Functional connectivity describes how different brain regions coordinate their activity over time, typically measured using noninvasive methods such as functional MRI, EEG, or MEG. Instead of focusing on single areas, it characterizes networks of regions whose signals fluctuate together, suggesting they work as a unit.

In resting state fMRI, spontaneous low frequency fluctuations are used to identify networks like the default mode, attention, and sensorimotor systems. Correlation or related metrics quantify how similar time series are across regions. These network patterns are highly reproducible within individuals yet can vary across people, developmental stages, and clinical conditions.

Research has shown that functional connectivity is dynamic, not static. Connectivity patterns change over seconds to minutes, reflecting shifting cognitive states, vigilance, and ongoing mental processes. Methods such as sliding window correlations, coactivation patterns, and hidden Markov models are used to capture this temporal variability.

Functional connectivity is sensitive to structural connectivity but is not determined by it alone. Regions without direct anatomical links can still show strong functional coupling, influenced by polysynaptic pathways, neuromodulatory systems, and common inputs. Comparing functional and structural networks reveals both convergences and important differences.

Connectivity measures are increasingly applied to study psychiatric and neurological disorders. Altered network organization, such as disrupted default mode or frontoparietal connectivity, has been associated with depression, schizophrenia, Alzheimer’s disease, and others. However, interpretational challenges remain, including confounds from motion, physiological noise, and vascular factors, as well as the correlational nature of the measures.

Ongoing work aims to refine analysis methods, improve reliability, and link connectivity patterns more directly to behavior, cognition, and underlying neural mechanisms.