Background: 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.
Methods: 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.
Results: 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 vs. 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.
Conclusions: 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.
Keywords: Grieving; Insula; MVPA; Neural-Decoding; Rumination; Thought-Prediction.