Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features

Bartosz Bohaterewicz; Anna M Sobczak; Igor Podolak; Bartosz Wójcik; Dagmara Mȩtel; Adrian A Chrobak; Magdalena Fa Frowicz; Marcin Siwek; Dominika Dudek; Tadeusz Marek

doi:10.3389/fnins.2020.605697

Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features

Front Neurosci. 2021 Jan 11:14:605697. doi: 10.3389/fnins.2020.605697. eCollection 2020.

Authors

Affiliations

¹ Department of Cognitive Neuroscience and Neuroergonomics, Institute of Applied Psychology, Jagiellonian University, Kraków, Poland.
² Department of Psychology of Individual Differences, Psychological Diagnosis, and Psychometrics, Institute of Psychology, University of Social Sciences and Humanities, Warsaw, Poland.
³ Institute of Computer Science, Faculty of Mathematics and Computer Science, Jagiellonian University, Kraków, Poland.
⁴ Department of Community Psychiatry, Jagiellonian University Medical College, Kraków, Poland.
⁵ Department of Adult Psychiatry, Jagiellonian University Medical College, Kraków, Poland.
⁶ Department of Affective Disorders, Jagiellonian University Medical College, Kraków, Poland.

Abstract

Background: Some studies suggest that as much as 40% of all causes of death in a group of patients with schizophrenia can be attributed to suicides and compared with the general population, patients with schizophrenia have an 8.5-fold greater suicide risk (SR). There is a vital need for accurate and reliable methods to predict the SR among patients with schizophrenia based on biological measures. However, it is unknown whether the suicidal risk in schizophrenia can be related to alterations in spontaneous brain activity, or if the resting-state functional magnetic resonance imaging (rsfMRI) measures can be used alongside machine learning (ML) algorithms in order to identify patients with SR.

Methods: Fifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 min resting-state functional magnetic resonance imaging. Both static and dynamic indexes of the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity as well as functional connectivity (FC) were calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine.

Results: All groups revealed different intra-network functional connectivity in ventral DMN and anterior SN. The best performance was reached for the LASSO applied to FC with an accuracy of 70% and AUROC of 0.76 (p < 0.05). Significant classification ability was also reached for GB and LR using fALFF and ALFF measures.

Conclusion: Our findings suggest that SR in schizophrenia can be seen on the level of DMN and SN functional connectivity alterations. ML algorithms were able to significantly differentiate SR patients. Our results could be useful in developing neuromarkers of SR in schizophrenia based on non-invasive rsfMRI.

Keywords: classification; feature selection; gradient boosting; machine learning; mental pain; resting state fMRI; schizophrenia; suicidal ideations.