We have demonstrated an attentional bias to reminders of the deceased versus a living attachment in grieving. Overlapping neural circuits related to living- and deceased-related attention suggest that the bereaved employ similar processes in attending to the deceased as they do in attending to the living. Deceased-related attentional bias appears to be linked primarily to intrusive thinking about the loss.

Clinically useful predictors of treatment outcome in major depressive disorder (MDD) remain elusive. We examined associations between functional magnetic resonance imaging (fMRI) blood oxygen level dependent (BOLD) signal during active negative word processing and subsequent selective serotonin reuptake inhibitor (SSRI) treatment outcome in MDD. Unmedicated MDD subjects (n=17) performed an emotional word processing fMRI task, and then received eight weeks of standardized antidepressant treatment with escitalopram. Lower pre-treatment BOLD responses to negative words in midbrain, dorsolateral prefrontal cortex, paracingulate, anterior cingulate, thalamus and caudate nuclei correlated significantly with greater improvement following escitalopram treatment. Activation of these regions in response to negative words correlated significantly with reaction time for rating word relevance. Maximally predictive clusters of voxels identified using a cross-validation approach predicted 48% of the variance in response to treatment. This study provides preliminary evidence that SSRIs may be most beneficial in patients who are less able to engage cognitive control networks while processing negative stimuli. Differences between these findings and previous fMRI studies of SSRI treatment outcome may relate to differences in task design. Regional BOLD responses to negative words predictive of SSRI outcome in this study were both overlapping and distinct from those predictive of outcome with cognitive behavioral therapy (CBT) in previous studies using the same task. Future studies may examine prediction of differential outcome across treatments in the context of a randomized controlled trial.

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.