Spectral imprint of structural embedding in effective connectivity.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Network Science

Background:

• Neural fluctuations display complex spectral profiles influenced by local dynamics and anatomical structure.
• Current resting-state effective connectivity models often overlook structural embedding and assume uniform regional fluctuation timescales.

Purpose of the Study:

• To introduce a chromatic dynamic causal model (DCM) integrating structural information into neural fluctuation analysis.
• To investigate how structural valency influences the spectral properties of endogenous neural fluctuations.

Main Methods:

• Developed a chromatic DCM where structural valency linearly maps to the spectral exponent of scale-free auto-spectra.
• Utilized simulations to demonstrate the emergence of this mapping from structural embedding in a non-equilibrium system.
• Validated the model's ability to recover parameters across various network sizes and noise levels, comparing it to standard spectral DCM.

Main Results:

• Chromatic DCM reliably recovers ground-truth parameters, outperforming standard spectral DCM.
• Analysis of empirical data showed that valency-exponent mappings differ across the cortical hierarchy.
• These mapping parameters were found to be conserved across homologous brain networks in humans, macaques, marmosets, and mice.

Conclusions:

• The chromatic DCM provides a generative framework for understanding structure-function coupling in the brain.
• This model expands the available biophysical mechanisms for effective connectivity inference.
• Findings highlight species- and hierarchy-specific variations in how brain structure shapes neural dynamics.