A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity

Griffiths, John; McIntosh, Anthony R.; Lefebvre, Jérémie

doi:10.3389/fncom.2020.575143

Cited by 16 publications

(15 citation statements)

References 104 publications

(170 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our results show that ARAS-mediated inputs can engage and interfere with cortical and subcortical networks to mediate such transitions spontaneously -and supports the hypothesis by which the suppression of α-power reflects enhanced activation of cortical activity. Furthermore, our results suggest that focal activation of task-relevant circuits would support spatially localized oscillatory transitions, such as those observed in experiments (Pfurtscheller and Silva 1999) and computational simulations (Griffith, McIntosh, and Lefebvre 2021).…”

Section: Discussionsupporting

confidence: 68%

Arousal Fluctuations Govern Oscillatory Transitions Between Dominant $$\gamma$$ and $$\alpha$$ Occipital Activity During Eyes Open/Closed Conditions

Hutt

Lefebvre

2021

Brain Topogr

View full text Add to dashboard Cite

Arousal results in widespread activation of brain areas to increase their response in task and behavior relevant ways. Mediated by the Ascending Reticular Arousal System (ARAS), arousal-dependent inputs interact with neural circuitry to shape their dynamics. In the occipital cortex, such inputs may trigger shifts between dominant oscillations, where α activity is replaced by γ activity, or vice versa. A salient example of this are spectral power alternations observed while eyes are opened and/or closed. These transitions closely follow fluctuations in arousal, suggesting a common origin.To better understand the mechanisms at play, we developed and analyzed a computational model composed of two modules: a thalamocortical feedback circuit coupled with a superficial cortical network. Upon activation by noise-like inputs originating from the ARAS, our model is able to demonstrate that noise-driven nonlinear interactions mediate transitions in dominant peak frequency, resulting in the simultaneous suppression of α limit cycle activity and the emergence of γ oscillations through coherence resonance. Reduction in input provoked the reverse effect -leading to anticorrelated transitions between α and γ power. Taken together, these results shed a new light on how arousal shapes oscillatory brain activity.

show abstract

Section: Discussionsupporting

confidence: 68%

Arousal Fluctuations Govern Oscillatory Transitions Between Dominant $$\gamma$$ and $$\alpha$$ Occipital Activity During Eyes Open/Closed Conditions

Hutt

Lefebvre

2021

Brain Topogr

View full text Add to dashboard Cite

show abstract

“…Anatomical connectivity matrices for 50 randomly chosen subjects were computed from HCP dwMRI data using deterministic tractography. Full details on the local tissue model and dwMRI and tractography analysis pipeline are given in a previous article (Griffiths et al, 2020), and the original raw data (Van Essen et al, 2013) is publicly accessible at https://db.humanconnectome.org. The parcellation used for both dwMRI and fMRI analyses was the scale-1 (83 node) ‘Lausanne 2008’ parcellation (Hagmann et al, 2008; Daducci et al, 2012), which consists of 68 cortical and 15 subcortical regions.…”

Section: Methodsmentioning

confidence: 99%

“…Connectome-based neural mass models (CNMMs) have, over the past decade, become one of the principal computational tools used to explore scientific questions in this line of research (e.g. Deco et al, 2013b,a, 2014; Deco and Kringelbach, 2014; Breakspear, 2017; Griffiths et al, 2020). In CNMMs, equations describing the mesoscopic collective neural population behaviour in a large patch of tissue are used to model regional neural activity, such as that measured by average blood oxygenation level-dependent (BOLD) fMRI signal within a brain region or ‘parcel’.…”

Section: Introductionmentioning

confidence: 99%

“…To date, CNMMs have been used with moderate success to simulate, and thereby better understand, several characteristics of functional neuroimaging data, including static resting-state fMRI (rsfMRI) functional connectivity (FC; Deco et al, 2013b), dynamic (moving-window) FC (Hansen et al, 2015; Kehoe et al, 2017), state switching (Kringelbach and Deco, 2020; Griffiths et al, 2020), power spectra (Xie et al, 2018), graph-theoretic properties (Vecchio et al, 2017), metastability (Deco et al, 2017), travelling waves (Muller et al, 2016; Roberts et al, 2019), and EEG-BOLD anticorrelations (Schirner et al, 2018; Pang and Robinson, 2018). These studies have employed a combination of mathematical/numerical systems analysis (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep Learning-Based Parameter Estimation for Neurophysiological Models of Neuroimaging Data

Griffiths

Wang

Ather

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Connectome-based neural mass modelling is the emerging computational neuroscience paradigm for simulating large-scale network dynamics observed in whole-brain activity measurements such as fMRI, M/EEG, and related techniques. Estimating physiological parameters by fitting these models to empirical data is challenging however, due to large network sizes, often physiologically detailed fast-timescale system equations, and the need for long (e.g. tens of minutes) simulation runs. Here we introduce a novel approach to connectome-based neural mass model parameter estimation by employing optimization tools developed for deep learning. We cast the system of differential equations representing both neural and haemodynamic activity dynamics as a deep neural network, implemented within a widely used machine learning programming environment (PyTorch). This allows us to use robust industry-standard optimization algorithms, automatic differentiation for computation of gradients, and other useful functionality. The approach is demonstrated using a connectome-based network with nodal dynamics specified by the two-state RWW mean-field neural mass model equations, which we use here as a model of fMRI-measured activity and correlation fluctuations. Additional optimization constraints are explored and prove fruitful, including restricting the model to domains of parameter space near a bifurcation point that yield metastable dynamics. Using these techniques, we first show robust recovery of physiological model parameters in synthetic data and then, as a proof-of-principle, apply the framework to modelling of empirical resting-state fMRI data from the Human Connectome Project database. For resting state activity, the system can be understood as a deep net that receives uncorrelated noise on its input layer, which is transformed into network-wide modelled functional connectivity on its output layer. This is consistent with the prevailing conception in theoretical neuroscience of resting-state functional connectivity patterns as an emergent phenomenon that is driven by (effectively) random activity fluctuations, which are then in turn spatiotemporally filtered by anatomical connectivity and local neural dynamics.

show abstract

“…In the past, whole-brain models have been employed in a wide range of problems, including demonstrating the ability of whole-brain models to reproduce BOLD correlations from functional magnetic resonance imaging (fMRI) during resting-state [9,10] and sleep [11], explaining features of EEG [12] and MEG [5,6] recordings, studying the role of signal transmission delays between brain areas [13,14], the differential effects of neuromodulators [7,15], modeling electrical stimulation of the brain in-silico [16][17][18][19], or explaining the propagation of brain waves [20] such as in slow-wave sleep [21]. Previous work often focused on finding the parameters of optimal working points of a wholebrain model, given a functional dataset [22].…”

Section: Introductionmentioning

confidence: 99%

neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

2021

View full text Add to dashboard Cite

Abstractneurolib is a computational framework for whole-brain modeling written in Python. It provides a set of neural mass models that represent the average activity of a brain region on a mesoscopic scale. In a whole-brain network model, brain regions are connected with each other based on biologically informed structural connectivity, i.e., the connectome of the brain. neurolib can load structural and functional datasets, set up a whole-brain model, manage its parameters, simulate it, and organize its outputs for later analysis. The activity of each brain region can be converted into a simulated BOLD signal in order to calibrate the model against empirical data from functional magnetic resonance imaging (fMRI). Extensive model analysis is made possible using a parameter exploration module, which allows one to characterize a model’s behavior as a function of changing parameters. An optimization module is provided for fitting models to multimodal empirical data using evolutionary algorithms. neurolib is designed to be extendable and allows for easy implementation of custom neural mass models, offering a versatile platform for computational neuroscientists for prototyping models, managing large numerical experiments, studying the structure–function relationship of brain networks, and for performing in-silico optimization of whole-brain models.

show abstract

A Connectome-Based, Corticothalamic Model of State- and Stimulation-Dependent Modulation of Rhythmic Neural Activity and Connectivity

Cited by 16 publications

References 104 publications

Arousal Fluctuations Govern Oscillatory Transitions Between Dominant $$\gamma$$ and $$\alpha$$ Occipital Activity During Eyes Open/Closed Conditions

Arousal Fluctuations Govern Oscillatory Transitions Between Dominant $$\gamma$$ and $$\alpha$$ Occipital Activity During Eyes Open/Closed Conditions

Deep Learning-Based Parameter Estimation for Neurophysiological Models of Neuroimaging Data

neurolib: A Simulation Framework for Whole-Brain Neural Mass Modeling

Contact Info

Product

Resources

About