Classifying suicidal behavior with resting‐state functional connectivity and structural neuroimaging

Abstract

R. Classifying suicidal behavior with resting-state functional connectivity and structural neuroimaging Objective: About 80% of patients who commit suicide do not report suicidal ideation the last time they speak to their mental health provider, highlighting the need to identify biomarkers of suicidal behavior. Our goal is to identify suicidal behavior neural biomarkers to classify suicidal psychiatric inpatients. Methods: Eighty percent of our sample [suicidal (n = 63) and nonsuicidal psychiatric inpatients (n = 65)] was used to determine significant differences in structural and resting-state functional connectivity measures throughout the brain. These measures were used in a random forest classification model on 80% of the sample for training the model. Results: The model built on 80% of the patients had sensitivity = 79.4% and specificity = 72.3%. This model was tested on an independent sample (20%; n = 32) with sensitivity = 81.3% and specificity = 75.0% for confirming the generalizability of the model. Altered resting-state functional connectivity features from frontal and middle temporal regions, as well as the amygdala, parahippocampus, putamen, and vermis were found to generalize best. Conclusion: This work demonstrates neuroimaging (an unbiased biomarker) can be used to classify suicidal behavior in psychiatric inpatients without observing any clinical features. Summations• Neural biomarkers can be used to reliably classify suicidal behavior/ideation. • Resting-state functional connectivity of frontal and middle temporal regions, as well as the amygdala, parahippocampus, putamen, and vermis are altered in suicidal psychiatric patients compared to nonsuicidal patients.• The identification of neural biomarkers for classifying suicide may benefit assessment of suicidal risk in the future. Limitations• Neural biomarkers can only be said to be correlative with suicidal behavior, not causative. • While this work successfully classifies patients as suicidal or not, classification is not necessarily prediction.

Multilayer MEG functional connectivity as a potential marker for suicidal thoughts in major depressive disorder

“…Several other fMRI studies examined suicidal ideation but did not focus solely on the triple network model. One study found that reduced connectivity in a network including the left OFC, left thalamus, and right thalamus was associated with greater suicidal ideation and behavior scores ( Kim et al, 2017 ), while a study in psychiatric inpatients (not necessarily with MDD) showed reduced connectivity between the right AI and right OFC, but increased connectivity within the left OFC ( Gosnell et al, 2019 ). Similarly, Weng and colleagues found reduced connectivity between the pgACC and both medial and lateral OFC and right middle temporal cortex ( Du et al, 2017 ).…”

An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging

“…Traditional statistics can only differentiate different groups with suicidal ideation from those without, rather than detect which individual is at risk. New analytical methods, such as machine learning, have been used in an attempt to develop algorithms to classify individual risk [8][9][10][11]. One study used machine learning algorithms based on functional magnetic resonance imaging (fMRI) neural signatures of death-and life-related concepts to detect individuals with suicidal ideation with 91% accuracy [12].…”

Discriminating Suicide Attempters and Predicting Suicide Risk Using Altered Frontolimbic Resting-State Functional Connectivity in Patients With Bipolar II Disorder

“…However, clinical suicide risk evaluations primarily rely on retrospective information or selfreports by patients, which are inevitably subjective. Additionally, almost 80% of patients who have attempted suicide did not report their suicidal ideation to their doctors or healthcare providers (11). Therefore, it is challenging to accurately assess the risk of suicidality in patients with BD-II during depressive episodes in clinical practice, highlighting the need to identify reliable and accurate biomarkers for suicidality.…”