Resting-state brain organization revealed by functional covariance networks

Area of Science:

• Neuroscience
• Brain Imaging
• Network Science

Background:

• Current brain network studies utilize intrinsic connectivity networks (ICN) from fMRI time series and structural covariance networks (SCN) to map brain organization at different timescales.
• A gap exists in understanding meso-timescale brain networks that could bridge functional and structural connectivity.

Purpose of the Study:

• To introduce a novel method for analyzing brain networks at an intermediate timescale.
• To bridge the gap between existing functional (ICN) and structural (SCN) brain network analyses.
• To investigate novel organizational patterns in resting-state brain activity.

Main Methods:

• Proposed a functional covariance network (FCN) method measuring the covariance of amplitude of low-frequency fluctuations (ALFF) in BOLD signals across subjects.
• Compared ALFF-FCNs with intrinsic connectivity networks (ICN) and structural covariance networks (SCN) for default, task-positive, and sensory networks.
• Utilized conjunctional analysis to assess overlap and modularity among FCNs, ICNs, and SCNs.

Main Results:

• Demonstrated significant overlap among functional covariance networks (FCNs), intrinsic connectivity networks (ICNs), and structural covariance networks (SCNs).
• Identified modular structures within FCNs and ICNs.
• Revealed a novel network dichotomy in FCN analysis, contrasting high-level cognitive and low-level perceptive systems, distinct from the ICN dichotomy.

Conclusions:

• The proposed ALFF-FCN approach effectively measures interregional correlations in brain activity over short periods.
• This method reveals novel organizational patterns of resting-state brain activity at an intermediate timescale.
• The findings provide new insights into the functional architecture of the human brain.