Noam Schneck1, Tao Tu2, George A Bonanno3, M Katherine Shear4, Paul Sajda5, J John Mann6. 1. Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York; Department of Biomedical Engineering, Columbia University, New York, New York. Electronic address: schneck@nyspi.columbia.edu. 2. Department of Biomedical Engineering, Columbia University, New York, New York. 3. Department of Clinical Psychology, Teachers College, Columbia University, New York, New York. 4. Department of Psychiatry, Columbia University, New York, New York; School of Social Work, Columbia University, New York, New York. 5. Department of Biomedical Engineering, Columbia University, New York, New York; Department of Radiology, Columbia University, New York, New York; Data Science Institute, Columbia University, New York, New York. 6. Division of Molecular Imaging and Neuropathology, New York State Psychiatric Institute, New York, New York; Department of Psychiatry, Columbia University, New York, New York.
Abstract
BACKGROUND: The 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. METHODS: A 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. RESULTS: d-SA expression during SART-PROBES blocks without conscious deceased-related thinking correlated negatively with Inventory for Complicated Grief score (r25 = -.711, p < .001, 95% confidence interval = -0.89 to -0.42), accounting for 50% of variance. This relationship remained significant independent of demographic correlates of Inventory for Complicated Grief (B25 = -30, t = -2.64, p = .02, 95% confidence interval = -56.2 to -4.6). Unconscious d-SA pattern expression also correlated with activity in dorsolateral prefrontal cortex and temporal parietal junction during the SART-PROBES (voxel: p < .001, cluster: p < .05). CONCLUSIONS: Self-generated unconscious loss processing correlated with reduced grief severity. This activity, supported by a cognitive social neural architecture, may advance adaptation to the loss.
BACKGROUND: The 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. METHODS: A 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. RESULTS:d-SA expression during SART-PROBES blocks without conscious deceased-related thinking correlated negatively with Inventory for Complicated Grief score (r25 = -.711, p < .001, 95% confidence interval = -0.89 to -0.42), accounting for 50% of variance. This relationship remained significant independent of demographic correlates of Inventory for Complicated Grief (B25 = -30, t = -2.64, p = .02, 95% confidence interval = -56.2 to -4.6). Unconscious d-SA pattern expression also correlated with activity in dorsolateral prefrontal cortex and temporal parietal junction during the SART-PROBES (voxel: p < .001, cluster: p < .05). CONCLUSIONS: Self-generated unconscious loss processing correlated with reduced grief severity. This activity, supported by a cognitive social neural architecture, may advance adaptation to the loss.
Authors: S E Kakarala; K E Roberts; M Rogers; T Coats; F Falzarano; J Gang; M Chilov; J Avery; P K Maciejewski; W G Lichtenthal; H G Prigerson Journal: Psychiatry Res Neuroimaging Date: 2020-07-03 Impact factor: 2.376
Authors: Noam Schneck; Tao Tu; Stefan Haufe; George A Bonanno; Hanga GalfaIvy; Kevin N Ochsner; J John Mann; Paul Sajda Journal: Soc Cogn Affect Neurosci Date: 2019-02-13 Impact factor: 3.436