A macaque connectome for large-scale network simulations in TheVirtualBrain

Shen, Kelly; Bezgin, Gleb; Schirner, Michael; Ritter, Petra; Everling, Stefan; McIntosh, Anthony R.

doi:10.1038/s41597-019-0129-z

Cited by 65 publications

(69 citation statements)

References 66 publications

(95 reference statements)

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“…future work is needed to establish whether a similar pattern holds across other species, such as C. elegans and non-human primates [75], where both structural connectivity and large-scale brain dynamics have been measured.…”

Section: Discussionmentioning

confidence: 99%

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Fallon

Ward

Parkes

et al. 2019

Preprint

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2 FALLON ET AL. motifs. Our findings demonstrate a conserved property of mouse and human brain organization, in which a brain region's spontaneous activity fluctuations are closely related to their surrounding structural scaffold. K E Y W O R D S structure-function relationship, time-series analysis, structural connectivity, resting-state fMRI, interspecies comparison 1 | INTRODUCTION The brain's complex spatiotemporal dynamics unfold on an intricate web of axonal connections: the connectome [1, 2]. These pathways facilitate information transfer between brain regions, manifesting in a complex relationship between connectome structure and neural dynamics. Reflecting the pairwise nature of structural connectivity (regionregion), existing studies have overwhelmingly compared pairwise measurements of anatomical connectivity to pairwise statistical relationships between neural activity time series, or functional connectivity, often using simulations of network dynamics to better understand how the observed relationships may arise [3-18]. Structural connectivity is highly informative of functional connectivity, consistent with the connectome as a physical substrate constraining inter-regional communication dynamics.Our understanding of pairwise structure-function relationships in the brain remains disconnected from our understanding of how a brain area's structural connectivity properties shape its activity dynamics. Indeed, the structural connectivity profile of a region's incoming and outgoing axonal connections is thought to define its function [19]. Furthermore, the activity dynamics of brain areas follow a functional hierarchy, with rapid dynamics in 'lower' sensory regions, and slower fluctuations in 'higher' regions associated with integrative processes [20][21][22][23][24]. The spatial variation of intrinsic timescales has been measured using ECOG [21], MEG [25,26], TMS-EEG [27], and fMRI [24,[28][29][30][31][32][33], and may a function variation in temporal receptive windows: timescales over which new information can be actively integrated with recently received information [20,33,34]. Spatial variation in intrinsic activity fluctuations may form a key basis for the brain's functional hierarchical organization, shaped by structural variation in the brain's microcircuitry [35][36][37]. This organization is thought to be important for behavior and cognition [20,[38][39][40], and its disruption has clinical implications: e.g., differences in intrinsic timescales are associated with symptom severity in autism [33]. While much is known about the structure-function relationship at the level of brain-region pairs, and how structural and functional connectivity architecture shape cognitive function and are affected in disease [41][42][43][44], relatively little is known about how it affects the information processing dynamics of individual brain areas. In particular, we do not yet understand the role structural connections play in the cortical organization of intrinsic timescales.Recent work has provided statistical e...

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Section: Discussionmentioning

confidence: 99%

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Fallon

Ward

Parkes

et al. 2019

Preprint

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“…The simulated data was fitted to determine clinical epilepsy propagation zones. TVB was also used to simulate rat brains (35) and macaque brains (36). With an evolutionary optimization algorithm and the Generic 2D oscillator the simulated FC was tuned to fit its empirical counterpart.…”

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confidence: 99%

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

Triebkorn

Zimmermann

Stefanovski

et al. 2020

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Using The Virtual Brain (TVB, thevirtualbrian.org) simulation platform, we explored for 50 individual adult human brains (ages 18-80), how personalized connectome based brain network modelling captures various empirical observations as measured by functional magnetic resonance imaging (fMRI) and electroencephalography (EEG). We compare simulated activity based on individual structural connectomes (SC) inferred from diffusion weighted imaging with fMRI and EEG in the resting state. We systematically explore the role of the following model parameters:conduction velocity, global coupling and graph theoretical features of individual SC. First, a subspace of the parameter space is identified for each subject that results in realistic brain activity, i.e. reproducing the following prominent features of empirical EEG-fMRI activity: topology of resting-state fMRI functional connectivity (FC), functional connectivity dynamics (FCD), electrophysiological oscillations in the delta (3-4 Hz) and alpha (8-12 Hz) frequency range and their bimodality, i.e. low and high energy modes. Interestingly, FCD fit, bimodality and static FC fit are highly correlated. They all show their optimum in the same range of global coupling. In other words, only when our local model is in a bistable regime we are able to generate switching of modes in our global network. Second, our simulations reveal the explicit network mechanisms that lead to electrophysiological oscillations, their bimodal behaviour and inter-regional differences. Third, we discuss biological interpretability of the Stefanescu-Jirsa-Hindmarsh-Rose-3D model when embedded inside the large-scale brain network and mechanisms underlying the emergence of bimodality of the neural signal.With the present study, we set the cornerstone for a systematic catalogue of spatiotemporal brain activity regimes generated with the connectome-based brain simulation platform The Virtual Brain. Author SummaryIn order to understand brain dynamics we use numerical simulations of brain network models. Combining the structural backbone of the brain, that is the white matter fibres connecting distinct regions in the grey matter, with dynamical systems describing the activity of neural populations we are able to simulate brain function on a large scale. In order to make accurate prediction with this network, it is crucial to determine optimal model parameters. We here use an explorative approach to adjust model parameters to individual brain activity, showing that subjects have their own optimal point in the parameter space, depending on their brain structure and function. At the same time, we investigate the relation between bistable phenomena on the scale of neural populations and the changed in functional connectivity on the brain network scale. Our results are important for future modelling approaches trying to make accurate predictions of brain function.Brain network models allow us to investigate how realistic neural behaviour arises as a function of variations in specific neural parameters. Large-sc...

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“…The next brain link prediction papers appear almost a decade later, which incorporate additional topological and spatial features (Costa et al, 2007;Nepusz et al, 2008), both based on the CoCoMac database (Kötter, 2004), with the latter using a stochastic graph fitting method to handle the uncertainties in the data. Several other publications followed these papers (Hoff, 2009;Cannistraci et al, 2013;Hinne et al, 2017;Røge et al, 2017;Chen et al, 2020;Shen et al, 2019), but all (including the earliest three) are based on preconceived network models whose parameters are fitted to the data, and then used to make predictions (usually at a binary level) on missing links. These network models quantify the belief that the existence or absence of a link is largely determined by some summary network statistics on the existing data.…”

Section: Discussionmentioning

confidence: 99%

Predictability of cortico-cortical connections in the mammalian brain

Molnár

Horvát

Gomes

et al. 2020

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Despite a five-order magnitude range in size, the mammalian brain exhibits many shared anatomical and functional characteristics that should translate into cortical network commonalities. Here we develop a framework employing machine learning to quantify the degree of predictability of the weighted interareal cortical matrix. Data were obtained with retrograde tract-tracing experiments supplemented by projection length measurements. Using this framework with consistent and edge-complete empirical datasets in the macaque and mouse cortex, we show that there is significant amount of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an Area Under the ROC curve of at least 0.8 for the macaque. At the weighted level, strengths of the medium and strong links are predictable with at least 85-90% accuracy in mouse and 70-80% in macaque, whereas weak links are not predictable in either species. These observations suggest that the formation and evolution of the cortical network at the mesoscale is to a large extent, rule-based, motivating further research on the architectural invariants of the cortical connectome.

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A macaque connectome for large-scale network simulations in TheVirtualBrain

Cited by 65 publications

References 66 publications

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Timescales of spontaneous fMRI fluctuations relate to structural connectivity in the brain

Fifty shades of The Virtual Brain: Converging optimal working points yield biologically plausible electrophysiological and imaging features

Predictability of cortico-cortical connections in the mammalian brain

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