Support‐vector machine (SVM) learning was then used to further interrogate the brain structures driving flashbacks and determine the extent to which their involvement could predict memory events. Specifically, the presence and extent (in mm3) of overlap between VTAs and structures (as defined using a manually segmented high‐fidelity diencephalic atlas) 27 within memory‐associated regions were calculated and used to classify VTAs as “memory‐yes” or “memory‐no”. Modeling was performed with balanced data sets of 343 observations for both “memory‐yes” and “memory‐no” groups; additional observations for the “memory‐yes” cohort were created by random sampling with replacement. The most parsimonious model that best classified these observations was identified and validated using a 10‐fold (random split in 10 balanced (“memory‐yes” vs “memory‐no”) groups, 3 with 35 members per group and 7 with 34) cross‐validation approach. In addition, an alternative model classifying memory events on the basis of voltage and electrode contact alone was created for comparative purposes.

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