Imaging the Suicide Mind using Neurosemantic Signatures as Markers of Suicidal Ideation and Behavior

使用神经语义特征作为自杀意念和行为的标记来想象自杀心理

• 批准号: 10386788
• 负责人: David A. Brent
• 金额: $ 70.02万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-06-06 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10386788
• 关键词: Affect; Age; Anterior; Area; Brain; Classification; Clinical; Clinical assessments; Computers; Cross-Sectional Studies; Detection; Diagnosis; Dimensions; Emotional; Emotions; Feeling; Feeling suicidal; Frequencies; Functional Magnetic Resonance Imaging; Future; Goals; Image; Implicit Association Test; Individual; Intake; Light; Machine Learning; Measures; Medial; Methods; Mind; Modernization; Monitor; Neurocognitive; Participant; Patient Self-Report; Patients; Pattern; Persons; Pilot Projects; Recording of previous events; Risk; Sampling; Severities; Shame; Suicide; Suicide attempt; Techniques; Testing; Therapeutic; Thinking; Time; Translating; Validation; Work; base; behavior measurement; cingulate cortex; classification algorithm; clinical practice; design; follow-up; frontal lobe; ideation; impression; improved; innovation; longitudinal design; machine learning classification; machine learning classifier; neural patterning; neuroimaging; novel strategies; personalized medicine; positive emotional state; predictive test; prospective; recruit; relating to nervous system; standard of care; suicidal; suicidal behavior; suicidal individual; suicidal morbidity; suicidal patient; suicidal risk; translational goal; treatment planning; treatment strategy; young adult

ABSTRACT: The assessment of suicidal risk is critical for treatment planning and monitoring of therapeutic progress for suicidal individuals. Current standard-of-care relies on patient self-report and clinician impression, which are not strongly predictive of imminent suicidal risk. This project advances a highly innovative approach to the assessment of suicidal risk, by using machine-learning detection of brain activation patterns that are neural signatures of individual concepts that have been altered in suicidal individuals. The overarching goal is to establish reliable neurocognitive markers of suicidal ideation (SI) and attempt (SA) in individual participants, and to assess these measures’ ability to predict future ideation and attempts. In previous work, this approach was applied to the fMRI-based neurosemantic signature (NSS’s) during the thinking about each of 30 words related to either to suicide, negative concepts, or positive concepts in 17 SI young adults and 17 healthy controls (HCs). A machine learning classifier was able to discriminate between the SI and HCs with 91% accuracy, based on differential brain activation patterns in the L superior medial frontal cortex and anterior cingulate, areas known to be involved in self-referential thinking. Within the ideators, NSS’s also discriminated between those with a history of a SA from those without such a history with 94% accuracy. Moreover, using the classification algorithm derived from this sample, we were able to accurately classify a second sample of suicidal individuals with 87% accuracy. It was also possible to assess the emotions differentially manifested during the thinking about these words, and thus to differentiate SI from HC with 85% accuracy, and SI with and without SA with 88% accuracy. On the basis of these promising pilot findings, we propose to study 300 young adult SI (about half of whom will have made a SA), 100 never-suicidal psychiatric controls, and 100 HCs, use fMRI to assess NSS at intake and 3 months, and assess for suicidal ideation and behavior at intake, 3, 6, and 9 months thereafter. The goals are to determine if: (1) NSS’s are sensitive to changes in level of suicidal ideation when repeated at 3 months; and (2) whether NSS can predict trajectories of suicidal ideation and behavior upon prospective follow-up. We will also examine the relationship between NSS activation of circuits related to self-referential thinking and the death/suicide Implicit Association Test (IAT) that examines the extent to which a person associates suicide-related concepts with self. Finally, as a translational goal, we aim to develop and test a neurally based IAT that examines associations of suicidal concepts with self and with emotions as informed by NSS findings. This study, by shedding light on alterations in suicidal individuals’ neural representation of suicide-relevant concepts could be extremely useful for: (1) identification of those with suicidal ideation who may not self-report their level of risk; (2) monitoring fluctuations in suicidal risk over time; (3) identification of emotional states associated with suicidal ideation; (4) guiding therapy to mitigate these alterations; and (5) the prediction of future suicidal ideation and behavior.

摘要：自杀风险的评估对于治疗计划和治疗监测至关重要 自杀个体的进展。当前的护理标准依赖于患者的自我报告和临床印象， 这不是强烈预测迫在眉睫的自杀风险。该项目推进了一种高度创新的方法 通过使用机器学习检测大脑激活模式的评估自杀风险 自杀个体已改变的个体概念的神经特征。总体目标是 建立自杀思想（SI）的可靠神经认知标志物，并在个人参与者中尝试（SA）， 并评估这些措施预测未来思想和尝试的能力。在以前的工作中，这种方法 在思考30个单词中的每个单词时，都应用于基于fMRI的神经版权签名（NSS） 与17名SI年轻人和17位健康的自杀，负面概念或积极概念有关 控件（HCS）。机器学习分类器能够以91％的范围区分SI和HCS 精度，基于L上培养基额叶和前部的差异大脑激活模式 绑架，已知参与自指思想的领域。在这些想法中，NSS也有歧视 在没有这样的历史悠久的人中有94％精度的人之间的SA历史。此外，使用 从该样本得出的分类算法，我们能够准确地对第二个样本进行分类 具有87％精度的自杀人。也可以评估不同表现的情绪 在思考这些单词的过程中，因此以85％的精度将SI与HC区分开，然后Si与 没有SA的精度为88％。根据这些有前途的飞行员发现，我们建议研究300名年轻人 成人SI（大约一半将制作SA），100个从未杀害精神病患者和100个HCS，使用 fMRI在摄入量和3个月时评估NSS，并评估摄入量3、6的自杀念头和行为 此后9个月。目标是确定是否：（1）NSS对自杀级别的变化敏感 重复3个月时的构想； （2）NSS是否可以预测自杀思想的轨迹和 前瞻性随访的行为。我们还将检查电路的NSS激活之间的关系 与自我指南的思维和死亡/Suciceide隐式协会测试（IAT）有关 一个人将与自杀有关的概念与自杀相关联。最后，作为翻译目标，我们的目标是 开发和测试一种自然基础的IAT，该IAT考试自杀概念与自我以及与 NSS发现所告知的情绪。这项研究，通过阐明自杀个体的改变 与自杀相关的概念的神经表示对于：（1）确定患有 自杀想法可能不会自我报告自己的风险水平； （2）随着时间的流逝，监测自杀风险的波动； （3）识别与自杀思想相关的情绪状态； （4）指导疗法减轻这些 改变； （5）预测未来的自杀思想和行为。

项目成果

The Elusive Phenotype of Preadolescent Suicidal Thoughts and Behaviors: Can Neuroimaging Deliver on Its Promise?

• DOI: 10.1176/appi.ajp.2020.21010022
• 发表时间: 2021-04-01
• 期刊: The American journal of psychiatry
• 作者: Auerbach RP;Chase HW;Brent DA
• 通讯作者: Brent DA