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    Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI

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    Author
    Yoo, PE; Oxley, TJ; John, SE; Opie, NL; Ordidge, RJ; O'Brien, TJ; Hagan, MA; Wong, YT; Moffat, BA
    Date
    2018-10-22
    Source Title
    SCIENTIFIC REPORTS
    Publisher
    NATURE PUBLISHING GROUP
    University of Melbourne Author/s
    Wong, Yan; Moffat, Bradford; Opie, Nicholas; Ordidge, Roger; O'Brien, Terence; John, Sam; Oxley, Thomas
    Affiliation
    Medicine and Radiology
    Medicine (RMH)
    Radiology
    Chemical and Biomedical Engineering
    Biomedical Engineering
    Metadata
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    Document Type
    Journal Article
    Citations
    Yoo, P. E., Oxley, T. J., John, S. E., Opie, N. L., Ordidge, R. J., O'Brien, T. J., Hagan, M. A., Wong, Y. T. & Moffat, B. A. (2018). Feasibility of identifying the ideal locations for motor intention decoding using unimodal and multimodal classification at 7T-fMRI. SCIENTIFIC REPORTS, 8 (1), https://doi.org/10.1038/s41598-018-33839-4.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/221128
    DOI
    10.1038/s41598-018-33839-4
    Open Access at PMC
    http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6197258
    NHMRC Grant code
    NHMRC/1148005
    NHMRC/1158912
    Abstract
    Invasive Brain-Computer Interfaces (BCIs) require surgeries with high health-risks. The risk-to-benefit ratio of the procedure could potentially be improved by pre-surgically identifying the ideal locations for mental strategy classification. We recorded high-spatiotemporal resolution blood-oxygenation-level-dependent (BOLD) signals using functional MRI at 7 Tesla in eleven healthy participants during two motor imagery tasks. BCI diagnostic task isolated the intent to imagine movements, while BCI simulation task simulated the neural states that may be yielded in a real-life BCI-operation scenario. Imagination of movements were classified from the BOLD signals in sub-regions of activation within a single or multiple dorsal motor network regions. Then, the participant's decoding performance during the BCI simulation task was predicted from the BCI diagnostic task. The results revealed that drawing information from multiple regions compared to a single region increased the classification accuracy of imagined movements. Importantly, systematic unimodal and multimodal classification revealed the ideal combination of regions that yielded the best classification accuracy at the individual-level. Lastly, a given participant's decoding performance achieved during the BCI simulation task could be predicted from the BCI diagnostic task. These results show the feasibility of 7T-fMRI with unimodal and multimodal classification being utilized for identifying ideal sites for mental strategy classification.

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