Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation using Functional Connectivity

No fucking clue what this means or usefulness for stroke survivors. I bet your doctor can't figure it out either, or anyone at our fucking failures of stroke associations .
https://www.frontiersin.org/articles/10.3389/fnins.2018.00353/abstract

Rosaleena Mohanty^1*, Anita M. Sinha¹, Alexander B. Remsik¹, Keith C. Dodd¹, Brittany M. Young¹, Tyler Jacobson¹, Matthew Mcmillan¹, Jaclyn Thoma¹, Hemali Advani¹, Veena A. Nair¹, Theresa Kang¹, Kristin Caldera¹, Dorothy F. Edwards¹, Justin C. Williams¹ and Vivek Prabhakaran¹

• ¹University of Wisconsin-Madison, United States

Interventional therapy using brain-computer interface (BCI) technology has shown promise in facilitating motor recovery in stroke survivors; however, the impact of this form of intervention on functional networks outside of the motor network specifically is not well understood. Here, we investigated resting-state functional connectivity (rs-FC) in stroke participants undergoing BCI therapy across stages, namely pre- and post-intervention, to identify discriminative functional changes using a machine learning classifier with the goal of categorizing participants into one of the two therapy stages. Twenty chronic stroke participants with persistent upper-extremity motor impairment received neuromodulatory training using a closed-loop neurofeedback BCI device, and rs-functional MRI (rs-fMRI) scans were collected at four time points: pre-, mid-, post- and one month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was followed by a principal component analysis-based feature transformation to reduce the number of features which were used by a linear binary support vector machine (SVM) classifier to classify each participant into a therapy stage. The SVM classifier achieved a cross-validation accuracy of 92.5% using a leave-one-out method. Outside of the motor network, seeds from the fronto-parietal task control, default mode, subcortical, and visual networks emerged as important contributors to the classification. Furthermore, a higher number of functional changes were observed to be strengthening from the pre- to post-therapy stage than the ones weakening, both of which involved motor and non-motor regions of the brain. These findings may provide new evidence to support the potential clinical utility of BCI therapy as a form of stroke rehabilitation that not only benefits motor recovery but also facilitates recovery in other brain networks. Moreover, delineation of stronger and weaker changes may inform more optimal designs of BCI interventional therapy so as to facilitate strengthened and suppress weakened changes in the recovery process.