Short-term Brain-Machine Interface (BMI) operation modulates the somato-cognitive action network
Poster No:
1686
Submission Type:
Abstract Submission
Authors:
Ko Hasebe1, Seitaro Iwama1, Masumi Morishige2, Junichi Ushiba1
Institutions:
1Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University, Kanagawa, Japan, 2Graduate School of Science and Technology, Keio University, Kanagawa, Japan
First Author:
Ko Hasebe
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University
Kanagawa, Japan
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University
Kanagawa, Japan
Co-Author(s):
Seitaro Iwama
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University
Kanagawa, Japan
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University
Kanagawa, Japan
Junichi Ushiba
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University
Kanagawa, Japan
Department of Biosciences and Informatics, Faculty of Science and Technology, Keio University
Kanagawa, Japan
Introduction:
The primary motor cortex (M1) has traditionally been thought to have an organized somatotopic map [1]. However, recent findings suggest that the somato-cognitive action network (SCAN), that is composed of a set of regions and distinct from effector-specific regions associated with the foot, hand, and mouth, represents a system for whole-body action planning and functions independently of somatotopy [2]. An intervention for the aberrant M1 activity, brain-machine interfaces (BMI) have demonstrated potential for improving motor functions [3, 4]. For instance, visual neurofeedback training with BMI has been found to modulate connectivity between M1 and the putamen [5]. Despite this, the specific effects of BMI on SCAN and its associated regions, particularly subcortical structures, remain poorly understood. To address this gap, we employed functional magnetic resonance imaging (fMRI) to investigate how BMI interventions modulate SCAN and its connectivity with other brain regions. Based on this evidence, we hypothesized that BMI interventions would modulate the connectivity between SCAN and other regions, including subcortical structures.
Methods:
The study utilized data obtained from experiments conducted in our laboratory [6], in compliance with the Declaration of Helsinki and approved by the Ethics Committee of Keio University (Approval No. 31-21). Participants trained to operate a brain-machine interface (BMI) through kinesthetic motor imagery of right-hand finger movements. Feedback on event-related desynchronization (ERD) of sensorimotor rhythm (SMR) magnitude from the contralateral sensorimotor cortex (SM1) was provided during training, or, in placebo conditions, feedback was derived from other participants [6]. Each participant completed five blocks of electroencephalography (EEG) recordings in 30-minute training sessions to enhance control of the SMR-based BMI.
Functional magnetic resonance imaging (fMRI) was performed before and after training. T1-weighted imaging was used for structural data acquisition, while resting-state fMRI (rsfMRI) involved 10-minute scans to assess functional activity at rest. The datasets were preprocessed using the fMRI Prep pipeline and converted to the CIFTI format for subsequent analysis [7]. Seed-based resting-state functional connectivity (rsFC) analysis was conducted to identify regions of interest, with seeds localized to sulcal regions. The Infomap algorithm was employed to cluster cortical resting-state fMRI data into functional modules based on the methodology [2].
Functional magnetic resonance imaging (fMRI) was performed before and after training. T1-weighted imaging was used for structural data acquisition, while resting-state fMRI (rsfMRI) involved 10-minute scans to assess functional activity at rest. The datasets were preprocessed using the fMRI Prep pipeline and converted to the CIFTI format for subsequent analysis [7]. Seed-based resting-state functional connectivity (rsFC) analysis was conducted to identify regions of interest, with seeds localized to sulcal regions. The Infomap algorithm was employed to cluster cortical resting-state fMRI data into functional modules based on the methodology [2].
Results:
Fig. 1 shows the results of an Infomap analysis using the S1200 HCP average data, allowing for a clear categorization of M1 regions. Fig. 2 illustrates the Seed-Based rsFC of pre-post sessions, suggesting rsFC outcomes closely align with the Infomap mask for the M1 region. We analyzed connectivity between subdivided M1 regions and the putamen in both hemispheres. Post-training, connectivity significantly decreased in the left inter-effector (p = 0.0187) and hand regions (p = 0.0284) compared to pre-training.
In the previous research, Resting-state fluctuations of EEG sensorimotor rhythm reflect BOLD activities in the pericentral areas [10]. Moreover, event-related desynchronization (ERD) is a reliable marker of increased neuronal excitability in thalamo-cortical systems [9]. Focusing on the Thalamo-Cortical loop, it is hypothesized that Event-Related Desynchronization (ERD) in cortical areas may be correlated with activity in subcortical regions. This connectivity reduction likely reflects changes in the correlation between the subcortical putamen and M1 regions as a result of feedback training.
In the previous research, Resting-state fluctuations of EEG sensorimotor rhythm reflect BOLD activities in the pericentral areas [10]. Moreover, event-related desynchronization (ERD) is a reliable marker of increased neuronal excitability in thalamo-cortical systems [9]. Focusing on the Thalamo-Cortical loop, it is hypothesized that Event-Related Desynchronization (ERD) in cortical areas may be correlated with activity in subcortical regions. This connectivity reduction likely reflects changes in the correlation between the subcortical putamen and M1 regions as a result of feedback training.
·Fig.1 M1 division by Infomap
·Fig.2 Pre-Post Sessions Seed-Based rsFC
Conclusions:
This study demonstrates that BMI training using right-hand motor imagery leads to a disconnection between the left M1 inter-effector, hand, and putamen regions. This finding supports the hypothesis that self-regulation during BMI training induces desynchronization in the thalamo-cortical loop, impacting both M1 and the subcortical putamen.
Learning and Memory:
Skill Learning
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Motor Behavior:
Brain Machine Interface 1
Novel Imaging Acquisition Methods:
BOLD fMRI
EEG
Keywords:
Cortex
Electroencephaolography (EEG)
Learning
Motor
Somatosensory
Thalamus
1|2Indicates the priority used for review
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[2] Gordon, E. M. et al. (2023), ‘A somato-cognitive action network alternates with effector regions in motor cortex’, Nature, vol. 617, pp. 351–384.
[3] Cervera, M. A. et al. (2018b), 'Brain-computer interfaces for post-stroke motor rehabilitation: a meta-analysis', Annals of Clinical and Translational Neurology, vol. 5, no. 5, pp. 651–663.
[4] Shindo, K. et al. (2011j), 'Effects of neurofeedback training with an electroencephalogram-based Brain-Computer Interface for hand paralysis in patients with chronic stroke: A preliminary case series study', Journal of Rehabilitation Medicine, vol. 43, no. 10, pp. 951–957.
[5] Kasahara, K. et al. (2022), ‘Basal ganglia-cortical connectivity underlies self-regulation of brain oscillations in humans’, Communications Biology, vol. 5, no. 712.
[6] Kodama, M. et al. (2023), ‘Thirty-minute motor imagery exercise aided by EEG sensorimotor rhythm neurofeedback enhances morphing of sensorimotor cortices: a double-blind sham-controlled study’, Cerebral Cortex, vol. 33, pp. 6573–6584.
[7] Glasser, M. et al. (2016), ‘A multi-modal parcellation of human cerebral cortex’, Nature, vol. 536, pp. 171–178.
[8] Song, X. W. et al. (2011), ‘REST: a toolkit for resting-state functional magnetic resonance imaging data processing’, PLoS One, vol. 6, no. 9.
[9] Steriade, M. et al. (1988), ‘The functional states of the thalamus and the associated neuronal interplay’, Physiological Review, vol. 68, pp. 649–742.
[10] Tsuchimoto, S. et al. (2017), ‘Resting-State Fluctuations of EEG Sensorimotor Rhythm Reflect BOLD Activities in the Pericentral Areas: A Simultaneous EEG-fMRI Study’, Brain Imaging and Stimulation, vol. 11, no. 356.