How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest

Nakagawa, Tristan T.; Woolrich, Mark W.; Luckhoo, Henry; Joensson, Morten; Mohseni, Hossein; Kringelbach, Morten L.; Jirsa, Viktor K.; Deco, Gustavo

doi:10.1016/j.neuroimage.2013.11.009

Cited by 55 publications

(74 citation statements)

References 81 publications

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“…We assume the long range afferents from the cortical pyramidal population project to both populations of the thalamic nuclei, and the long range afferents of the thalamic relay population project to both populations of the cortex, as depicted in Fig 1. The delays introduced by these long range afferents might play a crucial role in cortical dynamics [58, 59]. However, as the axonal conduction delay between thalamus and cortex is rather small [60–63], we approximate it by a convolution with an alpha function [64], which can be written as where ϕ k is the resulting delayed firing rate and ν depicts the axonal rate constant of that connection.…”

Section: Methodsmentioning

confidence: 99%

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation

et al. 2016

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Few models exist that accurately reproduce the complex rhythms of the thalamocortical system that are apparent in measured scalp EEG and at the same time, are suitable for large-scale simulations of brain activity. Here, we present a neural mass model of the thalamocortical system during natural non-REM sleep, which is able to generate fast sleep spindles (12–15 Hz), slow oscillations (<1 Hz) and K-complexes, as well as their distinct temporal relations, and response to auditory stimuli. We show that with the inclusion of detailed calcium currents, the thalamic neural mass model is able to generate different firing modes, and validate the model with EEG-data from a recent sleep study in humans, where closed-loop auditory stimulation was applied. The model output relates directly to the EEG, which makes it a useful basis to develop new stimulation protocols.

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Section: Methodsmentioning

confidence: 99%

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation

et al. 2016

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“…Although practically any computer model of cortical activity can be tuned, with suitable parameter choice, to oscillate at alpha frequency, e.g. 5,16,20,22,[61][62][63] , none of them are able to parsimoniously recapitulate the posterior origin of alpha. Thus the prominence of posterior alpha might be explained by the hypothesized existence of alpha generators in posterior areas.…”

Section: Macroscopic Brain Rhythms Are Governed By the Connectomementioning

confidence: 99%

“…Brain rhythms generally fall within identical frequency bands and spatial maps 4,16,33 . Based on the hypothesis that the emergent behavior of long-range interactions can be independent of detailed local dynamics of individual neurons [13][14][15][16][17][18] , and may be largely governed by long-range connectivity [19][20][21][22] , we have reported here a minimal linear model of how the brain connectome serves as a spatial-spectral filter that modulates the underlying non-linear signals emanating from local circuits. Nevertheless, we recognize the limitations of a linear model and its inability to capture inherent non-linearities across all levels in the system.…”

Section: Emergence Of Linearity From Chaotic Brain Dynamicsmentioning

confidence: 99%

“…Therefore the traditional computational neuroscience paradigm at the microscopic scale does not easily extend to whole-brain macroscopic phenomena, as large neuronal ensembles exhibit emergent properties that can be unrelated to individual neuronal behavior [13][14][15][16][17][18] , and are instead largely governed by long-range connectivity [19][20][21][22] . At this scale, graph theory involving network statistics can phenomenologically capture structure-function relationships [23][24][25] , but do not explicitly embody any details about neural physiology 14,15 .…”

Section: Introduction the Structure-function Problem In Neurosciencementioning

confidence: 99%

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Spectral graph theory of brain oscillations

Raj

Cai

Xie

et al. 2019

Preprint

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The relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structurefunction problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. The resulting parsimonious and analytical solution stands in contrast to complex numerical simulations of high dimensional coupled non-linear neural field models. This spectral graph model accurately predicts spatial and spectral features of neural oscillatory activity across the brain and was successful in simultaneously reproducing empirically observed spatial and spectral patterns of alpha-band (8-12 Hz) and beta-band (15-30Hz) activity estimated from source localized scalp magnetoencephalography (MEG). This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics. Significance StatementThe relationship between the brain's structural wiring and the functional patterns of neural activity is of fundamental interest in computational neuroscience. We examine a hierarchical, linear graph spectral model of brain activity at mesoscopic and macroscopic scales. The model formulation yields an elegant closed-form solution for the structurefunction problem, specified by the graph spectrum of the structural connectome's Laplacian, with simple, universal rules of dynamics specified by a minimal set of global parameters. This spectral graph model demonstrates that certain brain oscillations are emergent properties of the graph structure of the structural connectome and provides important insights towards understanding the fundamental relationship between network topology and macroscopic whole-brain dynamics.

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“…Recent BNMs have covered whole-brain activity, demonstrating, for example, the importance of conduction delays for explaining distributed oscillations in the ''resting state'' (Nakagawa et al, 2014). Neural field models have also been applied to empirical M/EEG data, elucidating general principles of brain dynamics, such as multistability and scale-invariance (Freyer et al, 2011(Freyer et al, , 2012, and providing important insights into disorders such as epilepsy (Breakspear et al, 2006).…”

Section: Reviewmentioning

confidence: 99%

Translational Perspectives for Computational Neuroimaging

Stephan

Iglesias

Heinzle

et al. 2015

Neuron

158

148

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Functional neuroimaging has made fundamental contributions to our understanding of brain function. It remains challenging, however, to translate these advances into diagnostic tools for psychiatry. Promising new avenues for translation are provided by computational modeling of neuroimaging data. This article reviews contemporary frameworks for computational neuroimaging, with a focus on forward models linking unobservable brain states to measurements. These approaches-biophysical network models, generative models, and model-based fMRI analyses of neuromodulation-strive to move beyond statistical characterizations and toward mechanistic explanations of neuroimaging data. Focusing on schizophrenia as a paradigmatic spectrum disease, we review applications of these models to psychiatric questions, identify methodological challenges, and highlight trends of convergence among computational neuroimaging approaches. We conclude by outlining a translational neuromodeling strategy, highlighting the importance of openly available datasets from prospective patient studies for evaluating the clinical utility of computational models.

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How delays matter in an oscillatory whole-brain spiking-neuron network model for MEG alpha-rhythms at rest

Cited by 55 publications

References 81 publications

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation

A Thalamocortical Neural Mass Model of the EEG during NREM Sleep and Its Response to Auditory Stimulation

Spectral graph theory of brain oscillations

Translational Perspectives for Computational Neuroimaging

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