Investigating Resting State Functional Networks and Their Neural Basis in the Rat
Open Access
- Author:
- Ma, Yuncong
- Graduate Program:
- Biomedical Engineering
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- August 19, 2020
- Committee Members:
- Nanyin Zhang, Dissertation Advisor/Co-Advisor
Nanyin Zhang, Committee Chair/Co-Chair
Xiao Liu, Committee Member
Kevin Douglas Alloway, Committee Member
Patrick James Drew, Outside Member
Daniel J Hayes, Program Head/Chair - Keywords:
- Rat
rsfMRI
multi-echo
GCaMP
Electrophysiology
RSFC - Abstract:
- Resting state functional magnetic resonance imaging (rsfMRI) has been developed to probe blood-oxygenation-level dependent (BOLD)-based functional connectivity in both the human and animal brain. It provides a measure of resting state functional networks (RSFNs) that are associated with different brain functions. However, our knowledge of the neural basis of the rsfMRI signal, and how RSFNs dynamically change during different physiologic states such as anesthetics-induced unconsciousness remains largely unclear. In this dissertation, we investigated the dynamics of RSFN at conscious and unconscious states, and revealed two specific brain states associated with high and low consciousness, respectively. We also applied the multi-echo rsfMRI technique in awake rats to separate BOLD and non-BOLD components in the rsfMRI signal, and provided evidence supporting the neural contribution to the global rsfMRI signal. To further investigate the neural basis of RSFN, we combined direct measurement of neural activity, including GCaMP-based fiber photometry or electrophysiology, with concurrent rsfMRI. We demonstrated that the GCaMP signal in the hippocampus was tightly linked to the global rsfMRI signal, further supporting the neural basis of the global brain signal. We also showed a band- specific relationship between neurophysiology and rsfMRI signals, with both gamma-band power and multi-unit activity (MUA) being positively correlated, but delta-band power negatively associated with rsfMRI signal. Collectively, studies in this dissertation have enhanced our understanding of the neural basis of rsfMRI signal, and highlighted the utility of rsfMRI in characterizing brain’s dynamics at different conditions.