Biology subjects

Jirsa, V.

Publications and source records attributed to Jirsa, V..

At least 37 records · Page 2Linked to original sources

bioRxiv2024

Magnetoencephalography dimensionality reduction informed by dynamic brain states

Complex spontaneous brain dynamics mirror the large number of interactions taking place among regions, supporting higher functions. Such complexity is manifested in the inter-regional dependencies among signals derived from different brain areas, as observed utilising neuroimaging techniques, like magnetoencephalography. The dynamics of this data produce numerous subsets of active regions at any moment as they evolve. Notably, converging evidence shows that these states can be understood in terms of transient coordinated events that spread across the brain over multiple spatial and temporal scales. Those can be used as a proxy of the "effectiveness" of the dynamics, as they become stereotyped or disorganised in neurological diseases. However, given the high dimensional nature of the data, representing them has been challenging thus far. Dimensionality reduction techniques are typically deployed to describe complex interdependencies and improve their interpretability. However, many dimensionality reduction techniques lose information about the sequence of configurations that took place. Here, we leverage a newly described algorithm, PHATE (Potential of Heat-diffusion for Affinity-based Transition Embedding), specifically designed to preserve the dynamics of the system in the low-dimensional embedding space. We analysed source-reconstructed resting-state magnetoencephalography from 18 healthy subjects to represent the dynamics of the configuration in low-dimensional space. After reduction with PHATE, unsupervised clustering via K-means is applied to identify distinct clusters. The topography of the states is described, and the dynamics are represented as a transition matrix. All the results have been checked against null models, providing a parsimonious account of the large-scale, fast, aperiodic dynamics during resting-state.

neuroscience↗

bioRxiv2024

Modeling impairment of ionic regulation with extended Adaptive Exponential integrate-and-fire models

To model the dynamics of neuron membrane excitability many models can be considered, from the most biophysically detailed to the highest level of phenomenological description. Recent works at the single neuron level have shown the importance of taking into account the evolution of slow variables such as ionic concentration. A reduction of such a model to models of the integrate-and-fire family is interesting to then go to large network models. In this paper, we introduce a way to consider the impairment of ionic regulation by adding a third, slow, variable to the adaptive Exponential integrate-and-fire model (AdEx). We then implement and simulate a network including this model. We find that this network was able to generate normal and epileptic discharges. This model should be useful for the design of network simulations of normal and pathological states.

neuroscience↗

bioRxiv2024

A neural mass model with neuromodulation

The study of brain activity and its function requires the development of computational models alongside experimental investigations to explore different effects of multiple mechanisms at play in the central nervous system. Chemical neuromodulators, such as dopamine, play central roles in regulating the dynamics of neuronal populations. In this work, we propose a modular framework to capture the effects of neuromodulators at the neural mass level. Using this framework, we formulate a specific model for dopamine dynamics affecting D1-type receptors. We detail the dynamical repertoire associated with dopamine concentration evolution and characterize the transitions across qualitatively different oscillatory states as a function of relevant parameters, along with the variations in frequency of the emergent multi-scale oscillations.

neuroscience↗

bioRxiv2024

Improved Temporal and Spatial Focality of Non-invasive Deep-brain Stimulation using Multipolar Single-pulse Temporal Interference with Applications in Epilepsy

Temporal Interference (TI) is an emerging method to non-invasively stimulate deep brain structures. This innovative technique is increasingly recognized for its potential applications in the treatment of various neurological disorders, including epilepsy, depression, and Alzheimers disease. However, several drawbacks to the TI method exist that we aim to improve upon. To begin, the applied electric field in the TI target is not much higher than what non-invasive transcranial alternating current stimulation (TACS) provides in the cortex. Additionally, the TI stimulation onset is dependent on the envelope of the amplitude modulated (AM) signal, where for example 1 Hz and 100 Hz envelopes have significantly different rise times to reach maximum envelope amplitude - unlike square biphasic pulses. This limitation in turn prevents classic TI, from applying bursts of pulses. Finally, the electric field intensity of TI cannot be increased or decreased at the target without dramatically altering the spatial profile of the stimulation focus. In the work presented here, we efficiently address all three of these limitations. First, we performed two-photon calcium imaging to show that individual neurons selectively respond to the TI envelope frequency, providing evidence that TI modulates neural activity with temporal specificity. This marks a significant advancement, representing the first empirical demonstration of neuronal activation at the {Delta}f frequency within the context of TI and in an imaging modality. Subsequently, we compared the AM signals of TI with phase-shift keying (PSK) modulated signals to highlight the superior effectiveness of noninvasive pulses in contrast to the traditional TI method, particularly in inducing epileptic activity (after-discharges) in mice. We also added a multipolar configuration to create a significant increase in the electric field at the target without significantly altering the spatial profile and applied Fourier components to replicate classic biphasic bursts of square pulses - all transcranially, without the use of penetrating electrodes. These innovations aim to enhance the precision and efficacy of TI stimulation, to advance its application in neurological research and therapy. Key Points / HighlightsO_LINon-invasive temporal interference stimulation modulates the activity of individual neurons at the envelope frequency. C_LIO_LIA non-invasive multi-pulse TI stimulation paradigm improves both temporal and spatial focality in the deep target neural tissue when compared to traditional continuous wave (amplitude-modulated) TI stimulation. C_LIO_LIPulse TI paradigms can stimulate deep neural targets with reduced amplitude of the topical high-frequency stimulation, decreasing off-target stimulation when compared to continuous wave TI patterns. As a consequence, pulse TI stimulation reduces the risk of undesired side effects such as high-frequency conduction block in off-target tissues or cortical areas. C_LIO_LIBoth temporal and spatial focality of the TI stimulation pattern positively correlate with the efficacy of the stimulation to induce seizures in the mouse hippocampus. C_LI

neuroscience↗

bioRxiv2023

Multipair phase-modulated temporal interference electrical stimulation combined with fMRI.

Temporal Interference stimulation (TIS) is a promising non-invasive brain stimulation technique exploiting frequency-shifted kHz fields to modulate oscillatory neural activity without invasive procedures. While human studies suggest TIS efficacy in targeting relatively deep brain structures, recent computational modeling and animal studies indicate potential off-target stimulations via the application of standard TIS protocols. Here, we computationally optimized TIS targeting for the prefrontal cortex (PFC) in mice. Combining in vivo electrophysiological recordings, intracellular calcium dynamics with fiber photometry, and functional MRI, we confirmed TIS-induced amplitude modulation, neuronal entrainment and hemodynamic responses in the PFC, while also identifying off-target modulations. To mitigate off-target effects, we propose a novel configuration with three electrode pairs, one of which is actively phase-shifted by 180 degrees. This cancelling field significantly enhanced TIS focality, reducing off-target effects without compromising the TIS efficacy in the target area. This work effectively addresses one of TIS most critical shortcomings and opens new avenues for research and clinical applications. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=178 SRC="FIGDIR/small/571679v2_ufig1.gif" ALT="Figure 1"> View larger version (40K): org.highwire.dtl.DTLVardef@b04067org.highwire.dtl.DTLVardef@154549aorg.highwire.dtl.DTLVardef@17e63c3org.highwire.dtl.DTLVardef@c7a341_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2023

Dynamics and bifurcation structure of a mean-field model of adaptive exponential integrate-and-fire networks

The study of brain activity spans diverse scales and levels of description, and requires the development of computational models alongside experimental investigations to explore integrations across scales. The high dimensionality of spiking networks presents challenges for understanding their dynamics. To tackle this, a mean-field formulation offers a potential approach for dimensionality reduction while retaining essential elements. Here, we focus on a previously developed mean-field model of Adaptive Exponential (AdEx) networks, utilized in various research works. We provide a systematic investigation of its properties and bifurcation structure, which was not available for this model. We show that this provides a comprehensive description and characterization of the model to assist future users in interpreting their results. The methodology includes model construction, stability analysis, and numerical simulations. Finally, we offer an overview of dynamical properties and methods to characterize the mean-field model, which should be useful for for other models.

neuroscience↗

bioRxiv2023

Compact bifurcation analysis of the Epileptor

The Epileptor is a phenomenological model able to reproduce the activity of the most common class, in terms of dynamics, of epileptic seizures, characterized by having square-wave bursting properties. It also encodes an additional mechanism to account for interictal spikes and spike and wave discharges. This model is being used in large-scale brain modeling of epileptic patients with the goal of improving surgical outcomes. Here we use insights from a more generic model for square-wave bursting, based on the Unfolding Theory approach, to guide the bifurcation analysis of the Epileptor. This allows to understand how the Epileptors parameters can be modified to produce activities for other seizures classes (i.e. other onset/offset bifurcation pairs) as observed in patients and to unveil how the interaction with the additional mechanism for spike and wave discharges alters the bifurcation structure of the main burster.

neuroscience↗

bioRxiv2023

Controlling focality and intensity of non-invasive deep brain stimulation using multipolar temporal interference in non-human primates and rodents

Temporal interference (TI) is a method of non-invasive brain stimulation using transcutaneous electrodes that allows the targeting and modulation of deeper brain structures, not normally associated with non-invasive simulation, while avoiding unwanted stimulation of shallower cortical structures. The properties of TI have been previously demonstrated, however, the problem of decoupling stimulation focality from stimulation intensity has not been addressed. In this paper, we provide a possible novel solution, multipolar TI (mTI), which allows increased independent control over both the size of the stimulated region and the stimulation intensity. The mTI method uses multiple carrier frequencies to create multiple overlapping amplitude-modulated envelopes, rather than using one envelope as in standard TI. The study presents an explanation of the concept of mTI along with experimental data gathered from Rhesus macaques and mice. We improved the focality at depth in anesthetized mice and monkeys, and using the new focality in awake monkeys, evoked targeted activity at depth in the superior colliculus. The mTI method could be an interesting and potentially useful new tool alongside other forms of non-invasive brain stimulation. Teaser: Multipolar Temporal Interference Stimulation can produce a more focal brain stimulation at depth compared to Temporal Interference.

neuroscience↗

bioRxiv2023

Intermodal consistency of whole-brain connectivity and signal propagation delays

Measuring the propagation of perturbations across the human brain and their transmission delays is critical for network neuroscience, but it is a challenging problem that still requires cross-validation approaches. Here, we compare results from a recently introduced, non-invasive technique of functional delays estimation from source-reconstructed electro/magnetoencephalography, to the corresponding findings from a large dataset of cortico-cortical evoked potentials estimated from intracerebral stimulations of epileptic pharmaco-resistant patients. The two methods yield significantly similar probabilistic connectivity maps and signal propagation delays. This similarity suggests a correspondence between the mechanisms underpinning the propagation of spontaneously generated scale-free perturbations (i.e. neuronal avalanches observed in resting state activity studied using magnetoencephalography) and the spreading of cortico-cortical evoked potentials. This manuscript provides evidence for the accuracy of a subject-specific estimate of functional delays obtained non-invasively from reconstructed sources. Conversely, our findings show that estimates obtained from externally-induced perturbations capture physiological activities. In conclusion, this manuscript constitutes a cross-validation between two different modalities. Importantly, the capability to measure delays non-invasively (as per MEG) paves the way for the inclusion of functional delays in personalized large-scale brain models as well as in diagnostic and prognostic algorithms.

neuroscience↗

bioRxiv2023

Incorporating slow NMDA-type receptors with nonlinear voltage-dependent magnesium block in a next generation neural mass model: derivation and dynamics

We derive a next generation neural mass model of a population of quadratic-integrate-and-fire neurons, with slow adaptation, and conductance-based AMPAR, GABAR and nonlinear NMDAR synapses. We show that the Lorentzian ansatz assumption can be satisfied by introducing a piece-wise polynomial approximation of the nonlinear voltage-dependent magnesium block of NMDAR current. We study the dynamics of the resulting system for two example cases of excitatory cortical neurons and inhibitory striatal neurons. Bifurcation diagrams are presented comparing the different dynamical regimes as compared to the case of linear NMDAR currents, along with sample comparison simulation time series demonstrating different possible oscillatory solutions. The omission of the nonlinearity of NMDAR currents results in a shift in the range (and possible disappearance) of the constant high firing rate regime, along with a modulation in the amplitude and frequency power spectrum of oscillations. Moreover, nonlinear NMDAR action is seen to be state-dependent and can have opposite effects depending on the type of neurons involved and the level of input firing rate received. The presented model can serve as a computationally efficient building block in whole brain network models for investigating the differential modulation of different types of synapses under neuromodulatory influence or receptor specific malfunction. Statements and DeclarationsThe authors have no competing interests to declare. FundingWe acknowledge support by H2020 Research and Innovation Action grants Human Brain Project SGA3 number 945539.

neuroscience↗

bioRxiv2023

Large-scale brain signatures of fluid dynamics and responsiveness linked to consciousness

Signatures of consciousness are found in spectral and temporal properties of neuronal activity. Among these, spatiotemporal complexity after a perturbation has recently emerged as a robust metric to infer levels of consciousness. Perturbation paradigms remain, however, difficult to perform routinely. To discover alternative paradigms and metrics, we systematically explore brain stimulation and resting-state activity in a whole-brain model. We find that perturbational complexity only occurs when the brain model operates within a specific dynamical regime, in which spontaneous activity produces a large degree of functional network reorganizations referred to as being fluid. The regime of high brain fluidity is characterized by a small battery of metrics drawn from dynamical systems theory and predicts the impact of consciousness-altering drugs (Xenon, Propofol, and Ketamine). We validate the predictions in a cohort of 15 subjects at various stages of consciousness and demonstrate their agreement with previously reported perturbational complexity, but in a more accessible paradigm. Beyond the facilitation in clinical use, the metrics highlight complexity properties of brain dynamics in support of the emergence of consciousness.

neuroscience↗

bioRxiv2023

Virtual brain simulations reveal network-specific parameters in neurodegenerative dementias

IntroductionNeural circuit alterations lay at the core of brain physiopathology, and yet are hard to unveil in living subjects. Virtual brain modelling (TVB), by exploiting structural and functional MRI, yields mesoscopic parameters of connectivity and synaptic transmission. MethodsWe used TVB to simulate brain networks, which are key for human brain function, in Alzheimers disease (AD) and Frontotemporal Dementia (FTD) patients, whose connectivity and synaptic parameters remain largely unknown; we then compared them to healthy controls, to reveal novel in vivo pathological hallmarks. ResultsThe pattern of simulated parameter differed between AD and FTD, shedding light on disease-specific alterations in brain networks. Individual subjects displayed subtle differences in network parameter patterns that significantly correlated with their individual neuropsychological, clinical, and pharmacological profiles. DiscussionThese TVB simulations, by informing about a new personalized set of networks parameters, open new perspectives for understanding dementias mechanisms and design personalized therapeutic approaches.

neuroscience↗

bioRxiv2022

Bayesian Pharmacometrics Analysis of Baclofen for Alcohol Use Disorder

Alcohol use disorder (AUD) also called alcohol dependence is a major public health problem, which affects almost 10% of the worlds population. Baclofen as a selective GABAB receptor agonist has emerged as a promising drug for the treatment of AUD, however, its optimal dosage varies according to individuals, and its exposure-response relationship has not been well established yet. In this study, we use a principled Bayesian workflow to estimate the parameters of a pharmacokinetic (PK) population model from Baclofen administration to patients with AUD. By monitoring various convergence diagnostics, the probabilistic methodology is first validated on synthetic longitudinal datasets and then, applied to infer the PK model parameters based on the clinical data that were retrospectively collected from outpatients treated with oral Baclofen. We show that state-of-the-art advances in automatic Bayesian inference using self-tuning Hamiltonian Monte Carlo (HMC) algorithms with a leveraged level of information in priors provide accurate predictions on Baclofen plasma concentration in individuals. This approach may pave the way to render non-parametric HMC sampling methods sufficiently easy and reliable to use in clinical schedules for personalized treatment of AUD.

pharmacology and toxicology↗

bioRxiv2022

Low-dimensional organization of global brain states of reduced consciousness

Brain states are frequently represented using a unidimensional scale measuring the richness of subjective experience (level of consciousness). This description assumes a mapping between the high-dimensional space of whole-brain configurations and the trajectories of brain states associated with changes in consciousness, yet this mapping and its properties remain unknown. We combined whole-brain modelling, data augmentation and deep learning for dimensionality reduction to determine a mapping representing states of consciousness in a low-dimensional space, where distances parallel similarities between states. An orderly trajectory from wakefulness to brain injured patients is revealed in a latent space whose coordinates represent metrics related to functional modularity and structure-function coupling, both increasing alongside loss of consciousness. Finally, we investigated the effects of model perturbations, providing geometrical interpretation for the stability and reversibility of states. We conclude that conscious awareness depends on functional patterns encoded as a low-dimensional trajectory within the vast space of brain configurations.

neuroscience↗

bioRxiv2022

Brain fingerprint is based on the aperiodic, scale-free, neuronal activity

The possibility to identify subjects from their brain activity was met enthusiastically, as it bears the possibility to individualize brain analyses. However, the nature of the processes generating subject-specific features remains unknown, as the literature does not point to specific mechanisms. In particular, most of the current literature uses techniques that are based on the assumption of stationarity (e.g. Pearsons correlation), which do not hypothesize any mechanisms, and crashes against a large body of literature showing the complex, highly non-linear nature of brain activity. In this paper, we hypothesize that intermittent moments when large, non-linear perturbations spread across the brain (defined as neuronal avalanches in the context of critical dynamics) are the ones that carry subject-specific information, and that contribute the most to identifiability. To test this hypothesis, we apply the recently-developed avalanche transition matrix (ATM) to source reconstructed magnetoencephalographic data, as to characterize subject-speficic fast dynamics. Then, we perform identifiability analysis based on the ATMs, and compared the performance to more classical ways of estimating large-scale connections (which assume stationareity). We demonstrate that selecting the moments and places where neuronal avalanches spread improves identifiability (p<0.0001, permutation testing), despite the fact that most ot the data (i.e. the linear part) are discarded. Our results show that the non-linear part of the brain signals carries most of the subject-specific information, shading light on the nature of the processes that underlie subject-identifiability. Borrowing from statistical mechanics, a solid branch of physics, we provide a principled way to link emergent large-scale personalized activations to non-observable, microscopic processes.

neuroscience↗

bioRxiv2022

Multiscale cosimulation design template implemented for a neuroscience application

Integration of information across heterogeneous sources creates added scientific value. It is, however, a challenge to progress, often a barrier, to interoperate data, tools and models across spatial and temporal scales. Here we present a design template for coupling simulators operating at different scales and enabling co-simulation. We illustrate its functioning along a neuroscience example, in which individual regions of interest are simulated on the cellular level to address mechanistic questions, while the remaining network is efficiently simulated on the population level. A workflow is illustrated for the use case of The Virtual Brain and NEST, in which the cellular-level hippocampus of the mouse is embedded into a full brain network involving micro and macro electrode recordings. This new tool allows integrating knowledge across scales in the same simulation framework and validate them against multiscale experiments, thereby largely widening the explanatory power of computational models.

neuroscience↗

bioRxiv2022

Measuring brain critical dynamics to inform Brain-Computer Interfaces

Large-scale interactions among multiple brain regions manifest as bursts of activations called neuronal avalanches, which reconfigure according to the task at hand and, hence, might constitute natural candidates to design brain-computer interfaces (BCI). To test this hypothesis, we used source-reconstructed magneto/electroencephalography, during resting state and a motor imagery task performed within a BCI protocol. To track the probability that an avalanche would spread across any two regions we built an avalanche transition matrix (ATM) and demonstrated that the edges whose transition probabilities significantly differed between conditions hinged selectively on premotor regions in all subjects. Furthermore, we showed that the topology of the ATMs allows task-decoding above the current gold standard. Hence, our results suggest that Neuronal Avalanches might capture interpretable differences between tasks that can be used to inform brain-computer interfaces.

neuroscience↗

bioRxiv2022

Focal Non-invasive Deep-brain Stimulation with Temporal Interference for the Suppression of Epileptic Biomarkers

Neurostimulation applied from deep brain stimulation (DBS) electrodes is an effective therapeutic intervention in patients suffering from intractable drug-resistant epilepsy when resective surgery is contraindicated or failed. Inhibitory DBS to suppress seizures and associated epileptogenic biomarkers could be performed with high-frequency stimulation (HFS), typically between 100 -165Hz, to various deep-seated targets such as for instance the Mesio-temporal lobe (MTL) which leads to changes in brain rhythms, specifically in the hippocampus. The most prominent alterations concern high-frequency oscillations (HFOs), namely increase in ripples, a reduction in pathological Fast Ripples (FRs), and a decrease in pathological interictal epileptiform discharges (IEDs). In the current study, we use Temporal Interference stimulation to provide a non-invasive focal DBS (130 Hz) of the MTL, specifically the hippocampus, which increases physiological ripples, and decreases the number of FRs and IEDs in a mouse model of epilepsy. Similarly, we show the inability of 130 Hz transcranial current stimulation (TCS) to achieve similar results. The method could potentially revolutionize how DBS, certainly in epilepsy, is performed, and we therefore further demonstrate the translatability to human subjects via measurements of the TI stimulation vs TCS in human cadavers. Results show the better penetration of TI fields into the human hippocampus as compared with TCS. Finally, we provide evidence of the efficacy of the specific form of Pulse-width Modulated TI (PWM-TI), implemented with square waves, which is used in this study. One Sentence SummaryA non-invasive deep brain stimulation applied via temporal interference achieves the suppression of biomarkers of epilepsy in mice and is scaled to humans.

neuroscience↗