Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder

Overview

Journal Behav Neurol

Publisher Wiley

Specialty Psychiatry

Date 2023 Aug 14

PMID 37575401

Authors

Nidal Moukaddam

Bishal Lamichhane

Ramiro Salas

Wayne Goodman

Ashutosh Sabharwal

Affiliations

Soon will be listed here.

Abstract

Introduction: Suicide is one of the leading causes of death across different age groups. The persistence of suicidal ideation and the progression of suicidal ideations to action could be related to impulsivity, the tendency to act on urges with low temporal latency, and little forethought. Quantifying impulsivity could thus help suicidality estimation and risk assessments in ideation-to-action suicidality frameworks.

Methods: To model suicidality with impulsivity quantification, we obtained questionnaires, behavioral tests, heart rate variability (HRV), and resting state functional magnetic resonance imaging measurements from 34 participants with mood disorders. The participants were categorized into three suicidality groups based on their Mini-International Neuropsychiatric Interview: none, low, and moderate to severe.

Results: Questionnaire and HRV-based impulsivity measures were significantly different between the suicidality groups with higher subscales of impulsivity associated with higher suicidality. A multimodal system to characterize impulsivity objectively resulted in a classification accuracy of 96.77% in the three-class suicidality group prediction task.

Conclusions: This study elucidates the relative sensitivity of various impulsivity measures in differentiating participants with suicidality and demonstrates suicidality prediction with high accuracy using a multimodal objective impulsivity characterization in participants with mood disorders.

Citing Articles

Modeling Suicidality with Multimodal Impulsivity Characterization in Participants with Mental Health Disorder.

Moukaddam N, Lamichhane B, Salas R, Goodman W, Sabharwal A Behav Neurol. 2023; 2023:8552180.

PMID: 37575401 PMC: 10423091. DOI: 10.1155/2023/8552180.

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