In this educational course, basic concepts of the resting state fMRI (rsfMRI) will be outlined, and several widely used data-driven analysis approaches for resting state functional connectivity (rsFC) and their applications will be introduced. The course will also briefly describe the recent emergence of dynamic functional connectivity (dFC) and several of its widely used data analysis approaches.
The goal of resting state fMRI (rsfMRI) is to investigate neurological processes that occur in the absence of external stimulation. The imaging modality is especially appealing for studies that involve pediatric and clinical populations, as it does not require explicit tasks and therefore allows the use of an identical protocol in all participants – regardless of their degree of cognitive and physical limitations. By observing the spontaneous, low frequency fluctuations in the blood oxygenation level dependent (BOLD) signal in the absence of external stimulation, one is able to investigate the functionally integrated relationship between spatially separated brain regions – in other words, resting state functional connectivity (rsFC). Significant changes in rsFC have been observed in many neurological and psychological disorders and provided valuable insights into the disorders.
There are several hypotheses- and data-driven analysis approaches for obtaining rsFC. In this educational course, we will concentrate on the data-driven analysis approaches, which only utilizes the data and does not require a priori assumptions. Examples of such data-driven approaches include independent component analysis (ICA), regional homogeneity (ReHo), and fractional amplitude of low-frequency fluctuations (fALFF). The course will also briefly describe the recent emergence of dynamic functional connectivity (dFC) and several of its widely used data analysis approaches, such as sliding window and dynamic conditional correlation (DCC) approaches.
After the educational course, the attendee will: