Towards a Distributed-Delay Paradigm in Neural Mass Modeling for High-Dimensional Brain Dynamics
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M4 (Mezzanine Level)
Poster No:
1320
Submission Type:
Abstract Submission
Authors:
Anisleidy Gonzalez-Mitjans1, Alejandro Salinas-Medina1, Paule Toussaint1, Pedro Valdes-Sosa2, Alan Evans1
Institutions:
1McGill University, Montreal, Quebec, Canada, 2University of Electronic Sciences and Technology of China, Sichuan, Chengdu, China
First Author:
Co-Author(s):
Introduction:
Neural Mass Models (NMMs) are essential tools for exploring the complex interactions among neuronal populations. However, classical models such as the Jansen and Rit NMM (JR-NMM) are constrained by oversimplified modularization and fixed conduction delays. These limitations hinder their ability to accurately represent brain dynamics, simulate neurological disorders (e.g., epilepsy), and model large-scale network interactions. Building on our previous work on Distributed-Delay NMMs (DD-NMMs) (Fig. 1), we now enhance this framework with biologically plausible distributed delays informed by axonal properties. Furthermore, we extend its utility to include sensitivity analyses and integration of realistic physiological mechanisms, such as electrophysiology, neurotransmitters, and chemoreceptor dynamics, enabling multi-modal studies of brain activity (e.g., EEG/MEG and fMRI).
Methods:
The DD-NMM models conduction delays as a spectrum of times rather than fixed values (Fig. 2A), capturing variability in white matter tract properties derived from axon diameter estimates (electron microscopy) and axon lengths (diffusion tractography). Sensitivity Analysis was conducted using a Latin Hypercube Sampling approach across an 11-parameter space, generating 12-second EEG simulations for each set. Classical machine learning techniques, such as decision trees and Random Forests, were applied to classify simulations into steady-state and seizure-like dynamics based on features such as amplitude, frequency, and peak counts.
Results:
The inclusion of distributed delays improves the model's ability to simulate biologically realistic brain dynamics. As shown in Fig. 2A, distributed delays introduce variability in signal propagation, breaking the excessive synchronization seen with fixed delays (red boxes) and broadening the frequency spectrum to include theta bands and harmonics (green boxes). This aligns more closely with observed EEG/MEG signals in healthy and pathological states, such as seizures.
Sensitivity Analysis identified excitatory (b_exc) and inhibitory (b_inh) gain parameters as key modulators of steady-state and seizure dynamics. The noise standard deviation (μ_Ste) also influenced transitions between brain states. Nonlinear interactions revealed that moderate excitation enhances inhibitory feedback, refining the balance between excitation and inhibition (Fig. 2B). These findings provide biologically grounded insights into diverse brain dynamics.
Sensitivity Analysis identified excitatory (b_exc) and inhibitory (b_inh) gain parameters as key modulators of steady-state and seizure dynamics. The noise standard deviation (μ_Ste) also influenced transitions between brain states. Nonlinear interactions revealed that moderate excitation enhances inhibitory feedback, refining the balance between excitation and inhibition (Fig. 2B). These findings provide biologically grounded insights into diverse brain dynamics.
Conclusions:
This enhanced DD-NMM framework represents a significant advance in the realism of Neural Mass Modeling, supporting high-dimensional simulations with distributed delays and enabling the integration of diverse biological mechanisms (Fig. 2A). The findings highlight the importance of excitation and inhibition parameters in shaping brain dynamics and suggest potential for parameter-specific interventions, such as optimizing neuromodulation therapies (e.g., DBS, TMS). Its applications extend to multi-modal imaging modalities, including EEG/MEG and fMRI, offering a robust platform for exploring neurophysiological processes. By bridging biophysical models with clinical applications, this open-source DD-NMM toolbox (https://github.com/anisleidygm/Brain_Modeling) provides a powerful tool for studying steady-state dynamics, seizure mechanisms, and other complex brain phenomena, while paving the way for personalized medicine approaches.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Methods Development 2
Keywords:
Computational Neuroscience
Cortical Columns
Design and Analysis
Electroencephaolography (EEG)
Machine Learning
Modeling
Open-Source Code
Open-Source Software
Other - Neural Mass Models
1|2Indicates the priority used for review
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[3] Caminiti, R., Carducci, F., Piervincenzi, C., Battaglia-Mayer, A., Confalone, G., Visco-Comandini, F., ... & Innocenti, G. M. (2013). Diameter, length, speed, and conduction delay of callosal axons in macaque monkeys and humans: comparing data from histology and magnetic resonance imaging diffusion tractography. Journal of Neuroscience, 33(36), 14501-14511.
[4] Ferrat, L. A., Goodfellow, M., & Terry, J. R. (2018). Classifying dynamic transitions in high dimensional neural mass models: A random forest approach. PLoS computational biology, 14(3), e1006009.
[5] Mitjans, A. G., Linares, D. P., Gonzalez, A. A., & Sosa, P. V. (2021, October). Neuroinformatic Tool to Study High Dimensional Dynamics with Distributed Delays in Neural Mass Models. In International Journal of Psychophysiology (Vol. 168, pp. S183-S183). RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS: Elsevier.