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Mercadal, B.

Publications and source records attributed to Mercadal, B..

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cyto7: an open, vertex-level cortical type atlas of the human cortex

The cerebral cortex is organized along a gradient of laminar complexity, indexed principally by the emergence and granularity of layer IV and by the size of layer III neuron somata, and cortical types are the ordered categories along it. The Structural Model builds on that gradient to predict laminar connectivity, plasticity and disease vulnerability (Barbas and Rempel-Clower, 1997; Garcia-Cabezas et al., 2019). Existing digital cortical-type maps are area-level: they assign one type per classical area and cannot represent a type boundary that runs through an area. We release cyto7, an open, vertex-level, seven-type cortical-type atlas delineated on the fsaverage surface following the Garcia-Cabezas protocol and revised by the protocol's authors. We distribute the atlas with a reusable quality-control and support framework. A topological audit reports the map against four spatial rules that follow from cortical type analysis and the dual origin of the neocortex theory: neighbouring vertices differ by at most one type, the least complex cortex forms one continuous limbic belt, eulaminate II forms a single matrix enclosing a few holes, and eulaminate III and koniocortex form isolated islands. These were imposed during construction rather than tested, and the audit reports what imposing them cost. A vertex-wise benchmark against the existing area-level map, one type per von Economo-Koskinas area, shows exact agreement at 52.5% of vertices, with disagreement almost always a single level and concentrated inside the large, heterogeneous areas that a single areal label cannot resolve. Where the two maps disagree, cyto7's label better matches the independent multimodal profile in 60.1% of pooled feature-by-vertex comparisons, against 46% under a rotation null and 54% under granularity-matched alternative partitions (p = 0.020), the harder of the two. A per-vertex anatomical support score, computed from anatomy alone, states how strongly each hand-drawn label is backed by the anatomical evidence, and is deliberately not a calibrated error probability. We then validate cyto7 against independent brain measures not used to define it, under a spatial-autocorrelation (spin) null with FDR control. Intracortical myelin (T1w/T2w) increases, cortical thickness decreases, and the principal functional-connectivity gradient reverses along the type axis (all q of about 0.006 or less). Source-reconstructed resting-state MEG dynamics also track the axis: the intrinsic timescale shortens toward koniocortex (rho of about -0.46) and survives the null, as do the spectral centroid and the slow/fast band-power ratio, whereas the aperiodic 1/f exponent does not. The laminar marker genes are where cyto7 differs most from a quantisation of a continuous map: it shows a larger absolute correlation with the layer-IV marker RORB (rho = +0.78) and the layer-V marker FEZF2 (-0.59) than seven-level septiles of myelin, of the functional gradient or of a histological gradient, on four of five markers, while tying or losing to those septiles on the macroscale proxies. These are larger observed associations against three tested quantisations, not a test that the differences between maps are reliable, and they do not establish the accuracy of any individual boundary. Defined from anatomy alone, cyto7 recovers the sensorimotor-to-association hierarchy and, on these markers, some of the laminar signal underlying it. It is released with its full pipeline and an uncalibrated anatomical support score as a reusable substrate for structure-function and modelling studies.

neuroscience↗

Variational autoencoder for interpretable seizure onset phases detection

ObjectiveWe present the first deep learning framework for automated, time-resolved, per-channel annotation of ictal and Low-Voltage Fast Activity (LVFA) onsets in stereo electroencephalography (SEEG) recordings of patients with focal epilepsy. To our knowledge, no prior system jointly addresses these tasks on continuous single-channel recordings. ApproachA one-dimensional Variational Autoencoder (VAE) encodes 2-second SEEG segments into a 60-dimensional latent space and classifies them as interictal, ictal, or LVFA via a linear classifier. A postprocessing algorithm converts segment-level probabilities into per-channel onset markers at 0.5-second resolution. The system was trained and evaluated using subject-wise 5-fold cross-validation on 37 patients with manual ictal and LVFA annotations. Main resultsAt the segment level, the VAE classified the three classes with an average recall of 0.88. At the channel level, it reached an ictal recall of 0.84 (0.91 on Seizure Onset Zone channels) and LVFA recall of 0.74, with median onset latencies of 5.0 s and 0.86 s, respectively. As a seizure detector, the system achieved 99.1 % recall with 1 % false positives. Latent dimensions correlated with physiologically interpretable features (amplitude, band powers, spectral flatness, energy ratio). An ablation study showed that the VAEs reconstruction objective provides dual benefits over a discriminative encoder-only baseline: improved detection performance and stronger alignment between latent dimensions and these clinically meaningful features. SignificanceBy providing the first time-resolved per-channel framework for joint ictal and LVFA annotation, this work establishes a robust and explainable platform for automated SEEG analysis with potential to substantially reduce clinician workload during presurgical epilepsy evaluation.

neuroscience↗

Bridging local and global dynamics: a biologically grounded model for cooperative and competitive interactions in the brain

Functional brain networks exhibit both cooperative and competitive interactions, yet existing models--assuming purely excitatory long-range coupling--fail to account for the widespread anti-correlations observed in fMRI. Starting from a laminar neural mass frame-work, where each mass comprises distinct slow (alpha-band) and fast (gamma-band) oscillatory pyramidal subpopulations (P1 and P2), we show how laminar-specific long-range excitatory projections across neural mass parcels can give rise to both cooperation and competition via cross-frequency envelope coupling. We demonstrate that homologous connections across parcels (e.g., P1[->]P1 or P2[->]P2) induce positive correlations between the infra-slow amplitude fluctuations of alpha band envelopes in each parcel, as well as in the simulated fMRI BOLD signals. Conversely, heterologous connections (P1[->]P2) induce negative correlations. We tested this mechanism by building personalized whole-brain models for a cohort of 60 subjects in two steps. First, we inferred signed inter-parcel generative effective connectivity directly from resting-state fMRI using regularized maximum-entropy (Ising) models. Then we connected laminar neural masses to simulate BOLD dynamics by implementing positive and negative Ising connections via homologous and heterologous projections, respectively. Ising-derived cooperative/competitive connectivity modeling faithfully reproduced both static and dynamic functional connectivity patterns, as well as gamma power-BOLD correlation and partial alpha power-BOLD anticorrelation-outperforming structurally constrained and cooperative-only variants. This further demonstrates that functional data alone suffices to infer individualized connectivity. Together, these results provide a biologically grounded mechanistic model on how long-range excitatory circuits and local cross-frequency interactions shape the balance of cooperation and competition in large-scale brain dynamics. HighlightsO_LIWe introduce a biologically grounded mechanism for brain-wide cooperation and competition, based on laminar-specific cross-frequency coupling (CFC) between alpha and gamma oscillations, mediated solely by excitatory long-range projections targeting different cortical layers. C_LIO_LIOur models can reproduce key empirical observations, including the negative correlation between alpha power and BOLD, the positive correlation between gamma power and BOLD, and the presence of local laminar cross-frequency interactions consistent with invasive and EEG findings. C_LIO_LIModels generate BOLD signals autonomously, without recourse to external stochastic inputs. C_LIO_LIWe introduce a method for personalized Ising modeling from Ising data using a sparsity (L1) constraint to infer signed connectivity from limited BOLD data. C_LIO_LIWe validate the proposed mechanism in a cohort of 60 subjects using subject-specific generative whole-brain models, which not only improve the replication of static functional connectivity but also accurately capture dynamic spatiotemporal brain state transitions. C_LIO_LIOur whole-brain models display biologically realistic local dynamics, with laminar neural mass models preserving plausible EEG-like alpha and gamma oscillations while aligning with large-scale BOLD patterns--offering a unifying framework that bridges microscale laminar physiology and macroscale functional connectivity. C_LI

neuroscience↗

Personalized whole-brain models of seizure propagation

Objective: Computational modeling has recently emerged as a powerful tool to better understand seizure dynamics and guide new treatment strategies. This work aims to develop and personalize whole-brain computational models in epilepsy using multimodal clinical data to simulate and evaluate individualized therapeutic strategies. Approach: We present a computational framework that constructs patient-specific whole-brain models of seizure propagation by integrating SEEG, MRI, and diffusion MRI data. The pipeline uses neural mass models for each node in the network, simulating whole-brain dynamics. Model personalization involves adjusting global and local parameters representing the excitability of individual brain areas, using an evolutionary algorithm that aims to maximize the correlation between empirical and synthetic functional connectivity matrices derived from SEEG data. Main results: The resulting personalized models successfully reproduce individual seizure propagation patterns and can be used to simulate therapeutic interventions like surgery, stimulation, or pharmacological interventions within a unified physiological framework. Notably, model predictions reveal distinct patient-specific responses across interventions, including variable sensitivity to different pharmacological agents and identification of critical regions whose removal or modulation reduced seizure spread. Significance: This framework provides a mechanistic, interpretable approach to simulate and compare individualized treatment strategies. By integrating multimodal data into a unified whole-brain model, it has the potential to improve clinical decision-making in epilepsy by identifying accessible and functionally relevant targets.

neuroscience↗

Fast Interneuron Dysfunction in Laminar Neural Mass Model Reproduces Alzheimer's Oscillatory Biomarkers

Alzheimers disease (AD) is characterized by a progressive cognitive decline underpinned by disruptions in neural circuit dynamics. Early-stage AD is associated with cortical hyperexcitability, whereas later stages exhibit oscillatory slowing and hypoactivity, a progression observable in electrophysiological spectral characteristics. While previous studies have linked these changes to the dysfunction of fast-spiking parvalbumin-positive (PV) interneurons and neuronal loss associated with amyloid-beta (A{beta}) and hyperphos-phorylated tau (hp-{tau}) pathology, the precise mechanistic relationship between cellular and altered electrophysiology remains unclear. To study this relationship, we employed a Laminar Neural Mass Model that integrates excitatory and inhibitory neural populations within a biophysically informed columnar framework. The connectivity constant from PV cells to pyramidal neurons was gradually reduced to simulate the progressive neurotoxic effects of A{beta} oligomers. Other model parameters were systematically varied to compare with existing modeling literature and also to simulate the effects of hp-{tau}. All model predictions were compared to empirical M/EEG findings in the literature. Our simulations of PV interneuron dysfunction successfully reproduced the biphasic electrophysiological progression observed in AD: an early phase of hyperexcitability with increased gamma and alpha power, followed by oscillatory slowing and reduced spectral power. Alternative mechanisms and model parameters, such as increased excitatory drive, failed to replicate the observed biomarker trajectory. Additionally, to reconcile the hypoactivity and decreased firing rates observed in advanced AD stages, we combined the PV dysfunction model with a disruption of the pyramidal cell populations that reflects the neurotoxicity induced by hp-{tau}. Although this additional mechanism is not necessary to reproduce oscillatory changes in the isolated neural mass, it is crucial for aligning the model with evidence of reduced firing rates, metabolic activity, and cell loss and will enhance its applicability in future whole-brain modeling studies. These results support the hypothesis that at the local level, PV interneuron dysfunction is a primary driver of cortical electrophysiological alterations, while pyramidal neuron loss underlies later-stage severe hypoactivity. Our model provides a mechanistic framework for interpreting excitation-inhibition imbalance across AD progression, demonstrating the value of biophysically constrained models for interpreting electrophysiological biomarkers. Author summaryAlzheimers disease (AD) is not just a disorder of memory--it is a disease of brain networks. Before widespread neuronal loss occurs, the brain enters a state of hyperexcitability, which may contribute to disease progression. This hyperactivity is thought to arise from the selective dysfunction of inhibitory interneurons, particularly parvalbumin-positive (PV) interneurons, which play a crucial role in maintaining balanced brain activity. However, as the disease advances, a dramatic shift occurs, with neurons becoming progressively less active, leading to network breakdown. Understanding how this transition unfolds is essential for identifying new targets for early intervention. In this study, we developed a computational model of AD that represents the effects of amyloid-beta (A{beta}) oligomers and hyperphosphorylated tau (hp-{tau}) on neural circuits in a biologically meaningful way. Our model reproduces key features of M/EEG biomarkers, demonstrating that PV interneuron dysfunction leads to early hyperexcitability and the electrophysiological oscillatory changes characteristic of AD, while pyramidal cell pathology is necessary to drive later hypoactivity and network failure. By bridging molecular pathology mechanisms with mesoscale neural activity, our model provides a powerful tool for studying AD-related circuit dysfunction. It also highlights the importance of PV interneurons as a potential therapeutic target, paving the way for biologically informed interventions that could alter the course of the disease.

neuroscience↗

Biophysical modeling of the electric field magnitude and distribution induced by electrical stimulation with intracerebral electrodes

Intracranial electrodes are used clinically for diagnostic or therapeutic purposes, notably in drug-refractory epilepsy (DRE) among others. Visualization and quantification of the energy delivered through such electrodes is key to understanding how the resulting electric fields modulate neuronal excitability, i.e. the ratio between excitation and inhibition. Quantifying the electric field induced by electrical stimulation in a patient-specific manner is challenging, because these electric fields depend on a number of factors: electrode trajectory with respect to folded brain anatomy, biophysical (electrical conductivity / permittivity) properties of brain tissue and stimulation parameters such as electrode contacts position and intensity. Here, we aimed to evaluate various biophysical models for characterizing the electric fields induced by electrical stimulation in DRE patients undergoing stereoelectroencephalography (SEEG) recordings in the context of pre-surgical evaluation. This stimulation was performed with multiple-contact intracranial electrodes used in routine clinical practice. We introduced realistic 3D models of electrode geometry and trajectory in the neocortex. For the electrodes, we compared point (0D) and line (1D) sources approximations. For brain tissue, we considered three configurations of increasing complexity: a 6-layer spherical model, a toy model with a sulcus representation, replicating results from previous approaches; and went beyond the state-of-the-art by using a realistic head model geometry. Electrode geometry influenced the electric field distribution at close distances (~3 mm) from the electrode axis. For larger distances, the volume conductor geometry and electrical conductivity dominated electric field distribution. These results are the first step towards accurate and computationally tractable patient-specific models of electric fields induced by neuromodulation and neurostimulation procedures.

neuroscience↗

A physical neural mass model framework for the analysis of oscillatory generators from laminar electrophysiological recordings

Cortical function emerges from the interactions of multi-scale networks that may be studied at a high level using neural mass models (NMM), representing the mean activity of large numbers of neurons. In order to properly reproduce experimental data, these models require the addition of further elements. Here we provide a framework integrating conduction physics that can be used to simulate cortical electrophysiology measurements, particularly those obtained from multi-contact laminar electrodes. This is achieved by endowing NMMs with basic physical properties, such as the average laminar location of the apical and basal dendrites of pyramidal cell populations. We call this framework laminar NMM, or LaNMM for short. We then employ this framework to infer the location of oscillatory generators from laminar-resolved data collected from the prefrontal cortex in the macaque monkey. Based on the literature on columnar connectivity, we define a minimal neural mass model capable of generating amplitude and phase coupled slow (alpha/beta, 4-22 Hz) and fast (gamma, 30-250 Hz) oscillations. The synapse layer locations of the two pyramidal cell populations are treated as optimization parameters, together with two more LaNMM-specific parameters, to compare the models with the multi-contact recordings. We rank the candidate models using an optimization function that evaluates the match between the functional connectivity (FC) of the model and data, where the FC is defined by the covariance between bipolar voltage measurements at different cortical depths. The family of best solutions reproduces the FC of the observed electrophysiology while selecting locations of pyramidal cells and their synapses that result in the generation of fast activity at superficial layers and slow activity across most depths, in line with recent literature proposals. HighlightsO_LIWe provide a neural mass modeling formalism that includes a physical layer to simulate electrophysiology measurements. C_LIO_LITo analyze in-vivo data collected in the macaque monkey during a memory task, we propose a specific model with two coupled main circuits that can generate realistic electrophysiological signals in two important oscillatory regimes--the alpha/beta and the gamma bands. C_LIO_LIPhysical elements in the model shed light on the generation of oscillations in the two regimes and on the relative power distribution of fast and slow oscillatory signals across cortical depth, which we show can be altered by the choice of the reference location or method. C_LIO_LIThe model is contrasted with in-vivo data, with parameters adjusted by matching voltage statistics in the alpha/beta and gamma bands, leading to a solution with slow frequency components generated by synapses spanning most cortical layers and fast oscillations in superficial layers. C_LIO_LIThe resulting formalism provides useful tools and concepts to analyze and model data, with implications for understanding altered oscillatory EEG activity in dementia, Alzheimers disease and other disorders with oscillatory features. C_LI

neuroscience↗

Towards a mesoscale physical modeling framework for stereotactic-EEG recordings

ObjectiveStereotactic-EEG (SEEG) and scalp EEG recordings can be modeled using mesoscale neural mass population models (NMM). However, the relationship between those mathematical models and the physics of the measurements is unclear. In addition, it is challenging to represent SEEG data by combining NMMs and volume conductor models due to the intermediate spatial scale represented by these measurements. ApproachWe provide a framework combining the multicompartmental modeling formalism and a detailed geometrical model to simulate the transmembrane currents that appear in layer 3, 5 and 6 pyramidal cells due to a synaptic input. With this approach, it is possible to realistically simulate the current source density (CSD) depth profile inside a cortical patch due to inputs localized into a single cortical layer and the induced voltage measured by two SEEG contacts using a volume conductor model. Based on this approach, we built a framework to connect the activity of a NMM with a volume conductor model and we simulated an example of SEEG signal as a proof of concept. Main resultsCSD depends strongly on the distribution of the synaptic inputs onto the different cortical layers and the equivalent current dipole strengths display substantial differences (of up to a factor of four in magnitude in our example). Thus, the inputs coming from different neural populations do not contribute equally to the electrophysiological recordings. A direct consequence of this is that the raw output of neural mass models is not a good proxy for electrical recordings. We also show that the simplest CSD model that can accurately reproduce SEEG measurements can be constructed from discrete monopolar sources (one per cortical layer). SignificanceOur results highlight the importance of including a physical model in NMMs to represent measurements. We provide a framework connecting microscale neuron models with the neural mass formalism and with physical models of the measurement process that can improve the accuracy of predicted electrophysiological recordings.

neuroscience↗

A personalizable autonomous neural mass model of epileptic seizures

Work in the last two decades has shown that neural mass models (NMM) can realistically reproduce and explain epileptic seizure transitions as recorded by electrophysiological methods (EEG, SEEG). In previous work, advances were achieved by increasing excitation and heuristically varying network inhibitory coupling parameters in the models. Based on these early studies, we provide a laminar NMM capable of realistically reproducing the electrical activity recorded by SEEG in the epileptogenic zone during interictal to ictal states. With the exception of the external noise input into the pyramidal cell population, the model dynamics are autonomous. By setting the system at a point close to bifurcation, seizure-like transitions are generated, including pre-ictal spikes, low voltage fast activity, and ictal rhythmic activity. A novel element in the model is a physiologically motivated algorithm for chloride dynamics: the gain of GABAergic post-synaptic potentials is modulated by the pathological accumulation of chloride in pyramidal cells due to high inhibitory input and/or dysfunctional chloride transport. In addition, in order to simulate SEEG signals for comparison with real seizure recordings, the NMM is embedded first in a layered model of the neocortex and then in a realistic physical model. We compare modeling results with data from four epilepsy patient cases. By including key pathophysiological mechanisms, the proposed framework captures succinctly the electrophysiological phenomenology observed in ictal states, paving the way for robust personalization methods based on NMMs.

neuroscience↗

Modeling implanted metals in electrical stimulation applications

BackgroundMetal implants impact the dosimetry assessment in electrical stimulation techniques. Therefore, they need to be included in numerical models. While currents in the body are ionic, metals only allow electron transport. In fact, charge transfer between tissues and metals requires electric fields to drive the electrochemical reactions at the interface. Thus, metal implants may act as insulators or as conductors depending on the scenario. Objective/HypothesisThe aim of this paper is to provide a theoretical argument that guides the choice of the correct representation of metal implants using purely electrical models but considering the electrochemical nature of the problem in the technology of interest. MethodsWe built a simple model of a metal implant exposed to a homogeneous electric field of various magnitudes to represent both weak (e.g., tDCS), medium (TMS) or strong field stimulation. The same geometry was solved using two different models: a purely electric one (with different conductivities for the implant), and an electrochemical one. As an example of application, we also modeled a transcranial electrical stimulation (tES) treatment in a realistic head model with a skull plate using a high and low conductivity value for the plate. ResultsMetal implants generally act as electric insulators when exposed to electric fields up to around 100 V/m (tES and TMS range) and they only resemble a perfect conductor for fields in the order of 1000 V/m and above. The results are independent of the implants metal, but they depend on its geometry. Conclusion(s)Metal implants can be accurately represented by a simple electrical model of constant conductivity, but an incorrect model choice can lead to large errors in the dosimetry assessment. In particular, tES modeling with implants incorrectly treated as conductors can lead to errors of 50% in induced fields or more. Our results can be used as a guide to select the correct model in each scenario.

bioengineering↗