Self-generated Unconscious Processing of Loss Linked to Less Severe Grieving.

backgroundThe intense loss processing that characterizes grieving may help people to adapt to the loss. However, empirical studies show that more conscious loss-related thinking and greater reactivity to reminders of the deceased correspond to poorer adaptation. These findings raise the possibility that loss processing that is unconscious rather than conscious and is self-generated rather than reactive may facilitate adaptation. Here, we used machine learning to detect a functional magnetic resonance imaging (fMRI) signature of self-generated unconscious loss processing that we hypothesized to correlate with lower grief severity.

methodsA total of 29 subjects bereaved within the past 14 months participated. Participants performed a modified Stroop fMRI task using deceased-related words. A machine-learning regression, trained on Stroop fMRI data, learned a neural pattern for deceased-related selective attention (d-SA), the allocation of attention to the deceased. Expression of this pattern was tracked during a subsequent sustained attention fMRI task interspersed with deceased-related thought probes (SART-PROBES). d-SA pattern expression during SART-PROBES blocks without reported thoughts of loss indicated self-generated unconscious loss processing. Grief severity was measured with the Inventory for Complicated Grief.

resultsd-SA expression during SART-PROBES blocks without conscious deceased-related thinking correlated negatively with Inventory for Complicated Grief score (r

conclusionsSelf-generated unconscious loss processing correlated with reduced grief severity. This activity, supported by a cognitive social neural architecture, may advance adaptation to the loss.