ArticleJAMA psychiatry2022
Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning: Dementia Praecox Revisited.
Article in JAMA psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
15 citing papers in PubMed, 32 citations in OpenAlex.
- A frontotemporal dementia-like phenotype in schizophrenia: links to striatal dopamine and iron accumulation.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Observational
- Risk of Psychosis among Patients with Dementia: the Roles of Loneliness, Life Satisfaction, Autonomy, Physical Health and Eyesight Loss.The Psychiatric quarterly · 2026Article
- Subtyping schizophrenia via machine learning by using structural neuroimaging.Translational psychiatry · 2025Review
- The potential role of machine learning and deep learning in differential diagnosis of Alzheimer's disease and FTD using imaging biomarkers: A review.The neuroradiology journal · 2025Review
- Pilot study of cerebrospinal fluid biomarkers reveals inflammatory changes in patients with paranoid schizophrenia.Scientific reports · 2025Article
- EEG-based Signatures of Schizophrenia, Depression, and Aberrant Aging: A Supervised Machine Learning Investigation.Schizophrenia bulletin · 2025Article
- Timing of risk factors, prodromal features, and comorbidities of dementia from a large health claims case-control study.Alzheimer's research & therapy · 2025Article
- Review
- Metal Toxicity and Dementia Including Frontotemporal Dementia: Current State of Knowledge.Antioxidants (Basel, Switzerland) · 2024Review
- Using brain structural neuroimaging measures to predict psychosis onset for individuals at clinical high-risk.Molecular psychiatry · 2024Article
- Insomnia in Forensic Detainees: Is Salience Network the Common Pathway for Sleep, Neuropsychiatric, and Neurodegenerative Disorders?Journal of clinical medicine · 2024Review
- Effects of Substance Use and Antisocial Personality on Neuroimaging-Based Machine Learning Prediction of Schizophrenia.Schizophrenia bulletin · 2023Article
- Causal associations between common musculoskeletal disorders and dementia: a Mendelian randomization study.Frontiers in aging neuroscience · 2023Article
- Neuropsychiatry in the Century of Neuroscience.Noro psikiyatri arsivi · 2022Article
- Schizophrenia and dementia across the lifespan: epidemiological links, cognitive trajectories, and the pathophysiological interplay.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
42 authors at 20 institutions in 6 countries.
Funding
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.
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