FBNs

Functional brain networks (FBNs) are sets of brain regions that exhibit temporally correlated activity, coordinating to support cognition and behavior. They can be observed during rest or during tasks and are typically identified from functional magnetic resonance imaging data using statistical measures of functional connectivity, such as correlations or coherence between regional time series.

Among the best-characterized FBNs are the default mode network, which is active during internally focused thought; the frontoparietal control network, implicated in executive function and adaptive control; the dorsal and ventral attention networks, associated with goal-directed and stimulus-driven attention; the salience network, which helps switch between other networks; and sensorimotor networks that subserve movement and sensation. These networks form a distributed, hierarchically organized architecture with hubs that coordinate information flow. Resting-state analyses suggest the brain maintains modular organization that can reconfigure when engaging in tasks.

Researchers map FBNs using seed-based correlation analyses, independent component analysis (ICA), or graph theoretical approaches. Functional connectivity (FC) is quantified as statistical dependence between region time series, and researchers examine properties such as hubness, modularity, and small-world topology. Dynamic connectivity methods also explore how FC patterns fluctuate over time, providing insight into moment-to-moment network coordination.

FBNs are relevant for understanding development, aging, and a range of neurological and psychiatric conditions, including Alzheimer's disease, schizophrenia, autism spectrum disorders, and mood disorders. They also inform theories of attention, memory, and executive function by describing how distributed networks support cognitive operations.

Limitations include substantial inter-individual variability, sensitivity to head motion and physiological noise, and dependencies on preprocessing choices. Ongoing work seeks methodological standardization, cross-cohort replication, and integration with other modalities to clarify the functional significance of observed network patterns.