Gradually evaluating of suicidal risk in depression by semi-supervised cluster analysis on resting-state fMRI

Abstract

A timely and effective evaluation of the suicidal ideation bears practical meaning, particularly for the depressive who tend to disguise the real suicide intent and without obvious symptoms. Measuring individual ideation of the depression with uncertain or transient suicide crisis is the purpose. Resting-state fMRI data were collected from 78 depressed patients with variable clinical suicidal crisis. Thirty subjects were well labeled as extremely serious individuals with suicide attempters or as without suicidal ideation. A feature mask was constructed via the two sample t-test on their regional conncectivities. Then, a semi-supervised machine learning frame using the feature mask was designed to assist in clarifying gradation of suicidal susceptibility for the residual forty-eight vaguely defined subjects, by a way of Iterative Self-Organizing Data analysis techniques (ISODATA). Such semi-supervised model was designed purposely to block out the effect of disease itself on the suicide intendancy evaluation. The vague-labeled patients were divided into another two different stages relating to their suicidal susceptibility. The distance ratio of each subject to the two well-defined extreme groups in the feature space can be utilized as the suicide risk index. The re-evaluation of the Nurses' Global Assessment of Suicide Risk (NGASR) via experts blind to original HAM-D rates was significantly correlated with the model estimation. The constructed model suggested its potential to examine the risk of suicidal in an objective way. The functional connectivity, locating mostly within the frontal-temporal circuit and involving the default mode network (DMN), were well integrated to discriminative the gradual susceptibility of suicidal.

Keywords: Depression; Semi-supervised model; Suicide; fMRI.