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：自杀风险评估对于治疗计划和治疗监测至关重要。 自杀者的进步。目前的护理标准依赖于患者自我报告和临床医生的印象， 这并不能很好地预测即将发生的自杀风险。该项目提出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， 在自杀个体中改变的个体概念的神经特征。总体目标是 在个体参与者中建立可靠的自杀意念（SI）和企图（SA）的神经认知标志物， 并评估这些措施的能力，以预测未来的想法和尝试。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，将其应用于基于fMRI的神经语义签名（NSS） 在17名SI年轻人和17名健康人中， 对照组（HC）。机器学习分类器能够区分SI和HC，91% 准确性，基于L上级内侧额叶皮层和前额叶皮层的不同大脑激活模式 扣带回是已知参与自我参照思维的区域。在创意者中，NSS也有歧视 有SA病史的人与没有SA病史的人之间的准确率为94%。此外，使用 分类算法从这个样本，我们能够准确地分类第二个样本， 有自杀倾向的人，准确率为87%。也有可能评估不同表现的情绪 在思考这些词的过程中，从而以85%的准确率区分SI和HC，以及SI和 准确率为88%。在这些有希望的试点研究结果的基础上，我们建议对300名年轻人进行研究。 成年SI（约一半的人将作出SA），100从未自杀的精神病控制，和100 HC，使用 fMRI评估摄入时和3个月时的NSS，并评估摄入时、3、6和7个月时的自杀意念和行为。 九个月后。目的是确定：（1）NSS对自杀水平的变化是否敏感 在3个月时重复时的意念;（2）NSS是否可以预测自杀意念的轨迹， 前瞻性随访时的行为。我们还将研究NSS激活电路之间的关系 与自我参照思维和死亡/自杀内隐联想测试（IAT）有关， 一个人将自杀相关的概念与自我联系在一起。最后，作为一个转化目标，我们的目标是 开发和测试基于神经的IAT，检查自杀概念与自我和 通过NSS的调查结果了解情绪。这项研究，通过揭示自杀个体的变化， 神经表征自杀相关的概念可能是非常有用的：（1）识别那些 可能不会自我报告其风险水平的自杀意念;（2）监测自杀风险随时间的波动; (3)识别与自杀念头相关的情绪状态;（4）指导治疗以缓解这些问题 改变;（5）预测未来的自杀意念和行为。