Dynamical models reveal anatomically reliable attractor landscapes embedded in resting state brain networks

Area of Science:

• Neuroscience
• Computational Neuroscience
• Systems Neuroscience

Background:

• Functional connectivity (FC) in resting-state brain networks (RSNs) is crucial for cognition but its mechanistic basis remains unclear.
• Debate exists whether resting-state activity reflects noise or a nonlinear dynamical system with attractors.

Approach:

• Developed whole-brain dynamical systems models using the Mesoscale Individualized NeuroDynamic (MINDy) platform from resting-state fMRI (rfMRI) data.
• Models comprised neural masses with individualized connection weights.
• Investigated attractor landscapes and their mapping onto canonical RSNs like the default mode network (DMN) and frontoparietal control network (FPN).

Key Points:

• MINDy models revealed diverse attractor landscapes, including multiple equilibria and limit cycles.
• These attractors reliably mapped to canonical RSNs at the individual level.
• Model combinations induced bifurcations, recapitulating the full spectrum of observed dynamics.

Conclusions:

• Resting brain dynamics are best characterized as a nonlinear system with multiple attractors, not merely noise.
• Conventional FC analysis may overlook critical attractor properties and structure.
• Neural dynamical modeling offers a more comprehensive approach to understanding intrinsic brain organization and generative mechanisms.