Schneck Noam, Haufe Stefan, Tu Tao, Bonanno George A, Ochsner Kevin, Sajda Paul, Mann J John
Division of Molecular Imaging and Neuropathology, Columbia University and New York State Psychiatric Institute, New York, NY.
Department of Psychiatry, Columbia University, New York, NY.
Biol Psychiatry Cogn Neurosci Neuroimaging. 2017 Jul;2(5):421-429. doi: 10.1016/j.bpsc.2017.02.004.
Deceased-related thinking is central to grieving and potentially critical to processing of the loss. Self-report measurements might fail to capture important elements of deceased-related thinking and processing. Here, we used a machine learning approach applied to fMRI - known as neural decoding - to develop a measure of ongoing deceased-related processing.
23 subjects grieving the loss of a first-degree relative, spouse or partner within 14 months underwent two fMRI tasks. They first viewed pictures and stories related to the deceased, a living control and a demographic control figure while providing ongoing valence and arousal ratings. Second, they performed a 10-minute Sustained Attention to Response Task (SART) with thought probes every 25-35 seconds to identify deceased, living and self-related thoughts.
A conjunction analysis, controlling for valence/arousal, identified neural clusters in basal ganglia, orbital prefrontal cortex and insula associated with both types of deceased-related stimuli the two control conditions in the first task. This pattern was applied to fMRI data collected during the SART, and discriminated deceased-related but not living or self-related thoughts, independently of grief-severity and time since loss. Deceased-related thoughts on the SART correlated with self-reported avoidance. The neural model predicted avoidance over and above deceased-related thoughts.
A neural pattern trained to identify mental representations of the deceased tracked deceased-related thinking during a sustained attention task and also predicted subject-level avoidance. This approach provides a new imaging tool to be used as an index of processing the deceased for future studies of complicated grief.
与逝者相关的思维是悲伤情绪的核心，对处理丧失感可能至关重要。自我报告测量可能无法捕捉与逝者相关的思维和处理过程中的重要元素。在此，我们使用了一种应用于功能磁共振成像（fMRI）的机器学习方法——即神经解码——来开发一种衡量当前与逝者相关处理过程的指标。
23名在14个月内经历一级亲属、配偶或伴侣死亡并处于悲伤中的受试者接受了两项fMRI任务。他们首先观看与逝者、在世对照人物和人口统计学对照人物相关的图片和故事，同时持续给出效价和唤醒评分。其次，他们进行了一项10分钟的持续注意力反应任务（SART），每隔25 - 35秒进行一次思维探测，以识别与逝者、在世者和自我相关的思维。
一项控制了效价/唤醒的联合分析，在基底神经节、眶额前皮质和岛叶中识别出与两种类型的与逝者相关刺激（第一项任务中的两种对照条件）相关的神经簇。这种模式被应用于SART期间收集的fMRI数据，并区分了与逝者相关的思维，而非与在世者或自我相关的思维，且与悲伤严重程度和丧失后的时间无关。SART上与逝者相关的思维与自我报告的回避相关。神经模型在与逝者相关的思维之外还预测了回避情况。
一种经过训练以识别逝者心理表征的神经模式，在持续注意力任务中追踪与逝者相关的思维，并且还预测了个体水平的回避情况。这种方法提供了一种新的成像工具，可作为处理逝者的指标，用于未来对复杂悲伤症的研究。
