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Dynamic Adaptation of Brain Networks from Rest to Motor Task and Application to Stroke Research

Thursday, August 17, 2017

9:00 PM-11:00 PM

BIOMED PhD Thesis Defense (DU-SJTU Dual PhD)

Title:
Dynamic Adaptation of Brain Networks from Rest to Motor Task and Application to Stroke Research

Speaker:
Lin Cheng, Dual PhD Candidate, School of Biomedical Engineering, Science and Health Systems, Drexel University, and Shanghai Jiao Tong University (SJTU)

Advisors:
Hasan Ayaz, PhD, Associate Research Professor, School of Biomedical Engineering, Science and Health Systems, Drexel University

Shanbao Tong, PhD, Professor, Shanghai Jiao Tong University (SJTU)

Junfeng Sun, PhD, Associate Professor, Shanghai Jiao Tong University (SJTU)

Abstract:
Examining how motor task modulates brain activity plays a critical role of understanding how cerebral motor system works and could further be applied in the research of motor disability due to neurological diseases. Recent advances in neuroimaging have resulted in numerous studies focusing on motor-induced modulation of brain activity and most widely used strategy of these studies was identifying activated brain regions during motor task through event-related/block-design experiment paradigms. Despite progress obtained, motor-related activation analysis mainly focused on modulation of brain activities for individual brain regions. However, human brain is known to be an integrated network, and the adaptation of brain in response to motor task could be reflected by the modulation of brain networks. Thus, investigating the spatiotemporal modulation of task-state brain networks during motor task would provide system-level information regarding the underlying adaptation of cerebral motor system in response to the motor task and could be further applied in the research of motor disability due to neurological diseases.

In this thesis, functional magnetic resonance imaging (fMRI) and functional near infrared spectroscopy (fNIRS) were employed to record the brain hemodynamic signals, and static functional connectivity (FC) and dynamic FC were used to investigate spatiotemporal pattern of task-state brain network during motor task. In addition, the fMRI data of stroke patients were recorded at four time points post stroke, and the reorganization of task-state brain network as well as its relationship to stroke recovery were examined. Specific results are described as follows:

(1) Through static FC analysis of fNIRS during rest and motor preparation, increased FC were identified during motor preparation, especially the FC connecting right dorsolateral prefrontal cortex (DLPFC) with contralateral primary somatosensory cortex (S1) and primary motor cortex (M1) as well as the FC connecting contralateral S1 with ipsilateral S1 and M1.

(2) Through dynamic FC analysis of fNIRS during rest and motor execution, increased variability of FC connecting contralateral premotor and supplementary motor cortex (PMSMC) and M1 was identified, and the nodal strength variability of these two brain regions were also increased during motor execution. Our findings demonstrated that contralateral M1 and PMSMC were interacting with each other actively and dynamically to facilitate the fist opening and closing.

(3) Through dynamic FC analysis on fMRI data. Our findings suggested that the principal states could show a link between the rest and task states, and verified our hypothesis on overall spatial similarity but distinct temporal patterns of dynamic brain networks between rest and task states.

(4) Task-state motor network was applied in the research of motor disability due to stroke and topological reorganization of task-state motor network was identified during sub-acute phase post stroke.

In summary, this thesis used two functional neuroimaging modalities (fMRI and fNIRS) to investigate how brain networks, specifically the motor network and high-level cognitive network, reorganize spatiotemporally from resting-state to motor tasks through both static and dynamic FC analysis. And, further applied the task-state brain network analysis in the research of motor disability due to stroke. Our findings revealed the underlying spatiotemporal adaptation of brain networks in response to motor task and demonstrated the potential clinical prognostic value of task-state motor network during stroke recovery.

Contact Information

Ken Barbee
215-895-1335
barbee@drexel.edu

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Location

Med-X Building, Room 218, Shanghai Jiao Tong University (SJTU), Shanghai, China

Audience

  • Undergraduate Students
  • Graduate Students
  • Faculty
  • Staff