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

Koutsouleris, N., Pantelis, C., Velakoulis, D., McGuire, P., Dwyer, D. B., Urquijo-Castro, M.-F., et al. (2022). Exploring links between psychosis and frontotemporal dementia using multimodal machine learning: Dementia praecox revisited. JAMA Psychiatry, 79(9), 907-919. doi:10.1001/jamapsychiatry.2022.2075.

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Koutsouleris, Nikolaos, Author
Pantelis, Christos, Author
Velakoulis, Dennis, Author
McGuire, Philip, Author
Dwyer, Dominic B., Author
Urquijo-Castro, Maria-Fernanda, Author
Paul, Riya, Author
Dong, Sen, Author
Popovic, David, Author
Oeztuerk, Oemer, Author
Kambeitz, Joseph, Author
Salokangas, Raimo K. R., Author
Hietala, Jarmo, Author
Bertolino, Alessandro, Author
Brambilla, Paolo, Author
Upthegrove, Rachel, Author
Wood, Stephen J., Author
Lencer, Rebekka, Author
Borgwardt, Stefan, Author
Maj, Carlo, Author
Nöthen, Markus, AuthorDegenhardt, Franziska, AuthorPolyakova, Maryna1, Author                 Mueller, Karsten2, 3, Author           Villringer, Arno1, Author                 Danek, Adrian, AuthorFassbender, Klaus, AuthorFliessbach, Klaus, AuthorJahn, Holger, AuthorKornhuber, Johannes, AuthorLandwehrmeyer, Bernhard, AuthorAnderl-Straub, Sarah, AuthorPrudlo, Johannes, AuthorSynofzik, Matthis, AuthorWiltfang, Jens, AuthorRiedl, Lina, AuthorDiehl-Schmid, Janine, AuthorOtto, Markus, AuthorMeisenzahl, Eva, AuthorFalkai, Peter, AuthorSchroeter, Matthias L.1, Author           International FTD-Genetics Consortium (IFGC), Author              German Frontotemporal Lobar Degeneration (FTLD) Consortium, Author              PRONIA Consortium, Author               more..
Affiliations:
1Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
2Methods and Development Unit Nuclear Magnetic Resonance, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634558              
3Method and Development Group Neural Data Science and Statistical Computing, MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_3282987              

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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.

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Language(s): eng - English
 Dates: 2022-06-122022-08-032022-09-01
 Publication Status: Published in print
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Title: JAMA Psychiatry
Source Genre: Journal
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Publ. Info: Chicago, Ill. : American Medical Association
Pages: - Volume / Issue: 79 (9) Sequence Number: - Start / End Page: 907 - 919 Identifier: Other: 2168-6238
CoNE: https://pure.mpg.de/cone/journals/resource/2168-6238