Exploring links between psychosis and frontotemporal dementia using multimodal machine learning: Dementia Praecox revisited

International FTD-Genetics Consortium (IFGC), the German Frontotemporal Lobar Degeneration (FTLD) Consortium, PRONIA Consortium, Nikolaos Koutsouleris, Christos Pantelis, Dennis Velakoulis, Philip Mcguire, Dominic B. Dwyer, Maria Fernanda Urquijo-Castro, Riya Paul, Sen Dong, David Popovic, Oemer Oeztuerk, Joseph Kambeitz, Raimo K.R. Salokangas, Jarmo Hietala, Alessandro Bertolino, Paolo Brambilla, Rachel Upthegrove, Stephen J. WoodRebekka Lencer, Stefan Borgwardt, Carlo Maj, Markus Nöthen, Franziska Degenhardt, Maryna Polyakova, Karsten Mueller, Arno Villringer, Adrian Danek, Klaus Fassbender, Klaus Fliessbach, Holger Jahn, Johannes Kornhuber, Bernhard Landwehrmeyer, Sarah Anderl-Straub, Johannes Prudlo, Matthis Synofzik, Jens Wiltfang, Lina Riedl, Janine Diehl-Schmid, Markus Otto, Eva Meisenzahl, Peter Falkai, Matthias L. Schroeter

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Abstract

Importance: The behavioral and cognitive symptoms of severe psychotic disorders overlap with those seen in dementia. However, shared brain alterations remain disputed, and their relevance for patients in at-risk disease stages has not been explored so far.

Objective: To use machine learning to compare the expression of structural magnetic resonance imaging (MRI) patterns of behavioral-variant frontotemporal dementia (bvFTD), Alzheimer disease (AD), and schizophrenia; estimate predictability in patients with bvFTD and schizophrenia based on sociodemographic, clinical, and biological data; and examine prognostic value, genetic underpinnings, and progression in patients with clinical high-risk (CHR) states for psychosis or recent-onset depression (ROD).

Design, Setting, and Participants: This study included 1870 individuals from 5 cohorts, including (1) patients with bvFTD (n = 108), established AD (n = 44), mild cognitive impairment or early-stage AD (n = 96), schizophrenia (n = 157), or major depression (n = 102) to derive and compare diagnostic patterns and (2) patients with CHR (n = 160) or ROD (n = 161) to test patterns' prognostic relevance and progression. Healthy individuals (n = 1042) were used for age-related and cohort-related data calibration. Data were collected from January 1996 to July 2019 and analyzed between April 2020 and April 2022.

Main Outcomes and Measures: Case assignments based on diagnostic patterns; sociodemographic, clinical, and biological data; 2-year functional outcomes and genetic separability of patients with CHR and ROD with high vs low pattern expression; and pattern progression from baseline to follow-up MRI scans in patients with nonrecovery vs preserved recovery.

Results: Of 1870 included patients, 902 (48.2%) were female, and the mean (SD) age was 38.0 (19.3) years. The bvFTD pattern comprising prefrontal, insular, and limbic volume reductions was more expressed in patients with schizophrenia (65 of 157 [41.2%]) and major depression (22 of 102 [21.6%]) than the temporo-limbic AD patterns (28 of 157 [17.8%] and 3 of 102 [2.9%], respectively). bvFTD expression was predicted by high body mass index, psychomotor slowing, affective disinhibition, and paranoid ideation (R2= 0.11). The schizophrenia pattern was expressed in 92 of 108 patients (85.5%) with bvFTD and was linked to the C9orf72 variant, oligoclonal banding in the cerebrospinal fluid, cognitive impairment, and younger age (R2= 0.29). bvFTD and schizophrenia pattern expressions forecasted 2-year psychosocial impairments in patients with CHR and were predicted by polygenic risk scores for frontotemporal dementia, AD, and schizophrenia. Findings were not associated with AD or accelerated brain aging. Finally, 1-year bvFTD/schizophrenia pattern progression distinguished patients with nonrecovery from those with preserved recovery.

Conclusions and Relevance: Neurobiological links may exist between bvFTD and psychosis focusing on prefrontal and salience system alterations. Further transdiagnostic investigations are needed to identify shared pathophysiological processes underlying the neuroanatomical interface between the 2 disease spectra.

Original languageEnglish
Pages (from-to)907-919
Number of pages13
JournalJAMA psychiatry
Volume79
Issue number9
Early online date3 Aug 2022
DOIs
Publication statusPublished - 1 Sept 2022

Bibliographical note

Publisher Copyright:
© 2022 American Medical Association. All rights reserved.

Keywords

  • Adult
  • Alzheimer Disease/diagnostic imaging
  • Brain/diagnostic imaging
  • Female
  • Frontotemporal Dementia/diagnostic imaging
  • Humans
  • Machine Learning
  • Magnetic Resonance Imaging/methods
  • Male
  • Neuropsychological Tests
  • Psychotic Disorders/diagnostic imaging
  • Schizophrenia/diagnostic imaging

ASJC Scopus subject areas

  • Psychiatry and Mental health

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