Insights on cognitive reorganization after hemispherectomy in Rasmussen's encephalitis. A narrative review

Rasmussen's encephalitis is a rare neurological pathology affecting one cerebral hemisphere, therefore, posing unique challenges. Patients may undergo hemispherectomy, a surgical procedure after which cognitive development occurs in the isolated contralateral hemisphere. This rare situation provides an excellent opportunity to evaluate brain plasticity and cognitive recovery at a hemispheric level. This literature review synthesizes the existing body of research on cognitive recovery following hemispherectomy in Rasmussen patients, considering cognitive domains and modulatory factors that influence cognitive outcomes. While language function has traditionally been the focus of postoperative assessments, there is a growing acknowledgment of the need to broaden the scope of language investigation in interaction with other cognitive domains and to consider cognitive scaffolding in development and recovery. By synthesizing findings reported in the literature, we delineate how language functions may find support from the right hemisphere after left hemispherectomy, but also how, beyond language, global cognitive functioning is affected. We highlight the critical influence of several factors on postoperative cognitive outcomes, including the timing of hemispherectomy and the baseline preoperative cognitive status, pointing to early surgical intervention as predictive of better cognitive outcomes. However, further specific studies are needed to confirm this correlation. This review aims to emphasize a better understanding of mechanisms underlying hemispheric specialization and plasticity in humans, which are particularly important for both clinical and research advancements. This narrative review underscores the need for an integrative approach based on cognitive scaffolding to provide a comprehensive understanding of mechanisms underlying the reorganization in Rasmussen patients after hemispherectomy.

Neuronal synchrony and critical bistability: Mechanistic biomarkers for localizing the epileptogenic network

OBJECTIVE

Postsurgical seizure freedom in drug-resistant epilepsy (DRE) patients varies from 30% to 80%, implying that in many cases the current approaches fail to fully map the epileptogenic zone (EZ). We aimed to advance a novel approach to better characterize epileptogenicity and investigate whether the EZ encompasses a broader epileptogenic network (EpiNet) beyond the seizure zone (SZ) that exhibits seizure activity.

METHODS

We first used computational modeling to test putative complex systems-driven and systems neuroscience-driven mechanistic biomarkers for epileptogenicity. We then used these biomarkers to extract features from resting-state stereoelectroencephalograms recorded from DRE patients and trained supervised classifiers to localize the SZ against gold standard clinical localization. To further explore the prevalence of pathological features in an extended brain network outside of the clinically identified SZ, we also used unsupervised classification.

RESULTS

Supervised SZ classification trained on individual features achieved accuracies of .6-.7 area under the receiver operating characteristic curve (AUC). Combining all criticality and synchrony features further improved the AUC to .85. Unsupervised classification discovered an EpiNet-like cluster of brain regions, in which 51% of brain regions were outside of the SZ. Brain regions in the EpiNet-like cluster engaged in interareal hypersynchrony and locally exhibited high-amplitude bistability and excessive inhibition, which was strikingly similar to the high seizure risk regime revealed by our computational modeling.

SIGNIFICANCE

The finding that combining biomarkers improves SZ localization accuracy indicates that the novel mechanistic biomarkers for epileptogenicity employed here yield synergistic information. On the other hand, the discovery of SZ-like brain dynamics outside of the clinically defined SZ provides empirical evidence of an extended pathophysiological EpiNet.

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy

Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

Modeling a neurological disorder as the result of an operator acting on the brain: A first sketch based on network channel modeling

The brain is a complex network, and diseases can alter its structures and connections between regions. Therefore, we can try to formalize the action of diseases by using operators acting on the brain network. Here, we propose a conceptual model of the brain, seen as a multilayer network, whose intra-lobe interactions are formalized as the diagonal blocks of an adjacency matrix. We propose a general and abstract definition of disease as an operator altering the weights of the connections between neural agglomerates, that is, the elements of the brain matrix. As models, we consider examples from three neurological disorders: epilepsy, Alzheimer-Perusini's disease, and schizophrenia. The alteration of neural connections can be seen as alterations of communication pathways, and thus, they can be described with a new channel model.

Critical-like Brain Dynamics in a Continuum from Second- to First-Order Phase Transition

The classic brain criticality hypothesis postulates that the brain benefits from operating near a continuous second-order phase transition. Slow feedback regulation of neuronal activity could, however, lead to a discontinuous first-order transition and thereby bistable activity. Observations of bistability in awake brain activity have nonetheless remained scarce and its functional significance unclear. Moreover, there is no empirical evidence to support the hypothesis that the human brain could flexibly operate near either a first- or second-order phase transition despite such a continuum being common in models. Here, using computational modeling, we found bistable synchronization dynamics to emerge through elevated positive feedback and occur exclusively in a regimen of critical-like dynamics. We then assessed bistability in vivo with resting-state MEG in healthy adults (7 females, 11 males) and stereo-electroencephalography in epilepsy patients (28 females, 36 males). This analysis revealed that a large fraction of the neocortices exhibited varying degrees of bistability in neuronal oscillations from 3 to 200 Hz. In line with our modeling results, the neuronal bistability was positively correlated with classic assessment of brain criticality across narrow-band frequencies. Excessive bistability was predictive of epileptic pathophysiology in the patients, whereas moderate bistability was positively correlated with task performance in the healthy subjects. These empirical findings thus reveal the human brain as a one-of-a-kind complex system that exhibits critical-like dynamics in a continuum between continuous and discontinuous phase transitions.SIGNIFICANCE STATEMENT In the model, while synchrony per se was controlled by connectivity, increasing positive local feedback led to gradually emerging bistable synchrony with scale-free dynamics, suggesting a continuum between second- and first-order phase transitions in synchrony dynamics inside a critical-like regimen. In resting-state MEG and SEEG, bistability of ongoing neuronal oscillations was pervasive across brain areas and frequency bands and was observed only with concurring critical-like dynamics as the modeling predicted. As evidence for functional relevance, moderate bistability was positively correlated with executive functioning in the healthy subjects, and excessive bistability was associated with epileptic pathophysiology. These findings show that critical-like neuronal dynamics in vivo involves both continuous and discontinuous phase transitions in a frequency-, neuroanatomy-, and state-dependent manner.

Brain criticality predicts individual levels of inter-areal synchronization in human electrophysiological data

Neuronal oscillations and their synchronization between brain areas are fundamental for healthy brain function. Yet, synchronization levels exhibit large inter-individual variability that is associated with behavioral variability. We test whether individual synchronization levels are predicted by individual brain states along an extended regime of critical-like dynamics - the Griffiths phase (GP). We use computational modelling to assess how synchronization is dependent on brain criticality indexed by long-range temporal correlations (LRTCs). We analyze LRTCs and synchronization of oscillations from resting-state magnetoencephalography and stereo-electroencephalography data. Synchronization and LRTCs are both positively linearly and quadratically correlated among healthy subjects, while in epileptogenic areas they are negatively linearly correlated. These results show that variability in synchronization levels is explained by the individual position along the GP with healthy brain areas operating in its subcritical and epileptogenic areas in its supercritical side. We suggest that the GP is fundamental for brain function allowing individual variability while retaining functional advantages of criticality.

Altered spreading of neuronal avalanches in temporal lobe epilepsy relates to cognitive performance: A resting-state hdEEG study

OBJECTIVE

Large aperiodic bursts of activations named neuronal avalanches have been used to characterize whole-brain activity, as their presence typically relates to optimal dynamics. Epilepsy is characterized by alterations in large-scale brain network dynamics. Here we exploited neuronal avalanches to characterize differences in electroencephalography (EEG) basal activity, free from seizures and/or interictal spikes, between patients with temporal lobe epilepsy (TLE) and matched controls.

METHOD

We defined neuronal avalanches as starting when the z-scored source-reconstructed EEG signals crossed a specific threshold in any region and ending when all regions returned to baseline. This technique avoids data manipulation or assumptions of signal stationarity, focusing on the aperiodic, scale-free components of the signals. We computed individual avalanche transition matrices to track the probability of avalanche spreading across any two regions, compared them between patients and controls, and related them to memory performance in patients.

RESULTS

We observed a robust topography of significant edges clustering in regions functionally and structurally relevant for the TLE, such as the entorhinal cortex, the inferior parietal and fusiform area, the inferior temporal gyrus, and the anterior cingulate cortex. We detected a significant correlation between the centrality of the entorhinal cortex in the transition matrix and the long-term memory performance (delay recall Rey-Osterrieth Complex Figure Test).

SIGNIFICANCE

Our results show that the propagation patterns of large-scale neuronal avalanches are altered in TLE during the resting state, suggesting a potential diagnostic application in epilepsy. Furthermore, the relationship between specific patterns of propagation and memory performance support the neurophysiological relevance of neuronal avalanches.

Fully Bayesian estimation of virtual brain parameters with self-tuning Hamiltonian Monte Carlo

Virtual brain models are data-driven patient-specific brain models integrating individual brain imaging data with neural mass modeling in a single computational framework, capable of autonomously generating brain activity and its associated brain imaging signals. Along the example of epilepsy, we develop an efficient and accurate Bayesian methodology estimating the parameters linked to the extent of the epileptogenic zone. State-of-the-art advances in Bayesian inference using Hamiltonian Monte Carlo (HMC) algorithms have remained elusive for large-scale differential-equations based models due to their slow convergence. We propose appropriate priors and a novel reparameterization to facilitate efficient exploration of the posterior distribution in terms of computational time and convergence diagnostics. The methodology is illustrated for in-silico dataset and then, applied to infer the personalized model parameters based on the empirical stereotactic electroencephalography (SEEG) recordings of retrospective patients. This improved methodology may pave the way to render HMC methods sufficiently easy and efficient to use, thus applicable in personalized medicine.

Identifying spatio-temporal seizure propagation patterns in epilepsy using Bayesian inference

Focal drug resistant epilepsy is a neurological disorder characterized by seizures caused by abnormal activity originating in one or more regions together called as epileptogenic zone. Treatment for such patients involves surgical resection of affected regions. Epileptogenic zone is typically identified using stereotactic EEG recordings from the electrodes implanted into the patient's brain. Identifying the epileptogenic zone is a challenging problem due to the spatial sparsity of electrode implantation. We propose a probabilistic hierarchical model of seizure propagation patterns, based on a phenomenological model of seizure dynamics called Epileptor. Using Bayesian inference, the Epileptor model is optimized to build patient specific virtual models that best fit to the log power of intracranial recordings. First, accuracy of the model predictions and identifiability of the model are investigated using synthetic data. Then, model predictions are evaluated against a retrospective patient cohort of 25 patients with varying surgical outcomes. In the patients who are seizure free after surgery, model predictions showed good match with the clinical hypothesis. In patients where surgery failed to achieve seizure freedom model predictions showed a strong mismatch. Our results demonstrate that proposed probabilistic model could be a valuable tool to aid the clinicians in identifying the seizure focus.

Patient-Specific Network Connectivity Combined With a Next Generation Neural Mass Model to Test Clinical Hypothesis of Seizure Propagation

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 mathematical modeling with structural information from non invasive brain imaging, thus building large-scale brain network models to explore emergent dynamics and test the 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 with the example of diffusion-weighted magnetic resonance imaging (dMRI) 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.

On seizure semiology

The clinical expression of seizures represents the main symptomatic burden of epilepsy. Neural mechanisms of semiologic production in epilepsy, especially for complex behaviors, remain poorly known. In a framework of epilepsy as a network rather than as a focal disorder, we can think of semiology as being dynamically produced by a set of interconnected structures, in which specific rhythmic interactions, and not just anatomical localization, are likely to play an important part in clinical expression. This requires a paradigm shift in how we think about seizure organization, including from a presurgical evaluation perspective. Semiology is a key data source, albeit with significant methodological challenges for its use in research, including observer bias and choice of semiologic categories. Better understanding of semiologic categorization and pathophysiological correlates is relevant to seizure classification systems. Advances in knowledge of neural mechanisms as well as anatomic correlates of different semiologic patterns could help improve knowledge of epilepsy networks and potentially contribute to therapeutic innovations.

Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks

Understanding the dynamics of brain-scale functional networks at rest and during cognitive tasks is the subject of intense research efforts to unveil fundamental principles of brain functions. To estimate these large-scale brain networks, the emergent method called "electroencephalography (EEG) source connectivity" has generated increasing interest in the network neuroscience community, due to its ability to identify cortical brain networks with satisfactory spatio-temporal resolution, while reducing mixing and volume conduction effects. However, no consensus has been reached yet regarding a unified EEG source connectivity pipeline, and several methodological issues have to be carefully accounted to avoid pitfalls. Thus, a validation toolbox that provides flexible "ground truth" models is needed for an objective methods/parameters evaluation and, thereby an optimization of the EEG source connectivity pipeline. In this paper, we show how a recently developed large-scale model of brain-scale activity, named COALIA, can provide to some extent such ground truth by providing realistic simulations of source-level and scalp-level activity. Using a bottom-up approach, the model bridges cortical micro-circuitry and large-scale network dynamics. Here, we provide an example of the potential use of COALIA to analyze, in the context of epileptiform activity, the effect of three key factors involved in the "EEG source connectivity" pipeline: (i) EEG sensors density, (ii) algorithm used to solve the inverse problem, and (iii) functional connectivity measure. Results showed that a high electrode density (at least 64 channels) is required to accurately estimate cortical networks. Regarding the inverse solution/connectivity measure combination, the best performance at high electrode density was obtained using the weighted minimum norm estimate (wMNE) combined with the weighted phase lag index (wPLI). Although those results are specific to the considered aforementioned context (epileptiform activity), we believe that this model-based approach can be successfully applied to other experimental questions/contexts. We aim at presenting a proof-of-concept of the interest of COALIA in the network neuroscience field, and its potential use in optimizing the EEG source-space network estimation pipeline.

Glucose Metabolism Characteristics of Extra-Hypothalamic Cortex in Patients With Hypothalamic Hamartomas (HH) Undergoing Epilepsy Evaluation: A Retrospective Study of 16 Cases

Purpose: There are few studies on the glucose metabolic characteristics of the extra-hypothalamic cortex in the hypothalamic hamartomas (HH). A comprehensive understanding of pathogenic progression of the disease is required from the perspective of cortical metabolism; therefore, we aimed to characterize metabolic characteristics of extra-hypothalamic in HH patients.

Methods: We investigated the metabolic characteristics of 16 HH patients, all of whom underwent epilepsy evaluation at Xuan Wu Hospital between 2017 and 2019. The lateralization and cortical distribution pattern of hypometabolism was assessed and related to HH mass neuroanatomy on magnetic resonance imaging (MRI) as well as scalp-electroencephalogram (scalp-EEG) abnormalities. Furthermore, asymmetry measurements of region of interest (ROI) in the temporal cortex (hippocampal formation, amygdala, and lateral temporal neocortex) were quantitatively assessed based on the normalized average positron emission tomography (PET) voxel values. The surgery prognosis was assessed using the International League Against Epilepsy (ILAE) classification system.

Results: The lateralization of hypometabolism in global visual ratings was consistent with the HH mass lateralization seen on MRI. Cortical hypometabolism showed three patterns depending whether the HH mass involved mammillary bodies, middle hypothalamus nucleus, or both. The three patterns were hypometabolism of the mesial temporal cortex with symptom of mesial temporal epilepsy (3/16, pattern I), lateral temporal, and extratemporal (frontal or parietal) cortex with symptom of neocortex temporal or frontal epilepsy (5/16, pattern II), and mesial and lateral temporal cortex and extratemporal (frontal or parietal) cortex with varied symptoms (8/16, pattern III), respectively. A significant difference in PET voxel values was found between bilateral hippocampal formation (P = 0.001) and lateral temporal neocortex in the third group (P = 0.005). We suggest that the hypometabolic characteristics of the extra-hypothalamic cortex in HH patients have three patterns. The final cortical hypometabolic pattern depends on the neuroanatomic location of the HH mass and was consistent with the main involved cortex of the interictal and ictal discharges. The third hypometabolic pattern with the most extensive cortical hypometabolism has a poorer prognosis.

Multiscale Functional Clustering Reveals Frequency Dependent Brain Organization in Type II Focal Cortical Dysplasia With Sleep Hypermotor Epilepsy

OBJECTIVE

A multiscale functional clustering approach is proposed to investigate the organization of the epileptic networks during different sleep stages and in relation with the occurrence of seizures.

METHOD

Stereo-electroencephalographic signals from seven pharmaco-resistant epileptic patients (focal cortical dysplasia type II) were analyzed. The discrete wavelet transform provided a multiscale framework on which a data-driven functional clustering procedure was applied, based on multivariate measures of integration and mutual information. The most interacting functional clusters (FCs) within the sampled brain areas were extracted.

RESULTS

FCs characterized by strongly integrated activity were observed mostly in the beta and alpha frequency bands, immediately before seizure onset and in deep sleep stages. These FCs generally included the electrodes from the epileptogenic zone. Furthermore, repeatable patterns were found across ictal events in the same patient.

CONCLUSION

In line with previous studies, our findings provide evidence of the important role of beta and alpha activity in seizures generation and support the relation between epileptic activity and sleep stages.

SIGNIFICANCE

Despite the small number of subjects included in the study, the present results suggest that the proposed multiscale functional clustering approach is a useful tool for the identification of the frequency-dependent mechanisms underlying seizure generation.

Optimization of surgical intervention outside the epileptogenic zone in the Virtual Epileptic Patient (VEP)

Studies to improve the efficacy of epilepsy surgery have focused on better refining the localization of the epileptogenic zone (EZ) with the aim of effectively resecting it. However, in a considerable number of patients, EZs are distributed across multiple brain regions and may involve eloquent areas that cannot be removed due to the risk of neurological complications. There is a clear need for developing alternative approaches to induce seizure relief, but minimal impact on normal brain functions. Here, we develop a personalized in-silico network approach, that suggests effective and safe surgical interventions for each patient. Based on the clinically identified EZ, we employ modularity analysis to identify target brain regions and fiber tracts involved in seizure propagation. We then construct and simulate a patient-specific brain network model comprising phenomenological neural mass models at the nodes, and patient-specific structural brain connectivity using the neuroinformatics platform The Virtual Brain (TVB), in order to evaluate effectiveness and safety of the target zones (TZs). In particular, we assess safety via electrical stimulation for pre- and post-surgical condition to quantify the impact on the signal transmission properties of the network. We demonstrate the existence of a large repertoire of efficient surgical interventions resulting in reduction of degree of seizure spread, but only a small subset of them proves safe. The identification of novel surgical interventions through modularity analysis and brain network simulations may provide exciting solutions to the treatment of inoperable epilepsies.

We propose a personalized in-silico surgical approach able to suggest effective and safe surgical options for each epilepsy patient. In particular, we focus on deriving effective alternative methods for those cases where EZs are inoperable because of issues related with neurological complications. Based on modularity analysis using structural brain connectivity from each patient, TZs that would be considered as surgical sites are obtained. The acquired TZs are evaluated by personalized brain network simulations in terms of effectiveness and safety. Through the feedback approach combining modularity analysis and brain network simulations, the optimized TZ options that minimize seizure propagation while not affecting normal brain functions are obtained. Our study has a great importance in that it demonstrates the possibility of computational neuroscience field being able to construct a paradigm for personalized medicine by deriving innovative surgical options suitable for each patient and predicting the surgical outcomes.

Hypothalamic hamartoma: Epileptogenesis beyond the lesion?

The discovery of intrinsic epileptogenicity of the hypothalamic hamartoma (HH) marked a new area in understanding the associated clinical syndrome, often manifesting as progressive epileptic encephalopathy. However, therapeutic procedures targeting the HH proved to be inefficient to cure seizures in up to 50% of cases, whereas in cases with partial improvement, the electroclinical patterns of persisting seizures suggest an involvement of distant cortical regions. The concept of kindling-like secondary epileptogenesis has been suggested as a possible underlying mechanism. Yet the role of the hypothalamic lesion in the pathophysiology of the syndrome remains debatable. In the Strasbourg-Kork series, the best outcomes were obtained when the duration of epilepsy before endoscopic HH surgery did not exceed 10 years. In two patients with HH ablation followed at a later time by a temporal lobectomy, only this second surgical step allowed complete seizure freedom. These findings suggest the existence of an independent, third stage of secondary epileptogenesis in human. In the Grenoble series, stereotactic intracerebral recordings (stereo electroencephalography [SEEG]) of five HH cases demonstrated that gelastic/dacrystic seizures were correlated with discharges within the HH, whereas other seizure types were related to discharges affecting cortical regions, which sometimes seemed to be triggered by HH. In the Marseille series, two cases explored by SEEG provided evidence of extended epileptogenicity outside the limits of the HH, forming complex epileptogenic networks, with HH still triggering clusters of neocortical seizures in the first, but not obligatory involved in spontaneous seizures in the second case. Taken together, our data argue for the existence of dynamic ictal network organization, with possible "kindling-like" relationships between the HH and the neocortex or widespread epileptogenesis. Despite the existence of secondary epileptogenesis, the epileptogenic zone could still be limited to the hamartoma, for which early surgical treatment should be pragmatically considered as a first surgical step.

Individual brain structure and modelling predict seizure propagation

See Lytton (doi:10.1093/awx018) for a scientific commentary on this article.Neural network oscillations are a fundamental mechanism for cognition, perception and consciousness. Consequently, perturbations of network activity play an important role in the pathophysiology of brain disorders. When structural information from non-invasive brain imaging is merged with mathematical modelling, then generative brain network models constitute personalized in silico platforms for the exploration of causal mechanisms of brain function and clinical hypothesis testing. We here demonstrate with the example of drug-resistant epilepsy that patient-specific virtual brain models derived from diffusion magnetic resonance imaging have sufficient predictive power to improve diagnosis and surgery outcome. In partial epilepsy, seizures originate in a local network, the so-called epileptogenic zone, before recruiting other close or distant brain regions. We create personalized large-scale brain networks for 15 patients and simulate the individual seizure propagation patterns. Model validation is performed against the presurgical stereotactic electroencephalography data and the standard-of-care clinical evaluation. We demonstrate that the individual brain models account for the patient seizure propagation patterns, explain the variability in postsurgical success, but do not reliably augment with the use of patient-specific connectivity. Our results show that connectome-based brain network models have the capacity to explain changes in the organization of brain activity as observed in some brain disorders, thus opening up avenues towards discovery of novel clinical interventions.

Chimera states in a Hodgkin-Huxley model of thermally sensitive neurons

Chimera states occur when identically coupled groups of nonlinear oscillators exhibit radically different dynamics, with one group exhibiting synchronized oscillations and the other desynchronized behavior. This dynamical phenomenon has recently been studied in computational models and demonstrated experimentally in mechanical, optical, and chemical systems. The theoretical basis of these states is currently under active investigation. Chimera behavior is of particular relevance in the context of neural synchronization, given the phenomenon of unihemispheric sleep and the recent observation of asymmetric sleep in human patients with sleep apnea. The similarity of neural chimera states to neural "bump" states, which have been suggested as a model for working memory and visual orientation tuning in the cortex, adds to their interest as objects of study. Chimera states have been demonstrated in the FitzHugh-Nagumo model of excitable cells and in the Hindmarsh-Rose neural model. Here, we demonstrate chimera states and chimera-like behaviors in a Hodgkin-Huxley-type model of thermally sensitive neurons both in a system with Abrams-Strogatz (mean field) coupling and in a system with Kuramoto (distance-dependent) coupling. We map the regions of parameter space for which chimera behavior occurs in each of the two coupling schemes.

Nodal approach reveals differential impact of lateralized focal epilepsies on hub reorganization

The impact of the hemisphere affected by impairment in models of network disease is not fully understood. Among such models, focal epilepsies are characterised by recurrent seizures generated in epileptogenic areas also responsible for wider network dysfunction between seizures. Previous work focusing on functional connectivity within circumscribed networks suggests a divergence of network integrity and compensatory capacity between epilepsies as a function of the laterality of seizure onset. We evaluated the ability of complex network theory to reveal changes in focal epilepsy in global and nodal parameters using graph theoretical analysis of functional connectivity data obtained with resting-state fMRI. Graphs of functional connectivity networks were derived from 19 right and 13 left focal epilepsy patients and 15 controls. Topological metrics (degree, local efficiency, global efficiency and modularity) were computed for a whole-brain, atlas-defined network. We also calculated a hub disruption index for each graph metric, measuring the capacity of the brain network to demonstrate increased connectivity in some nodes for decreased connectivity in others. Our data demonstrate that the patient group as a whole is characterised by network-wide pattern of reorganization, even while global parameters fail to distinguish between groups. Furthermore, multiple metrics indicate that epilepsies with differently lateralized epileptic networks are asymmetric in their burden on functional brain networks; with left epilepsy patients being characterised by reduced efficiency and modularity, while in right epilepsy patients we provide the first evidence that functional brain networks are characterised by enhanced connectivity and efficiency at some nodes whereas reduced in others.

Chow-Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20-30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow-Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Alteration of consciousness in focal epilepsy: the global workspace alteration theory

Alteration of consciousness (AOC) is an important clinical manifestation of partial seizures that greatly impacts the quality of life of patients with epilepsy. Several theories have been proposed in the last fifty years. An emerging concept in neurology is the global workspace (GW) theory that postulates that access to consciousness (from several sensorial modalities) requires transient coordinated activity from associative cortices, in particular the prefrontal cortex and the posterior parietal associative cortex. Several lines of evidence support the view that partial seizures alter consciousness through disturbance of the GW. In particular, a nonlinear relation has been shown between excess of synchronization in the GW regions and the degree of AOC. Changes in thalamocortical synchrony occurring during the spreading of the ictal activity seem particularly involved in the mechanism of altered consciousness. This link between abnormal synchrony and AOC offers new perspectives in the treatment of the AOC since means of decreasing consciousness alteration in seizures could improve patients' quality of life.