Brain connectivity research investigates how different parts of the brain communicate and work together to support perception, thought, and behavior. It typically distinguishes three complementary levels.

Structural connectivity maps the physical wiring of the brain. Using techniques such as diffusion MRI and tractography, researchers trace white matter pathways that link distant regions. This reveals major networks and how their integrity changes with development, aging, and disease. For example, weakened structural connections are associated with disorders such as schizophrenia, Alzheimer’s disease, and traumatic brain injury.

Functional connectivity looks at how brain regions show correlated activity over time, usually measured with functional MRI or EEG. Regions whose activity rises and falls together are considered functionally connected, forming large scale networks such as the default mode, attention, and sensorimotor networks. These patterns reorganize during tasks, sleep, and under anesthesia, and they differ systematically between individuals.

Effective connectivity goes further by asking which regions exert a causal influence on others. Methods like dynamic causal modeling and Granger causality use time series data to infer directionality and strength of influence. This helps to identify information flow within circuits and how it is altered in psychiatric and neurological conditions.

Current research combines these three levels in multimodal approaches, linking structure, function, and causality in the same brain. Longitudinal studies track how connectivity develops from childhood to old age and how it is reshaped by learning or therapy. Large datasets and machine learning are used to build predictive models, aiming to personalize diagnosis and treatment by characterizing each person’s unique connectome.