Changes in cortical network connectivity with long-term brain-machine interface exposure after chronic amputation

Balasubramanian, Karthikeyan; Vaidya, Mukta; Southerland, Joshua; Badreldin, Islam S.; Eleryan, Ahmed; Takahashi, Kazutaka; Qian, Kai; Slutzky, Marc W.; Fagg, Andrew H.; Oweiss, Karim G.; Hatsopoulos, Nicholas G.

doi:10.1038/s41467-017-01909-2

Cited by 18 publications

(14 citation statements)

References 52 publications

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“…Recent studies have revealed that BMI training in a closed-loop condition improves BMI performance. It has been demonstrated that closed-loop training improves the control of a neuroprosthetic device using multi-unit activities in accordance with some network plasticity and reorganization (Orsborn et al, 2014 ; Balasubramanian et al, 2017 ). Similarly, the performance of non-invasive BMI can be predicted by cortical activities and improved by closed-loop neurofeedback training (Hwang et al, 2009 ; Blankertz et al, 2010 ; Sugata et al, 2016 ; Wan et al, 2016 ).…”

Section: Discussionmentioning

confidence: 99%

Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

et al. 2018

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Objective: Brain-machine interfaces (BMIs) are useful for inducing plastic changes in cortical representation. A BMI first decodes hand movements using cortical signals and then converts the decoded information into movements of a robotic hand. By using the BMI robotic hand, the cortical representation decoded by the BMI is modulated to improve decoding accuracy. We developed a BMI based on real-time magnetoencephalography (MEG) signals to control a robotic hand using decoded hand movements. Subjects were trained to use the BMI robotic hand freely for 10 min to evaluate plastic changes in the cortical representation due to the training.Method: We trained nine young healthy subjects with normal motor function. In open-loop conditions, they were instructed to grasp or open their right hands during MEG recording. Time-averaged MEG signals were then used to train a real decoder to control the robotic arm in real time. Then, subjects were instructed to control the BMI-controlled robotic hand by moving their right hands for 10 min while watching the robot's movement. During this closed-loop session, subjects tried to improve their ability to control the robot. Finally, subjects performed the same offline task to compare cortical activities related to the hand movements. As a control, we used a random decoder trained by the MEG signals with shuffled movement labels. We performed the same experiments with the random decoder as a crossover trial. To evaluate the cortical representation, cortical currents were estimated using a source localization technique. Hand movements were also decoded by a support vector machine using the MEG signals during the offline task. The classification accuracy of the movements was compared among offline tasks.Results: During the BMI training with the real decoder, the subjects succeeded in improving their accuracy in controlling the BMI robotic hand with correct rates of 0.28 ± 0.13 to 0.50 ± 0.11 (p = 0.017, n = 8, paired Student's t-test). Moreover, the classification accuracy of hand movements during the offline task was significantly increased after BMI training with the real decoder from 62.7 ± 6.5 to 70.0 ± 11.1% (p = 0.022, n = 8, t(7) = 2.93, paired Student's t-test), whereas accuracy did not significantly change after BMI training with the random decoder from 63.0 ± 8.8 to 66.4 ± 9.0% (p = 0.225, n = 8, t(7) = 1.33).Conclusion: BMI training is a useful tool to train the cortical activity necessary for BMI control and to induce some plastic changes in the activity.

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Section: Discussionmentioning

confidence: 99%

Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

et al. 2018

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“…We therefore compared effective connectivity among both BCI and non-BCI units during joystick trials and during BCI trials. We evaluated pairwise effective connectivity among all BCI units ( Table 1 , top row, left number) and all non-BCI units that were modulated significantly during both joystick and BCI trials ( Table 1 , other rows, right number in each cell) using Granger causality adapted for point process models ( Kim et al, 2011 ; Balasubramanian et al, 2017 ), as described in Materials and Methods. Figure 8 shows the Granger connectivity matrices from an example session, computed using equal numbers of joystick ( Fig.…”

Section: Resultsmentioning

confidence: 99%

“…A previous study has shown that effective connectivity among the M1 BCI units controlling a reach-to-grasp robot changes progressively as non-human primates acquired proficient control, although the time course of these changes differed depending on whether the M1 BCI population was contralateral or ipsilateral to an upper extremity amputation ( Balasubramanian et al, 2017 ). Here, we found that effective connectivity increased during BCI as compared with joystick control in one monkey but not the other.…”

Section: Discussionmentioning

confidence: 99%

“…To examine effective connectivity among simultaneously recorded units, we used Granger causality adapted for point processes ( Kim et al, 2011 ; Balasubramanian et al, 2017 ). This adaptation replaces the standard multivariate vector autoregressive models with point process likelihood functions, where the point process of a spike train is characterized by the logarithm of a conditional intensity function (CIF) modeled with a generalized linear model (GLM).…”

Section: Methodsmentioning

confidence: 99%

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Neuronal Activity Distributed in Multiple Cortical Areas during Voluntary Control of the Native Arm or a Brain-Computer Interface

Liu

Schieber

2020

eNeuro

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Voluntary control of visually-guided upper extremity movements involves neuronal activity in multiple areas of the cerebral cortex. Studies of brain-computer interfaces (BCIs) that use spike recordings for input, however, have focused largely on activity in the region from which those neurons that directly control the BCI, which we call BCI units, are recorded. We hypothesized that just as voluntary control of the arm and hand involves activity in multiple cortical areas, so does voluntary control of a BCI. In two subjects ( Macaca mulatta ) performing a center-out task both with a hand-held joystick and with a BCI directly controlled by four primary motor cortex (M1) BCI units, we recorded the activity of other, non-BCI units in M1, dorsal premotor cortex (PMd) and ventral premotor cortex (PMv), primary somatosensory cortex (S1), dorsal posterior parietal cortex (dPPC), and the anterior intraparietal area (AIP). In most of these areas, non-BCI units were active in similar percentages and at similar modulation depths during both joystick and BCI trials. Both BCI and non-BCI units showed changes in preferred direction (PD). Additionally, the prevalence of effective connectivity between BCI and non-BCI units was similar during both tasks. The subject with better BCI performance showed increased percentages of modulated non-BCI units with increased modulation depth and increased effective connectivity during BCI as compared with joystick trials; such increases were not found in the subject with poorer BCI performance. During voluntary, closed-loop control, non-BCI units in a given cortical area may function similarly whether the effector is the native upper extremity or a BCI-controlled device.

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“…This asymmetric stability likely reflects 1) flexibility in the activity of individual neurons within the intrinsic manifold or 2) constraints imposed by the intrinsic manifold on behavioral improvement (and changes in neural connectivity) possible within a training session. Note that the density of functional network connections increased monotonically in monkey Z; the existing network connections were first pruned before comparably denser connectivity emerged in monkey K (Balasubramanian et al 2017). Monkey K was forced to alter an existing intrinsic manifold of which the neurons were arbitrarily assigned for reaching and grasping.…”

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confidence: 99%

Emergent coordination with a brain–machine interface: implications for the neural basis of motor learning

Mangalam

2018

Journal of Neurophysiology

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How patterns of covariance in motor output and neural activity emerge over the course of learning is a topic of ongoing investigation. Vaidya et al. (Vaidya M, Balasubramanian K, Southerland J, Badreldin I, Eleryan A, Shattuck K, Gururangan S, Slutzky M, Osborne L, Fagg A, Oweiss K, Hatsopoulos NG. J Neurophysiol 119: 1291-1304, 2018) investigate the emergence of patterns of covariance in the motor output and neural activity in chronically amputated macaques learning reach-to-grasp movements with a brain-machine interface. The authors' findings have implications for uncovering general principles of how neural coordination unfolds while learning a different motor behavior.

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Changes in cortical network connectivity with long-term brain-machine interface exposure after chronic amputation

Cited by 18 publications

References 52 publications

Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

Training in Use of Brain–Machine Interface-Controlled Robotic Hand Improves Accuracy Decoding Two Types of Hand Movements

Neuronal Activity Distributed in Multiple Cortical Areas during Voluntary Control of the Native Arm or a Brain-Computer Interface

Emergent coordination with a brain–machine interface: implications for the neural basis of motor learning

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