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. Author manuscript; available in PMC: 2020 Mar 1.
Published in final edited form as: Biol Psychiatry Cogn Neurosci Neuroimaging. 2018 Aug 25;4(3):271–279. doi: 10.1016/j.bpsc.2018.08.003

Figure 1. Identifying Self-Generated Unconscious Processing of Loss.

Figure 1.

A. A modified Stroop fMRI task was used to learn a neural pattern corresponding to deceased-related selective attention (d-SA). To do this, a machine learning regression delineated a voxelwise d-SA weighting matrix (green) that optimized the prediction of longer reaction times to deceased-related words from trial level Stroop fMRI data. B. Subjects completed 16 blocks of an extended neutral sustained attention task (SART-PROBES). During this task they were instructed to push a button everytime they saw a number except for the number “3”, intermittent probes assessed the occurrence of a thought about the deceased, i.e. an intrusion. The d-SA weighting matrix identified in the prior step was applied to fMRI data produced during the SART-PROBES. Expression of the d-SA neural pattern indicated the similarity between SART-PROBES neural data at each time point and the neural pattern associated with slower Stroop trials (green oscillations). This served as an ongoing measure of d-SA transpiring during the SART-PROBES. C. Expression of the d-SA neural pattern during SART-PROBES blocks without reported thoughts of loss was taken as a measure of self-generated unconscious processing of the loss.