Connectome spectral analysis to track EEG task dynamics on a subsecond scale

Glomb, Katharina; Rué-Queralt, Joan; Pascucci, David; Defferrard, Michaël; Tourbier, Sébastien; Carboni, Margherita; Rubega, Maria; Vulliémoz, Serge; Plomp, Gijs; Hagmann, Patric

doi:10.1016/j.neuroimage.2020.117137

Cited by 52 publications

(69 citation statements)

References 76 publications

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“…In the graph domain, it was shown that harmonic modes of the structural connectome can explain functional connectivity, in particular, resting state networks (Atasoy et al, 2016). More generally, harmonic modes of the structural connectome are useful for our understanding of how functional activity is variably shaped by underlying white matter connectivity (Preti and Van De Ville, 2019;Glomb et al, 2020). Moreover, harmonic modes of the structural connectivity have been found to predict disease progression in dementia (Raj et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The link between structural and functional connectivity is made by dynamical models, which suggest that space and time are linked via the oscillatory frequencies of certain brain networks (Atasoy et al, 2018). Further evidence for a link between spatial patterns and oscillations comes from applications of harmonic modes of the structural connectivity to faster timescales (i.e., M/EEG) (Glomb et al, 2020;Tokariev et al, 2019;Raj et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Functional harmonics reveal multi-dimensional basis functions underlying cortical organization

et al. 2021

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Functional harmonics reveal multi-dimensional basis functions underlying cortical organization

et al. 2021

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“…In short, MRI data is employed to obtain local graph edges based on the surface mesh; DTI data is employed to add long-range white-matter connections to the graph. The main difference with previous studies analyzing brain activity in terms of the anatomical connectome graph Laplacian [11] is that instead of constructing the combinatorial (binary) graph Laplacian, here we construct a distance-weighted graph Laplacian (Eqs (19)(20)(21)(22)). This allows us to take into account physical distance properties of the cortex that are relevant for graph neural fields, and that are otherwise lost.…”

Section: Author Summarymentioning

confidence: 99%

“…Furthermore in [11], for the first time to the best of our knowledge, a model of neural activity making use of the graph Laplacian was implemented, and used to suggest the role of Excitatory-Inhibitory dynamics as possible underlying mechanism for the self-organization of resting-state activity patterns. In other very recent work [19,20] techniques based on the graph Laplacian were employed to model EEG and MEG oscillations. Considering these developments, the combination of neural activity modelling and graph signal processing techniques appears as a promising direction for further inquiry.…”

Section: Introductionmentioning

confidence: 99%

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome

et al. 2021

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Tools from the field of graph signal processing, in particular the graph Laplacian operator, have recently been successfully applied to the investigation of structure-function relationships in the human brain. The eigenvectors of the human connectome graph Laplacian, dubbed “connectome harmonics”, have been shown to relate to the functionally relevant resting-state networks. Whole-brain modelling of brain activity combines structural connectivity with local dynamical models to provide insight into the large-scale functional organization of the human brain. In this study, we employ the graph Laplacian and its properties to define and implement a large class of neural activity models directly on the human connectome. These models, consisting of systems of stochastic integrodifferential equations on graphs, are dubbed graph neural fields, in analogy with the well-established continuous neural fields. We obtain analytic predictions for harmonic and temporal power spectra, as well as functional connectivity and coherence matrices, of graph neural fields, with a technique dubbed CHAOSS (shorthand for Connectome-Harmonic Analysis Of Spatiotemporal Spectra). Combining graph neural fields with appropriate observation models allows for estimating model parameters from experimental data as obtained from electroencephalography (EEG), magnetoencephalography (MEG), or functional magnetic resonance imaging (fMRI). As an example application, we study a stochastic Wilson-Cowan graph neural field model on a high-resolution connectome graph constructed from diffusion tensor imaging (DTI) and structural MRI data. We show that the model equilibrium fluctuations can reproduce the empirically observed harmonic power spectrum of resting-state fMRI data, and predict its functional connectivity, with a high level of detail. Graph neural fields natively allow the inclusion of important features of cortical anatomy and fast computations of observable quantities for comparison with multimodal empirical data. They thus appear particularly suitable for modelling whole-brain activity at mesoscopic scales, and opening new potential avenues for connectome-graph-based investigations of structure-function relationships.

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“…Abdelnour et al report that only the first few Laplacian eigenmodes (with smallest eigenvalues) are required to resemble empirical functional connectivity of resting state BOLD fMRI [5,41]; similar results were reported by Atasoy and others [42][43][44]. Other studies involve a series expansion of the graph adjacency or Laplacian matrices [45][46][47][48][49], which also amount to weighting the first natural eigenvectors more heavily in the summation. This discrepancy might reflect the different context here: MEG rather than fMRI, and modeling of spectral power instead of functional connectivity.…”

Section: No Natural Ordering Of Eigenmodesmentioning

confidence: 66%

Spectral graph theory of brain oscillations – revisited and improved

Verma

Nagarajan

Raj

2021

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Mathematical modeling of the relationship between the functional activity and the structural wiring of the brain has largely been undertaken using non-linear and biophysically detailed mathematical models with regionally varying parameters. While this approach provides us a rich repertoire of multistable dynamics that can be displayed by the brain, it is computationally demanding. Moreover, although neuronal dynamics at the microscopic level are nonlinear and chaotic, it is unclear if such detailed nonlinear models are required to capture the emergent meso- (regional population ensemble) and macro-scale (whole brain) behavior, which is largely deterministic and reproducible across individuals. Indeed, recent modeling effort based on spectral graph theory has shown that an analytical model without regionally varying parameters can capture the empirical magnetoencephalography frequency spectra and the spatial patterns of the alpha and beta frequency bands accurately. In this work, we demonstrate an improved hierarchical, linearized, and analytic spectral graph theory-based model that can capture the frequency spectra obtained from magnetoencephalography recordings of resting healthy subjects. We reformulated the spectral graph theory model in line with classical neural mass models, therefore providing more biologically interpretable parameters, especially at the local scale. We demonstrated that this model performs better than the original model when comparing the spectral correlation of modeled frequency spectra and that obtained from the magnetoencephalography recordings. This model also performs equally well in predicting the spatial patterns of the empirical alpha and beta frequency bands.

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Connectome spectral analysis to track EEG task dynamics on a subsecond scale

Cited by 52 publications

References 76 publications

Functional harmonics reveal multi-dimensional basis functions underlying cortical organization

Functional harmonics reveal multi-dimensional basis functions underlying cortical organization

Graph neural fields: A framework for spatiotemporal dynamical models on the human connectome

Spectral graph theory of brain oscillations – revisited and improved

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