Lieslehto, J., Jääskeläinen, E., Kiviniemi, V. et al. The progression of disorder-specific brain pattern expression in schizophrenia over 9 years. npj Schizophr 7, 32 (2021). https://doi.org/10.1038/s41537-021-00157-0
The progression of disorder-specific brain pattern expression in schizophrenia over 9 years
|Author:||Lieslehto, Johannes1,2,3; Jääskeläinen, Erika1,4,5; Kiviniemi, Vesa5,6;|
1Center for Life Course Health Research, University of Oulu, Oulu, Finland
2Department of Psychiatry and Psychotherapy, Ludwig-Maximilian-University, Munich, Germany
3Department of Forensic Psychiatry, University of Eastern Finland, Niuvanniemi Hospital, Kuopio, Finland
4Department of Psychiatry, Oulu University Hospital, Oulu, Finland
5Medical Research Center Oulu, Oulu University Hospital and University of Oulu, Oulu, Finland
6Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
7Department of Psychiatry, University of Cambridge, Cambridge, UK
8Department of Psychiatry, Research Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
9Institute for Translational Psychiatry, University of Münster, Münster, Germany
10International Max Planck Research School for Translational Psychiatry, Munich, Germany
11Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK
|Online Access:||PDF Full Text (PDF, 2.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2021082544243
|Publish Date:|| 2021-08-25
Age plays a crucial role in the performance of schizophrenia vs. controls (SZ-HC) neuroimaging-based machine learning (ML) models as the accuracy of identifying first-episode psychosis from controls is poor compared to chronic patients. Resolving whether this finding reflects longitudinal progression in a disorder-specific brain pattern or a systematic but non-disorder-specific deviation from a normal brain aging (BA) trajectory in schizophrenia would help the clinical translation of diagnostic ML models. We trained two ML models on structural MRI data: an SZ-HC model based on 70 schizophrenia patients and 74 controls and a BA model (based on 561 healthy individuals, age range = 66 years). We then investigated the two models’ predictions in the naturalistic longitudinal Northern Finland Birth Cohort 1966 (NFBC1966) following 29 schizophrenia and 61 controls for nine years. The SZ-HC model’s schizophrenia-specificity was further assessed by utilizing independent validation (62 schizophrenia, 95 controls) and depression samples (203 depression, 203 controls). We found better performance at the NFBC1966 follow-up (sensitivity = 75.9%, specificity = 83.6%) compared to the baseline (sensitivity = 58.6%, specificity = 86.9%). This finding resulted from progression in disorder-specific pattern expression in schizophrenia and was not explained by concomitant acceleration of brain aging. The disorder-specific pattern’s progression reflected longitudinal changes in cognition, outcomes, and local brain changes, while BA captured treatment-related and global brain alterations. The SZ-HC model was also generalizable to independent schizophrenia validation samples but classified depression as control subjects. Our research underlines the importance of taking account of longitudinal progression in a disorder-specific pattern in schizophrenia when developing ML classifiers for different age groups.
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3124 Neurology and psychiatry
The study was funded by grants from the Finnish Medical Association (author J.L.), Yrjö Jahnsson’s Foundation (author JL), Jalmari and Rauha Ahokas Foundation (author J.L.). The NFBC1966 received financial support from the University of Oulu Grant no. 65354, Oulu University Hospital Grant no. 2/97, 8/97, Ministry of Health and Social Affairs Grant no. 23/251/97, 160/97, 190/97, National Institute for Health and Welfare, Helsinki Grant no. 54121, Regional Institute of Occupational Health, Oulu, Finland Grant no. 50621, 54231. Data collection and sharing of the NMorphCH project was funded by NIMH grant R01 MH056584. We thank all NFBC1966 cohort members and researchers who participated in the 43 and 34 years studies. We also wish to acknowledge the work of the NFBC project centre.
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