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

Abstract

BackgroundSome 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.MethodsFifty-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.ResultsAll 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.ConclusionOur 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.

Machine Learning of Schizophrenia Detection with Structural and Functional Neuroimaging

“…Our study has some limitations. First, although the sample size is limited, it is relatively larger when compared to previous studies [ 1 , 21 , 31 , 50 , 65 ]. A larger sample size should be recruited in the future to replicate and enrich our findings.…”

“…Different studies have reported inconsistent results on the effect of GSR on SZ [ 28 – 30 ]. As far as we know, very few studies reported the effect of GSR on ML of SZ classification [ 31 ]. However, the sample size of that study was limited, and it did not specifically discuss the effect of GSR on SZ classification.…”

Neurocognitive Suicide and Homicide Markers in Patients with Schizophrenia Spectrum Disorders: A Systematic Review

“…These cognitive deficits, especially those related to working memory, have an impact on executive control, decision making, and higher reasoning skills, which are protective factors against suicidal ideation and attempts [ 53 , 65 ]. Additionally, recent studies on brain dysfunctions have reported reduced neural activity in suicide attempters who were exposed to fMRI (functional magnetic resonance imaging) [ 66 , 67 ].…”

Generalisable functional imaging classifiers of schizophrenia have multifunctionality as trait, state, and staging biomarkers

“…While disease trait markers, including diagnostic markers, assist early intervention, other biomarkers are also essential: state markers for estimating dynamic changes in response to treatment 13,14 and staging markers for disease prevention or personalising interventions 15 . These biomarkers have been developed separately in some studies 16,17 , and other studies assumed a shared background 18–20 . In SSD research, few studies have succeeded in developing state markers using neuroimaging, and for staging markers, there is no consensus on biological staging models 21 .…”