Multi-modal and multi-model interrogation of large-scale functional brain networks

Castaldo, Francesca; Santos, Francisco Páscoa dos; Timms, Ryan C; Cabral, Joana; Vohryzek, Jakub; Deco, Gustavo; Woolrich, Mark W.; Friston, Karl J.; Verschure, Paul F. M. J.

doi:10.1016/j.neuroimage.2023.120236

Cited by 16 publications

(17 citation statements)

References 185 publications

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“…as opposed to the most common implementation [36,37,[39][40][41]85]. Furthermore, we argue that it may still be in line with literature.…”

Section: S2 Appendix Homeostasis Of Excitatory Synapses Unto Inhibito...supporting

confidence: 82%

Excitatory-Inhibitory Homeostasis and Bifurcation Control in the Wilson-Cowan Model of Cortical Dynamics

Páscoa dos Santos,

Verschure

2024

Preprint

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Although the primary function of excitatory-inhibitory (E-I) homeostasis is the maintenance of mean firing rates, the conjugation of multiple homeostatic mechanisms is thought to be pivotal to ensuring edge-of-bifurcation dynamics in cortical circuits. However, computational studies on E-I homeostasis have focused solely on the plasticity of inhibition, neglecting the impact of different modes of E-I homeostasis on cortical dynamics. Therefore, we investigate how oscillations and edge-of-bifurcation dynamics are shaped by the diverse mechanisms of E-I homeostasis employed by cortical networks. Using the Wilson-Cowan model, we explore how distinct modes of E-I homeostasis maintain stable firing rates in models with varying levels of input and how it affects circuit dynamics. Our results confirm that E-I homeostasis can be leveraged to control edge-of-bifurcation dynamics and that some modes of homeostasis maintain mean firing rates under higher levels of input by modulating the distance to the bifurcation.Additionally, relying on multiple modes of homeostasis ensures stable activity while keeping oscillation frequencies within a physiological range. Our findings tie relevant features of cortical networks, such as E-I balance, the generation of gamma oscillations, and edge-of-bifurcation dynamics, under the framework of firing-rate homeostasis, providing a mechanistic explanation for the heterogeneity in the distance to the bifurcation found across cortical areas. In addition, we reveal the functional benefits of relying upon different homeostatic mechanisms, providing a robust method to regulate network dynamics with minimal perturbation to the generation of gamma rhythms and explaining the correlation between inhibition and gamma frequencies found in cortical networks.Author summaryWe study how excitatory-inhibitory (E-I) homeostasis controls edge-of-bifurcation dynamics in cortical networks and how it impacts the generation of gamma oscillations. Importantly, while previous studies have limited E-I homeostasis to the plasticity of inhibition, we explore the wide range of mechanisms employed by cortical networks and, more importantly, how they interact. Here, we derive the mathematical solution for the Wilson-Cowan model under distinct modes of homeostasis and study how they shape model dynamics and the generation of gamma oscillations. That said, we demonstrate that E-I homeostasis, particularly of excitation and intrinsic excitability, modulates model dynamics relative to the bifurcation between damped and sustained oscillations in a manner previously unaccounted for, providing a mechanism for the implementation of heterogeneous distances to the bifurcation across cortical areas. Furthermore, our results stress the functional benefits of relying on multiple modes of homeostasis, allowing for the control of firing rates and circuit dynamics while ensuring that gamma oscillations remain within a physiological range and explaining the relationship between inhibition and gamma frequencies found in empirical data. With these results, we unify E-I balance, edge-of-bifurcation dynamics, and gamma oscillations under the lens of firing-rate homeostasis.

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“…as opposed to the most common implementation [36,37,[39][40][41]85]. Furthermore, we argue that it may still be in line with literature.…”

Section: S2 Appendix Homeostasis Of Excitatory Synapses Unto Inhibito...supporting

confidence: 82%

Excitatory-Inhibitory Homeostasis and Bifurcation Control in the Wilson-Cowan Model of Cortical Dynamics

Páscoa dos Santos,

Verschure

2024

Preprint

Self Cite

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“…The SL model is arguably the simplest phase-amplitude model used in neuroscience [26, 27, 28, 29], and is therefore ideally suited to obtain analytical insights on PAC-enhancing stimulation. The model represents the canonical form of a Hopf bifurcation, and can therefore operate in the fixed-point or limit-cycle regime.…”

Section: Resultsmentioning

confidence: 99%

“…PAC was also found to be abnormal in various neurological disorders -see [11] for a review. In Parkinson's disease (PD), coupling between the beta phase (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) and gamma amplitude in the motor cortex is exaggerated compared to patients with dystonia and patients with epilepsy, both at rest and during movement [12]. Elevated PAC was reported in patients with PD off dopaminergic medication compared to patients on medication, as well as compared to humans without a movement disorder [13].…”

Section: Introductionmentioning

confidence: 99%

How to design optimal brain stimulation to modulate phase-amplitude coupling?

Duchet,

Bogacz

2024

Preprint

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Objective. Phase-amplitude coupling (PAC), the coupling of the amplitude of a faster brain rhythm to the phase of a slower brain rhythm, plays a significant role in brain activity and has been implicated in various neurological disorders. For example, in Parkinson's disease, PAC between the beta (13-30 Hz) and gamma (50-200 Hz) rhythms in the motor cortex is exaggerated, while in Alzheimer's disease, PAC between the theta (4-8 Hz) and gamma rhythms is diminished. Modulating PAC (i.e. reducing or enhancing PAC) using brain stimulation could therefore open new therapeutic avenues. However, while it has been previously reported that phase-locked stimulation can increase PAC, it is unclear what the optimal stimulation strategy to modulate PAC might be. Here, we provide a theoretical framework to narrow down the experimental optimisation of stimulation aimed at modulating PAC, which would otherwise rely on trial and error. Approach. We make analytical predictions using a Stuart-Landau model, and confirm these predictions in a more realistic model of coupled neural populations. Main results. Our framework specifies the critical Fourier coefficients of the stimulation waveform which should be tuned to optimally modulate PAC. Depending on the characteristics of the amplitude response curve of the fast population, these components may include the slow frequency, the fast frequency, combinations of these, as well as their harmonics. We also show that the optimal balance of energy between these Fourier components depends on the relative strength of the endogenous slow and fast rhythms, and that the alignment of fast components with the fast rhythm should change throughout the slow cycle. Furthermore, we identify the conditions requiring to phase-lock stimulation to the fast and/or slow rhythms. Significance. Together, our theoretical framework lays the foundation for guiding the development of innovative and more effective brain stimulation aimed at modulating PAC for therapeutic benefit.

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“…With the development of science and technology, many technologies such as biomarker research ( Singhal et al, 2023 ), neuroimaging technology ( Aberathne et al, 2023 ; Castaldo et al, 2023 ), artificial intelligence and machine learning ( Rahaman et al, 2020 , 2021 ; Chen A. et al, 2022 ; Inoue et al, 2023 ), and remote monitoring and sensor technology ( Lu et al, 2023 ; Rahman et al, 2023 ) have been widely used to explore various diseases and achieve interesting results. Machine learning discussed in this article has been the hottest topic in the field of artificial intelligence in the past 10 years, among which deep learning, classification, neural networks, etc.…”

Section: Current Status and Future Prospectsmentioning

confidence: 99%

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Li,

Zhang,

Chen

et al. 2023

Front. Hum. Neurosci.

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Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.

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Multi-modal and multi-model interrogation of large-scale functional brain networks

Cited by 16 publications

References 185 publications

Excitatory-Inhibitory Homeostasis and Bifurcation Control in the Wilson-Cowan Model of Cortical Dynamics

Excitatory-Inhibitory Homeostasis and Bifurcation Control in the Wilson-Cowan Model of Cortical Dynamics

How to design optimal brain stimulation to modulate phase-amplitude coupling?

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

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