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.
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
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
Functional magnetic resonance imaging (fMRI) has been widely utilized to study the motor deficits and rehabilitation following stroke. In particular, functional connectivity(FC) analyses with fMRI at rest can be employed to reveal the neural connectivity rationale behind this post-stroke motor function impairment and recovery. However, the methods and findings have not been summarized in a review focusing on post-stroke functional connectivity analysis. In this context, we broadly review the static functional connectivity network analysis (SFC) and dynamic functional connectivity network analysis (DFC) for post-stroke motor dysfunction patients, aiming to provide method guides and the latest findings regarding post-stroke motor function recovery. Specifically, a brief overview of the SFC and DFC methods for fMRI analysis is provided, along with the preprocessing and denoising procedures that go into these methods. Following that, the current status of research in functional connectivity networks for post-stoke patients under these two views was synthesized individually. Results show that SFC is the most frequent post-stroke functional connectivity analysis method. The SFC findings demonstrate that the stroke lesion reduces FC between motor areas, and the FC increase positively correlates with functional recovery. Meanwhile, the current DFC analysis in post-stroke has just been uncovered as the tip of the iceberg of its prospect, and its exceptionally rapidly progressing development can be expected.
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.
Introduction: Functional magnetic resonance imaging (fMRI) has shown that aging disturbs healthy brain organization and functional connectivity. However, how this age-induced alteration impacts dynamic brain function interaction has not yet been fully investigated. Dynamic function network connectivity (DFNC) analysis can produce a brain representation based on the time-varying network connectivity changes, which can be further used to study the brain aging mechanism for people at different age stages.
Method: This presented investigation examined the dynamic functional connectivity representation and its relationship with brain age for people at an elderly stage as well as in early adulthood. Specifically, the resting-state fMRI data from the University of North Carolina cohort of 34 young adults and 28 elderly participants were fed into a DFNC analysis pipeline. This DFNC pipeline forms an integrated dynamic functional connectivity (FC) analysis framework, which consists of brain functional network parcellation, dynamic FC feature extraction, and FC dynamics examination.
Results: The statistical analysis demonstrates that extensive dynamic connection changes in the elderly concerning the transient brain state and the method of functional interaction in the brain. In addition, various machine learning algorithms have been developed to verify the ability of dynamic FC features to distinguish the age stage. The fraction time of DFNC states has the highest performance, which can achieve a classification accuracy of over 88% by a decision tree.
Discussion: The results proved there are dynamic FC alterations in the elderly, and the alteration was found to be correlated with mnemonic discrimination ability and could have an impact on the balance of functional integration and segregation.
