Dissecting Static and Dynamic Functional Connectivity: Example From the Autism Spectrum

Abstract

The ability to measure the intrinsic functional architecture of the brain has grown exponentially over the last 2 decades. Measures of intrinsic connectivity within the brain, typically measured using resting-state functional magnetic resonance imaging (MRI), have evolved from primarily "static" approaches, to include dynamic measures of functional connectivity. Measures of dynamic functional connectivity expand the assumptions to allow brain regions to have temporally different patterns of communication between different regions. That is, connections within the brain can differentially fire between different regions at different times, and these differences can be quantified. Applying approaches that measure the dynamic characteristics of functional brain connectivity have been fruitful in identifying differences during brain development and psychopathology. We provide a brief overview of static and dynamic measures of functional connectivity and illustrate the synergy in applying these approaches to identify both age-related differences in children and differences between typically developing children and children with autistic symptoms.

Keywords: Resting-state functional magnetic resonance imaging; autism spectrum disorders; children; development; neurodevelopment.

Figure 1.

Simulated example of a network where the (C) right parietal region communicates differentially between the (B) left parietal region and the (A) right prefrontal region. The light blue rectangles reflect the regions in the time series in which the correlations are highest between (A) and (B) and the light red rectangles reflect the regions of highest correlation between (A) and (C).