Does Fractional Anisotropy Predict Motor Imagery Neurofeedback Performance in Healthy Older Adults

Motor imagery neurofeedback (MI-NF) training has been proposed as a potential add-on therapy for motor impairment after stroke, but not everyone can successfully use an MI-NF system. Previous work has used fractional anisotropy (FA), a measure of white matter integrity, to predict MI-NF aptitude in healthy young adults. We set out to extend this finding by assessing its replicability in an MI-NF system that is closer to those used for stroke rehabilitation and in a sample whose age is closer to that of typical stroke patients. Using shrinkage linear discriminant analysis with FA values in 48 white matter regions as predictors, we predicted whether each participant in a sample of 21 healthy older adults (48 – 77 years old) was a good or a bad performer with 84.8% accuracy. The regions used for prediction in our sample differed from those identified previously, and previously suggested regions did not yield significant prediction in our sample. Furthermore, within our own sample the results for online MI-NF performance did not generalize to offline performance. Accounting for the effects of age on MI-NF performance and white matter structure by including age as a predictor led to loss of statistical significance and somewhat poorer prediction accuracy (71.3%). Our results suggest that if predictions are used to determine the potential benefit of MI-NF, those predictions should be based on data collected using the same paradigm and with subjects whose characteristics match those of the target case as close as possible.