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INTRODUCTION: Machine learning models were used to discover novel disease trajectories for autosomal dominant Alzheimer's disease. METHODS: Longitudinal structural magnetic resonance imaging, amyloid positron emission tomography (PET), and fluorodeoxyglucose PET were acquired in 131 mutation carriers and 74 non-carriers from the Dominantly Inherited Alzheimer Network; the groups were matched for age, education, sex, and apolipoprotein e4 (APOE e4). A deep neural network was trained to predict disease progression for each modality. Relief algorithms identified the strongest predictors of mutation status. RESULTS: The Relief algorithm identified the caudate, cingulate, and precuneus as the strongest predictors among all modalities. The model yielded accurate results for predicting future Pittsburgh compound B (R2 = 0.95), fluorodeoxyglucose (R2 = 0.93), and atrophy (R2 = 0.95) in mutation carriers compared to non-carriers. DISCUSSION: Results suggest a sigmoidal trajectory for amyloid, a biphasic response for metabolism, and a gradual decrease in volume, with disease progression primarily in subcortical, middle frontal, and posterior parietal regions.

© 2020 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited., Introduction: Apathy adversely affects prognosis and survival of patients with frontotemporal dementia (FTD). We test whether apathy develops in presymptomatic genetic FTD, and is associated with cognitive decline and brain atrophy. Methods: Presymptomatic carriers of MAPT, GRN or C9orf72 mutations (N = 304), and relatives without mutations (N = 296) underwent clinical assessments and MRI at baseline, and annually for 2 years. Longitudinal changes in apathy, cognition, gray matter volumes, and their relationships were analyzed with latent growth curve modeling. Results: Apathy severity increased over time in presymptomatic carriers, but not in non-carriers. In presymptomatic carriers, baseline apathy predicted cognitive decline over two years, but not vice versa. Apathy progression was associated with baseline low gray matter volume in frontal and cingulate regions. Discussion: Apathy is an early marker of FTD-related changes and predicts a subsequent subclinical deterioration of cognition before dementia onset. Apathy may be a modifiable factor in those at risk of FTD., This work is co‐funded by the UK Medical Research Council (MR/M023664/1), the Italian Ministry of Health and the Canadian Institutes of Health Research as part of a Centres of Excellence in Neurodegeneration grant, a Canadian Institutes of Health Research operating grant and the Bluefield Project, as well as a JPND grant “GENFI‐prox” (by DLR/BMBF to MS, joined with JDR, JvS, MO, BB and CG). MM is supported by the Cambridge Trust & Sidney Sussex College Scholarship. PSJ is supported by the Cambridge Centre for Parkinson Plus. KAT is supported by the British Academy Postdoctoral Fellowship (KAT: PF160048) and Guarantors of Brain (KAT: 101149). TR is supported by the Cambridge Centre for Parkinson Plus and Cambridge Biomedical Resource Centre. RAK is supported by Medical Research Council (RAK: SUAG/047/G101400). JBR reports grants from the NIHR Cambridge Biomedical research centre, Wellcome Trust (103838), and Medical Research Council (SUAG051/G101400; MR/T033371/1); personal fees from Asceneuron, WAVE, Astex, and Biogen; and grants from Janssen, AZ Medimmune, and Eli Lilly, outside the submitted work. JDR is supported by an MRC Clinician Scientist Fellowship (MR/M008525/1) and has received funding from the NIHR Rare Disease Translational Research Collaboration (BRC149/NS/MH). MB is supported by a Fellowship award from the Alzheimer's Society, UK (AS‐JF‐19a‐004‐517), and by the UK Dementia Research Institute which receives its funding from DRI Ltd, funded by the UK Medical Research Council, Alzheimer's Society, and Alzheimer's Research UK.

Introduction: Although some members of families with autosomal dominant Alzheimer's disease mutations learn their mutation status, most do not. How knowledge of mutation status affects clinical disease progression is unknown. This study quantifies the influence of mutation awareness on clinical symptoms, cognition, and biomarkers. / Methods: Mutation carriers and non‐carriers from the Dominantly Inherited Alzheimer Network (DIAN) were stratified based on knowledge of mutation status. Rates of change on standard clinical, cognitive, and neuroimaging outcomes were examined. / Results: Mutation knowledge had no associations with cognitive decline, clinical progression, amyloid deposition, hippocampal volume, or depression in either carriers or non‐carriers. Carriers who learned their status mid‐study had slightly higher levels of depression and lower cognitive scores. / Discussion: Knowledge of mutation status does not affect rates of change on any measured outcome. Learning of status mid‐study may confer short‐term changes in cognitive functioning, or changes in cognition may influence the determination of mutation status.