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Cortical activity predicts good variation in human motor output

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Abstract

Human movement patterns have been shown to be particularly variable if many combinations of activity in different muscles all achieve the same task goal (i.e., are goal-equivalent). The nervous system appears to automatically vary its output among goal-equivalent combinations of muscle activity to minimize muscle fatigue or distribute tissue loading, but the neural mechanism of this “good” variation is unknown. Here we use a bimanual finger task, electroencephalography (EEG), and machine learning to determine if cortical signals can predict goal-equivalent variation in finger force output. 18 healthy participants applied left and right index finger forces to repeatedly perform a task that involved matching a total (sum of right and left) finger force. As in previous studies, we observed significantly more variability in goal-equivalent muscle activity across task repetitions compared to variability in muscle activity that would not achieve the goal: participants achieved the task in some repetitions with more right finger force and less left finger force (right > left) and in other repetitions with less right finger force and more left finger force (left > right). We found that EEG signals from the 500 milliseconds (ms) prior to each task repetition could make a significant prediction of which repetitions would have right > left and which would have left > right. We also found that cortical maps of sites contributing to the prediction contain both motor and pre-motor representation in the appropriate hemisphere. Thus, goal-equivalent variation in motor output may be implemented at a cortical level.

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Acknowledgments

The contents of this research paper were developed in part under support to J.J.K. from the USC Division of Biokinesiology and Physical Therapy. The work of S.B. and E.K. is supported by an NSF CMMI Grant (13-63404), an Army Research Office Grant (W911NF-16-1-0074), and support from the USC Women in Science and Engineering (WiSE) program. EK acknowledges the support of the Army Research Office through the Grant number II.A.1.3.2.

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    Authors and Affiliations

    1. Aerospace and Mechanical Engineering, University of Southern California, Los Angeles, CA, 90089, USA

      Sarine Babikian & Eva Kanso

    2. Division of Biokinesiology and Physical Therapy, University of Southern California, 1540 E. Alcazar Street, CHP 155, Los Angeles, CA, 90033, USA

      Jason J. Kutch

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    1. Sarine Babikian
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    2. Eva Kanso
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    Correspondence to Jason J. Kutch.

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    Eva Kanso and Jason J. Kutch contributed equally.

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    Babikian, S., Kanso, E. & Kutch, J.J. Cortical activity predicts good variation in human motor output. Exp Brain Res 235, 1139–1147 (2017). https://doi.org/10.1007/s00221-017-4876-9

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