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.
Highlights
Schizophrenia research suggests that as much as 40% of all death causes in this group can be attributed to suicides (Wildgust et al, 2010), while 25–50% of individuals with schizophrenia attempt to commit suicide during their lifetime (Bohaterewicz et al, 2018; Cassidy et al, 2018)
Previous studies indicate gray matter volume reduction in dorsolateral prefrontal cortex (DLPFC), superior temporal gyrus, as well as insular cortex in patients after suicide attempt, compared to the ones without suicide attempt in the past (Besteher et al, 2016; Zhang et al, 2020), whereas fMRI studies revealed that during a simple task based on cognitive control, suicide thoughts were associated with decreased activity in PFC and the history of previous suicide attempt resulted in decreased activity of premotor cortex (Minzenberg et al, 2014; Potvin et al, 2018)
suicidal risk (SR) and non-suicidal risk (NSR) groups were significantly different in Suicide Behavior Questionnaire—Revised (SBQ-R) score (t = 7.645; p < 0.001) and illness duration (t = 1.69; p = 0.01), but no differences were found in the case of The Psychache Scale (TPS) scores (t = 1.904; p = 0.064) The range of SBQ-R score in the SR group was 8–17 points (Table 1)
Summary
Schizophrenia research suggests that as much as 40% of all death causes in this group can be attributed to suicides (Wildgust et al, 2010), while 25–50% of individuals with schizophrenia attempt to commit suicide during their lifetime (Bohaterewicz et al, 2018; Cassidy et al, 2018). 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. 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
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