MRI functional connectivity-based predictive models of brain organization and cognitive state for healthy and clinical populations
Embargo Date
2027-02-04
OA Version
Citation
Abstract
Connectome Fingerprinting (CF) and Connectome-Based Predictive Modeling (CPM) are emerging data science approaches within cognitive neuroscience that utilize brain connectivity data to predict the functional organization of the brain and behavior. Here, I examined the robustness and data needs of these approaches and developed applications for specific clinical populations, including neurosurgical glioma patients and early-stage Alzheimer’s Disease (AD) patients.In the first study, I utilized the Human Connectome Project dataset (n=208) to understand the relationship between function and connectivity using CF across cognitive tasks, and functional connectivity paradigms of fixation and movie-watching. I benchmarked the CF method to establish the quantity and quality of connectivity and task activation data required to build robust models across different cortical regions, cognitive tasks, scan quality, learning algorithms, and scanners. I also modeled the link of cerebellar-cortical connectivity with function, suggesting an integrated role of the cerebellum across cognitive domains.
Neurosurgeons routinely use task fMRI protocols to map out motor and language networks to aid in presurgical planning for brain tumor resection to minimize cognitive damage. In the second study, I investigated the possibility of using CF models to help neurosurgeons predict motor and language networks in presurgical patients with gliomas (n=16), who are unable to perform complex tasks in the scanner. I tested the model validity across healthy control adults (n=16) from different scanners with varying data quality and quantity.
The deposition of tau and amyloid-β plaques in AD results in brain degeneration, memory loss, and behavioral changes. Resting-state functional connectivity gets affected due to the progression of AD. In the third study, I analyzed the relationship between functional connectivity and tau/amyloid depositions and tested the predictive ability of CPM across two different cohorts: Autosomal Dominant with Presenilin1 mutation from the Colombia-Boston study with early-onset AD (n=32) and late-onset sporadic AD with APOE4 marker from the Harvard Aging Brain Study (n=78) and compared them with healthy age-matched adults (n=35/n=206) and healthy young adults (n=1570) from the Genomic Superstruct Project.
Combined, these studies highlight the potential of data-driven approaches to model human brain function and connectivity, facilitating fMRI's translational applications.
Description
2023
License
Attribution-NonCommercial-ShareAlike 4.0 International