Biology subjects

Jirsa, V. K.

Publications and source records attributed to Jirsa, V. K..

8 recordsLinked to original sources

bioRxiv2024

Efficient Inference on a Network of Spiking Neurons using Deep Learning

The process of making inference on networks of spiking neurons is crucial to decipher the underlying mechanisms of neural computation. Mean-field theory simplifies the interactions between neurons to produce macroscopic network behavior, facilitating the study of information processing and computation within the brain. In this study, we perform inference on a mean-field model of spiking neurons to gain insight into likely parameter values, uniqueness and degeneracies, and also to explore how well the statistical relationship between parameters is maintained by traversing across scales. We benchmark against state-of-the-art optimization and Bayesian estimation algorithms to identify their strengths and weaknesses in our analysis. We show that when confronted with dynamical noise or in the case of missing data in the presence of bistability, generating probability distributions using deep neural density estimators outperforms other algorithms, such as adaptive Monte Carlo sampling. However, this class of deep generative models may result in an overestimation of uncertainty and correlation between parameters. Nevertheless, this issue can be improved by incorporating time-delay embedding. Moreover, we show that training deep Neural ODEs on spiking neurons enables the inference of system dynamics from microscopic states. In summary, this work demonstrates the enhanced accuracy and efficiency of inference on networks of spiking neurons when deep learning is harnessed to solve inverse problems in neural computation.

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bioRxiv2023

Whole-brain modelling of low-dimensional manifold modes reveals organising principle of brain dynamics

The discovery of resting state networks shifted the focus from the role of local regions in cognitive tasks to the ongoing spontaneous dynamics in global networks. Recently, efforts have been invested to reduce the complexity of brain activity recordings through the application of nonlinear dimensionality reduction algorithms. Here, we investigate how the interaction between these networks emerges as an organising principle in human cognition. We combine deep variational auto-encoders with computational modelling to construct a dynamical model of brain networks fitted to the whole-brain dynamics measured with functional magnetic resonance imaging (fMRI). Crucially, this allows us to infer the interaction between these networks in resting state and 7 different cognitive tasks by determining the effective functional connectivity between networks. We found a high flexible reconfiguration of task-driven network interaction patterns and we demonstrate that can be used to classify different cognitive tasks. Importantly, compared to using all the nodes in a parcellation, we obtain better results by modelling the dynamics of interacting networks in both model and classification performance. These findings show the key causal role of manifolds as a fundamental organising principle of brain function, providing evidence that interacting networks are the computational engines brain during cognitive tasks.

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bioRxiv2023

Parameter estimation in a whole-brain network model of epilepsy: comparison of parallel global optimization solvers

The Virtual Epileptic Patient (VEP) refers to a computer-based representation of a patient with epilepsy that combines personalized anatomical data with dynamical models of abnormal brain activities. It is capable of generating spatio-temporal seizure patterns that resemble those recorded with invasive methods such as stereoelectro EEG data, allowing for the evaluation of clinical hypotheses before planning surgery. This study highlights the effectiveness of calibrating VEP models using a global optimization approach. The approach utilizes SaCeSS, a cooperative metaheuristic capable of parallel computation, to yield high-quality solutions without requiring excessive computational time. Through extensive benchmarking, our proposal successfully solved a set of different configurations of VEP models, demonstrating better scalability and superior performance against other parallel solvers. These results were further enhanced using a Bayesian optimization framework for hyperparameter tuning, with significant gains in terms of both accuracy and computational cost. Additionally, we added an scalable uncertainty quantification phase after model calibration, and used it to assess the variability in estimated parameters across different problems. Overall, this study has the potential to improve the estimation of pathological brain areas in drug-resistant epilepsy, thereby to inform the clinical decision-making process. Author summaryMotivated by the problem of parameter estimation in a set of whole-brain network models of epilepsy (of increasing complexity), this study addresses the question of choosing a robust global optimization solver that can be accelerated by exploiting parallelism in different infrastructures, from desktop workstations to supercomputers. By leveraging data-driven techniques with robust cooperative global optimization methods, we aim to achieve accurate parameter estimation with reduced reliance on prior information. This is due to the dependency of Bayesian inference on the level of information in the prior, while this approach allows us to quantify uncertainty in the absence of any prior knowledge effectively. In this work, we construct an efficient and accurate method to perform parameter estimation and uncertainty quantification for the VEP model, and we use it to infer the brain regional epileptogenicity from source and sensor level whole-brain data. Of specific interest is the ability of our method to produce inference for high-dimensional state-space models governed by deterministic, stochastic, well-behaved, and stiff differential equations, using only partial observations and sparse encoding from system states to the observation.

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bioRxiv2023

Probing the Mechanisms of Global Brain Reconfiguration after Local Manipulations

Understanding how localized brain interventions translate into whole-brain dynamics is crucial for deciphering neural function and tailoring therapeutic strategies. Combining mouse experimental datasets of focal interventions (thalamic lesion and chemogenetic silencing of cortical hubs), we demonstrate both local and global effects. Using whole-brain simulations of experimental data, we not only confirm the distributed nature of local manipulations but also offer mechanistic insights into these processes. Our simulations predict specific alterations in firing rates and spectral characteristics across specific brain networks, leading to structured changes in functional connectivity patterns. Some of these predictions have been empirically validated. Notably, the affected brain subnetworks--and their resultant signatures of change--are contingent on the original intervention site, suggesting a method to accurately localize the source of alteration. Our results provide a general framework for interpreting localized intervention effects, offering insights that could refine clinical interventions for focal brain disorders by enabling targeted circuit-level neuromodulation strategies.

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bioRxiv2021

Parameter inference on brain network models with unknown node dynamics and spatial heterogeneity

Model-based data analysis of whole-brain dynamics links the observed data to model parameters in a network of neural masses. In recent years a special focus was placed on the role of regional variance of model parameters for the emergent activity. Such analyses however depend on the properties of the employed neural mass model, which is often obtained through a series of major simplifications or analogies. Here we propose a data-driven approach where the neural mass model needs not to be specified. Building on the recent progresses in identification of dynamical systems with neural networks, we propose a method to infer from the functional data both the neural mass model representing the regional dynamics as well as the region- and subject-specific parameters, while respecting the known network structure. We demonstrate on two synthetic data sets that our method is able to recover the original model parameters, and that the trained generative model produces dynamics resembling the training data both on the regional level and on the whole-brain level. We further apply the method to resting-state fMRI data from Human Connectome Project. We find that to achieve best fit, the model needs two dimensional state space and three regional parameters, one of which is strongly correlated with the map of genetic expression in human brain. The present approach opens a novel way to the analysis of resting-state fMRI with possible applications in understanding the changes of whole-brain dynamics during aging or in neurodegenerative diseases.

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bioRxiv2019

A mathematical model of ephaptic interactions in neuronal fiber pathways: could there be more than transmission along the tracts?

In the past several decades, there has been numerous experimental and modeling efforts to study ephaptic interactions in neuronal systems. While studies on the matter have looked at either axons of the peripheral nervous system or cortical neuronal structures, no attention has be given to the possibility of ephaptic interactions in the white matter tracts of the brain. Inspired by the highly organized and tightly packed geometry of axons in neuronal fiber pathways, we aim to theoretically investigate the potential effects of ephaptic interactions along these structures that are resilient to experimental probing. For that end, we use axonal cable theory to derive a minimal model of a sheet of N ephaptically coupled axons. We numerically solve the equations and explore the dynamics of the system as the ephaptic coupling parameter is varied. We demonstrate that ephaptic interactions can lead to local phase locking between impulses traveling along adjacent axons. As ephaptic coupling is increased, traveling impulses trigger new impulses along adjacent axons resulting in finite size traveling fronts. For strong enough coupling, impulses propagate laterally and backwards, resulting in complex spatio-temporal patterns. While it is common for large scale brain network models to assume the role of brain fiber pathways to be that of mere transmission of signals between different brain regions, our work calls for a closer re-examination of the validity of such a view. The results suggest that in the presence of significant ephaptic interactions the brain fiber tracts can act as a dynamic active medium.\n\nAuthor summaryStarting from local circuit theory and the Fitzhugh-Nagumo cable model of an axon, we derive a system of nonlinear coupled partial differential equations (PDEs) to model a sheet of N ephaptically coupled axons. We also put forward a continuous limit approximation that transforms the model into a field equation in the form of a two-dimensional PDE that allows for the extension of the model to a 3D domain. We numerically solve the equations and explore the dynamic responses as the ephaptic coupling strength is varied. We observe that ephaptic interaction allows for phase locking of adjacent impulses and coordination of subthreshold dynamics. In addition, when strong enough, ephaptic interaction can lead to the generation of new impulses along the axons as well as lateral and backward propagation in the form of traveling fronts and complex spatio-temporal patterns. The transition between different dynamic regimes happens abruptly at critical values of the parameter. We also compare the dynamics of the two models and find good qualitative correspondence in certain parameter regimes. The results put into question the validity of assuming the role of fiber pathways to be that of mere interneuronal transmission and calls for further investigation of the matter.

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bioRxiv2019

In-silico Exploration of Mouse Brain Dynamics by Stimulation explains Functional Networks and Sensory Processing

Sensory and direct stimulation of the brain probes its functional repertoire and the information processing capacity of networks. However, a systematic exploration can only be performed in silico. Stimulation takes the system out of its attractor states and samples the environment of the flow to gain insight into the stability and multiplicity of trajectories. It is the only means of obtaining a complete understanding of the healthy brain networks dynamic properties. We built a whole mouse brain model with connectivity derived from tracer studies. We systematically varied the stimulation location, the ratio of long- to short-range interactions, and the range of short connections. Functional networks appeared in the spatial motifs of simulated brain activity. Several motifs included the default mode network, suggesting a junction of functional networks. The model explains processing in sensory systems and replicates the in vivo dynamics after stimulation without parameter tuning, emphasizing the role of connectivity.

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bioRxiv2018

Controlling seizure propagation in large-scale brain networks

Information transmission in the human brain is a fundamentally dynamic network process. In partial epilepsy, this process is perturbed and highly synchronous seizures originate in a local network, the so-called epileptogenic zone (EZ), before recruiting other close or distant brain regions. We studied patient-specific brain network models of 15 drug-resistant epilepsy patients with implanted stereotactic electroencephalography (SEEG) electrodes. Each personalized brain model was derived from structural data of magnetic resonance imaging (MRI) and diffusion tensor weighted imaging (DTI), comprising 88 nodes equipped with region specific neural mass models capable of demonstrating a range of epileptiform discharges. Each patients virtual brain was further personalized through the integration of the clinically hypothesized EZ. Subsequent simulations and connectivity modulations were performed and uncovered a finite repertoire of seizure propagation patterns. Across patients, we found that (i) patient-specific network connectivity is predictive for the subsequent seizure propagation pattern; (ii) seizure propagation is characterized by a systematic sequence of brain states; (iii) propagation can be controlled by an optimal intervention on the connectivity matrix; (iv) the degree of invasiveness can be significantly reduced via the proposed seizure control as compared to traditional resective surgery. To stop seizures, neurosurgeons typically resect the EZ completely. We showed that stability analysis of the network dynamics, employing structural and dynamical information, estimates reliably the spatiotemporal properties of seizure propagation. This suggests novel less invasive paradigms of surgical interventions to treat and manage partial epilepsy.\n\nPACS numbers:\n\nI. AUTHOR SUMMARYEpilepsy is characterized by perturbed dynamics that originate in a local network before spreading to other brain regions. We studied patient-specific brain network models of epilepsy patients, comprising 88 nodes equipped with region specific neural mass models capable of demonstrating epileptiform discharges. Applying stability analysis led to a seizure control strategy that is significantly less invasive than the traditional surgery, which typically resects the epileptogenic regions. The invasiveness of the procedure correlates with graph theoretical importance of the nodes. The novel method subsequently removes the most unstable links, a procedure possible by advent of novel surgery techniques. Our approach is entirely based on structural data, allowing creation of a brain model based on purely non-invasive data prior to any surgery.

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