Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity
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...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-05-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/article/10.3389/fnins.2018.00353/full |
| _version_ | 1819176619815731200 |
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| author | Rosaleena Mohanty Rosaleena Mohanty Anita M. Sinha Anita M. Sinha Alexander B. Remsik Alexander B. Remsik Keith C. Dodd Keith C. Dodd Brittany M. Young Brittany M. Young Tyler Jacobson Tyler Jacobson Matthew McMillan Matthew McMillan Jaclyn Thoma Jaclyn Thoma Hemali Advani Veena A. Nair Theresa J. Kang Kristin Caldera Dorothy F. Edwards Justin C. Williams Vivek Prabhakaran Vivek Prabhakaran Vivek Prabhakaran Vivek Prabhakaran |
| author_facet | Rosaleena Mohanty Rosaleena Mohanty Anita M. Sinha Anita M. Sinha Alexander B. Remsik Alexander B. Remsik Keith C. Dodd Keith C. Dodd Brittany M. Young Brittany M. Young Tyler Jacobson Tyler Jacobson Matthew McMillan Matthew McMillan Jaclyn Thoma Jaclyn Thoma Hemali Advani Veena A. Nair Theresa J. Kang Kristin Caldera Dorothy F. Edwards Justin C. Williams Vivek Prabhakaran Vivek Prabhakaran Vivek Prabhakaran Vivek Prabhakaran |
| author_sort | Rosaleena Mohanty |
| collection | DOAJ |
| description | 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 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation 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. |
| first_indexed | 2024-12-22T21:13:39Z |
| format | Article |
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| issn | 1662-453X |
| language | English |
| last_indexed | 2024-12-22T21:13:39Z |
| publishDate | 2018-05-01 |
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| series | Frontiers in Neuroscience |
| spelling | doaj.art-0f4ecfa208624edcb81131c4e96fe0a62022-12-21T18:12:25ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2018-05-011210.3389/fnins.2018.00353359732Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional ConnectivityRosaleena Mohanty0Rosaleena Mohanty1Anita M. Sinha2Anita M. Sinha3Alexander B. Remsik4Alexander B. Remsik5Keith C. Dodd6Keith C. Dodd7Brittany M. Young8Brittany M. Young9Tyler Jacobson10Tyler Jacobson11Matthew McMillan12Matthew McMillan13Jaclyn Thoma14Jaclyn Thoma15Hemali Advani16Veena A. Nair17Theresa J. Kang18Kristin Caldera19Dorothy F. Edwards20Justin C. Williams21Vivek Prabhakaran22Vivek Prabhakaran23Vivek Prabhakaran24Vivek Prabhakaran25Department of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Electrical Engineering, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Kinesiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United StatesMedical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, United StatesNeuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDeparment of Psychology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesNeuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Orthopedics and Rehabilitation, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Kinesiology, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Radiology, University of Wisconsin-Madison, Madison, WI, United StatesMedical Scientist Training Program, University of Wisconsin-Madison, Madison, WI, United StatesNeuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United StatesDepartment of Medical Physics, University of Wisconsin-Madison, Madison, WI, United StatesInterventional 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 1 month post-therapy. To evaluate the peak effects of this intervention, rs-FC was analyzed from two specific stages, namely pre- and post-therapy. In total, 236 seeds spanning both motor and non-motor regions of the brain were computed at each stage. A univariate feature selection was applied to reduce the number of features followed by a principal component-based data transformation 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.https://www.frontiersin.org/article/10.3389/fnins.2018.00353/fullBCI therapystroke recoveryfunctional MRIfunctional connectivitymotor networknon-motor networks |
| spellingShingle | Rosaleena Mohanty Rosaleena Mohanty Anita M. Sinha Anita M. Sinha Alexander B. Remsik Alexander B. Remsik Keith C. Dodd Keith C. Dodd Brittany M. Young Brittany M. Young Tyler Jacobson Tyler Jacobson Matthew McMillan Matthew McMillan Jaclyn Thoma Jaclyn Thoma Hemali Advani Veena A. Nair Theresa J. Kang Kristin Caldera Dorothy F. Edwards Justin C. Williams Vivek Prabhakaran Vivek Prabhakaran Vivek Prabhakaran Vivek Prabhakaran Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity Frontiers in Neuroscience BCI therapy stroke recovery functional MRI functional connectivity motor network non-motor networks |
| title | Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity |
| title_full | Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity |
| title_fullStr | Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity |
| title_full_unstemmed | Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity |
| title_short | Machine Learning Classification to Identify the Stage of Brain-Computer Interface Therapy for Stroke Rehabilitation Using Functional Connectivity |
| title_sort | machine learning classification to identify the stage of brain computer interface therapy for stroke rehabilitation using functional connectivity |
| topic | BCI therapy stroke recovery functional MRI functional connectivity motor network non-motor networks |
| url | https://www.frontiersin.org/article/10.3389/fnins.2018.00353/full |
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