Whole-brain simulation of interictal epileptic discharges for patient-specific interpretation of interictal SEEG data.

In patients with refractory epilepsy, the clinical interpretation of stereoelectroencephalographic (SEEG) signals is crucial to delineate the epileptogenic network that should be targeted by surgery. We propose a pipeline of patient-specific computational modeling of interictal epileptic activity to improve the definition of regions of interest. Comparison between the computationally defined regions of interest and the resected region confirmed the efficiency of the pipeline. This result suggests that computational modeling can be used to reconstruct signals and aid clinical interpretation.

Modification of brain conductivity in human focal epilepsy: A model-based estimation from stereoelectroencephalography.

OBJECTIVE: We have developed a novel method for estimating brain tissue electrical conductivity using low-intensity pulse stereoelectroencephalography (SEEG) stimulation coupled with biophysical modeling. We evaluated the hypothesis that brain conductivity is correlated with the degree of epileptogenicity in patients with drug-resistant focal epilepsy. METHODS: We used bipolar low-intensity biphasic pulse stimulation (.2 mA) followed by a postprocessing pipeline for estimating brain conductivity. This processing is based on biophysical modeling of the electrical potential induced in brain tissue between the stimulated contacts in response to pulse stimulation. We estimated the degree of epileptogenicity using a semi-automatic method quantifying the dynamic of fast discharge at seizure onset: the epileptogenicity index (EI). We also investigated how the location of stimulation within specific anatomical brain regions or within lesional tissue impacts brain conductivity. RESULTS: We performed 1034 stimulations of 511 bipolar channels in 16 patients. We found that brain conductivity was lower in the epileptogenic zone (EZ; unpaired median difference = .064, p < .001) and inversely correlated with the epileptogenic index value (p < .001, Spearman rho = -.32). Conductivity values were also influenced by anatomical site, location within lesion, and delay between SEEG electrode implantation and stimulation, and had significant interpatient variability. Mixed model multivariate analysis showed that conductivity is significantly associated with EI (F = 13.45, p < .001), anatomical regions (F = 5.586, p < .001), delay since implantation (F = 14.71, p = .003), and age at SEEG (F = 6.591, p = .027), but not with the type of lesion (F = .372, p = .773) or the delay since last seizure (F = 1.592, p = .235). SIGNIFICANCE: We provide a novel model-based method for estimating brain conductivity from SEEG low-intensity pulse stimulations. The brain tissue conductivity is lower in EZ as compared to non-EZ. Conductivity also varies significantly across anatomical brain regions. Involved pathophysiological processes may include changes in the extracellular space (especially volume or tortuosity) in epileptic tissue.

Tuning Microelectrodes' Impedance to Improve Fast Ripples Recording.

Epilepsy is a chronic neurological disorder characterized by recurrent seizures resulting from abnormal neuronal hyperexcitability. In the case of pharmacoresistant epilepsy requiring resection surgery, the identification of the Epileptogenic Zone (EZ) is critical. Fast Ripples (FRs; 200-600 Hz) are one of the promising biomarkers that can aid in EZ delineation. However, recording FRs requires physically small electrodes. These microelectrodes suffer from high impedance, which significantly impacts FRs' observability and detection. In this study, we investigated the potential of a conductive polymer coating to enhance FR observability. We employed biophysical modeling to compare two types of microelectrodes: Gold (Au) and Au coated with the conductive polymer poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (Au/PEDOT:PSS). These electrodes were then implanted into the CA1 hippocampal neural network of epileptic mice to record FRs during epileptogenesis. The results showed that the polymer-coated electrodes had a two-order lower impedance as well as a higher transfer function amplitude and cut-off frequency. Consequently, FRs recorded with the PEDOT:PSS-coated microelectrode yielded significantly higher signal energy compared to the uncoated one. The PEDOT:PSS coating improved the observability of the recorded FRs and thus their detection. This work paves the way for the development of signal-specific microelectrode designs that allow for better targeting of pathological biomarkers.

Interictal Functional Connectivity in Focal Refractory Epilepsies Investigated by Intracranial EEG.

Introduction: Focal epilepsies are diseases of neuronal excitability affecting macroscopic networks of cortical and subcortical neural structures. These networks ("epileptogenic networks") can generate pathological electrophysiological activities during seizures, and also between seizures (interictal period). Many works attempt to describe these networks by using quantification methods, particularly based on the estimation of statistical relationships between signals produced by brain regions, namely functional connectivity (FC). Results: FC has been shown to be greatly altered during seizures and in the immediate peri-ictal period. An increasing number of studies have shown that FC is also altered during the interictal period depending on the degree of epileptogenicity of the structures. Furthermore, connectivity values could be correlated with other clinical variables including surgical outcome. Significance: This leads to a conceptual change and to consider epileptic areas as both hyperexcitable and abnormally connected. These data open the door to the use of interictal FC as a marker of epileptogenicity and as a complementary tool for predicting the effect of surgery. Aim: In this article, we review the available data concerning interictal FC estimated from intracranial electroencephalograhy (EEG) in focal epilepsies and discuss it in the light of data obtained from other modalities (EEG imaging) and modeling studies.

In silico model reveals the key role of GABA in KCNT1-epilepsy in infancy with migrating focal seizures.

OBJECTIVE: This study was undertaken to investigate how gain of function (GOF) of slack channel due to a KCNT1 pathogenic variant induces abnormal neuronal cortical network activity and generates specific electroencephalographic (EEG) patterns of epilepsy in infancy with migrating focal seizures. METHODS: We used detailed microscopic computational models of neurons to explore the impact of GOF of slack channel (explicitly coded) on each subtype of neurons and on a cortical micronetwork. Then, we adapted a thalamocortical macroscopic model considering results obtained in detailed models and immature properties related to epileptic brain in infancy. Finally, we compared simulated EEGs resulting from the macroscopic model with interictal and ictal patterns of affected individuals using our previously reported EEG markers. RESULTS: The pathogenic variants of KCNT1 strongly decreased the firing rate properties of γ-aminobutyric acidergic (GABAergic) interneurons and, to a lesser extent, those of pyramidal cells. This change led to hyperexcitability with increased synchronization in a cortical micronetwork. At the macroscopic scale, introducing slack GOF effect resulted in epilepsy of infancy with migrating focal seizures (EIMFS) EEG interictal patterns. Increased excitation-to-inhibition ratio triggered seizure, but we had to add dynamic depolarizing GABA between somatostatin-positive interneurons and pyramidal cells to obtain migrating seizure. The simulated migrating seizures were close to EIMFS seizures, with similar values regarding the delay between the different ictal activities (one of the specific EEG markers of migrating focal seizures due to KCNT1 pathogenic variants). SIGNIFICANCE: This study illustrates the interest of biomathematical models to explore pathophysiological mechanisms bridging the gap between the functional effect of gene pathogenic variants and specific EEG phenotype. Such models can be complementary to in vitro cellular and animal models. This multiscale approach provides an in silico framework that can be further used to identify candidate innovative therapies.

The "Connectivity Epileptogenicity Index " (cEI), a method for mapping the different seizure onset patterns in StereoElectroEncephalography recorded seizures.

OBJECTIVE: Localization of epileptogenic brain regions is a crucial aim of pre-surgical evaluation of patients with drug-resistant epilepsy. Several methods have been proposed to identify the seizure onset zone, particularly based on the detection of fast activity. Most of these methods are inefficient to detect slower patterns of onset that account for 20-30% of commonly observed Stereo-Electro-Encephalography (SEEG) patterns. We seek to evaluate the performance of a new quantified measure called the Connectivity Epileptogenicity Index (cEI) in various types of seizure onset patterns. METHODS: We studied SEEG recorded seizures from 51 patients, suffering from focal drug-resistant epilepsy. The cEI combines a directed connectivity measure ("out-degrees") and the original epileptogenicity index (EI). Quantified results (Out-degrees, cEI and EI) were compared to visually defined seizure onset zone (vSOZ). We computed recall (sensitivity) and precision (proportion of correct detections within all detections) with vSOZ as a reference. The quality of the detector was quantified by the area under the precision-recall curve. RESULTS: Best results (in terms of match with vSOZ) were obtained for cEI. For seizures with fast onset patterns, cEI and EI gave comparable results. For seizures with slow onset patterns, cEI gave a better estimation of the vSOZ than EI. CONCLUSIONS: We observed that cEI discloses better performance than EI when seizures starts with slower patterns and equal to EI in seizures with fast onset patterns. SIGNIFICANCE: The cEI is a promising new tool for epileptologists, that helps characterizing the seizure onset zone in sEEG, in a robust way despite variations in seizure onset patterns.

Quantitative analysis and EEG markers of KCNT1 epilepsy of infancy with migrating focal seizures.

OBJECTIVE: We aimed to characterize epilepsy of infancy with migrating focal seizures (EIMFS), a rare, severe early onset developmental epilepsy related to KCNT1 mutation, and to define specific electroencephalography (EEG) markers using EEG quantitative analysis. The ultimate goal would be to improve early diagnosis and to better understand seizure onset and propagation of EIMFS as compared to other early onset developmental epilepsy. METHODS: EEG of 7 EIMFS patients with KCNT1 mutations (115 seizures) and 17 patients with other early onset epilepsies (30 seizures) was included in this study. After detection of seizure onset and termination, spatiotemporal characteristics were quantified. Seizure propagation dynamics were analyzed using chronograms and phase coherence. RESULTS: In patients with EIMFS, seizures started and were localized predominantly in temporal and occipital areas, and evolved with a stable frequency (4-10 Hz). Inter- and intrahemispheric migrations were present in 60% of EIMFS seizures with high intraindividual reproducibility of temporospatial dynamics. Interhemispheric migrating seizures spread in 71% from temporal or occipital channels to the homologous contralateral ones, whereas intrahemispheric seizures involved mainly frontotemporal, temporal, and occipital channels. Causality links were present between ictal activities detected under different channels during migrating seizures. Finally, time delay index (based on delays between the different ictal onsets) and phase correlation index (based on coherence of ictal activities) allowed discrimination of EIMFS and non-EIMFS seizures with a specificity of 91.2% and a sensitivity of 84.4%. SIGNIFICANCE: We showed that the migrating pattern in EIMFS is not a random process, as suggested previously, and that it is a particular propagation pattern that follows the classical propagation pathways. It is notable that this study reveals specific EEG markers (time delay and phase correlation) accessible to visual evaluation, which will improve EIMFS diagnosis.

Model-guided control of hippocampal discharges by local direct current stimulation.

Neurostimulation is an emerging treatment for drug-resistant epilepsies when surgery is contraindicated. Recent clinical results demonstrate significant seizure frequency reduction in epileptic patients, however the mechanisms underlying this therapeutic effect are largely unknown. This study aimed at gaining insights into local direct current stimulation (LDCS) effects on hyperexcitable tissue, by i) analyzing the impact of electrical currents locally applied on epileptogenic brain regions, and ii) characterizing currents achieving an "anti-epileptic" effect (excitability reduction). First, a neural mass model of hippocampal circuits was extended to accurately reproduce the features of hippocampal paroxysmal discharges (HPD) observed in a mouse model of epilepsy. Second, model predictions regarding current intensity and stimulation polarity were confronted to in vivo mice recordings during LDCS (n = 8). The neural mass model was able to generate realistic hippocampal discharges. Simulation of LDCS in the model pointed at a significant decrease of simulated HPD (in duration and occurrence rate, not in amplitude) for cathodal stimulation, which was successfully verified experimentally in epileptic mice. Despite the simplicity of our stimulation protocol, these results contribute to a better understanding of clinical benefits observed in epileptic patients with implanted neurostimulators. Our results also provide further support for model-guided design of neuromodulation therapy.

Defining epileptogenic networks: Contribution of SEEG and signal analysis.

Epileptogenic networks are defined by the brain regions involved in the production and propagation of epileptic activities. In this review we describe the historical, methodologic, and conceptual bases of this model in the analysis of electrophysiologic intracerebral recordings. In the context of epilepsy surgery, the determination of cerebral regions producing seizures (i.e., the "epileptogenic zone") is a crucial objective. In contrast with a traditional focal vision of focal drug-resistant epilepsies, the concept of epileptogenic networks has been progressively introduced as a model better able to describe the complexity of seizure dynamics and realistically describe the distribution of epileptogenic anomalies in the brain. The concept of epileptogenic networks is historically linked to the development of the stereoelectroencephalography (SEEG) method and subsequent introduction of means of quantifying the recorded signals. Seizures, and preictal and interictal discharges produce clear patterns on SEEG. These patterns can be analyzed utilizing signal analysis methods that quantify high-frequency oscillations or changes in functional connectivity. Dramatic changes in SEEG brain connectivity can be described during seizure genesis and propagation within cortical and subcortical regions, associated with the production of different patterns of seizure semiology. The interictal state is characterized by networks generating abnormal activities (interictal spikes) and also by modified functional properties. The introduction of novel approaches to large-scale modeling of these networks offers new methods in the goal of better predicting the effects of epilepsy surgery. The epileptogenic network concept is a key factor in identifying the anatomic distribution of the epileptogenic process, which is particularly important in the context of epilepsy surgery.

Automatic Detection and Classification of High-Frequency Oscillations in Depth-EEG Signals.

GOAL: Interictal high-frequency oscillations (HFOs [30-600 Hz]) have proven to be relevant biomarkers in epilepsy. In this paper, four categories of HFOs are considered: Gamma ([30-80 Hz]), high-gamma ([80-120 Hz]), ripples ([120-250 Hz]), and fast-ripples ([250-600 Hz]). A universal detector of the four types of HFOs is proposed. It has the advantages of 1) classifying HFOs, and thus, being robust to inter and intrasubject variability; 2) rejecting artefacts, thus being specific. METHODS: Gabor atoms are tuned to cover the physiological bands. Gabor transform is then used to detect HFOs in intracerebral electroencephalography (iEEG) signals recorded in patients candidate to epilepsy surgery. To extract relevant features, energy ratios, along with event duration, are investigated. Discriminant ratios are optimized so as to maximize among the four types of HFOs and artefacts. A multiclass support vector machine (SVM) is used to classify detected events. Pseudoreal signals are simulated to measure the performance of the method when the ground truth is known. RESULTS: Experiments are conducted on simulated and on human iEEG signals. The proposed method shows high performance in terms of sensitivity and false discovery rate. CONCLUSION: The methods have the advantages of detecting and discriminating all types of HFOs as well as avoiding false detections caused by artefacts. SIGNIFICANCE: Experimental results show the feasibility of a robust and universal detector.

The role of sub-hippocampal versus hippocampal regions in bitemporal lobe epilepsies.

OBJECTIVE: We aimed at better delineating the functional anatomical organization of bitemporal lobe epilepsy. METHODS: We studied the epileptogenic zone (EZ) by quantifying the epileptogenicity of brain structures explored by depth electrodes in patients investigated by stereoelectroencephalography (SEEG). We compared 15 patients with bilateral mesial temporal lobe epilepsy (BTLE) and 15 patients with unilateral mesial temporal lobe epilepsy (UTLE). This quantification was performed using the 'Epileptogenicity Index' (EI). RESULTS: Age at epilepsy onset, and epilepsy duration, were not statistically different in both groups. UTLE patients more frequently displayed maximal epileptogenicity in hippocampal structures, whereas BTLE patients had maximal values in subhippocampal areas (entorhinal cortex, temporal pole, parahippocampal cortex). CONCLUSIONS: Our results suggest different organization of the EZ in the two groups. SIGNIFICANCE: BTLE was associated with more involvement of subhippocampal regions, a result in agreement with known anatomical connections between the two temporal lobes.

Epilepsy in young Tsc1(+/-) mice exhibits age-dependent expression that mimics that of human tuberous sclerosis complex.

OBJECTIVE: To describe the epileptic phenotype of Tsc1(+/-) mice pups in comparison with age-related seizures in human tuberous sclerosis complex (TSC). METHODS: Tsc1(+/-) and control mice underwent intracranial electroencephalography (EEG) recording at postnatal ages (P)8 to P33, with linear silicon probe implanted in the somatosensory cortex of one or both hemispheres for 8-24 h. Ictal events were classified visually by independent analyzers; distinct EEG patterns were related to age and analyzed to quantify field potential characteristics and signal dynamics between hemispheres. We collected retrospectively 20 infants with prenatally diagnosed TSC and EEG before seizure onset, and analyzed the electroclinical course of epilepsy, taking into account a first-line treatment by vigabatrin. RESULTS: Spontaneous seizures were disclosed in 55% of Tsc1(+/-) mice at P9-18. Three ictal patterns were identified: from P9 to P12 "spike clusters" consisted of recurring large spikes without clinical correlate; "spasm-like" discharges dominated from P13 to P16 consisting of high amplitude large field potential superimposed with or followed by fast activity repeated every 2-10 s for at least 20 s, accompanied by rhythmic limb contractions; from P14 to P18 a "tonic-clonic like" pattern comprised rhythmic spikes of increasing amplitude with tonic-clonic movements. Early onset "spike clusters" were mainly unilateral, whereas "spasm-like" and "tonic-clonic like" patterns were bilateral. Interhemispheric propagation was significantly faster for "tonic-clonic like" than for "spasm-like" events. In infants diagnosed prenatally with TSC, clusters of sharp waves or spikes preceded the first seizure, and vigabatrin prevented the development of seizures. Patients treated after seizure onset developed spasms or focal seizures that were pharmacoresistant in 66.7% of cases. SIGNIFICANCE: Tsc1(+/-) mice pups exhibit an age-dependent seizure pattern sequence mimicking early human TSC epilepsy features. Spike clusters before seizure onset in TSC should be considered as a first stage of epilepsy reinforcing the concept of preventive antiepileptic therapy.

What is the concordance between the seizure onset zone and the irritative zone? A SEEG quantified study.

OBJECTIVE: In focal epilepsies, the accurate delineation of the epileptogenic network is a fundamental step before surgery. For years, the relationship between the interictal epileptic spikes (defining the "irritative zone", IZ) and the sites of seizure initiation (SOZ) has been a matter of debate. METHODS: Our goal was to investigate from intracerebral recordings (stereoelectroencephalography, SEEG) the distribution of interictal epileptic spikes (based on a spike frequency index, SI) and the topography of the SOZ (based on the Epileptogenicity Index, EI) in patients having focal neocortical epilepsies. Thirty-one patients were studied. A total of 539 brain regions were quantified in term of both spike generation (SI) and seizure initiation (EI). RESULTS: We found a 56% (18/32) rate of agreement between maximal EI and maximal SI values. When considering separately patients with focal cortical dysplasia (FCD), the proportion of patients with good concordance was ∼75% (15/20), whereas it was only 33% (4/12) in the non FCD group. CONCLUSIONS: Our results show that a significant part of patients have some dissociation between regions showing pronounced spiking activity and those showing high epileptogenicity. e is clinically important. SIGNIFICANCE: For patients with these dissociations, other markers than spiking frequency remain to be investigated. In the FCD group, the good concordance between SI and EI confirms that the mapping of the irritative zone is clinically important.

Classification of high frequency oscillations in epileptic intracerebral EEG.

High Frequency Oscillations (HFOs 40-500 Hz), recorded from intracerebral electroencephalography (iEEG) in epileptic patients, are categorized into four distinct sub-bands (Gamma, High-Gamma, Ripples and Fast Ripples). They have recently been used as a reliable biomarker of epileptogenic zones. The objective of this paper is to investigate the possibility of discriminating between the different classes of HFOs which physiological/pathological value is critical for diagnostic but remains to be clarified. The proposed method is based on the definition of a relevant feature vector built from energy ratios (computed using Wavelet Transform-WT) in a-priori-defined frequency bands. It makes use of a multiclass Linear Discriminant Analysis (LDA) and is applied to iEEG signals recorded in patients candidate to epilepsy surgery. Results obtained from bootstrap on training/test datasets indicate high performances in terms of sensitivity and specificity.

Localization of Epileptogenic Zone on Pre-surgical Intracranial EEG Recordings: Toward a Validation of Quantitative Signal Analysis Approaches.

In patients diagnosed with pharmaco-resistant epilepsy, cerebral areas responsible for seizure generation can be defined by performing implantation of intracranial electrodes. The identification of the epileptogenic zone (EZ) is based on visual inspection of the intracranial electroencephalogram (IEEG) performed by highly qualified neurophysiologists. New computer-based quantitative EEG analyses have been developed in collaboration with the signal analysis community to expedite EZ detection. The aim of the present report is to compare different signal analysis approaches developed in four different European laboratories working in close collaboration with four European Epilepsy Centers. Computer-based signal analysis methods were retrospectively applied to IEEG recordings performed in four patients undergoing pre-surgical exploration of pharmaco-resistant epilepsy. The four methods elaborated by the different teams to identify the EZ are based either on frequency analysis, on nonlinear signal analysis, on connectivity measures or on statistical parametric mapping of epileptogenicity indices. All methods converge on the identification of EZ in patients that present with fast activity at seizure onset. When traditional visual inspection was not successful in detecting EZ on IEEG, the different signal analysis methods produced highly discordant results. Quantitative analysis of IEEG recordings complement clinical evaluation by contributing to the study of epileptogenic networks during seizures. We demonstrate that the degree of sensitivity of different computer-based methods to detect the EZ in respect to visual EEG inspection depends on the specific seizure pattern.

Beyond the lesion: the epileptogenic networks around cavernous angiomas.

The relationship between epileptogenic lesions and the extension of epileptogenicity is a major challenge in presurgical evaluation of drug resistant epilepsies. In this study, we aimed at quantifying the epileptogenic properties of brain structures explored by depth electrodes in patients investigated by stereoelectroencephalography (SEEG) and suffering from focal drug-resistant epilepsy associated with cavernous angioma (CA). Epileptogenicity of the perilesional region and distant brain areas was calculated according to the "epileptogenicity index" (EI), a technique that allows mathematical quantification of rapid discharges at seizure onset taking into account the time at which the discharge occurs. Thirteen seizures from 6 patients were studied. Localization of the cavernoma was the frontal lobe (two cases), the temporal lobe (three cases) or the anterior insula (one case). Visual inspection of the ictal discharge showed that in the majority of cases (5/6) the perilesional region was either not involved or involved with other distant sites. Using EI quantification, complex patterns of epileptogenicity were observed in five patients. A large number of brain regions out of the lesional region disclosed higher values than the lesion site. Mean values in the perilesional region and in the extralesional sites were not significantly different (p=0.34). Complex organization of the epileptogenic zone may be found in drug-resistant CA associated epilepsy. Thus, this result should be borne in mind when patients with CA and drug resistant epilepsy are investigated. If there is a suspicion of a larger epileptogenic zone than the lesion, intra-cerebral exploration by SEEG may be required before surgery that may be guided by the definition of the EZ.

Modulation of epileptic activity by deep brain stimulation: a model-based study of frequency-dependent effects.

A number of studies showed that deep brain stimulation (DBS) can modulate the activity in the epileptic brain and that a decrease of seizures can be achieved in "responding" patients. In most of these studies, the choice of stimulation parameters is critical to obtain desired clinical effects. In particular, the stimulation frequency is a key parameter that is difficult to tune. A reason is that our knowledge about the frequency-dependant mechanisms according to which DBS indirectly impacts the dynamics of pathological neuronal systems located in the neocortex is still limited. We address this issue using both computational modeling and intracerebral EEG (iEEG) data. We developed a macroscopic (neural mass) model of the thalamocortical network. In line with already-existing models, it includes interconnected neocortical pyramidal cells and interneurons, thalamocortical cells and reticular neurons. The novelty was to introduce, in the thalamic compartment, the biophysical effects of direct stimulation. Regarding clinical data, we used a quite unique data set recorded in a patient (drug-resistant epilepsy) with a focal cortical dysplasia (FCD). In this patient, DBS strongly reduced the sustained epileptic activity of the FCD for low-frequency (LFS, < 2 Hz) and high-frequency stimulation (HFS, > 70 Hz) while intermediate-frequency stimulation (IFS, around 50 Hz) had no effect. Signal processing, clustering, and optimization techniques allowed us to identify the necessary conditions for reproducing, in the model, the observed frequency-dependent stimulation effects. Key elements which explain the suppression of epileptic activity in the FCD include: (a) feed-forward inhibition and synaptic short-term depression of thalamocortical connections at LFS, and (b) inhibition of the thalamic output at HFS. Conversely, modeling results indicate that IFS favors thalamic oscillations and entrains epileptic dynamics.

Automatic detection of fast ripples.

OBJECTIVE: We propose a new method for automatic detection of fast ripples (FRs) which have been identified as a potential biomarker of epileptogenic processes. METHODS: This method is based on a two-stage procedure: (i) global detection of events of interest (EOIs, defined as transient signals accompanied with an energy increase in the frequency band of interest 250-600Hz) and (ii) local energy vs. frequency analysis of detected EOIs for classification as FRs, interictal epileptic spikes or artifacts. For this second stage, two variants were implemented based either on Fourier or wavelet transform. The method was evaluated on simulated and real depth-EEG signals (human, animal). The performance criterion was based on receiving operator characteristics. RESULTS: The proposed detector showed high performance in terms of sensitivity and specificity. CONCLUSIONS: As designed to specifically detect FRs, the method outperforms any method simply based on the detection of energy changes in high-pass filtered signals and avoids spurious detections caused by sharp transient events often present in raw signals. SIGNIFICANCE: In most of epilepsy surgery units, huge data sets are generated during pre-surgical evaluation. We think that the proposed detection method can dramatically decrease the workload in assessing the presence of FRs in intracranial EEGs.

Transcranial direct-current stimulation modulates synaptic mechanisms involved in associative learning in behaving rabbits.

Transcranial direct-current stimulation (tDCS) is a noninvasive brain stimulation technique that has been successfully applied for modulation of cortical excitability. tDCS is capable of inducing changes in neuronal membrane potentials in a polarity-dependent manner. When tDCS is of sufficient length, synaptically driven after-effects are induced. The mechanisms underlying these after-effects are largely unknown, and there is a compelling need for animal models to test the immediate effects and after-effects induced by tDCS in different cortical areas and evaluate the implications in complex cerebral processes. Here we show in behaving rabbits that tDCS applied over the somatosensory cortex modulates cortical processes consequent to localized stimulation of the whisker pad or of the corresponding area of the ventroposterior medial (VPM) thalamic nucleus. With longer stimulation periods, poststimulation effects were observed in the somatosensory cortex only after cathodal tDCS. Consistent with the polarity-specific effects, the acquisition of classical eyeblink conditioning was potentiated or depressed by the simultaneous application of anodal or cathodal tDCS, respectively, when stimulation of the whisker pad was used as conditioned stimulus, suggesting that tDCS modulates the sensory perception process necessary for associative learning. We also studied the putative mechanisms underlying immediate effects and after-effects of tDCS observed in the somatosensory cortex. Results when pairs of pulses applied to the thalamic VPM nucleus (mediating sensory input) during anodal and cathodal tDCS suggest that tDCS modifies thalamocortical synapses at presynaptic sites. Finally, we show that blocking the activation of adenosine A1 receptors prevents the long-term depression (LTD) evoked in the somatosensory cortex after cathodal tDCS.

Relationship between flow and metabolism in BOLD signals: insights from biophysical models.

In many physiological or pathological situations, the interpretation of BOLD signals remains elusive as the intimate link between neuronal activity and subsequent flow/metabolic changes is not fully understood. During the past decades, a number of biophysical models of the neurovascular coupling have been proposed. It is now well-admitted that these models may bridge between observations (fMRI data) and underlying biophysical and (patho-)physiological mechanisms (related to flow and metabolism) by providing mechanistic explanations. In this study, three well-established models (Buxton's, Friston's and Sotero's) are investigated. An exhaustive parameter sensitivity analysis (PSA) was conducted to study the marginal and joint influences of model parameters on the three main features of the BOLD response (namely the principal peak, the post-stimulus undershoot and the initial dip). In each model, parameters that have the greatest (and least) influence on the BOLD features as well as on the direction of variation of these features were identified. Among the three studied models, parameters were shown to affect the output features in different manners. Indeed, the main parameters revealed by the PSA were found to strongly depend on the way the flow(CBF)-metabolism(CMRO(2)) relationship is implemented (serial vs. parallel). This study confirmed that the model structure which accounts for the representation of the CBF-CMRO(2) relationship (oxygen supply by the flow vs. oxygen demand from neurons) plays a key role. More generally, this work provides substantial information about the tuning of parameters in the three considered models and about the subsequent interpretation of BOLD signals based on these models.