Arousal-driven parametric fluctuations improve the performance of computational models of dFC
Poster No:
1177
Submission Type:
Abstract Submission
Authors:
Anagh Pathak1, Demian Battaglia1
Institutions:
1University of Strasbourg, Strasbourg, France
First Author:
Co-Author:
Introduction:
Spontaneous neural dynamics, reflected in correlated fluctuations of brain activity, provide critical insights into network organization and may serve as markers of cognition and neurodegeneration. However, methodological challenges complicate their interpretation, particularly regarding whether dynamic functional connectivity (dFC) arises from genuine neural processes or non-neural sources like noise. Even under neural origins, the mechanisms remain debated, including contributions from cognitive processes, arousal fluctuations, or intrinsic neural dynamics. Whole-brain computational modeling offers a valuable approach to address these issues by linking empirical dFC patterns to mechanistic principles. Existing models attribute dFC to transient dynamics, such as multistability (Hansen et al. 2014) or metastable state switching (Cabral et al. 2022), driven by structural connectivity. While these frameworks capture basic dFC properties, they fail to reproduce higher-order features observed in resting-state fMRI (rsfMRI) data. We hypothesize that neuromodulatory processes, which regulate neuronal excitability and synaptic transmission across timescales, are crucial for bridging this gap. By incorporating non-autonomous terms representing neuromodulatory inputs (e.g., arousal fluctuations) into computational models, we introduce external influences that interact with intrinsic dynamics, replicating the variability and non-stationarity seen in empirical dFC. Our results reveal that neuromodulation-enhanced models better capture key dFC features, including higher-order temporal properties like dFC speed. These findings suggest that neuromodulation operates as a hierarchical control mechanism, modulating network transitions and stabilizing specific brain states in context-dependent ways. This work highlights neuromodulation's critical role in resting-state dFC and its potential implications for understanding normal and pathological brain function.
Methods:
This study utilized resting-state fMRI data from 100 participants (HCP), including two sessions per subject using a 3T Siemens Connectome Skyra scanner. Functional connectivity (FC) was analyzed with 89 AAL-parcelated ROIs, and whole-brain models were constructed using averaged HCP structural connectivity. Simulations used an oscillatory Stuart-Landau model and a multistable Wong-Wang model, extended with a time-varying global excitability term (tMFM). Features such as dFC speed, fluidity, and dimensionality were extracted to characterize dynamics. A Genetic Algorithm optimized model parameters by minimizing errors between simulated and empirical data. AIC/BIC metrics guided model selection for robust conclusions.
Results:
Systematic analysis of resting-state dFC matrices (N=200) revealed two distinct phenotypes: drift, characterized by progressive slowing over time, and pulsatile, featuring brief, well-defined slow dynamics. Autonomous models, including noise-driven bistability (eMFM) and delay-induced metastability (SLM), partially captured empirical dFC but had limited success. MFM produced low-dimensional dFC dynamics via noise-driven stochastic resonance and intermediate global excitability (G). SLM showed robust fast dFC at intermediate delays (~4 ms) and G, with slow dFC emerging from filtered BOLD-like signals. Both models ruled out noise artifacts using control analyses. Incorporating arousal-linked, time-varying excitability (tMFM) enhanced model performance, replicating dFC and speed distributions. AIC/BIC analysis confirmed tMFM as superior, with drift and pulsatile phenotypes explained by distinct excitability dynamics.
·tMFM model fit for drift phenotype
·tMFM model fit for pulsatile phenotype
Conclusions:
Our findings identify distinct dFC phenotypes (drift and pulsatile) and show that while autonomous models (MFM, SLM) capture key dynamics, they fall short of fully explaining empirical data. By integrating time-varying excitability, tMFM outperformed static models, offering a unified framework to link arousal-linked processes with dFC variability and distinct phenotypes.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals
Keywords:
Computational Neuroscience
FUNCTIONAL MRI
Modeling
Neurotransmitter
Other - Dynamic Functional Connectivity; Arousal; Whole brain modeling; functional connectivity; neuromodulation
1|2Indicates the priority used for review
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Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Hansen, E. C., Battaglia, D., Spiegler, A., Deco, G., & Jirsa, V. K. (2015). Functional connectivity dynamics: modeling the switching behavior of the resting state. Neuroimage, 105, 525-535.