Biology subjects

Bartolomei, F.

Publications and source records attributed to Bartolomei, F..

At least 19 recordsLinked to original sources

bioRxiv2026

Higher-order interdependencies of the epileptogenic network in presurgical resting-state fMRI

Drug-resistant focal epilepsy (DRFE) is understood as a complex brain network disorder. As a result, conventional pair-wise functional connectivity may fail to capture the higher-order interactions between groups of epileptogenic regions. We applied multivariate information theory to stereotactic electroencephalography-defined networks in retrospective resting-state fMRI analysis to determine whether the overall strength and dominant type of interdependencies between three or more regions, represented by the S-information and O-information respectively, could offer clinically relevant insights prior to surgical intervention in DRFE. We found networks involving the epileptogenic zone to be mostly dominated by re-dundant information-sharing, which was predicted by stronger structural and functional connections between regions. Non-temporal lobe epilepsy patients demonstrated greater redundancy and stronger comparative interdependencies than temporal lobe epilepsy patients, in line with a more complex network architecture. Among non-temporal lobe cases with postoperative follow-up, not seizure-free patients displayed more positive O-information deviation scores than seizure-free patients, suggesting elevated redundancy in networks associated with poorer surgical outcomes. Our findings potentiate the S-information and O-information as complementary higher-order descriptors of the epileptogenic network within personalised presurgical assessments.

neuroscience↗

bioRxiv2026

Causal mapping of self-motion networks in the human brain

Although functional neuroimaging studies using caloric and galvanic vestibular stimulation have identified a distributed cortical network involved in human vestibular processing, including the posterior insula, parietal operculum, temporo-parietal junction, cingulate and frontal areas, the causal contribution of specific brain regions to vestibular self-motion perception remains poorly understood. Invasive electrical brain stimulation (EBS) during stereoelectroencephalography (SEEG) offers a unique opportunity to causally map the cortical networks underlying vestibular self-motion perception in humans. Here, we retrospectively analyzed EBS-induced vestibular percepts in 354 patients with drug-resistant epilepsy undergoing SEEG. A total of 19,708 stimulations (11,004 at 50 Hz and 8,704 at 1 Hz) yielded 3,015 clinical responses. Vestibular self-motion illusions were defined as sensations of vertigo, dizziness, whole-body rotation, or translation occurring without corresponding physical movement. Stimulation sites were assigned to the seven large-scale functional networks of the Schaefer-Yeo atlas to characterize the network organization of vestibular self-motion perception. Vestibular self-motion sensations were elicited in 46 patients during 86 EBS delivered outside epileptogenic and lesional regions. Percepts ranged from nonspecific vertigo and dizziness (54.7%) to more explicit rotational (30.2%) and translational (15.1%) self-motion illusions. Vestibular responses were most commonly evoked by stimulation of the insula, medial temporal regions, cingulate cortex, inferior frontal gyrus, and premotor cortices. Network-level mapping using the Schaefer-Yeo atlas revealed a non-uniform distribution of vestibular sites across large-scale functional networks, with the highest representation within the salience/ventral attention, visual, and dorsal attention networks. Together, these findings provide causal evidence that vestibular self-motion perception emerges from activity within a distributed cortical network involved in attentional control and multisensory processing. Beyond advancing our understanding of human vestibular processing, these findings may have clinical relevance for disorders involving altered self-motion perception, including vestibular epilepsy and functional neurological disorders.

neuroscience↗

bioRxiv2026

Surprise gates two distinct mechanisms to support memorability in music

Music is a uniquely memorable human creation that, when skillfully composed, can persist in individual memory (as in e.g., earworms) and cultural transmission (e.g. global hits or anthems). While both acoustic and statistical properties are known to influence a songs memorability, the neural mechanisms that facilitate the engramming of certain musical sequences remain unclear. Current theories suggest that memory systems function as predictive internal models, enhancing learning when expectations are violated. Yet expected stimuli, by aligning with and reinforcing prior knowledge, also enhance memorability. How these two opposing processes arise from the brains sensitivity to statistical regularities, especially in naturalistic sequences, is not well understood. Here, we leveraged musics intrinsic balance between expectancy and surprise, and examined the neural correlates of minutes-scale memorability using intracranial EEG recordings from nine patients with epilepsy performing a musical memory task. Quantifying the statistical surprise of each musical excerpt with PolyRNN, a polyphonic model of musical expectations, we uncovered a U-shaped relationship between musical surprise and memory performance: both highly expected and highly surprising melodies led to greater memorability. While neural pattern similarity between song repetitions was enhanced for low-surprise stimuli, high-surprise stimuli enhanced neural pattern separability in medial temporal regions, each mediating memorability in distinguishable ways. These findings reveal complementary neural mechanisms through which statistical structure shapes musical sequence memory, clarifying how the brain encodes complex, ecologically valid stimuli.

neuroscience↗

bioRxiv2025

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↗

bioRxiv2025

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↗

bioRxiv2024

Multi-stream predictions in human auditory cortex during natural music listening

Musical expectations shape how we perceive and process music, yet current computational models are limited to monophonic or simplified stimuli. The study of the neural processes underlying musical expectations in real-world music therefore requires significant advances in our statistical modeling of these stimuli. We present PolyRNN, a recurrent neural network designed to model expectations in naturalistic, polyphonic music. We recorded neurophysiological activity non invasively (MEG) and within the human brain (intracranial EEG) while participants listened to naturally expressive piano recordings. The musical expectations estimated by the model are encoded in evoked P2- and P3-like components in auditory regions. Comparing PolyRNN to a state-of-the-art generative music model, we show that piano roll representations are best suited to represent expectations in polyphonic contexts. Overall, our approach provides a new way to capture the musical expectations emerging from natural music listening, and enables the study of predictive processes in more ecologically valid settings.

neuroscience↗

bioRxiv2024

NeoCOMM: A Neocortical Neuroinspired Computational Model for the Reconstruction and Simulation of Epileptiform Events

Understanding the pathophysiological dynamics which underline interictal epileptiform events (IEEs) such as epileptic spikes, spike-and-waves or High-frequency oscillations (HFOs) is of major importance in the context of neocortical refractory epilepsy, as it paves the way for the development of novel therapies. Typically, these events are detected in local field potential (LFP) recordings obtained through depth electrodes during pre-surgical investigations. Although essential, the underlying pathophysiological mechanisms for the generation of these epileptic neuromarkers remain unclear. The aim of this paper is to propose a novel neurophysiologically relevant reconstruction of the neocortical microcircuitry in the context of epilepsy. This reconstruction intends to facilitate the analysis of a comprehensive set of parameters encompassing physiological, morphological, and biophysical aspects that directly impact the generation and recording of different IEEs. Accordingly, a novel microscale computational model of an epileptic neocortical column was introduced. This model incorporates the intricate multilayered structure of the cortex and allows for the simulation of realistic interictal epileptic signals. The proposed model was validated through comparisons with real IEEs recorded using intracranial stereo-electroencephalography (SEEG) signals from both humans and animals. Using the model, the user can recreate epileptiform patterns observed in different species (human, rodent, and mouse) and study the intracellular activity associated with these patterns. Our model allowed us to unravel the relationship between glutamatergic and GABAergic synaptic transmission of the epileptic neural network and the type of generated IEE. Moreover, sensitivity analyses allowed for the exploration of the pathophysiological parameters responsible for the transitions between these events. Finally, the presented modeling framework also provides an Electrode Tissue Model (ETI) that adds realism to the simulated signals and offers the possibility of studying their sensitivity to the electrode characteristics. The model (NeoCoMM) presented in this work can be of great use in different applications since it offers an in silico framework for sensitivity analysis and hypothesis testing. It can also be used as a starting point for more complex studies.

bioengineering↗

bioRxiv2023

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↗

bioRxiv2022

The role of the thalamus in human reinforcement learning

Although the thalamus is supposed to be involved in reinforcement-based decision-making, there is no direct evidence regarding the involvement of this subcortical structure in humans. To fill this gap, we leveraged rare intra-thalamic electrophysiological recordings in patients and found that temporally structured thalamic oscillations encode key learning signals. Our findings also provide neural insight into the computational mechanisms of action inhibition in punishment avoidance learning.

neuroscience↗

bioRxiv2022

Multi-scale structural alterations of basal ganglia in focal epilepsy as demonstrated by 7T MRI

Focal epilepsy is characterized by repeated spontaneous seizures that originate from cortical epileptogenic zone networks (EZN). More recently, analysis of intracerebral recordings showed that subcortical structures, and in particular the thalamus, play an important role in facilitating and/or propagating epileptic activity. This supports previously reported structural alterations of these structures. Nonetheless, between-patient differences in EZN (e.g., temporal vs. non-temporal lobe epilepsy) as well as other clinical features (e.g., number of epileptogenic regions) might impact the magnitude as well as spatial distribution of subcortical structural changes. Here we used 7 Tesla MRI T1 data to provide a comprehensive description of subcortical morphological (volume, tissue deformation, and shape) and longitudinal relaxation (T1) changes in focal epilepsy patients to evaluate the impact of the EZN and patient-specific clinical features. Our results showed widespread morphometric and T1 changes. Focusing on the thalamus, atrophy varied across nuclei but appeared most prominent for the TLE group and the ipsilateral side, while shortening of T1 was observed for the lateral thalamus, in particular. Multivariate analyses across thalamic nuclei and basal ganglia showed that volume acted as the dominant discriminator between patients and controls, while (posterolateral) thalamic T1 measures looked promising to further differentiate patients based on EZN. In particular, the observed differences in T1 changes between thalamic nuclei indicated differential involvement of thalamic nuclei based on EZN. Finally, the number of epileptogenic regions was found to best explain the observed variability between patients. To conclude, this work revealed multi-scale subcortical alterations in focal epilepsy as well as their dependence on several clinical characteristics. Our results provide a basis for further, in-depth investigations using (quantitative) MRI and SEEG data and warrant further personalization of intervention strategies, such as deep brain stimulation, for treating focal epilepsy patients.

neuroscience↗

bioRxiv2022

A spherical harmonics-based framework for representing steady-state shifts in neuron models induced by weak electric fields

ObjectiveWe provide a systematic framework for quantifying the effect of externally applied weak electric fields on realistic neuron compartment models as captured by physiologically relevant quantities such as the membrane potential or transmembrane current as a function of the orientation of the field. ApproachWe define a response function as the steady-state change of the membrane potential induced by a canonical external field of 1 V/m as a function of its orientation. We estimate the function values through simulations employing reconstructions of the rat somatosensory cortex from the Blue Brain Project. The response of different cell types is simulated using the NEURON simulation environment. We represent and analyze the angular response as an expansion in spherical harmonics. Main resultsWe report membrane perturbation values comparable to those in the literature, extend them to different cell types, and provide their profiles as spherical harmonic coefficients. We show that at rest, responses are dominated by their dipole terms ({ell} = 1), in agreement with experimental findings and compartment theory. Indeed, we show analytically that for a passive cell, only the dipole term is nonzero. However, while minor, other terms are relevant for states different from resting. In particular, we show how{ell} = 0 and{ell} = 2 terms can modify the function to induce asymmetries in the response. SignificanceThis work provides a practical framework for the representation of the effects of weak electric fields on different neuron types and their main regions--an important milestone for developing micro- and mesoscale models and optimizing brain stimulation solutions.

neuroscience↗

bioRxiv2022

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↗

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↗

bioRxiv2022

Magnetoencephalography can reveal deep brain network activities linked to memory processes

Recording from deep neural structures such as hippocampus non-invasively and yet with high temporal resolution remains a major challenge for human neuroscience. Although it has been proposed that deep neuronal activity might be recordable during cognitive tasks using magnetoencephalography (MEG), this remains to be demonstrated as the contribution of deep structures to MEG recordings may be too small to be detected or might be eclipsed by the activity of large-scale neocortical networks. In the present study, we disentangled mesial activity and large-scale networks from the MEG signals thanks to blind source separation (BSS). We then validated the MEG BSS components using intracerebral EEG signals recorded simultaneously in patients during their presurgical evaluation of epilepsy. In the MEG signals obtained during a memory task involving the recognition of old and new images, we identified with BSS a putative mesial component, which was present in all patients and all control subjects. The time course of the component selectively correlated with SEEG signals recorded from hippocampus and rhinal cortex, thus confirming its mesial origin. This finding complements previous studies with epileptic activity and opens new possibilities for using MEG to study deep brain structures in cognition and in brain disorders.

neuroscience↗

bioRxiv2021

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↗

bioRxiv2021

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↗

bioRxiv2021

Patient-specific network connectivitycombined with a next generation neural massmodel to test clinical hypothesis of seizurepropagation

Dynamics underlying epileptic seizures span multiple scales in space and time, therefore, understanding seizure mechanisms requires identifying the relations between seizure components within and across these scales, together with the analysis of their dynamical repertoire. In this view, mathematical models have been developed, ranging from single neuron to neural population. In this study we consider a neural mass model able to exactly reproduce the dynamics of heterogeneous spiking neural networks. We combine the mathematical modelling with structural information from non-invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test clinical hypothesis. We provide a comprehensive study on the effect of external drives on neuronal networks exhibiting multistability, in order to investigate the role played by the neuroanatomical connectivity matrices in shaping the emergent dynamics. In particular we systematically investigate the conditions under which the network displays a transition from a low activity regime to a high activity state, which we identify with a seizure-like event. This approach allows us to study the biophysical parameters and variables leading to multiple recruitment events at the network level. We further exploit topological network measures in order to explain the differences and the analogies among the subjects and their brain regions, in showing recruitment events at different parameter values. We demonstrate, along the example of diffusion-weighted magnetic resonance imaging (MRI) connectomes of 20 healthy subjects and 15 epileptic patients, that individual variations in structural connectivity, when linked with mathematical dynamic models, have the capacity to explain changes in spatiotemporal organization of brain dynamics, as observed in network-based brain disorders. In particular, for epileptic patients, by means of the integration of the clinical hypotheses on the epileptogenic zone (EZ), i.e. the local network where highly synchronous seizures originate, we have identified the sequence of recruitment events and discussed their links with the topological properties of the specific connectomes. The predictions made on the basis of the implemented set of exact mean-field equations turn out to be in line with the clinical pre-surgical evaluation on recruited secondary networks.

neuroscience↗

bioRxiv2020

Computational modeling of seizure spread on a cortical surface

In the field of computational epilepsy, neural field models helped to understand some large-scale features of seizure dynamics. These insights however remain on general levels, without translation to the clinical settings via personalization of the model with the patient-specific structure. In particular, a link was suggested between epileptic seizures spreading across the cortical surface and the so-called theta-alpha activity (TAA) pattern seen on intracranial electrographic signals, yet this link was not demonstrated on a patient-specific level. Here we present a single patient computational study linking the seizure spreading across the patient-specific cortical surface with a specific instance of the TAA pattern recorded in the patient. Using the realistic geometry of the cortical surface we perform the simulations of seizure dynamics in The Virtual Brain platform, and we show that the simulated electrographic signals qualitatively agree with the recorded signals. Furthermore, the comparison with the simulations performed on surrogate surfaces reveals that the best quantitative fit is obtained for the real surface. The work illustrates how the patient-specific cortical geometry can be utilized in The Virtual Brain for personalized model building, and the importance of such approach.

neuroscience↗