RestingStatefMRI

Resting-state functional magnetic resonance imaging (rs-fMRI) is a neuroimaging technique that measures spontaneous fluctuations in the BOLD signal when the subject is at rest and not engaged in a specific task. It is used to study functional connectivity, defined as the statistical dependence between distant brain regions.

During rs-fMRI, participants lie still in the scanner, often with eyes closed or fixated, while blood-oxygenation level dependent signals are collected with echo-planar imaging. Data are analyzed to identify temporally correlated low-frequency fluctuations (roughly 0.01–0.1 Hz) that are thought to reflect intrinsic brain networks.

Analytical approaches include seed-based correlation, where a region of interest is correlated with the rest of the brain, and independent component analysis (ICA), which extracts spatially independent networks without predefined seeds. Graph-theoretic methods describe the brain as a network of nodes and edges, enabling metrics such as connectivity strength and modular organization.

Prominent resting-state networks include the default mode network, salience network, and frontoparietal, sensorimotor, and visual networks. rs-fMRI has been used to study aging, development, and a range of conditions including Alzheimer’s disease, schizophrenia, autism, and depression, and to inform pre-surgical planning in some cases.

Limitations include sensitivity to head motion and physiological noise, variability in scan parameters and preprocessing, and the correlative nature of functional connectivity that does not indicate directionality or causality. Cross-site standardization and interpretation remain active areas of methodological development.