Effects of regional heterogeneity on the neural dynamics of the human cortex
Poster No:
1666
Submission Type:
Abstract Submission
Authors:
Victor Barnes1, Jace Cruddas1, Trang Cao2, James Pang3, Alex Fornito3
Institutions:
1Monash University, Melbourne, Victoria, 2Monash Biomedical Imaging, Monash University, Clayton, Victoria, 3Monash University, Clayton, Victoria
First Author:
Co-Author(s):
Introduction:
Brain function is constrained by its underlying structure and anatomy, but explaining the mechanisms behind this link has proven challenging. In many areas of physics and engineering, the dynamics of a system can be understood with respect to the eigenmodes of its structure, representing the fundamental, resonant vibrations of the system. Recent work has shown that a diverse array of functional brain maps derived from task-evoked and resting-state fMRI can be parsimoniously explained as excitations of the eigenmodes of cortical geometry (Pang et al., 2023). The success of these eigenmodes is somewhat surprising given that they are estimated using a minimal set of features, simply defining how the shape of the cortex varies through space. However, alternative anatomical properties, such as regional variations in myelination or synaptic density, may also play a role in determining the spatial pattern of the brain's anatomical modes.
Methods:
Here, we developed a framework for deriving geometric eigenmodes that can account for spatial heterogeneities in any arbitrary cortical property. We derived the modes by using a triangular mesh representation of the cortical surface from a FreeSurfer template (Fischl, 1999) and solving the generalised form of the Helmholtz equation:
∇(c ·∇)ψ=-λψ
where ψ are the eigenmodes, λ are the eigenvalues, and c is the heterogeneous term describing local variations across the cortical mesh and corresponds to the wave speed of the resulting dynamics, as captured in biophysical models such as neural field theory (Wright, 1995; Jirsa 1996; Robinson, 1997). We used a generalised form of the isotropic damped wave equation without regeneration (Robinson, 1997) to develop 10 large-scale models of cortical activity that incorporate different heterogenous properties of the cortex and fit each model using neuroimaging data from the Human Connectome Project. We evaluated model fit using three metrics: static pairwise FC (edge-level FC), static node-level average FC (node-level FC) and time-resolved dynamic properties of FC (FCD). To optimise the model we ran a 5-fold cross validation analysis where we defined the objective function to be an aggregation of the three aforementioned metrics.
∇(c ·∇)ψ=-λψ
where ψ are the eigenmodes, λ are the eigenvalues, and c is the heterogeneous term describing local variations across the cortical mesh and corresponds to the wave speed of the resulting dynamics, as captured in biophysical models such as neural field theory (Wright, 1995; Jirsa 1996; Robinson, 1997). We used a generalised form of the isotropic damped wave equation without regeneration (Robinson, 1997) to develop 10 large-scale models of cortical activity that incorporate different heterogenous properties of the cortex and fit each model using neuroimaging data from the Human Connectome Project. We evaluated model fit using three metrics: static pairwise FC (edge-level FC), static node-level average FC (node-level FC) and time-resolved dynamic properties of FC (FCD). To optimise the model we ran a 5-fold cross validation analysis where we defined the objective function to be an aggregation of the three aforementioned metrics.
Results:
We found that the similarity between model and empirical FC was significantly higher for 7 of the 10 heterogeneous models compared to the homogeneous model (Figure 1). The heterogeneous model parameterised by the first PC of gene expression in Layer IV had the highest similarity with the empirical data for both edge-level and node-level FC indicating an influential property of cortical specialisation shaping resting-state FC. This aligns with existing work showing that specific thalamic nuclei, which are thought to act as drivers of cortical activity, send axonal projections into Layer IV of the cortex (Shine, 2023). We also tested whether these improvements in model-empirical fits were network specific by examining how model performance varied within and across different functionally relevant cortical networks. We found that the heterogeneous model preferentially improved model-empirical fits in association networks compared to sensory networks (Figure 2).
·Cortical heterogeneity improves model fit to resting-state fMRI.
·Improvement in model fit varies across networks.
Conclusions:
We show that refining the geometric eigenmode model by incorporating information about spatial heterogeneities in cortical tissue can better capture the spatiotemporal neural dynamics of resting-state fMRI. This indicates the importance of anatomical and physiological properties of the cortex in shaping large-scale neural dynamics.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Task-Independent and Resting-State Analysis 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Cortical Anatomy and Brain Mapping 2
Cortical Cyto- and Myeloarchitecture
Keywords:
Computational Neuroscience
Cortex
Design and Analysis
FUNCTIONAL MRI
Modeling
Other - Brain geometry; Eigenmodes; Regional heterogeneity
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Jirsa, V. 1996. "Field theory of electromagnetic brain activity." Physical Review Letters. 77, 960–963.
Pang, J. C., Aquino, K. M. 2023. “Geometric Constraints on Human Brain Function.” Nature. 618 (7965): 566–74.
Robinson, P. A. 1997. “Propagation and Stability of Waves of Electrical Activity in the Cerebral Cortex.” Physical Review E. 56 (1): 826–40.
Shine J. M. 2023. "The impact of the human thalamus on brain-wide information processing." Nature Reviews Neuroscience. 24(7), 416–430.
Wright, J. J. 1995. "Simulation of electrocortical waves." Biological Cybernetics. 72, 347–356.