Approved Research

Evaluation of gBOLD-CSF Coupling as a Functional Index in the UK Biobank Cohort

Lay summary

Rationale: The circulation of cerebrospinal fluid (CSF) has been suggested to play critical roles in clearing brain waste. Failure of this process can lead to toxic protein accumulation, impacting cognitive functions during aging, and in Parkinson's and other neurodegenerative disorders. Emerging evidence has suggested gBOLD-CSF coupling as a promising metric for studying such process. This metric, initially reported as the correlation between low-frequency resting-state fMRI BOLD signals and CSF dynamics, was subsequently associated with cognitive decline in patients with Alzheimer's and Parkinson's disease.

Goals: We aim to explore the potential of gBOLD-CSF coupling to reflect individual variations using a large-scale dataset. We will 1) verify the reliability of this index; 2) correlate this index with sleep patterns indexed through self-reports and wrist-band recorded data; and 3) explore its variations among different genders and ages.

Methods: We will follow the existing procedures to quantify gBOLD-CSF coupling. The cross-correlation function between gBOLD and CSF signals at different time lags will be computed, and the evaluation at the time lag of peak time point is used to quantify the gBOLD-CSF coupling for each subject. The anticipated project duration is 2-3 years. Given that the extraction of the CSF signal requires manually defining structural labels, conducting this procedure in a large-scale dataset is notably time-consuming.

Expected impact: This project is projected to contribute to the public health in the following aspects: Firstly, it promises to illuminate research on the sleep, thus bridging the existing gap between rodent models and human studies. Secondly, by establishing a robust functional measure in a large-scale general population, it opens the possibility of exploring new research avenues. Lastly, understanding individual variations in sleep-related function may enable the development of personalized interventions. These could specifically target conditions such as cognitive decline in Alzheimer's and Parkinson's disease, thereby addressing the urgent needs of aging populations.