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Individuals with drug-resistant focal epilepsy are candidates for surgical treatment as a curative option. Before surgery can take place, the patient must have a presurgical evaluation to establish whether and how surgical treatment might stop their seizures without causing neurological deficits. Virtual brains are a new digital modelling technology that map the brain network of a person with epilepsy, using data derived from MRI. This technique produces a computer simulation of seizures and brain imaging signals, such as those that would be recorded with intracranial EEG. When combined with machine learning, virtual brains can be used to estimate the extent and organisation of the epileptogenic zone (ie, the brain regions related to seizure generation and the spatiotemporal dynamics during seizure onset). Virtual brains could, in the future, be used for clinical decision making, to improve precision in localisation of seizure activity, and for surgical planning, but at the moment these models have some limitations, such as low spatial resolution. As evidence accumulates in support of the predictive power of personalised virtual brain models, and as methods are tested in clinical trials, virtual brains might inform clinical practice in the near future.

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

In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.

Objective Microburst vagus nerve stimulation (μVNS) may reduce seizure frequency in drug‐resistant epilepsy (DRE) via targeted thalamic plasticity. We prospectively investigated the role of thalamic resting‐state functional connectivity (rsFC) in μVNS effects and hypothesized a relationship between thalamic rsFC and long‐term seizure frequency changes. Methods Eighteen patients with focal (Foc) and 10 with generalized (Gen) DRE underwent 3T resting‐state fMRI at 2 weeks post‐implantation and before starting (baseline) and after 6 months of μVNS. Resting‐state fMRI analyses were performed using in‐house Matlab scripts. Nonparametric permutation tests assessed baseline group differences in rsFC between automated anatomical atlas (AAL) brain regions and longitudinal rsFC changes. Spearman correlations were performed between baseline thalamus rsFC and percent change in seizures after 12 months of μVNS (PCHG‐12). Results Gen had stronger baseline rsFC between AAL regions compared with Foc. Gen showed significant baseline‐to‐6‐month rsFC changes, including a decrease between the left thalamus and left superior parietal lobule. Foc showed significant rsFC changes, including increases between the left thalamus and four regions: right middle cingulum (Lthal‐Rmidcing), right angular gyrus (Lthal‐Rangular), left angular gyrus (Lthal‐Langular), and left precuneus (Lthal‐Lprecuneus), and increases between the right thalamus and left angular gyrus (Rthal‐Langular). Foc showed negative correlations between PCHG‐12 and baseline rsFC for Lthal‐Rmidcing (ρ = −0.61; p = 0.007), Lthal‐Rangular (ρ = −0.51; p = 0.032), and Rthal‐Langular (ρ = −0.52; p = 0.028). Significance Baseline thalamic rsFC is correlated with long‐term seizure frequency reduction with μVNS in focal DRE. RsFC may be a potential biomarker for μVNS response in focal‐onset seizure patients; this relationship may be different in DRE with generalized seizures. Plain Language Summary We investigated how stimulating the vagus nerve in a specific manner may reduce seizure frequency in people with drug‐resistant epilepsy through its actions on how a part of the brain called the thalamus is functionally connected to other brain regions. People with focal seizures showed a different pattern of changes in thalamus functional connectivity than those with generalized seizures after 6 months of stimulation. People with focal seizures also showed that stronger functional connectivity between the thalamus and other brain regions at baseline was related to a greater decrease in seizure frequency after 12 months of stimulation.

Abstract BACKGROUND Deep brain stimulation (DBS) of the anterior nucleus of the thalamus (ANT) is an increasingly utilized treatment of drug-resistant epilepsy. To date, the effect of high-frequency stimulation (HFS) vs low-frequency stimulation (LFS) in ANT DBS is poorly understood. OBJECTIVE To assess differences in the acute effect of LFS vs HFS in ANT DBS utilizing blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). METHODS In this prospective study of 5 patients with ANT DBS for epilepsy, BOLD activation and deactivation were modeled for 145-Hz and 30-Hz ANT stimulation using an fMRI block design. Data were analyzed with a general linear model and combined via 2-stage mixed-effects analysis. Z-score difference maps were nonparametrically thresholded using cluster threshold of z > 3.1 and a (corrected) cluster significance threshold of P = .05. RESULTS HFS produced significantly greater activation within multiple regions, in particular the limbic and default mode network (DMN). LFS produced minimal activation and failed to produce significant activation within these same networks. HFS produced widespread cortical and subcortical deactivation sparing most of the limbic and DMN regions. Meanwhile, LFS produced deactivation in most DMN and limbic structures. CONCLUSION Our results show that HFS and LFS produce substantial variability in both local and downstream network effects. In particular, largely opposing effects were identified within the limbic network and DMN. These findings may serve as a mechanistic basis for understanding the potential of HFS vs LFS in various epilepsy syndromes.

The vast net of fibres within and underneath the cortex is optimised to support the convergence of different levels of brain organisation. Here, we propose a novel coordinate system of the human cortex based on an advanced model of its connectivity. Our approach is inspired by seminal, but so far largely neglected models of cortico–cortical wiring established by postmortem anatomical studies and capitalises on cutting-edge in vivo neuroimaging and machine learning. The new model expands the currently prevailing diffusion magnetic resonance imaging (MRI) tractography approach by incorporation of additional features of cortical microstructure and cortico–cortical proximity. Studying several datasets and different parcellation schemes, we could show that our coordinate system robustly recapitulates established sensory-limbic and anterior–posterior dimensions of brain organisation. A series of validation experiments showed that the new wiring space reflects cortical microcircuit features (including pyramidal neuron depth and glial expression) and allowed for competitive simulations of functional connectivity and dynamics based on resting-state functional magnetic resonance imaging (rs-fMRI) and human intracranial electroencephalography (EEG) coherence. Our results advance our understanding of how cell-specific neurobiological gradients produce a hierarchical cortical wiring scheme that is concordant with increasing functional sophistication of human brain organisation. Our evaluations demonstrate the cortical wiring space bridges across scales of neural organisation and can be easily translated to single individuals.

•A novel physiology-based driven patient specific DL model for EEG inverse problem in epilepsy.•Realistic simulated intracerebral electrical activity with patient's head model for the model training.•Simultaneous extraction of spatial feature and temporal dependency of HR-EEG signals using a well-tailored DL-Model.•Higher performance in terms of epileptic source localization and time course reconstruction for both simulated and real epileptic HR-EEG signals. Identifying the location, the spatial extent and the electrical activity of distributed brain sources in the context of epilepsy through ElectroEncephaloGraphy (EEG) recordings is a challenging task because of the highly ill-posed nature of the underlying Electrophysiological Source Imaging (ESI) problem. To guarantee a unique solution, most existing ESI methods pay more attention to solve this inverse problem by imposing physiological constraints. This paper proposes an efficient ESI approach based on simulation-driven deep learning. Epileptic High-resolution 256-channels scalp EEG (Hr-EEG) signals are simulated in a realistic manner to train the proposed patient-specific model. More particularly, a computational neural mass model developed in our team is used to generate the temporal dynamics of the activity of each dipole while the forward problem is solved using a patient-specific three-shell realistic head model and the boundary element method. A Temporal Convolutional Network (TCN) is considered in the proposed model to capture local spatial patterns. To enable the model to observe the EEG signals from different scale levels, the multi-scale strategy is leveraged to capture the overall features and fine-grain features by adjusting the convolutional kernel size. Then, the Long Short-Term Memory (LSTM) is used to extract temporal dependencies among the computed spatial features. The performance of the proposed method is evaluated through three different scenarios of realistic synthetic interictal Hr-EEG data as well as on real interictal Hr-EEG data acquired in three patients with drug-resistant partial epilepsy, during their presurgical evaluation. A performance comparison study is also conducted with two other deep learning-based methods and four classical ESI techniques. The proposed model achieved a Dipole Localization Error (DLE) of 1.39 and Normalized Hamming Distance (NHD) of 0.28 in the case of one patch with SNR of 10 dB. In the case of two uncorrelated patches with an SNR of 10 dB, obtained DLE and NHD were respectively 1.50 and 0.28. Even in the more challenging scenario of two correlated patches with an SNR of 10 dB, the proposed approach still achieved a DLE of 3.74 and an NHD of 0.43. The results obtained on simulated data demonstrate that the proposed method outperforms the existing methods for different signal-to-noise and source configurations. The good behavior of the proposed method is also confirmed on real interictal EEG data. The robustness with respect to noise makes it a promising and alternative tool to localize epileptic brain areas and to reconstruct their electrical activities from EEG signals.

PurposeTo review recent advances in structural MRI post-processing for pediatric drug-resistant epilepsy, with emphasis on artificial intelligence–driven and quantitative techniques, including MELD-Graph, MAP18, FLAT1, and SUPR-FLAIR, and to evaluate their impact on lesion detection, epileptogenic zone localization, and presurgical planning.MethodsNovel post-processing approaches were examined with respect to their computational foundations, imaging requirements, and diagnostic performance. Techniques employing machine learning, deep learning, voxel-based morphometry, cortical surface projection, and FLAIR/T1 ratio mapping were assessed for their applicability in children and their integration into multimodal evaluation pathways alongside electrophysiology and functional imaging.ResultsAdvanced post-processing tools substantially increase sensitivity for detecting subtle cortical abnormalities, particularly in MRI-negative pediatric epilepsy. MELD-Graph identify features of focal cortical dysplasia through automated surface-based analysis and deep neural network classification, achieving notable lesion detection even when conventional MRI findings are normal. MAP18 provides complementary voxel-wise morphometric assessment, improving specificity and benefiting from optimized structural sequences. FLAT1 enhances lesion conspicuity by quantifying FLAIR/T1 signal relationships, while SUPR-FLAIR improves visualization of cortical signal abnormalities through normalized FLAIR intensity projection onto the cortical surface. When incorporated into multimodal diagnostic workflows, these methods refine epileptogenic zone localization, inform individualized surgical strategies, and can reduce reliance on invasive testing.ConclusionAdvanced structural MRI post-processing is transforming the neuroradiological evaluation of pediatric drug-resistant epilepsy. By revealing subtle cortical abnormalities not visible on conventional imaging, these tools support more precise lesion characterization and surgical planning. Ongoing efforts toward standardization, clinical validation, and workflow integration will be essential to ensure widespread adoption and maximize clinical impact within precision-medicine approaches to pediatric epilepsy.Advanced structural MRI post-processing tools significantly improve the detection of subtle epileptogenic lesions like focal cortical dysplasia in pediatric epilepsy.Accurate localization requires multimodal integration of structural, metabolic, and functional data, with electrical imaging.Imaging informs personalized surgical planning by mapping eloquent cortex and predicting post-surgical seizure and cognitive outcomes using virtual models

Objective This study aimed to investigate [ 18 F]fluorodeoxyglucose positron emission tomography ([ 18 F]FDG PET) mapping for cerebral glucose metabolism in drug-sensitive and drug-resistant pediatric epilepsy patients. Methods This retrospective study enrolled 40 patients and 25 controls. Patients were categorized into drug-sensitive epilepsy ( n  = 22) and drug-resistant epilepsy ( n  = 18) according to the seizure frequency at follow-up. All patients underwent two [ 18 F]FDG PET scans separated by a minimum of one year. Absolute asymmetry index (|AI|) was calculated for assessing metabolic differences and changes in epileptic foci. Statistical Parametric Mapping (SPM) was utilized to reveal voxel-wise metabolic characteristics and alterations throughout the brain. Network analysis based on graph theory was used to investigate network-level differences between the two patient groups. Results The drug-sensitive group showed a lower |AI| at both baseline ( P  = 0.038) and follow-up ( P  = 0.003) PET scans than the drug-resistant group. |AI| decreased in the drug-sensitive group and increased in the drug-resistant group across scans, but these trends were not statistically significant ( P  = 0.240 and P  = 0.450, respectively). Both groups exhibited hypometabolism at baseline. The drug-sensitive group showed less hypometabolic brain regions than the drug-resistant group. The drug-sensitive maintained stable level of hypometabolism between the two scans, whereas the drug-resistant group showed an increasing hypometabolism. Network analysis demonstrated that the drug-sensitive group had a higher global efficiency, average degree, and clustering, along with a shorter characteristic path length compared to the drug-resistant group. Conclusions For the first time, this study revealed in vivo cerebral glucose metabolic pattern of nonsurgical pediatric epilepsy patients treated by antiepileptic drugs. Especially, drug-resistant epilepsy patients represented significantly extensive and progressive hypometabolism with inefficient brain network connectivity compared with drug-sensitive epilepsy. [ 18 F]FDG PET imaging may be a potential visual approach for theranostics of epilepsy patients.

In patients with drug-resistant epilepsy, difficulties in identifying the epileptogenic zone are well known to correlate with poorer clinical outcomes post-surgery. The integration of PET and MRI in the presurgical assessment of pediatric patients likely improves diagnostic precision by confirming or widening treatment targets. PET and MRI together offer superior insights compared to either modality alone. For instance, PET highlights abnormal glucose metabolism, while MRI precisely localizes structural anomalies, providing a comprehensive understanding of the epileptogenic zone. Furthermore, both methodologies, whether utilized through simultaneous PET/MRI scanning or the co-registration of separately acquired PET and MRI data, present unique advantages, having complementary roles in lesional and non-lesional cases. Simultaneous FDG-PET/MRI provides precise co-registration of functional (PET) and structural (MR) imaging in a convenient one-stop-shop approach, which minimizes sedation time and reduces radiation exposure in children. Commercially available fusion software that allows retrospective co-registration of separately acquired PET and MRI images is a commonly used alternative. This review provides an overview and illustrative cases that highlight the role of combining 18F-FDG-PET and MRI imaging and shares the authors’ decade-long experience utilizing simultaneous PET/MRI in the presurgical evaluation of pediatric epilepsy.