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 have looked at the reconfiguration of the functional brain network after stroke.
Multilayer dynamics analysis framework.
Investigating the changes in the recruitment, integration and flexibility there were clear differences depending on the severity of the stroke (missing reference).
Summary of the reconfiguration patterns of stroke patients (A) Mild (B) Severe.
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, Aiqun Cai, and John Q. Fang
Functional magnetic resonance imaging (fMRI) has emerged as a prevalent tool for investigating motor deficits and rehabilitation in the context of stroke. Particularly, the exploration of functional connectivity (FC) through resting-state fMRI has the potential to unveil the neural connectivity mechanisms underlying post-stroke motor impairment and recovery. Despite the significance of this approach, there exists a gap in the literature where a comprehensive reviewdedicated to post-stroke functional connectivity analysis is lacking. In this paper, we undertake an extensive review of both static functional connectivity network analysis (SFC) and dynamic functional connectivity network analysis (DFC) in the context of post-stroke motor dysfunction. Our primary goal is to present comprehensive methodological insights and the latest research findings pertaining to motor function recovery after stroke. We commence by providing a succinct overviewof SFC and DFC methods, elucidating the preprocessing and denoising techniques essential to these analyses. Subsequently, we summarize the application of two methods in stroke disease, mainly focusing on the extracted insight into post-stroke brain dysfunction and rehabilitation. Our review indicates a prevalence of SFC as the method of choice for post-stroke functional connectivity investigations. Specifically, SFC studies reveal a reduction in FC between motor areas due to stroke lesions, with increased FC correlating positively with functional recovery. Nevertheless, the DFC for post-stroke analysis has only begun to unveil its potential due to its ability in temporal dynamics. In summary, this review paper presents a thorough understanding of post-stroke functional connectivity analysis and its implications for the study of motor function recovery, offering valuable insights for future research and clinical applications.
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.
