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

This goes with all these other research pieces in not knowing what exactly helps recovery using neurofeedback. It is a simple question. How should neurofeedback be used to help stroke recovery?  WHOM is going to answer that?  

• Near-infrared spectroscopy-mediated neurofeedback (1)  

• neurofeedback (31) 

•  biofeedback (31)

• feedback (7)

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

Joost Meekes^1*, Stefan Debener^{1, 2, 3}, Catharina Zich^{1, 4, 5}, Martin G. Bleichner^{1, 2} and Cornelia Kranczioch^{1, 3}

• ¹Department of Psychology, University of Oldenburg, Germany
• ²Cluster of Excellence Hearing4all, Germany
• ³Research Center Neurosensory Science, University of Oldenburg, Germany
• ⁴Oxford Centre for Human Brain Activity, Medical Sciences Division, University of Oxford, United Kingdom
• ⁵Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, U.K.

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.

Keywords: Motor Imagery (MI), Electroencephalography (EEG), neurofeedback (NF), White matter (WM), Fractional Anisotropy (FA), magnetic resonance imaging (MRI), Shrinkage linear discriminant analysis (SLDA)

Received: 18 Oct 2018; Accepted: 11 Feb 2019.

Edited by:
Juan H. Zhou, Duke-NUS Medical School, Singapore

Reviewed by:
Kang Sim, Institute of Mental Health, Singapore
Sebastian Walther, Universitätsklinik für Psychiatrie und Psychotherapie, Universität Bern, Switzerland  

Copyright: © 2019 Meekes, Debener, Zich, Bleichner and Kranczioch. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.