Effective Connectivity of Neural Networks During Motor Imagery and Execution of a Gross Manual Dexterity Task using Dynamic Causal Modeling: an fNIRS Study
Presenter Type
UNO Graduate Student (Doctoral)
Major/Field of Study
Biomechanics
Author ORCID Identifier
https://orcid.org/0000-0001-7759-7800
Advisor Information
Jorge M Zuniga
Location
MBSC Ballroom Poster # 502 - G (Doctoral)
Presentation Type
Poster
Start Date
24-3-2023 9:00 AM
End Date
24-3-2023 10:15 AM
Abstract
Motor Imagery is the mental rehearsal of a movement, without actually performing the movement. Previous work has shown that motor imagery activates similar areas within the brain as actual motor execution. However, the majority of these studies have been performed using brain imaging modalities that restrict movement and have been limited to movements such as finger-tapping. Functional near-infrared spectroscopy, or fNIRS is a rapidly developing brain imaging technique that tolerates movement and allows for imaging during a wide array of naturalistic tasks. In this study, we propose to utilize fNIRS to measure the activity of the motor areas of 15 participants during a modified Box and Blocks task. Participants will be tasked to move 16 blocks in standardized positions from one side of a partitioned box to another during the motor execution condition and imagine doing the same task during the motor imagery condition. Activity within the brain will be investigated by measuring effective connectivity using Dynamic Causal Modeling. Effective connectivity is the assessment of how one neural network influences another. Dynamic Causal Modeling produces a generative model of how information flows within the brain, which can then be explored to make inferences on how different brain regions interact. Previous literature using tapping has shown that during motor imagery, the primary motor cortex (M1) has significantly reduced activation due to a suppressive influence placed on it from the supplementary motor area (SMA), which remains highly active. We expect to find similar results during the Box and Block Task, where the SMA will remain active during motor imagery, but suppress the primary motor cortex. We additionally expect that during motor execution, the SMA and M1 will work in tandem, positively influencing each other in a closed feedback loop. Verification of how these networks communicate during a more naturalistic task will expand the literature on the motor system within the brain and can form the basis for future studies seeking to understand differences in connectivity in pathologies such as stroke or amputation.
Scheduling
9:15-10:30 a.m., 10:45 a.m.-Noon
Effective Connectivity of Neural Networks During Motor Imagery and Execution of a Gross Manual Dexterity Task using Dynamic Causal Modeling: an fNIRS Study
MBSC Ballroom Poster # 502 - G (Doctoral)
Motor Imagery is the mental rehearsal of a movement, without actually performing the movement. Previous work has shown that motor imagery activates similar areas within the brain as actual motor execution. However, the majority of these studies have been performed using brain imaging modalities that restrict movement and have been limited to movements such as finger-tapping. Functional near-infrared spectroscopy, or fNIRS is a rapidly developing brain imaging technique that tolerates movement and allows for imaging during a wide array of naturalistic tasks. In this study, we propose to utilize fNIRS to measure the activity of the motor areas of 15 participants during a modified Box and Blocks task. Participants will be tasked to move 16 blocks in standardized positions from one side of a partitioned box to another during the motor execution condition and imagine doing the same task during the motor imagery condition. Activity within the brain will be investigated by measuring effective connectivity using Dynamic Causal Modeling. Effective connectivity is the assessment of how one neural network influences another. Dynamic Causal Modeling produces a generative model of how information flows within the brain, which can then be explored to make inferences on how different brain regions interact. Previous literature using tapping has shown that during motor imagery, the primary motor cortex (M1) has significantly reduced activation due to a suppressive influence placed on it from the supplementary motor area (SMA), which remains highly active. We expect to find similar results during the Box and Block Task, where the SMA will remain active during motor imagery, but suppress the primary motor cortex. We additionally expect that during motor execution, the SMA and M1 will work in tandem, positively influencing each other in a closed feedback loop. Verification of how these networks communicate during a more naturalistic task will expand the literature on the motor system within the brain and can form the basis for future studies seeking to understand differences in connectivity in pathologies such as stroke or amputation.