A multiplex of connectome trajectories enables several connectivity patterns in parallel

Area of Science:

• Neuroscience
• Cognitive Science
• Systems Neuroscience

Background:

• Complex brain functions involve parallel neural operations across distributed networks.
• Understanding how the brain facilitates parallel processing across different timescales remains a challenge.
• Functional connectivity (FC) dynamics are crucial for brain function, but their multi-timescale nature is not fully understood, especially with slow fMRI.

Purpose of the Study:

• To investigate whether the brain utilizes multi-timescale network dynamics for parallel processing.
• To examine the spatial and temporal convergence of brain network patterns across various timescales.
• To determine if different timescales of functional connectivity operate independently or in synchrony.

Main Methods:

• Simultaneous intracranial electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) were recorded in humans.
• Source-localized scalp EEG-fMRI data were analyzed across a broad range of timescales (infraslow to gamma).
• Spatial and temporal convergence of connectome trajectories were assessed between EEG frequency bands and fMRI.

Main Results:

• Spatial convergence was observed, indicating similar large-scale network patterns between EEG and fMRI.
• Temporal divergence was found, with connectome states occurring asynchronously across different timescales.
• Recurrent functional connectivity patterns showed partial spatial similarity but differed in their temporal occurrence across timescales.

Conclusions:

• Hemodynamic (fMRI) and electrophysiological (EEG) signals reflect distinct, parallel connectome trajectories operating at different speeds.
• The brain leverages a multiplex of dynamic network patterns across multiple timescales to enable independent concurrent connectivity.
• This multi-timescale parallel processing is fundamental to complex brain function.