross-sectional subset of the data emulating clinical trials, performance of the best algorithms at predicting clinical diagnosis decreased only slightly (2 percentage points) compared to the full longitudinal dataset. The submission system remains open via the website https://tadpole.grand-challenge.org, while TADPOLE SHARE (https://tadpole-share.github.io/) collates code for submissions. TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease. However, results call into question the usage of cognitive test scores for patient selection and as a primary endpoint in clinical trials.

AbstractIndividuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

AbstractIntroductionHarboring two copies of the apolipoprotein E (APOE) ε2 allele strongly protects against Alzheimer's disease (AD). However, the effect of this genotype on gray matter (GM) volume in cognitively unimpaired individuals has not yet been described.MethodsMulticenter brain magnetic resonance images (MRIs) from cognitively unimpaired ε2 homozygotes were matched (1:1) against all other APOE genotypes for relevant confounders (n = 223). GM volumes of ε2 genotypic groups were compared to each other and to the reference group (APOE ε3/ε3).ResultsCarrying at least one ε2 allele was associated with larger GM volumes in brain areas typically affected by AD and also in areas associated with cognitive resilience. APOE ε2 homozygotes, but not APOE ε2 heterozygotes, showed larger GM volumes in areas related to successful aging.DiscussionIn addition to the known resistance against amyloid‐β deposition, the larger GM volumes in key brain regions may confer APOE ε2 homozygotes additional protection against AD‐related cognitive decline.