Machine Learning-Based Prediction of Changes in Behavioral Outcomes Using Functional Connectivity and Clinical Measures in Brain-Computer Interface Stroke Rehabilitation

  • Rosaleena Mohanty
  • Anita Sinha
  • Alexander Remsik
  • Janerra Allen
  • Veena Nair
  • Kristin Caldera
  • Justin Sattin
  • Dorothy Edwards
  • Justin C. Williams
  • Vivek PrabhakaranEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10284)


The goal of this work is to evaluate if changes in brain connectivity can predict behavioral changes among subjects who have suffered stroke and have completed brain-computer interface (BCI) interventional therapy. A total of 23 stroke subjects, with persistent upper-extremity motor deficits, received the stroke rehabilitation therapy using a closed-loop neurofeedback BCI device. Over the course of the entire interventional therapy, resting-state fMRI were collected at two time points: prior to start and immediately upon completion of therapy. Behavioral assessments were administered at each time point via neuropsychological testing to collect measures on Action Research Arm Test, Nine-Hole Peg Test, Barthel Index and Stroke Impact Scale. Resting-state functional connectivity changes in the motor network were computed from pre- to post-interventional therapy and were combined with clinical data corresponding to each subject to estimate the change in behavioral performance between the two time-points using a machine learning based predictive model. Inter-hemispheric correlations emerged as stronger predictors of changes across multiple behavioral measures in comparison to intra-hemispheric links. Additionally, age predicted behavioral changes better than other clinical variables such as gender, pre-stroke handedness, etc. Machine learning model serves as a valuable tool in predicting BCI therapy-induced behavioral changes on the basis of functional connectivity and clinical data.


Brain-computer interface Stroke rehabilitation BCI therapy Upper extremity motor recovery Resting-state fMRI Machine learning Predictive model 



The authors would like to thank all the subjects and their families for their participation in the stroke rehabilitation program. Thanks to the study coordinator Theresa Jungae Kang and the MRI technicians Sara John and Jenelle Fuller at the Wisconsin Institutes for Medical Research. This study was supported by NIH grants RC1MH090912-01, K23NS086852, T32GM008692, UL1TR000427, T32EB011434 and TL1TR000429. Additional funding was also provided through a Coulter Translational Research Award, an American Heart Association Postdoctoral Fellow Research Award, AHA Midwest Grant-in-Aid Award, AHA National Innovation Award, UW Milwaukee-Madison Intercampus Grants, UW Graduate School, Grants from Shapiro Foundation and Foundation of ASNR award.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Rosaleena Mohanty
    • 1
  • Anita Sinha
    • 2
  • Alexander Remsik
    • 3
  • Janerra Allen
    • 4
  • Veena Nair
    • 5
  • Kristin Caldera
    • 6
  • Justin Sattin
    • 7
  • Dorothy Edwards
    • 3
    • 7
  • Justin C. Williams
    • 2
  • Vivek Prabhakaran
    • 2
    • 5
    • 7
    Email author
  1. 1.Department of Electrical EngineeringUniversity of Wisconsin-MadisonMadisonUSA
  2. 2.Department of Biomedical EngineeringUniversity of Wisconsin-MadisonMadisonUSA
  3. 3.Department of KinesiologyUniversity of Wisconsin-MadisonMadisonUSA
  4. 4.Department of Material Science and EngineeringUniversity of Wisconsin-MadisonMadisonUSA
  5. 5.Department of RadiologyUniversity of Wisconsin-MadisonMadisonUSA
  6. 6.Department of RehabilitationUniversity of Wisconsin School of Medicine and Public HealthMadisonUSA
  7. 7.Department of NeurologyUniversity of Wisconsin School of Medicine and Public HealthMadisonUSA

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