The virtual multiple sclerosis patient.

Multiple sclerosis (MS) diagnosis typically involves assessing clinical symptoms, MRI findings, and ruling out alternative explanations. While myelin damage broadly affects conduction speeds, traditional tests focus on specific white-matter tracts, which may not reflect overall impairment accurately. In this study, we integrate diffusion tensor immaging (DTI) and magnetoencephalography (MEG) data into individualized virtual brain models to estimate conduction velocities for MS patients and controls. Using Bayesian inference, we demonstrated a causal link between empirical spectral changes and inferred slower conduction velocities in patients. Remarkably, these velocities proved superior predictors of clinical disability compared to structural damage. Our findings underscore a nuanced relationship between conduction delays and large-scale brain dynamics, suggesting that individualized velocity alterations at the whole-brain level contribute causatively to clinical outcomes in MS.

Dynamical interactions reconfigure the gradient of cortical timescales.

The functional organization of the brain is usually presented with a back-to-front gradient of timescales, reflecting regional specialization with sensory areas (back) processing information faster than associative areas (front), which perform information integration. However, cognitive processes require not only local information processing but also coordinated activity across regions. Using magnetoencephalography recordings, we find that the functional connectivity at the edge level (between two regions) is also characterized by a back-to-front gradient of timescales following that of the regional gradient. Unexpectedly, we demonstrate a reverse front-to-back gradient when nonlocal interactions are prominent. Thus, the timescales are dynamic and can switch between back-to-front and front-to-back patterns.

Whole-Brain Propagation Delays in Multiple Sclerosis, a Combined Tractography-Magnetoencephalography Study.

Two structurally connected brain regions are more likely to interact, with the lengths of the structural bundles, their widths, myelination, and the topology of the structural connectome influencing the timing of the interactions. We introduce an in vivo approach for measuring functional delays across the whole brain in humans (of either sex) using magneto/electroencephalography (MEG/EEG) and integrating them with the structural bundles. The resulting topochronic map of the functional delays/velocities shows that larger bundles have faster velocities. We estimated the topochronic map in multiple sclerosis patients, who have damaged myelin sheaths, and controls, demonstrating greater delays in patients across the network and that structurally lesioned tracts were slowed down more than unaffected ones. We provide a novel framework for estimating functional transmission delays in vivo at the single-subject and single-tract level.SIGNIFICANCE STATEMENT This article provides a straightforward way to estimate patient-specific delays and conduction velocities in the CNS, at the individual level, in healthy and diseased subjects. To do so, it uses a principled way to merge magnetoencephalography (MEG)/electroencephalography (EEG) and tractography.

The Bayesian Virtual Epileptic Patient: A probabilistic framework designed to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread.

Despite the importance and frequent use of Bayesian frameworks in brain network modeling for parameter inference and model prediction, the advanced sampling algorithms implemented in probabilistic programming languages to overcome the inference difficulties have received relatively little attention in this context. In this technical note, we propose a probabilistic framework, namely the Bayesian Virtual Epileptic Patient (BVEP), which relies on the fusion of structural data of individuals to infer the spatial map of epileptogenicity in a personalized large-scale brain model of epilepsy spread. To invert the individualized whole-brain model employed in this study, we use the recently developed algorithms known as No-U-Turn Sampler (NUTS) as well as Automatic Differentiation Variational Inference (ADVI). Our results indicate that NUTS and ADVI accurately estimate the degree of epileptogenicity of brain regions, therefore, the hypothetical brain areas responsible for the seizure initiation and propagation, while the convergence diagnostics and posterior behavior analysis validate the reliability of the estimations. Moreover, we illustrate the efficiency of the transformed non-centered parameters in comparison to centered form of parameterization. The Bayesian framework used in this work proposes an appropriate patient-specific strategy for estimating the epileptogenicity of the brain regions to improve outcome after epilepsy surgery.

Differences in MEG and EEG power-law scaling explained by a coupling between spatial coherence and frequency: a simulation study.

Electrophysiological signals (electroencephalography, EEG, and magnetoencephalography, MEG), as many natural processes, exhibit scale-invariance properties resulting in a power-law (1/f) spectrum. Interestingly, EEG and MEG differ in their slopes, which could be explained by several mechanisms, including non-resistive properties of tissues. Our goal in the present study is to estimate the impact of space/frequency structure of source signals as a putative mechanism to explain spectral scaling properties of neuroimaging signals. We performed simulations based on the summed contribution of cortical patches with different sizes (ranging from 0.4 to 104.2 cm2). Small patches were attributed signals of high frequencies, whereas large patches were associated with signals of low frequencies, on a logarithmic scale. The tested parameters included i) the space/frequency structure (range of patch sizes and frequencies) and ii) the amplitude factor c parametrizing the spatial scale ratios. We found that the space/frequency structure may cause differences between EEG and MEG scale-free spectra that are compatible with real data findings reported in previous studies. We also found that below a certain spatial scale, there were no more differences between EEG and MEG, suggesting a limit for the resolution of both methods.Our work provides an explanation of experimental findings. This does not rule out other mechanisms for differences between EEG and MEG, but suggests an important role of spatio-temporal structure of neural dynamics. This can help the analysis and interpretation of power-law measures in EEG and MEG, and we believe our results can also impact computational modeling of brain dynamics, where different local connectivity structures could be used at different frequencies.

The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread.

Individual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the individual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of individual patients. Along the example of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.

[What can we expect from clinical trials in psychiatry?] / Que peut-on attendre des essais cliniques en psychiatrie ?

Clinical trials in psychiatry allow to build the regulatory dossiers for market authorization but also to document the mechanism of action of new drugs, to build pharmacodynamics models, evaluate the treatment effects, propose prognosis, efficacy or tolerability biomarkers and altogether to assess the impact of drugs for patient, caregiver and society. However, clinical trials have shown some limitations. Number of recent dossiers failed to convince the regulators. The clinical and biological heterogeneity of psychiatric disorders, the pharmacokinetic and pharmacodynamics properties of the compounds, the lack of translatable biomarkers possibly explain these difficulties. Several breakthrough options are now available: quantitative system pharmacology analysis of drug effects variability, pharmacometry and pharmacoepidemiology, Big Data analysis, brain modelling. In addition to more classical approaches, these opportunities lead to a paradigm change for clinical trials in psychiatry.

Systematic approximations of neural fields through networks of neural masses in the virtual brain.

Full brain network models comprise a large-scale connectivity (the connectome) and neural mass models as the network's nodes. Neural mass models absorb implicitly a variety of properties in their constant parameters to achieve a reduction in complexity. In situations, where the local network connectivity undergoes major changes, such as in development or epilepsy, it becomes crucial to model local connectivity explicitly. This leads naturally to a description of neural fields on folded cortical sheets with local and global connectivities. The numerical approximation of neural fields in biologically realistic situations as addressed in Virtual Brain simulations (see http://thevirtualbrain.org/app/ (version 1.0)) is challenging and requires a thorough evaluation if the Virtual Brain approach is to be adapted for systematic studies of disease and disorders. Here we analyze the sampling problem of neural fields for arbitrary dimensions and provide explicit results for one, two and three dimensions relevant to realistically folded cortical surfaces. We characterize (i) the error due to sampling of spatial distribution functions; (ii) useful sampling parameter ranges in the context of encephalographic (EEG, MEG, ECoG and functional MRI) signals; (iii) guidelines for choosing the right spatial distribution function for given anatomical and geometrical constraints.

Anatomical connectivity and the resting state activity of large cortical networks.

This paper uses mathematical modelling and simulations to explore the dynamics that emerge in large scale cortical networks, with a particular focus on the topological properties of the structural connectivity and its relationship to functional connectivity. We exploit realistic anatomical connectivity matrices (from diffusion spectrum imaging) and investigate their capacity to generate various types of resting state activity. In particular, we study emergent patterns of activity for realistic connectivity configurations together with approximations formulated in terms of neural mass or field models. We find that homogenous connectivity matrices, of the sort of assumed in certain neural field models give rise to damped spatially periodic modes, while more localised modes reflect heterogeneous coupling topologies. When simulating resting state fluctuations under realistic connectivity, we find no evidence for a spectrum of spatially periodic patterns, even when grouping together cortical nodes into communities, using graph theory. We conclude that neural field models with translationally invariant connectivity may be best applied at the mesoscopic scale and that more general models of cortical networks that embed local neural fields, may provide appropriate models of macroscopic cortical dynamics over the whole brain.

Spatiotemporal multi-resolution approximation of the Amari type neural field model.

Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic example is provided to demonstrate the framework.

Towards the virtual brain: network modeling of the intact and the damaged brain.

Neurocomputational models of large-scale brain dynamics utilizing realistic connectivity matrices have advanced our understanding of the operational network principles in the brain. In particular, spontaneous or resting state activity has been studied on various scales of spatial and temporal organization including those that relate to physiological, encephalographic and hemodynamic data. In this article we focus on the brain from the perspective of a dynamic network and discuss the role of its network constituents in shaping brain dynamics. These constituents include the brain's structural connectivity, the population dynamics of its network nodes and the time delays involved in signal transmission. In addition, no discussion of brain dynamics would be complete without considering noise and stochastic effects. In fact, there is mounting evidence that the interaction between noise and dynamics plays an important functional role in shaping key brain processes. In particular, we discuss a unifying theoretical framework that explains how structured spatio-temporal resting state patterns emerge from noise driven explorations of unstable or stable oscillatory states. Embracing this perspective, we explore the consequences of network manipulations to understand some of the brain's dysfunctions, as well as network effects that offer new insights into routes towards therapy, recovery and brain repair. These collective insights will be at the core of a new computational environment, the Virtual Brain, which will allow flexible incorporation of empirical data constraining the brain models to integrate, unify and predict network responses to incipient pathological processes.

The development of a noisy brain.

Early in life, brain development carries with it a large number of structural changes that impact the functional interactions of distributed neuronal networks. Such changes enhance information processing capacity, moving the brain from a deterministic system to one that is more stochastic. The evidence from empirical studies with EEG and functional MRI suggests that this stochastic property is a result of an increased number of possible functional network configurations for a given situation. This is captured in the variability of endogenous and evoked responses or "brain noise ". In empirical data from infants and children, brain noise increases with maturation and correlates positively with stable behavior and accuracy. The noise increase is best explained through increased noise from network level interactions with a concomitant decrease of local noise. In old adults, brain noise continues to change, although the pattern of changes is not as global as in early development. The relation between high brain noise and stable behavior is maintained, but the relationships differ by region, suggesting changes in local dynamics that then impact potential network configurations. These data, when considered in concert with our extant modeling work, suggest that maturational changes in brain noise represent the enhancement offunctional network potential--the brain's dynamic repertoire.

The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models.

Functionally relevant large scale brain dynamics operates within the framework imposed by anatomical connectivity and time delays due to finite transmission speeds. To gain insight on the reliability and comparability of large scale brain network simulations, we investigate the effects of variations in the anatomical connectivity. Two different sets of detailed global connectivity structures are explored, the first extracted from the CoCoMac database and rescaled to the spatial extent of the human brain, the second derived from white-matter tractography applied to diffusion spectrum imaging (DSI) for a human subject. We use the combination of graph theoretical measures of the connection matrices and numerical simulations to explicate the importance of both connectivity strength and delays in shaping dynamic behaviour. Our results demonstrate that the brain dynamics derived from the CoCoMac database are more complex and biologically more realistic than the one based on the DSI database. We propose that the reason for this difference is the absence of directed weights in the DSI connectivity matrix.

Cortical network dynamics with time delays reveals functional connectivity in the resting brain.

In absence of all goal-directed behavior, a characteristic network of cortical regions involving prefrontal and cingulate cortices consistently shows temporally coherent fluctuations. The origin of these fluctuations is unknown, but has been hypothesized to be of stochastic nature. In the present paper we test the hypothesis that time delays in the network dynamics play a crucial role in the generation of these fluctuations. By tuning the propagation velocity in a network based on primate connectivity, we scale the time delays and demonstrate the emergence of the resting state networks for biophysically realistic parameters.

Noise during rest enables the exploration of the brain's dynamic repertoire.

Traditionally brain function is studied through measuring physiological responses in controlled sensory, motor, and cognitive paradigms. However, even at rest, in the absence of overt goal-directed behavior, collections of cortical regions consistently show temporally coherent activity. In humans, these resting state networks have been shown to greatly overlap with functional architectures present during consciously directed activity, which motivates the interpretation of rest activity as day dreaming, free association, stream of consciousness, and inner rehearsal. In monkeys, it has been shown though that similar coherent fluctuations are present during deep anesthesia when there is no consciousness. Here, we show that comparable resting state networks emerge from a stability analysis of the network dynamics using biologically realistic primate brain connectivity, although anatomical information alone does not identify the network. We specifically demonstrate that noise and time delays via propagation along connecting fibres are essential for the emergence of the coherent fluctuations of the default network. The spatiotemporal network dynamics evolves on multiple temporal scales and displays the intermittent neuroelectric oscillations in the fast frequency regimes, 1-100 Hz, commonly observed in electroencephalographic and magnetoencephalographic recordings, as well as the hemodynamic oscillations in the ultraslow regimes, <0.1 Hz, observed in functional magnetic resonance imaging. The combination of anatomical structure and time delays creates a space-time structure in which the neural noise enables the brain to explore various functional configurations representing its dynamic repertoire.

Local and global stabilization of coordination by sensory information.

In studies of rhythmic coordination, where sensory information is often generated by an auditory stimulus, spatial and temporal variability are known to decrease at points in the movement cycle coincident with the stimulus, a phenomenon known as anchoring (Byblow et al. 1994). Here we hypothesize that the role of anchoring may be to globally stabilize coordination under conditions in which it would otherwise undergo a global coordinative change such as a phase transition. To test this hypothesis, anchoring was studied in a bimanual coordination paradigm in which either inphase or antiphase coordination was produced as auditory pacing stimuli (and hence movement frequency) were scaled over a wide range of frequencies. Two different anchoring conditions were used: a single-metronome condition, in which peak amplitude of right finger flexion coincided with the auditory stimulus; and a double-metronome condition, in which each finger reversal (flexion and extension) occurred simultaneously with the auditory stimuli. Anchored reversal points displayed lower spatial variation than unanchored reversal points, resulting in more symmetric phase plane trajectories in the double- than the single-metronome condition. The global coordination dynamics of the double-metronome condition was also more stable, with transitions from antiphase to inphase occurring less often and at higher movement frequencies than in the single-metronome condition. An extension of the Haken-Kelso-Bunz model of bimanual coordination is presented briefly which includes specific coupling of sensory information to movement through a process we call parametric stabilization. The parametric stabilization model provides a theoretical account of both local effects on the individual movement trajectories (anchoring) and global stabilization of observed coordination patterns, including the delay of phase transitions.

Recruitment of degrees of freedom stabilizes coordination.

By showing that transitions may be obviated by recruiting degrees of freedom in the coupled pendulum paradigm, the authors reveal a novel mechanism for coordinative flexibility. In Experiment 1, participants swung pairs of unconstrained pendulums in 2 planes of motion (sagittal and frontal) at 8 movement frequencies starting from either an in-phase or antiphase mode. Few transitions were observed. Measures of spatial trajectory showed recruitment effects tied to the stability of the initial coordinative pattern. When the motion of the pendulums was physically restricted to a single plane in Experiment 2, transitions were more common, indicating that recruitment delays--or even eliminates--transitions. Such recruitment complements transitions as a source of coordinative flexibility and is incorporated in a simple extension of the Haken-Kelso-Bunz (1985) model.

Theory of the relation between human brain activity (MEG) and hand movements.

Earlier research established that spontaneous changes in human sensorimotor coordination are accompanied by qualitative changes in the spatiotemporal dynamics of neural activity measured by multisensor electroencephalography and magnetoencephalography. More recent research has demonstrated that a robust relation exists between brain activity and the movement profile produced. In particular, brain activity has been shown to correlate strongly with movement velocity independent of movement direction and mode of coordination. Using a recently developed field theoretical model of large-scale brain activity itself based on neuroanatomical and neurophysiological constraints we show here how these experimental findings relate to the field theory and how it is possible to reconstruct the movement profile via spatial and temporal integration of the brain signal. There is a unique relation between the quantities in the theory and the experimental data, and fit between the shape of the measured and the reconstructed time series for the movement is remarkably good given that there are no free parameters.

Spatiotemporal pattern formation in neural systems with heterogeneous connection topologies.

Biological systems like the human cortex show homogeneous connectivity, with additional strongly heterogeneous projections from one area to another. Here we report how such a dynamic system performs a macroscopically coherent pattern formation. The connection topology is used systematically as a control parameter to guide the neural system through a series of phase transitions. We discuss the example of a two-point connection, and its destabilization mechanism.