First steps in using machine learning on fMRI data to predict intrusive memories of traumatic film footage.
Bottom Line: After psychological trauma, why do some only some parts of the traumatic event return as intrusive memories while others do not?Using MVPA and a machine learning classifier, it was possible to predict later intrusive memories across participants with 68% accuracy, and within a participant with 97% accuracy; i.e. the classifier could identify out of multiple scenes those that would later return as an intrusive memory.MVPA opens the possibility of decoding brain activity to reconstruct idiosyncratic cognitive events with relevance to understanding and predicting mental health symptoms.
Affiliation: University Department of Psychiatry, Warneford Hospital, University of Oxford, United Kingdom.Show MeSH
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Mentions: A total of 117 input features (i.e. averaged activation across the 39 ICA brain networks during the 3 defined time points of the scenes; the initial 6 s of the scene, the remaining duration of the scene after the initial 6 s, and the 12 s post scene) contributed to intrusive memory scene prediction. Below we describe the top weighted input features of the classifier for predicting Flashback versus Potential events (i.e. the features contributing most strongly towards prediction in terms of their weighting within the classifier). We also note their possible cognitive function. While these networks are those top weighted by the classifier, this is not a statistical measure and can only provide a guide towards their predictive contribution. There are 2 components of each feature; the location in the brain (i.e. the ICA component) and the timing of activation. The top weighted input features comprise 8 ICA components, 3 of which were important for intrusive memory prediction at 2 time points (see Fig. 3; ICA components (a–h) are displayed according to their weighting, activation time points are displayed in brackets).
Affiliation: University Department of Psychiatry, Warneford Hospital, University of Oxford, United Kingdom.