TY - GEN
T1 - Methods for tracking dynamically coupled brain-body activities during natural movement
AU - Ryu, Jihye
AU - Vero, Joseph
AU - Torres, Elizabeth B.
N1 - Funding Information:
The study was supported by the Nancy Lurie Marks Family Foundation Development Career Award to EBT and the New Jersey Governor’s Council for Research and Treatment of Autism to EBT and JSR. We thank the participants and lab members who helped during data collection.
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/6/28
Y1 - 2017/6/28
N2 - A fundamental property of movement is its dynamically changing variability and its adaptive nature. These features seem to be connected to the cognitive control of our actions by the brain. However, it has been a challenge to connect cognitive neuroscience and movement science in developing a framework amenable to study the coupled dynamics of the brain and body during natural movements. Part of the problem has been the lack of proper sensors to probe both activities in tandem. Fortunately, contemporary advances in wireless technology with high sampling resolution have paved the way to address this challenge. In this paper, we make use of wireless wearable sensors and a new statistical platform to study the dynamic interactions of the brain, body and heart during natural walking. To examine the influence of cognitive tasks on deliberate (self-emergent), spontaneous, or inevitable (autonomic) processes, we combine the use of a metronome and specific instructions on paced breathing, while harnessing the heart signal underlying the evoked behaviors. This paper presents a new platform for the individualized behavioral analyses, which incorporates a new set of data types and visualization tools, to quantify the outcome of such experimental paradigm. We discuss our results and suggest that these new methods and paradigm may serve to unify and advance the fields of cognitive neuroscience and neuro-motor control.
AB - A fundamental property of movement is its dynamically changing variability and its adaptive nature. These features seem to be connected to the cognitive control of our actions by the brain. However, it has been a challenge to connect cognitive neuroscience and movement science in developing a framework amenable to study the coupled dynamics of the brain and body during natural movements. Part of the problem has been the lack of proper sensors to probe both activities in tandem. Fortunately, contemporary advances in wireless technology with high sampling resolution have paved the way to address this challenge. In this paper, we make use of wireless wearable sensors and a new statistical platform to study the dynamic interactions of the brain, body and heart during natural walking. To examine the influence of cognitive tasks on deliberate (self-emergent), spontaneous, or inevitable (autonomic) processes, we combine the use of a metronome and specific instructions on paced breathing, while harnessing the heart signal underlying the evoked behaviors. This paper presents a new platform for the individualized behavioral analyses, which incorporates a new set of data types and visualization tools, to quantify the outcome of such experimental paradigm. We discuss our results and suggest that these new methods and paradigm may serve to unify and advance the fields of cognitive neuroscience and neuro-motor control.
KW - Brain-body interface
KW - Gait
KW - Graphical network analysis
KW - Network connectivity
KW - Stochastic analysis
KW - Weighted directed graph
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UR - http://www.scopus.com/inward/citedby.url?scp=85037712703&partnerID=8YFLogxK
U2 - 10.1145/3077981.3078054
DO - 10.1145/3077981.3078054
M3 - Conference contribution
AN - SCOPUS:85037712703
T3 - ACM International Conference Proceeding Series
BT - MOCO 2017 - Proceedings of the 4th International Conference on Movement Computing
A2 - Niehaus, Kiona
PB - Association for Computing Machinery
T2 - 4th International Conference on Movement Computing, MOCO 2017
Y2 - 28 June 2017 through 30 June 2017
ER -