Tracking Functional Connectivity

Understanding changes in functional connectivity over time

Functional connectivity describes the functional relationship between different regions of the brain. This research focuses on the use of noninvasive brain imaging modalities (fMRI, fNIRS, EEG) to track changes in functional connectivity over time, in particular we are investigating the use of resting state data to understand how the brain network changes without stimulus.

Illustration of functional network connectivity.

Current projects include longitudinal studies related to stroke patients and cochlear implant users.


Dynamic Functional Connectivity after Stroke

Our review of current literature [1] shows there is clear evidence that the functional network in the brain changes following a stroke and continuously adapts as part of the recovery process. Despite this there has been little work done related to dynamic functional connectivity in stroke patients.

Our current work is investigating the longitudinal trends in dynamic functional connectivity of stroke patients. The first step in this has been to understand how dynamic functional connectivity changes in the normal course of aging [2].

Next we will be looking at changes in the modularity, integration and flexibility of the functional brain network after stroke.
Dynamic functional connectivity analysis pipeline.

Static Functional Connectivity after Cochlear Implant

Despite hearing improving after cochlear implant, there are differences in the amount of improvement for each patient. We are using fNIRS to study the changes in functional connectivity during the first year after implantation.


References

  1. fMRI-based Static and Dynamic Functional Connectivity Analysis for Post-stroke Motor Dysfunction Patient: A Review
    Kaichao Wu, Beth Jelfs, Katrina Neville, and John Q. Fang
    arXiv:2301.07171
  2. Tracking Functional Network Connectivity Dynamics in the Elderly
    Kaichao Wu, Beth Jelfs, Seedahmed S. Mahmoud, Katrina Neville, and John Q. Fang
    Frontiers in Neuroscience, 2023, vol. 17, no. 1146264.