Exploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning Dementia Praecox Revisited

dc.contributor.authorKoutsouleris Nikolaos
dc.contributor.authorPantelis Christos
dc.contributor.authorVelakoulis Dennis
dc.contributor.authorMcGuire Philip
dc.contributor.authorDwyer Dominic B.
dc.contributor.authorUrquijo-Castro Maria-Fernanda
dc.contributor.authorPaul Riya
dc.contributor.authorDong Sen
dc.contributor.authorPopovic David
dc.contributor.authorOeztuerk Oemer
dc.contributor.authorKambeitz Joseph
dc.contributor.authorSalokangas Raimo K. R.
dc.contributor.authorHietala Jarmo
dc.contributor.authorBertolino Alessandro
dc.contributor.authorBrambilla Paolo
dc.contributor.authorUpthegrove Rachel
dc.contributor.authorWood Stephen J.
dc.contributor.authorLencer Rebekka
dc.contributor.authorBorgwardt Stefan
dc.contributor.authorMaj Carlo
dc.contributor.authorNöthen Markus
dc.contributor.authorDegenhardt Franziska
dc.contributor.authorPolyakova Maryna
dc.contributor.authorMueller Karsten
dc.contributor.authorVillringer Arno
dc.contributor.authorDanek Adrian
dc.contributor.authorFassbender Klaus
dc.contributor.authorFliessbach Klaus
dc.contributor.authorJahn Holger
dc.contributor.authorKornhuber Johannes
dc.contributor.authorLandwehrmeyer Bernhard
dc.contributor.authorAnderl-Straub Sarah
dc.contributor.authorPrudlo Johannes
dc.contributor.authorSynofzik Matthis
dc.contributor.authorWiltfang Jens
dc.contributor.authorRiedl Lina
dc.contributor.authorDiehl-Schmid Janine
dc.contributor.authorOtto Markus
dc.contributor.authorMeisenzahl Eva
dc.contributor.authorFalkai Peter
dc.contributor.authorSchroeter Matthias L.
dc.contributor.authorInternational FTD-Genetics Consortium (IFGC)
dc.contributor.authorthe German Frontotemporal Lobar Degeneration (FTLD) Consortium
dc.contributor.authorand the PRONIA Consortium
dc.contributor.organizationfi=psykiatria|en=Psychiatry|
dc.contributor.organizationfi=tyks, vsshp|en=tyks, varha|
dc.contributor.organization-code1.2.246.10.2458963.20.16217176722
dc.converis.publication-id176218146
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/176218146
dc.date.accessioned2022-10-28T13:57:14Z
dc.date.available2022-10-28T13:57:14Z
dc.description.abstract<p>Importance<br>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.</p><p>Objective<br>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).</p><p>Design, Setting, and Participants<br>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.</p><p>Main Outcomes and Measures<br>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.</p><p>Results<br>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.</p><p>Conclusions and Relevance<br>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.<br></p>
dc.format.pagerange907
dc.format.pagerange919
dc.identifier.eissn2168-6238
dc.identifier.jour-issn2168-622X
dc.identifier.olddbid185400
dc.identifier.oldhandle10024/168494
dc.identifier.urihttps://www.utupub.fi/handle/11111/42198
dc.identifier.urlhttps://jamanetwork.com/journals/jamapsychiatry/fullarticle/2794930
dc.identifier.urnURN:NBN:fi-fe2022091258743
dc.language.isoen
dc.okm.affiliatedauthorSalokangas, Raimo
dc.okm.affiliatedauthorHietala, Jarmo
dc.okm.affiliatedauthorDataimport, tyks, vsshp
dc.okm.discipline3124 Neurology and psychiatryen_GB
dc.okm.discipline3124 Neurologia ja psykiatriafi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA1 ScientificArticle
dc.publisherAMER MEDICAL ASSOC
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.doi10.1001/jamapsychiatry.2022.2075
dc.relation.ispartofjournalJAMA Psychiatry
dc.relation.issue9
dc.relation.volume79
dc.source.identifierhttps://www.utupub.fi/handle/10024/168494
dc.titleExploring Links Between Psychosis and Frontotemporal Dementia Using Multimodal Machine Learning Dementia Praecox Revisited
dc.year.issued2022

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