{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T06:38:52Z","timestamp":1765262332212,"version":"3.46.0"},"reference-count":66,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      Suicidality is highly prevalent with vast public health implications. The association between schizophrenia spectrum disorders (SSDs) and suicidal ideation and related behaviors (SIBs) is well established. One construct that has received less attention is\n                      <jats:italic>emotion-based impulsivity<\/jats:italic>\n                      or\n                      <jats:italic>urgency<\/jats:italic>\n                      , defined as impulsive action in the context of high positive or negative emotion states. The current study leverages a machine learning approach to examine the association of SSD symptoms, urgency, and suicide risk.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Method<\/jats:title>\n                    <jats:p>\n                      We used least absolute shrinkage and selection operator (LASSO) logistic regression to generate classifications of individuals recruited as high-risk (n=14) and low-risk (n=16) for SIBs (n=30, M\n                      <jats:sub>age<\/jats:sub>\n                      =41.07, SD\n                      <jats:sub>age<\/jats:sub>\n                      =10.95, 13.3% female) Model 1 included demographics, childhood trauma, SSD symptoms, global cognitive ability, and urgency. In addition, Model 2 added neuroimaging data. The use of LASSO logistic regression allows for the identification of the strongest predictors among a large number of predictors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Model 1 suggested that greater hostility, guilt, and negative urgency predicted higher likelihood of belonging to the high-risk group, whereas greater difficulty with abstract thinking predicted lower likelihood of belonging to the high-risk group. Model 2 suggested that greater hostility predicted higher likelihood of high-risk, whereas greater anterior cingulate thickness and increased activity in six brain regions\u2014right superior frontal gyrus, left superior medial frontal gyrus, left superior frontal gyrus, right middle frontal gyrus, and right middle cingulate gyrus\u2014predicted a lower likelihood of high SIB.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Negative urgency and SSD symptoms can classify high-and low-risk groups accurately, and inclusion of brain data slightly increased predictive accuracy.<\/jats:p>\n                  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