Using the Virtual Brain to Reveal the Role of Oscillations and Plasticity in Shaping Brain's Dynamical Landscape

Structural-and-dynamical similarity predicts compensatory brain areas driving the post-lesion functional recovery mechanism

The focal lesion alters the excitation-inhibition (E-I) balance and healthy functional connectivity patterns, which may recover over time. One possible mechanism for the brain to counter the insult is global reshaping functional connectivity alterations. However, the operational principles by which this can be achieved remain unknown. We propose a novel equivalence principle based on structural and dynamic similarity analysis to predict whether specific compensatory areas initiate lost E-I regulation after lesion. We hypothesize that similar structural areas (SSAs) and dynamically similar areas (DSAs) corresponding to a lesioned site are the crucial dynamical units to restore lost homeostatic balance within the surviving cortical brain regions. SSAs and DSAs are independent measures, one based on structural similarity properties measured by Jaccard Index and the other based on post-lesion recovery time. We unravel the relationship between SSA and DSA by simulating a whole brain mean field model deployed on top of a virtually lesioned structural connectome from human neuroimaging data to characterize global brain dynamics and functional connectivity at the level of individual subjects. Our results suggest that wiring proximity and similarity are the 2 major guiding principles of compensation-related utilization of hemisphere in the post-lesion functional connectivity re-organization process.

Global dynamics of neural mass models

Neural mass models are used to simulate cortical dynamics and to explain the electrical and magnetic fields measured using electro- and magnetoencephalography. Simulations evince a complex phase-space structure for these kinds of models; including stationary points and limit cycles and the possibility for bifurcations and transitions among different modes of activity. This complexity allows neural mass models to describe the itinerant features of brain dynamics. However, expressive, nonlinear neural mass models are often difficult to fit to empirical data without additional simplifying assumptions: e.g., that the system can be modelled as linear perturbations around a fixed point. In this study we offer a mathematical analysis of neural mass models, specifically the canonical microcircuit model, providing analytical solutions describing slow changes in the type of cortical activity, i.e. dynamical itinerancy. We derive a perturbation analysis up to second order of the phase flow, together with adiabatic approximations. This allows us to describe amplitude modulations in a relatively simple mathematical format providing analytic proof-of-principle for the existence of semi-stable states of cortical dynamics at the scale of a cortical column. This work allows for model inversion of neural mass models, not only around fixed points, but over regions of phase space that encompass transitions among semi or multi-stable states of oscillatory activity. Crucially, these theoretical results speak to model inversion in the context of multiple semi-stable brain states, such as the transition between interictal, pre-ictal and ictal activity in epilepsy.

Whole-Brain Network Models: From Physics to Bedside

Computational neuroscience has come a long way from its humble origins in the pioneering work of Hodgkin and Huxley. Contemporary computational models of the brain span multiple spatiotemporal scales, from single neuronal compartments to models of social cognition. Each spatial scale comes with its own unique set of promises and challenges. Here, we review models of large-scale neural communication facilitated by white matter tracts, also known as whole-brain models (WBMs). Whole-brain approaches employ inputs from neuroimaging data and insights from graph theory and non-linear systems theory to model brain-wide dynamics. Over the years, WBM models have shown promise in providing predictive insights into various facets of neuropathologies such as Alzheimer's disease, Schizophrenia, Epilepsy, Traumatic brain injury, while also offering mechanistic insights into large-scale cortical communication. First, we briefly trace the history of WBMs, leading up to the state-of-the-art. We discuss various methodological considerations for implementing a whole-brain modeling pipeline, such as choice of node dynamics, model fitting and appropriate parcellations. We then demonstrate the applicability of WBMs toward understanding various neuropathologies. We conclude by discussing ways of augmenting the biological and clinical validity of whole-brain models.

Personalized Connectome-Based Modeling in Patients with Semi-Acute Phase TBI: Relationship to Acute Neuroimaging and 6 Month Follow-Up

Following traumatic brain injury (TBI), cognitive impairments manifest through interactions between microscopic and macroscopic changes. On the microscale, a neurometabolic cascade alters neurotransmission, while on the macroscale diffuse axonal injury impacts the integrity of long-range connections. Large-scale brain network modeling allows us to make predictions across these spatial scales by integrating neuroimaging data with biophysically based models to investigate how microscale changes invisible to conventional neuroimaging influence large-scale brain dynamics. To this end, we analyzed structural and functional neuroimaging data from a well characterized sample of 44 adult TBI patients recruited from a regional trauma center, scanned at 1–2 weeks postinjury, and with follow-up behavioral outcome assessed 6 months later. Thirty-six age-matched healthy adults served as comparison participants. Using The Virtual Brain, we fit simulations of whole-brain resting-state functional MRI to the empirical static and dynamic functional connectivity of each participant. Multivariate partial least squares (PLS) analysis showed that patients with acute traumatic intracranial lesions had lower cortical regional inhibitory connection strengths than comparison participants, while patients without acute lesions did not differ from the comparison group. Further multivariate PLS analyses found correlations between lower semiacute regional inhibitory connection strengths and more symptoms and lower cognitive performance at a 6 month follow-up. Critically, patients without acute lesions drove this relationship, suggesting clinical relevance of regional inhibitory connection strengths even when traumatic intracranial lesions were not present. Our results suggest that large-scale connectome-based models may be sensitive to pathophysiological changes in semi-acute phase TBI patients and predictive of their chronic outcomes.

Bridging Scales in Alzheimer's Disease: Biological Framework for Brain Simulation With The Virtual Brain

Despite the acceleration of knowledge and data accumulation in neuroscience over the last years, the highly prevalent neurodegenerative disease of AD remains a growing problem. Alzheimer's Disease (AD) is the most common cause of dementia and represents the most prevalent neurodegenerative disease. For AD, disease-modifying treatments are presently lacking, and the understanding of disease mechanisms continues to be incomplete. In the present review, we discuss candidate contributing factors leading to AD, and evaluate novel computational brain simulation methods to further disentangle their potential roles. We first present an overview of existing computational models for AD that aim to provide a mechanistic understanding of the disease. Next, we outline the potential to link molecular aspects of neurodegeneration in AD with large-scale brain network modeling using The Virtual Brain (www.thevirtualbrain.org), an open-source, multiscale, whole-brain simulation neuroinformatics platform. Finally, we discuss how this methodological approach may contribute to the understanding, improved diagnostics, and treatment optimization of AD.

Parcels and particles: Markov blankets in the brain

At the inception of human brain mapping, two principles of functional anatomy underwrote most conceptions-and analyses-of distributed brain responses: namely, functional segregation and integration. There are currently two main approaches to characterizing functional integration. The first is a mechanistic modeling of connectomics in terms of directed effective connectivity that mediates neuronal message passing and dynamics on neuronal circuits. The second phenomenological approach usually characterizes undirected functional connectivity (i.e., measurable correlations), in terms of intrinsic brain networks, self-organized criticality, dynamical instability, and so on. This paper describes a treatment of effective connectivity that speaks to the emergence of intrinsic brain networks and critical dynamics. It is predicated on the notion of Markov blankets that play a fundamental role in the self-organization of far from equilibrium systems. Using the apparatus of the renormalization group, we show that much of the phenomenology found in network neuroscience is an emergent property of a particular partition of neuronal states, over progressively coarser scales. As such, it offers a way of linking dynamics on directed graphs to the phenomenology of intrinsic brain networks.

Biophysical mechanisms governing large-scale brain network dynamics underlying individual-specific variability of perception

Perception necessitates interaction among neuronal ensembles, the dynamics of which can be conceptualized as the emergent behavior of coupled dynamical systems. Here, we propose a detailed neurobiologically realistic model that captures the neural mechanisms of inter-individual variability observed in cross-modal speech perception. From raw EEG signals recorded from human participants when they were presented with speech vocalizations of McGurk-incongruent and congruent audio-visual (AV) stimuli, we computed the global coherence metric to capture the neural variability of large-scale networks. We identified that participants' McGurk susceptibility was negatively correlated to their alpha band global coherence. The proposed biophysical model conceptualized the global coherence dynamics emerge from coupling between the interacting neural masses-representing the sensory-specific auditory/visual areas and modality nonspecific associative/integrative regions. Subsequently, we could predict that an extremely weak direct AV coupling results in a decrease in alpha band global coherence-mimicking the cortical dynamics of participants with higher McGurk susceptibility. Source connectivity analysis also showed decreased connectivity between sensory-specific regions in participants more susceptible to McGurk effect, thus establishing an empirical validation to the prediction. Overall, our study provides an outline to link variability in structural and functional connectivity metrics to variability of performance that can be useful for several perception and action task paradigms.

Long-Lasting Response Changes in Deep Cerebellar Nuclei in vivo Correlate With Low-Frequency Oscillations

The deep cerebellar nuclei (DCN) have been suggested to play a critical role in sensorimotor learning and some forms of long-term synaptic plasticity observed in vitro have been proposed as a possible substrate. However, till now it was not clear whether and how DCN neuron responses manifest long-lasting changes in vivo. Here, we have characterized DCN unit responses to tactile stimulation of the facial area in anesthetized mice and evaluated the changes induced by theta-sensory stimulation (TSS), a 4 Hz stimulation pattern that is known to induce plasticity in the cerebellar cortex in vivo. DCN units responded to tactile stimulation generating bursts and pauses, which reflected combinations of excitatory inputs most likely relayed by mossy fiber collaterals, inhibitory inputs relayed by Purkinje cells, and intrinsic rebound firing. Interestingly, initial bursts and pauses were often followed by stimulus-induced oscillations in the peri-stimulus time histograms (PSTH). TSS induced long-lasting changes in DCN unit responses. Spike-related potentiation and suppression (SR-P and SR-S), either in units initiating the response with bursts or pauses, were correlated with stimulus-induced oscillations. Fitting with resonant functions suggested the existence of peaks in the theta-band (burst SR-P at 9 Hz, pause SR-S at 5 Hz). Optogenetic stimulation of the cerebellar cortex altered stimulus-induced oscillations suggesting that Purkinje cells play a critical role in the circuits controlling DCN oscillations and plasticity. This observation complements those reported before on the granular and molecular layers supporting the generation of multiple distributed plasticities in the cerebellum following naturally patterned sensory entrainment. The unique dependency of DCN plasticity on circuit oscillations discloses a potential relationship between cerebellar learning and activity patterns generated in the cerebellar network.

Quantifying Differences Between Passive and Task-Evoked Intrinsic Functional Connectivity in a Large-Scale Brain Simulation

Establishing a connection between intrinsic and task-evoked brain activities is critical because it would provide a way to map task-related brain regions in patients unable to comply with such tasks. A crucial question within this realm is to what extent the execution of a cognitive task affects the intrinsic activity of brain regions not involved in the task. Computational models can be useful to answer this question because they allow us to distinguish task from nontask neural elements while giving us the effects of task execution on nontask regions of interest at the neuroimaging level. The quantification of those effects in a computational model would represent a step toward elucidating the intrinsic versus task-evoked connection. In this study we used computational modeling and graph theoretical metrics to quantify changes in intrinsic functional brain connectivity due to task execution. We used our large-scale neural modeling framework to embed a computational model of visual short-term memory into an empirically derived connectome. We simulated a neuroimaging study consisting of 10 subjects performing passive fixation (PF), passive viewing (PV), and delayed match-to-sample (DMS) tasks. We used the simulated blood oxygen level-dependent functional magnetic resonance imaging time series to calculate functional connectivity (FC) matrices and used those matrices to compute several graph theoretical measures. After determining that the simulated graph theoretical measures were largely consistent with experiments, we were able to quantify the differences between the graph metrics of the PF condition and those of the PV and DMS conditions. Thus, we show that we can use graph theoretical methods applied to simulated brain networks to aid in the quantification of changes in intrinsic brain FC during task execution. Our results represent a step toward establishing a connection between intrinsic and task-related brain activities.

Subject specificity of the correlation between large-scale structural and functional connectivity

Structural connectivity (SC), the physical pathways connecting regions in the brain, and functional connectivity (FC), the temporal coactivations, are known to be tightly linked. However, the nature of this relationship is still not understood. In the present study, we examined this relation more closely in six separate human neuroimaging datasets with different acquisition and preprocessing methods. We show that using simple linear associations, the relation between an individual's SC and FC is not subject specific for five of the datasets. Subject specificity of SC-FC fit is achieved only for one of the six datasets, the multimodal Glasser Human Connectome Project (HCP) parcellated dataset. We show that subject specificity of SC-FC correspondence is limited across datasets due to relatively small variability between subjects in SC compared with the larger variability in FC.

On the nature and use of models in network neuroscience

Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.

Toward Rigorous Parameterization of Underconstrained Neural Network Models Through Interactive Visualization and Steering of Connectivity Generation

Simulation models in many scientific fields can have non-unique solutions or unique solutions which can be difficult to find. Moreover, in evolving systems, unique final state solutions can be reached by multiple different trajectories. Neuroscience is no exception. Often, neural network models are subject to parameter fitting to obtain desirable output comparable to experimental data. Parameter fitting without sufficient constraints and a systematic exploration of the possible solution space can lead to conclusions valid only around local minima or around non-minima. To address this issue, we have developed an interactive tool for visualizing and steering parameters in neural network simulation models. In this work, we focus particularly on connectivity generation, since finding suitable connectivity configurations for neural network models constitutes a complex parameter search scenario. The development of the tool has been guided by several use cases-the tool allows researchers to steer the parameters of the connectivity generation during the simulation, thus quickly growing networks composed of multiple populations with a targeted mean activity. The flexibility of the software allows scientists to explore other connectivity and neuron variables apart from the ones presented as use cases. With this tool, we enable an interactive exploration of parameter spaces and a better understanding of neural network models and grapple with the crucial problem of non-unique network solutions and trajectories. In addition, we observe a reduction in turn around times for the assessment of these models, due to interactive visualization while the simulation is computed.

Differentiation of Alzheimer's disease based on local and global parameters in personalized Virtual Brain models

Alzheimer's disease (AD) is marked by cognitive dysfunction emerging from neuropathological processes impacting brain function. AD affects brain dynamics at the local level, such as changes in the balance of inhibitory and excitatory neuronal populations, as well as long-range changes to the global network. Individual differences in these changes as they relate to behaviour are poorly understood. Here, we use a multi-scale neurophysiological model, “The Virtual Brain (TVB)”, based on empirical multi-modal neuroimaging data, to study how local and global dynamics correlate with individual differences in cognition. In particular, we modeled individual resting-state functional activity of 124 individuals across the behavioural spectrum from healthy aging, to amnesic Mild Cognitive Impairment (MCI), to AD. The model parameters required to accurately simulate empirical functional brain imaging data correlated significantly with cognition, and exceeded the predictive capacity of empirical connectomes.

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Modeled local and global dynamics correlate with individual cognition in Alzheimer's.

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Proof of concept of The Virtual Brain to characterize individual dynamics

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Brain-behaviour relations depend on the network modeled (whole brain or limbic).

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Model parameters predict cognition better than metrics of neuroimaging data.

The graphical brain: Belief propagation and active inference

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium.

AUTHOR SUMMARY

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference-and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain.

The energy landscape underpinning module dynamics in the human brain connectome

Human brain dynamics can be viewed through the lens of statistical mechanics, where neurophysiological activity evolves around and between local attractors representing mental states. Many physically-inspired models of these dynamics define brain states based on instantaneous measurements of regional activity. Yet, recent work in network neuroscience has provided evidence that the brain might also be well-characterized by time-varying states composed of locally coherent activity or functional modules. We study this network-based notion of brain state to understand how functional modules dynamically interact with one another to perform cognitive functions. We estimate the functional relationships between regions of interest (ROIs) by fitting a pair-wise maximum entropy model to each ROI's pattern of allegiance to functional modules. This process uses an information theoretic notion of energy (as opposed to a metabolic one) to produce an energy landscape in which local minima represent attractor states characterized by specific patterns of modular structure. The clustering of local minima highlights three classes of ROIs with similar patterns of allegiance to community states. Visual, attention, sensorimotor, and subcortical ROIs are well-characterized by a single functional community. The remaining ROIs affiliate with a putative executive control community or a putative default mode and salience community. We simulate the brain's dynamic transitions between these community states using a random walk process. We observe that simulated transition probabilities between basins are statistically consistent with empirically observed transitions in resting state fMRI data. These results offer a view of the brain as a dynamical system that transitions between basins of attraction characterized by coherent activity in groups of brain regions, and that the strength of these attractors depends on the ongoing cognitive computations.

Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains"

Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging. Both modalities are combined by means of available interspecies registration tools and a newly developed Bayesian probabilistic modeling approach to extract common connectivity evidence. We demonstrate how this novel framework is embedded in the whole-brain simulation platform called The Virtual Brain (TVB). Hum Brain Mapp 38:2080-2093, 2017. © 2017 Wiley Periodicals, Inc.

A new neuroinformatics approach to personalized medicine in neurology: The Virtual Brain

PURPOSE OF REVIEW

An exciting advance in the field of neuroimaging is the acquisition and processing of very large data sets (so called 'big data'), permitting large-scale inferences that foster a greater understanding of brain function in health and disease. Yet what we are clearly lacking are quantitative integrative tools to translate this understanding to the individual level to lay the basis for personalized medicine.

RECENT FINDINGS

Here we address this challenge through a review on how the relatively new field of neuroinformatics modeling has the capacity to track brain network function at different levels of inquiry, from microscopic to macroscopic and from the localized to the distributed. In this context, we introduce a new and unique multiscale approach, The Virtual Brain (TVB), that effectively models individualized brain activity, linking large-scale (macroscopic) brain dynamics with biophysical parameters at the microscopic level. We also show how TVB modeling provides unique biological interpretable data in epilepsy and stroke.

SUMMARY

These results establish the basis for a deliberate integration of computational biology and neuroscience into clinical approaches for elucidating cellular mechanisms of disease. In the future, this can provide the means to create a collection of disease-specific models that can be applied on the individual level to personalize therapeutic interventions. VIDEO ABSTRACT.

Analytical Operations Relate Structural and Functional Connectivity in the Brain

Resting-state large-scale brain models vary in the amount of biological elements they incorporate and in the way they are being tested. One might expect that the more realistic the model is, the closer it should reproduce real functional data. It has been shown, instead, that when linear correlation across long BOLD fMRI time-series is used as a measure for functional connectivity (FC) to compare simulated and real data, a simple model performs just as well, or even better, than more sophisticated ones. The model in question is a simple linear model, which considers the physiological noise that is pervasively present in our brain while it diffuses across the white-matter connections, that is structural connectivity (SC). We deeply investigate this linear model, providing an analytical solution to straightforwardly compute FC from SC without the need of computationally costly simulations of time-series. We provide a few examples how this analytical solution could be used to perform a fast and detailed parameter exploration or to investigate resting-state non-stationarities. Most importantly, by inverting the analytical solution, we propose a method to retrieve information on the anatomical structure directly from functional data. This simple method can be used to complement or guide DTI/DSI and tractography results, especially for a better assessment of inter-hemispheric connections, or to provide an estimate of SC when only functional data are available.

Near-Infrared Spectroscopy - Electroencephalography-Based Brain-State-Dependent Electrotherapy: A Computational Approach Based on Excitation-Inhibition Balance Hypothesis

Stroke is the leading cause of severe chronic disability and the second cause of death worldwide with 15 million new cases and 50 million stroke survivors. The poststroke chronic disability may be ameliorated with early neuro rehabilitation where non-invasive brain stimulation (NIBS) techniques can be used as an adjuvant treatment to hasten the effects. However, the heterogeneity in the lesioned brain will require individualized NIBS intervention where innovative neuroimaging technologies of portable electroencephalography (EEG) and functional-near-infrared spectroscopy (fNIRS) can be leveraged for Brain State Dependent Electrotherapy (BSDE). In this hypothesis and theory article, we propose a computational approach based on excitation–inhibition (E–I) balance hypothesis to objectively quantify the poststroke individual brain state using online fNIRS–EEG joint imaging. One of the key events that occurs following Stroke is the imbalance in local E–I (that is the ratio of Glutamate/GABA), which may be targeted with NIBS using a computational pipeline that includes individual “forward models” to predict current flow patterns through the lesioned brain or brain target region. The current flow will polarize the neurons, which can be captured with E–I-based brain models. Furthermore, E–I balance hypothesis can be used to find the consequences of cellular polarization on neuronal information processing, which can then be implicated in changes in function. We first review the evidence that shows how this local imbalance between E–I leading to functional dysfunction can be restored in targeted sites with NIBS (motor cortex and somatosensory cortex) resulting in large-scale plastic reorganization over the cortex, and probably facilitating recovery of functions. Second, we show evidence how BSDE based on E–I balance hypothesis may target a specific brain site or network as an adjuvant treatment. Hence, computational neural mass model-based integration of neurostimulation with online neuroimaging systems may provide less ambiguous, robust optimization of NIBS, and its application in neurological conditions and disorders across individual patients.

Promises and pitfalls of relating alteration of white matter pathways causing improvement in cognitive performance

Here, we argue systematically about the promises and pitfalls of relating Human Connectome to cognitive enhancement. We also highlight three key areas where further resolution is required before the generalization of the white-matter-related cause of cognitive enhancement across a variety of cognitive modalities is made. These key areas are: (a) inherent limitations in estimating of diffusion-weighted anisotropy index near volumes with high abundance of crossing fibers; (b) species differences in cell types and only a putative link between brain rhythms and modulation of activity in precursor cells in rodents;

Does the regulation of local excitation-inhibition balance aid in recovery of functional connectivity? A computational account

Computational modeling of the spontaneous dynamics over the whole brain provides critical insight into the spatiotemporal organization of brain dynamics at multiple resolutions and their alteration to changes in brain structure (e.g. in diseased states, aging, across individuals). Recent experimental evidence further suggests that the adverse effect of lesions is visible on spontaneous dynamics characterized by changes in resting state functional connectivity and its graph theoretical properties (e.g. modularity). These changes originate from altered neural dynamics in individual brain areas that are otherwise poised towards a homeostatic equilibrium to maintain a stable excitatory and inhibitory activity. In this work, we employ a homeostatic inhibitory mechanism, balancing excitation and inhibition in the local brain areas of the entire cortex under neurological impairments like lesions to understand global functional recovery (across brain networks and individuals). Previous computational and empirical studies have demonstrated that the resting state functional connectivity varies primarily due to the location and specific topological characteristics of the lesion. We show that local homeostatic balance provides a functional recovery by re-establishing excitation-inhibition balance in all areas that are affected by lesion. We systematically compare the extent of recovery in the primary hub areas (e.g. default mode network (DMN), medial temporal lobe, medial prefrontal cortex) as well as other sensory areas like primary motor area, supplementary motor area, fronto-parietal and temporo-parietal networks. Our findings suggest that stability and richness similar to the normal brain dynamics at rest are achievable by re-establishment of balance.

Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics

Brain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and electroencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed inter-regional differences with very good interhemispheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of "functional cytoarchitectonics", that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts.

Functional Mechanisms of Recovery after Chronic Stroke: Modeling with the Virtual Brain

We have seen important strides in our understanding of mechanisms underlying stroke recovery, yet effective translational links between basic and applied sciences, as well as from big data to individualized therapies, are needed to truly develop a cure for stroke. We present such an approach using The Virtual Brain (TVB), a neuroinformatics platform that uses empirical neuroimaging data to create dynamic models of an individual’s human brain; specifically, we simulate fMRI signals by modeling parameters associated with brain dynamics after stroke.

In 20 individuals with stroke and 11 controls, we obtained rest fMRI, T1w, and diffusion tensor imaging (DTI) data. Motor performance was assessed pre-therapy, post-therapy, and 6–12 months post-therapy. Based on individual structural connectomes derived from DTI, the following steps were performed in the TVB platform: (1) optimization of local and global parameters (conduction velocity, global coupling); (2) simulation of BOLD signal using optimized parameter values; (3) validation of simulated time series by comparing frequency, amplitude, and phase of the simulated signal with empirical time series; and (4) multivariate linear regression of model parameters with clinical phenotype. Compared with controls, individuals with stroke demonstrated a consistent reduction in conduction velocity, increased local dynamics, and reduced local inhibitory coupling. A negative relationship between local excitation and motor recovery, and a positive correlation between local dynamics and motor recovery were seen.

TVB reveals a disrupted post-stroke system favoring excitation-over-inhibition and local-over-global dynamics, consistent with existing mammal literature on stroke mechanisms. Our results point to the potential of TVB to determine individualized biomarkers of stroke recovery.

TVB-EduPack-An Interactive Learning and Scripting Platform for The Virtual Brain

The Virtual Brain (TVB; thevirtualbrain.org) is a neuroinformatics platform for full brain network simulation based on individual anatomical connectivity data. The framework addresses clinical and neuroscientific questions by simulating multi-scale neural dynamics that range from local population activity to large-scale brain function and related macroscopic signals like electroencephalography and functional magnetic resonance imaging. TVB is equipped with a graphical and a command-line interface to create models that capture the characteristic biological variability to predict the brain activity of individual subjects. To enable researchers from various backgrounds a quick start into TVB and brain network modeling in general, we developed an educational module: TVB-EduPack. EduPack offers two educational functionalities that seamlessly integrate into TVB's graphical user interface (GUI): (i) interactive tutorials introduce GUI elements, guide through the basic mechanics of software usage and develop complex use-case scenarios; animations, videos and textual descriptions transport essential principles of computational neuroscience and brain modeling; (ii) an automatic script generator records model parameters and produces input files for TVB's Python programming interface; thereby, simulation configurations can be exported as scripts that allow flexible customization of the modeling process and self-defined batch- and post-processing applications while benefitting from the full power of the Python language and its toolboxes. This article covers the implementation of TVB-EduPack and its integration into TVB architecture. Like TVB, EduPack is an open source community project that lives from the participation and contribution of its users. TVB-EduPack can be obtained as part of TVB from thevirtualbrain.org.

Relating Alpha Power and Phase to Population Firing and Hemodynamic Activity Using a Thalamo-cortical Neural Mass Model

Oscillations are ubiquitous phenomena in the animal and human brain. Among them, the alpha rhythm in human EEG is one of the most prominent examples. However, its precise mechanisms of generation are still poorly understood. It was mainly this lack of knowledge that motivated a number of simultaneous electroencephalography (EEG) - functional magnetic resonance imaging (fMRI) studies. This approach revealed how oscillatory neuronal signatures such as the alpha rhythm are paralleled by changes of the blood oxygenation level dependent (BOLD) signal. Several such studies revealed a negative correlation between the alpha rhythm and the hemodynamic BOLD signal in visual cortex and a positive correlation in the thalamus. In this study we explore the potential generative mechanisms that lead to those observations. We use a bursting capable Stefanescu-Jirsa 3D (SJ3D) neural-mass model that reproduces a wide repertoire of prominent features of local neuronal-population dynamics. We construct a thalamo-cortical network of coupled SJ3D nodes considering excitatory and inhibitory directed connections. The model suggests that an inverse correlation between cortical multi-unit activity, i.e. the firing of neuronal populations, and narrow band local field potential oscillations in the alpha band underlies the empirically observed negative correlation between alpha-rhythm power and fMRI signal in visual cortex. Furthermore the model suggests that the interplay between tonic and bursting mode in thalamus and cortex is critical for this relation. This demonstrates how biophysically meaningful modelling can generate precise and testable hypotheses about the underpinnings of large-scale neuroimaging signals.

An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data

Large amounts of multimodal neuroimaging data are acquired every year worldwide. In order to extract high-dimensional information for computational neuroscience applications standardized data fusion and efficient reduction into integrative data structures are required. Such self-consistent multimodal data sets can be used for computational brain modeling to constrain models with individual measurable features of the brain, such as done with The Virtual Brain (TVB). TVB is a simulation platform that uses empirical structural and functional data to build full brain models of individual humans. For convenient model construction, we developed a processing pipeline for structural, functional and diffusion-weighted magnetic resonance imaging (MRI) and optionally electroencephalography (EEG) data. The pipeline combines several state-of-the-art neuroinformatics tools to generate subject-specific cortical and subcortical parcellations, surface-tessellations, structural and functional connectomes, lead field matrices, electrical source activity estimates and region-wise aggregated blood oxygen level dependent (BOLD) functional MRI (fMRI) time-series. The output files of the pipeline can be directly uploaded to TVB to create and simulate individualized large-scale network models that incorporate intra- and intercortical interaction on the basis of cortical surface triangulations and white matter tractograpy. We detail the pitfalls of the individual processing streams and discuss ways of validation. With the pipeline we also introduce novel ways of estimating the transmission strengths of fiber tracts in whole-brain structural connectivity (SC) networks and compare the outcomes of different tractography or parcellation approaches. We tested the functionality of the pipeline on 50 multimodal data sets. In order to quantify the robustness of the connectome extraction part of the pipeline we computed several metrics that quantify its rescan reliability and compared them to other tractography approaches. Together with the pipeline we present several principles to guide future efforts to standardize brain model construction. The code of the pipeline and the fully processed data sets are made available to the public via The Virtual Brain website (thevirtualbrain.org) and via github (https://github.com/BrainModes/TVB-empirical-data-pipeline). Furthermore, the pipeline can be directly used with High Performance Computing (HPC) resources on the Neuroscience Gateway Portal (http://www.nsgportal.org) through a convenient web-interface.

State-dependencies of learning across brain scales

Learning is a complex brain function operating on different time scales, from milliseconds to years, which induces enduring changes in brain dynamics. The brain also undergoes continuous “spontaneous” shifts in states, which, amongst others, are characterized by rhythmic activity of various frequencies. Besides the most obvious distinct modes of waking and sleep, wake-associated brain states comprise modulations of vigilance and attention. Recent findings show that certain brain states, particularly during sleep, are essential for learning and memory consolidation. Oscillatory activity plays a crucial role on several spatial scales, for example in plasticity at a synaptic level or in communication across brain areas. However, the underlying mechanisms and computational rules linking brain states and rhythms to learning, though relevant for our understanding of brain function and therapeutic approaches in brain disease, have not yet been elucidated. Here we review known mechanisms of how brain states mediate and modulate learning by their characteristic rhythmic signatures. To understand the critical interplay between brain states, brain rhythms, and learning processes, a wide range of experimental and theoretical work in animal models and human subjects from the single synapse to the large-scale cortical level needs to be integrated. By discussing results from experiments and theoretical approaches, we illuminate new avenues for utilizing neuronal learning mechanisms in developing tools and therapies, e.g., for stroke patients and to devise memory enhancement strategies for the elderly.

Dynamic changes in network synchrony reveal resting-state functional networks

Experimental functional magnetic resonance imaging studies have shown that spontaneous brain activity, i.e., in the absence of any external input, exhibit complex spatial and temporal patterns of co-activity between segregated brain regions. These so-called large-scale resting-state functional connectivity networks represent dynamically organized neural assemblies interacting with each other in a complex way. It has been suggested that looking at the dynamical properties of complex patterns of brain functional co-activity may reveal neural mechanisms underlying the dynamic changes in functional interactions. Here, we examine how global network dynamics is shaped by different network configurations, derived from realistic brain functional interactions. We focus on two main dynamics measures: synchrony and variations in synchrony. Neural activity and the inferred hemodynamic response of the network nodes are simulated using a system of 90 FitzHugh-Nagumo neural models subject to system noise and time-delayed interactions. These models are embedded into the topology of the complex brain functional interactions, whose architecture is additionally reduced to its main structural pathways. In the simulated functional networks, patterns of correlated regional activity clearly arise from dynamical properties that maximize synchrony and variations in synchrony. Our results on the fast changes of the level of the network synchrony also show how flexible changes in the large-scale network dynamics could be.