Assessing the reliability of dynamic Functional Connectivity (dFC) measurement through a comprehensive validation framework
My Ph.D. project aims to improve our understanding of dynamic functional connectivity (dFC) by evaluating its analytical flexibility across various methodologies and developing a comprehensive framework for its validation. By utilizing a combination of task-based, simulated, and electrophysiology data, I will investigate the reliability and interpretation of dFC assessments and determine their correlation with neural activity. The goal is to provide a deeper understanding of dFC as a biomarker with significant potential and as a tool for understanding brain function.
To facilitate implementation of multi-analysis dFC assessment, I have developed an open-source Python toolbox, PydFC. PydFC allows users to apply multiple dFC assessment methods on a desired dataset and compare their results.
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