Preictal short-term plasticity induced by intracerebral 1 Hz stimulation

Differential cortical network engagement during states of un/consciousness in humans

What happens in the human brain when we are unconscious? Despite substantial work, we are still unsure which brain regions are involved and how they are impacted when consciousness is disrupted. Using intracranial recordings and direct electrical stimulation, we mapped global, network, and regional involvement during wake vs. arousable unconsciousness (sleep) vs. non-arousable unconsciousness (propofol-induced general anesthesia). Information integration and complex processing we`re reduced, while variability increased in any type of unconscious state. These changes were more pronounced during anesthesia than sleep and involved different cortical engagement. During sleep, changes were mostly uniformly distributed across the brain, whereas during anesthesia, the prefrontal cortex was the most disrupted, suggesting that the lack of arousability during anesthesia results not from just altered overall physiology but from a disconnection between the prefrontal and other brain areas. These findings provide direct evidence for different neural dynamics during loss of consciousness compared with loss of arousability.

Variability of Single Pulse Electrical Stimulation Responses Recorded with Intracranial Electroencephalography in Epileptic Patients

Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and threshold value Z = 3, correlation between measurements drops almost to zero. In general, it remains below 0.5 when the threshold is smaller than Z = 4 or the stimulating current intensity is 1 mA. It grows with an increase of either of these factors. Variability is independent of interictal spiking rates in the stimulating and recording sites.We conclude that responses to SEEG stimulation in the human brain are variable, i.e. in a subject at rest, two stimulation trains performed at the same electrode contacts and with the same protocol can give discrepant results. Our findings highlight an advantage of probabilistic interpretation of such results even in the context of a single individual.

Dynamic causal modelling shows a prominent role of local inhibition in alpha power modulation in higher visual cortex

During resting-state EEG recordings, alpha activity is more prominent over the posterior cortex in eyes-closed (EC) conditions compared to eyes-open (EO). In this study, we characterized the difference in spectra between EO and EC conditions using dynamic causal modelling. Specifically, we investigated the role of intrinsic and extrinsic connectivity-within the visual cortex-in generating EC-EO alpha power differences over posterior electrodes. The primary visual cortex (V1) and the bilateral middle temporal visual areas (V5) were equipped with bidirectional extrinsic connections using a canonical microcircuit. The states of four intrinsically coupled subpopulations-within each occipital source-were also modelled. Using Bayesian model selection, we tested whether modulations of the intrinsic connections in V1, V5 or extrinsic connections (or a combination thereof) provided the best evidence for the data. In addition, using parametric empirical Bayes (PEB), we estimated group averages under the winning model. Bayesian model selection showed that the winning model contained both extrinsic connectivity modulations, as well as intrinsic connectivity modulations in all sources. The PEB analysis revealed increased extrinsic connectivity during EC. Overall, we found a reduction in the inhibitory intrinsic connections during EC. The results suggest that the intrinsic modulations in V5 played the most important role in producing EC-EO alpha differences, suggesting an intrinsic disinhibition in higher order visual cortex, during EC resting state.

A brain atlas of axonal and synaptic delays based on modelling of cortico-cortical evoked potentials

Epilepsy presurgical investigation may include focal intracortical single-pulse electrical stimulations with depth electrodes, which induce cortico-cortical evoked potentials at distant sites because of white matter connectivity. Cortico-cortical evoked potentials provide a unique window on functional brain networks because they contain sufficient information to infer dynamical properties of large-scale brain connectivity, such as preferred directionality and propagation latencies.

 Here, we developed a biologically informed modelling approach to estimate the neural physiological parameters of brain functional networks from the cortico-cortical evoked potentials recorded in a large multicentric database. Specifically, we considered each cortico-cortical evoked potential as the output of a transient stimulus entering the stimulated region, which directly propagated to the recording region. Both regions were modelled as coupled neural mass models, the parameters of which were estimated from the first cortico-cortical evoked potential component, occurring before 80 ms, using dynamic causal modelling and Bayesian model inversion. This methodology was applied to the data of 780 patients with epilepsy from the F-TRACT database, providing a total of 34 354 bipolar stimulations and 774 445 cortico-cortical evoked potentials. The cortical mapping of the local excitatory and inhibitory synaptic time constants and of the axonal conduction delays between cortical regions was obtained at the population level using anatomy-based averaging procedures, based on the Lausanne2008 and the HCP-MMP1 parcellation schemes, containing 130 and 360 parcels, respectively. To rule out brain maturation effects, a separate analysis was performed for older (>15 years) and younger patients (<15 years). In the group of older subjects, we found that the cortico-cortical axonal conduction delays between parcels were globally short (median = 10.2 ms) and only 16% were larger than 20 ms. This was associated to a median velocity of 3.9 m/s. Although a general lengthening of these delays with the distance between the stimulating and recording contacts was observed across the cortex, some regions were less affected by this rule, such as the insula for which almost all efferent and afferent connections were faster than 10 ms. Synaptic time constants were found to be shorter in the sensorimotor, medial occipital and latero-temporal regions, than in other cortical areas. Finally, we found that axonal conduction delays were significantly larger in the group of subjects younger than 15 years, which corroborates that brain maturation increases the speed of brain dynamics.

To our knowledge, this study is the first to provide a local estimation of axonal conduction delays and synaptic time constants across the whole human cortex in vivo, based on intracerebral electrophysiological recordings.

In vivo-assessment of the human temporal network: Evidence for asymmetrical effective connectivity

The human temporal lobe is a multimodal association area which plays a key role in various aspects of cognition, particularly in memory formation and spatial navigation. Functional and anatomical connectivity of temporal structures is thus a subject of intense research, yet by far underexplored in humans due to ethical and technical limitations. We assessed intratemporal cortico-cortical interactions in the living human brain by means of single pulse electrical stimulation, an invasive method allowing mapping effective intracortical connectivity with a high spatiotemporal resolution. Eighteen subjects with normal anterior-mesial temporal MR imaging undergoing intracranial presurgical epilepsy diagnostics with multiple depth electrodes were included. The investigated structures were temporal pole, hippocampus, amygdala and parahippocampal gyrus. Intratemporal cortical connectivity was assessed as a function of amplitude of the early component of the cortico-cortical evoked potentials (CCEP). While the analysis revealed robust interconnectivity between all explored structures, a clear asymmetry in bidirectional connectivity was detected for the hippocampal network and for the connections between the temporal pole and parahippocampal gyrus. The amygdala showed bidirectional asymmetry only to the hippocampus. The provided evidence of asymmetrically weighed intratemporal effective connectivity in humans in vivo is important for understanding of functional interactions within the temporal lobe since asymmetries in the brain connectivity define hierarchies in information processing. The findings are in exact accord with the anatomical tracing studies in non-human primates and open a translational route for interventions employing modulation of temporal lobe function.

Intracortical Dynamics Underlying Repetitive Stimulation Predicts Changes in Network Connectivity

Targeted stimulation can be used to modulate the activity of brain networks. Previously we demonstrated that direct electrical stimulation produces predictable poststimulation changes in brain excitability. However, understanding the neural dynamics during stimulation and its relationship to poststimulation effects is limited but critical for treatment optimization. Here, we applied 10 Hz direct electrical stimulation across several cortical regions in 14 human subjects (6 males) implanted with intracranial electrodes for seizure monitoring. The stimulation train was characterized by a consistent increase in high gamma (70-170 Hz) power. Immediately post-train, low-frequency (1-8 Hz) power increased, resulting in an evoked response that was highly correlated with the neural response during stimulation. Using two measures of network connectivity, corticocortical evoked potentials (indexing effective connectivity), and theta coherence (indexing functional connectivity), we found a stronger response to stimulation in regions that were highly connected to the stimulation site. In these regions, repeated cycles of stimulation trains and rest progressively altered the stimulation response. Finally, after just 2 min (∼10%) of repetitive stimulation, we were able to predict poststimulation connectivity changes with high discriminability. Together, this work reveals a relationship between stimulation dynamics and poststimulation connectivity changes in humans. Thus, measuring neural activity during stimulation can inform future plasticity-inducing protocols.SIGNIFICANCE STATEMENT Brain stimulation tools have the potential to revolutionize the treatment of neuropsychiatric disorders. Despite the widespread use of brain stimulation techniques such as transcranial magnetic stimulation, the therapeutic efficacy of these technologies remains suboptimal. This is in part because of a lack of understanding of the dynamic neural changes that occur during stimulation. In this study, we provide the first detailed characterization of neural activity during plasticity induction through intracranial electrode stimulation and recording in 14 medication-resistant epilepsy patients. These results fill a missing gap in our understanding of stimulation-induced plasticity in humans. In the longer-term, these data will also guide our translational efforts toward non-invasive, personalized, closed-loop neuromodulation therapy for neurological and psychiatric disorders in humans.

Optimizing Data for Modeling Neuronal Responses

In this technical note, we address an unresolved challenge in neuroimaging statistics: how to determine which of several datasets is the best for inferring neuronal responses. Comparisons of this kind are important for experimenters when choosing an imaging protocol-and for developers of new acquisition methods. However, the hypothesis that one dataset is better than another cannot be tested using conventional statistics (based on likelihood ratios), as these require the data to be the same under each hypothesis. Here we present Bayesian data comparison (BDC), a principled framework for evaluating the quality of functional imaging data, in terms of the precision with which neuronal connectivity parameters can be estimated and competing models can be disambiguated. For each of several candidate datasets, neuronal responses are modeled using Bayesian (probabilistic) forward models, such as General Linear Models (GLMs) or Dynamic Casual Models (DCMs). Next, the parameters from subject-specific models are summarized at the group level using a Bayesian GLM. A series of measures, which we introduce here, are then used to evaluate each dataset in terms of the precision of (group-level) parameter estimates and the ability of the data to distinguish similar models. To exemplify the approach, we compared four datasets that were acquired in a study evaluating multiband fMRI acquisition schemes, and we used simulations to establish the face validity of the comparison measures. To enable people to reproduce these analyses using their own data and experimental paradigms, we provide general-purpose Matlab code via the SPM software.

Induction and Quantification of Excitability Changes in Human Cortical Networks

How does human brain stimulation result in lasting changes in cortical excitability? Uncertainty on this question hinders the development of personalized brain stimulation therapies. To characterize how cortical excitability is altered by stimulation, we applied repetitive direct electrical stimulation in eight human subjects (male and female) undergoing intracranial monitoring. We evaluated single-pulse corticocortical-evoked potentials (CCEPs) before and after repetitive stimulation across prefrontal (n = 4), temporal (n = 1), and motor (n = 3) cortices. We asked whether a single session of repetitive stimulation was sufficient to induce excitability changes across distributed cortical sites. We found a subset of regions at which 10 Hz prefrontal repetitive stimulation resulted in both potentiation and suppression of excitability that persisted for at least 10 min. We then asked whether these dynamics could be modeled by the prestimulation connectivity profile of each subject. We found that cortical regions (1) anatomically close to the stimulated site and (2) exhibiting high-amplitude CCEPs underwent changes in excitability following repetitive stimulation. We demonstrate high accuracy (72-95%) and discriminability (81-99%) in predicting regions exhibiting changes using individual subjects' prestimulation connectivity profile, and show that adding prestimulation connectivity features significantly improved model performance. The same features predicted regions of modulation following motor and temporal cortices stimulation in an independent dataset. Together, baseline connectivity profile can be used to predict regions susceptible to brain changes and provides a basis for personalizing brain stimulation.SIGNIFICANCE STATEMENT Brain stimulation is increasingly used to treat neuropsychiatric disorders by inducing excitability changes at specific brain regions. However, our understanding of how, when, and where these changes are induced is critically lacking. We inferred plasticity in the human brain after applying electrical stimulation to the brain's surface and measuring changes in excitability. We observed excitability changes in regions anatomically and functionally closer to the stimulation site. Those in responsive regions were accurately predicted using a classifier trained on baseline brain network characteristics. Finally, we showed that the excitability changes can potentially be monitored in real-time. These results begin to fill basic gaps in our understanding of stimulation-induced brain dynamics in humans and offer pathways to optimize stimulation protocols.

Facial Expressions Evoke Differential Neural Coupling in Macaques

In humans and monkeys, face perception activates a distributed cortical network that includes extrastriate, limbic, and prefrontal regions. Within face-responsive regions, emotional faces evoke stronger responses than neutral faces ("valence effect"). We used fMRI and Dynamic Causal Modeling (DCM) to test the hypothesis that emotional faces differentially alter the functional coupling among face-responsive regions. Three monkeys viewed conspecific faces with neutral, threatening, fearful, and appeasing expressions. Using Bayesian model selection, various models of neural interactions between the posterior (TEO) and anterior (TE) portions of inferior temporal (IT) cortex, the amygdala, the orbitofrontal (OFC), and ventrolateral prefrontal cortex (VLPFC) were tested. The valence effect was mediated by feedback connections from the amygdala to TE and TEO, and feedback connections from VLPFC to the amygdala and TE. Emotional faces were associated with differential effective connectivity: Fearful faces evoked stronger modulations in the connections from the amygdala to TE and TEO; threatening faces evoked weaker modulations in the connections from the amygdala and VLPFC to TE; and appeasing faces evoked weaker modulations in the connection from VLPFC to the amygdala. Our results suggest dynamic alterations in neural coupling during the perception of behaviorally relevant facial expressions that are vital for social communication.

Discrimination of a medial functional module within the temporal lobe using an effective connectivity model: A CCEP study

The temporal lobe is classically divided in two functional systems: the ventral visual pathway and the medial temporal memory system. However, their functional separation has been challenged by studies suggesting that the medial temporal lobe could be best understood as an extension of the hierarchically organized ventral visual pathway. Our purpose was to investigate (i) whether cerebral regions within the temporal lobe could be grouped into distinct functional assemblies, and (ii) which regions were central within these functional assemblies. We studied low intensity and low frequency electrical stimulations (0.5 mA, 1 Hz, 4 ms) performed during sixteen pre-surgical intracerebral EEG investigations in patients with medically intractable temporal or temporo-occipital lobe epilepsies. Eleven regions of interest were delineated per anatomical landmarks such as gyri and sulci. Effective connectivity based on electrophysiological feature (amplitude) of cortico-cortical evoked potentials (CCEPs) was evaluated and subjected to graph metrics. The amplitudes discriminated one medial module where the hippocampus could act as a signal amplifier. Mean amplitudes of CCEPs in regions of the temporal lobe showed a generalized Pareto distribution of probability suggesting neural synchronies to be self-organized critically. Our description of effective interactions within the temporal lobe provides a regional electrophysiological model of effective connectivity which is discussed in the context of the current hypothesis of pattern completion.

Stimulation artifact correction method for estimation of early cortico-cortical evoked potentials

BACKGROUND

Effective connectivity can be explored using direct electrical stimulations in patients suffering from drug-resistant focal epilepsies and investigated with intracranial electrodes. Responses to brief electrical pulses mimic the physiological propagation of signals and manifest as cortico-cortical evoked potentials (CCEP). The first CCEP component is believed to reflect direct connectivity with the stimulated region but the stimulation artifact, a sharp deflection occurring during a few milliseconds, frequently contaminates it.

NEW METHOD

In order to recover the characteristics of early CCEP responses, we developed an artifact correction method based on electrical modeling of the electrode-tissue interface. The biophysically motivated artifact templates are then regressed out of the recorded data as in any classical template-matching removal artifact methods.

RESULTS

Our approach is able to make the distinction between the physiological responses time-locked to the stimulation pulses and the non-physiological component. We tested the correction on simulated CCEP data in order to quantify its efficiency for different stimulation and recording parameters. We demonstrated the efficiency of the new correction method on simulations of single trial recordings for early responses contaminated with the stimulation artifact. The results highlight the importance of sampling frequency for an accurate analysis of CCEP. We then applied the approach to experimental data.

COMPARISON WITH EXISTING METHOD

The model-based template removal was compared to a correction based on the subtraction of the averaged artifact.

CONCLUSIONS

This new correction method of stimulation artifact will enable investigators to better analyze early CCEP components and infer direct effective connectivity in future CCEP studies.

Fast Activity Evoked by Intracranial 50 Hz Electrical Stimulation as a Marker of the Epileptogenic Zone

Epilepsy is a disease characterized by aberrant connections between brain areas. The altered activity patterns generated by epileptic networks can be analyzed with intracerebral electrodes during pre-surgical stereo-electroencephalographic (EEG) monitoring in patients candidate to epilepsy surgery. The responses to high frequency stimulation (HFS) at 50 Hz performed for diagnostic purposes during SEEG were analyzed with a new algorithm, to evaluate signal parameters that are masked to visual inspection and to define the boundaries of the epileptogenic network. The analysis was focused on 60-80 Hz activity that represented the largest frequency component evoked by HFS. The distribution of HFS-evoked fast activity across all (up to 162) recording contacts allowed to define different clusters of contacts that retrospectively correlated to the epileptogenic zone identified by the clinicians on the basis of traditional visual analysis. The study demonstrates that computer-assisted analysis of HFS-evoked activities may contribute to the definition of the epileptogenic network on intracranial recordings performed in a pre-surgical setting.

Tracking slow modulations in synaptic gain using dynamic causal modelling: validation in epilepsy

In this work we propose a proof of principle that dynamic causal modelling can identify plausible mechanisms at the synaptic level underlying brain state changes over a timescale of seconds. As a benchmark example for validation we used intracranial electroencephalographic signals in a human subject. These data were used to infer the (effective connectivity) architecture of synaptic connections among neural populations assumed to generate seizure activity. Dynamic causal modelling allowed us to quantify empirical changes in spectral activity in terms of a trajectory in parameter space — identifying key synaptic parameters or connections that cause observed signals. Using recordings from three seizures in one patient, we considered a network of two sources (within and just outside the putative ictal zone). Bayesian model selection was used to identify the intrinsic (within-source) and extrinsic (between-source) connectivity. Having established the underlying architecture, we were able to track the evolution of key connectivity parameters (e.g., inhibitory connections to superficial pyramidal cells) and test specific hypotheses about the synaptic mechanisms involved in ictogenesis. Our key finding was that intrinsic synaptic changes were sufficient to explain seizure onset, where these changes showed dissociable time courses over several seconds. Crucially, these changes spoke to an increase in the sensitivity of principal cells to intrinsic inhibitory afferents and a transient loss of excitatory–inhibitory balance.

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We propose a framework to characterise slow dynamical changes in the brain.

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Dynamical causal modelling finds the most likely connectivity among two brain areas.

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The synaptic weights defining these connections are tracked in time.

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We analyse brain activity of an epileptic subject, at the focus and just outside it.

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We point to modulations of synaptic connections as responsible of the seizure.

Electrical stimulation for cortical mapping reduces the density of high frequency oscillations

BACKGROUND

High frequency oscillations (HFOs, 80-500 Hz) are EEG biomarkers for epileptogenic areas. HFOs are also indicators of disease activity as HFO rates increase after reduction of antiepileptic medication. Electrical stimulation (ES) can be used for diagnostic purposes as well as therapy in patients with refractory epilepsy. This study investigates the occurrence and changes of HFOs during ES in patients with refractory epilepsy.

OBJECTIVE

Analysis of the effects of ES using intracranial ES on the occurrence of epileptic HFOs.

METHODS

Patients underwent ES for diagnostic purposes. Ripples (80-200 Hz) and fast ripples (200-500 Hz) were visually marked in a baseline EEG segment prior to ES, after each period of ES as well as after the end of ES. In patients in whom ES triggered a seizure a pre- and postictal segment was marked. Rates of HFOs were compared for the different time periods using a Spearman's correlation and Wilcoxon rank sum test (p<0.05).

RESULTS

12 patients with 911 EEG channels were analyzed. Ripple (r=-0.42, p<0.001) as well as fast ripple (r=-0.21, p<0.001) rates decreased significantly over the course of stimulation. This phenomenon was not focal over the seizure onset or neighboring contacts but even observed over distant contacts.

CONCLUSIONS

ES resulted in a gradual decrease of HFO-Rates over time. The decrease of HFOs was not limited to SOZ areas. If HFOs are considered as markers of disease activity the reduction in HFO-rates as a result of intracranial ES has to be interpreted as a reduction of disease activity.

On the modelling of seizure dynamics

This scientific commentary refers to ‘On the nature of seizure dynamics’, by V. Jirsa et al. (doi:10.1093/brain/awu133).

Stimulus-evoked potentials contribute to map the epileptogenic zone during stereo-EEG presurgical monitoring

Presurgical monitoring with intracerebral electrodes in patients with drug-resistant focal epilepsy represents a standard invasive procedure to localize the sites of seizures origin, defined as the epileptogenic zone (EZ). During presurgical evaluation, intracerebral single-pulse electrical stimulation (SPES) is performed to define the boundaries of eloquent areas and to evoke seizure-associated symptoms. Extensive intracranial exploration and stimulation generate a large dataset on brain connectivity that can be used to improve EZ detection and to understand the organization of the human epileptic brain. We developed a protocol to analyse field responses evoked by intracranial stimulation. Intracerebral recordings were performed with 105-162 recording sites positioned in fronto-temporal regions in 12 patients with pharmacoresistant focal epilepsy. Recording sites were used for bipolar SPES at 1 Hz. Reproducible early and late phases (<60 ms and 60-500 ms from stimulus artefact, respectively) were identified on averaged evoked responses. Phase 1 and 2 responses recorded at all and each recording sites were plotted on a 3D brain reconstructions. Based on connectivity properties, electrode contacts were primarily identified as receivers, mainly activators or bidirectional. We used connectivity patterns to construct networks and applied cluster partitioning to study the proprieties between potentials evoked/stimulated in different regions. We demonstrate that bidirectional connectivity during phase 1 is a prevalent feature that characterize contacts included in the EZ. This study shows that the application of an analytical protocol on intracerebral stimulus-evoked recordings provides useful information that may contribute to EZ detection and to the management of surgical-remediable epilepsies.

Functional brain connectivity from EEG in epilepsy: seizure prediction and epileptogenic focus localization

Today, neuroimaging techniques are frequently used to investigate the integration of functionally specialized brain regions in a network. Functional connectivity, which quantifies the statistical dependencies among the dynamics of simultaneously recorded signals, allows to infer the dynamical interactions of segregated brain regions. In this review we discuss how the functional connectivity patterns obtained from intracranial and scalp electroencephalographic (EEG) recordings reveal information about the dynamics of the epileptic brain and can be used to predict upcoming seizures and to localize the seizure onset zone. The added value of extracting information that is not visibly identifiable in the EEG data using functional connectivity analysis is stressed. Despite the fact that many studies have showed promising results, we must conclude that functional connectivity analysis has not made its way into clinical practice yet.

Widespread EEG changes precede focal seizures

The process by which the brain transitions into an epileptic seizure is unknown. In this study, we investigated whether the transition to seizure is associated with changes in brain dynamics detectable in the wideband EEG, and whether differences exist across underlying pathologies. Depth electrode ictal EEG recordings from 40 consecutive patients with pharmacoresistant lesional focal epilepsy were low-pass filtered at 500 Hz and sampled at 2,000 Hz. Predefined EEG sections were selected immediately before (immediate preictal), and 30 seconds before the earliest EEG sign suggestive of seizure activity (baseline). Spectral analysis, visual inspection and discrete wavelet transform were used to detect standard (delta, theta, alpha, beta and gamma) and high-frequency bands (ripples and fast ripples). At the group level, each EEG frequency band activity increased significantly from baseline to the immediate preictal section, mostly in a progressive manner and independently of any modification in the state of vigilance. Preictal increases in each frequency band activity were widespread, being observed in the seizure-onset zone and lesional tissue, as well as in remote regions. These changes occurred in all the investigated pathologies (mesial temporal atrophy/sclerosis, local/regional cortical atrophy, and malformations of cortical development), but were more pronounced in mesial temporal atrophy/sclerosis. Our findings indicate that a brain state change with distinctive features, in the form of unidirectional changes across the entire EEG bandwidth, occurs immediately prior to seizure onset. We postulate that these changes might reflect a facilitating state of the brain which enables a susceptible region to generate seizures.

A realistic neural mass model of the cortex with laminar-specific connections and synaptic plasticity - evaluation with auditory habituation

In this work we propose a biologically realistic local cortical circuit model (LCCM), based on neural masses, that incorporates important aspects of the functional organization of the brain that have not been covered by previous models: (1) activity dependent plasticity of excitatory synaptic couplings via depleting and recycling of neurotransmitters and (2) realistic inter-laminar dynamics via laminar-specific distribution of and connections between neural populations. The potential of the LCCM was demonstrated by accounting for the process of auditory habituation. The model parameters were specified using Bayesian inference. It was found that: (1) besides the major serial excitatory information pathway (layer 4 to layer 2/3 to layer 5/6), there exists a parallel "short-cut" pathway (layer 4 to layer 5/6), (2) the excitatory signal flow from the pyramidal cells to the inhibitory interneurons seems to be more intra-laminar while, in contrast, the inhibitory signal flow from inhibitory interneurons to the pyramidal cells seems to be both intra- and inter-laminar, and (3) the habituation rates of the connections are unsymmetrical: forward connections (from layer 4 to layer 2/3) are more strongly habituated than backward connections (from Layer 5/6 to layer 4). Our evaluation demonstrates that the novel features of the LCCM are of crucial importance for mechanistic explanations of brain function. The incorporation of these features into a mass model makes them applicable to modeling based on macroscopic data (like EEG or MEG), which are usually available in human experiments. Our LCCM is therefore a valuable building block for future realistic models of human cognitive function.

Imaging the seizure onset zone with stereo-electroencephalography

Stereo-electroencephalography is used to localize the seizure onset zone and connected neuronal networks in surgical candidates suffering from intractable focal epilepsy. The concept of an epileptogenicity index has been proposed recently to represent the likelihood of various regions being part of the seizure onset zone. It quantifies low-voltage fast activity, the electrophysiological signature of seizure onset usually assessed visually by neurologists. Here, we revisit epileptogenicity in light of neuroimaging tools such as those provided in statistical parametric mapping software. Our goal is to propose a robust approach, allowing easy exploration of patients' brains in time and space. The procedure is based upon statistical parametric mapping, which is an established framework for comparing multi-dimensional image data that allows one to correct for inherent multiple comparisons. Statistics can also be performed at the group level, between seizures in the same patient or between patients suffering from the same type of epilepsy using normalization of brains to a common anatomic atlas. Results are obtained from three case studies (insular reflex epilepsy, cryptogenic frontal epilepsy and lesional occipital epilepsy) where tailored resection was performed, and from a group of 10 patients suffering from mesial temporal lobe epilepsy. They illustrate the basics of the technique and demonstrate its very good reproducibility and specificity. Most importantly, the proposed approach to the quantification of the seizure onset zone allows one to summarize complex signals in terms of a time-series of statistical parametric maps that can support clinical decisions. Quantitative neuroimaging of stereo-electroencephalographic features of seizures might thus help to provide better pre-surgical assessment of patients undergoing resective surgery.

Electrical probing of cortical excitability in patients with epilepsy

Standard methods for seizure prediction involve passive monitoring of intracranial electroencephalography (iEEG) in order to track the 'state' of the brain. This paper introduces a new method for measuring cortical excitability using an electrical probing stimulus. Electrical probing enables feature extraction in a more robust and controlled manner compared to passively tracking features of iEEG signals. The probing stimuli consist of 100 bi-phasic pulses, delivered every 10 min. Features representing neural excitability are estimated from the iEEG responses to the stimuli. These features include the amplitude of the electrically evoked potential, the mean phase variance (univariate), and the phase-locking value (bivariate). In one patient, it is shown how the features vary over time in relation to the sleep-wake cycle and an epileptic seizure. For a second patient, it is demonstrated how the features vary with the rate of interictal discharges. In addition, the spatial pattern of increases and decreases in phase synchrony is explored when comparing periods of low and high interictal discharge rates, or sleep and awake states. The results demonstrate a proof-of-principle for the method to be applied in a seizure anticipation framework. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.

Cortical stimulation of the epileptogenic zone for the treatment of focal motor seizures: an experimental study in the nonhuman primate

BACKGROUND

Cortical stimulation is under investigation in clinical trials of drug-resistant epilepsy. Results are heterogeneous; therefore, more evidence from animal studies is required.

OBJECTIVE

To investigate the therapeutic effects of parameters of direct stimulation of the cortical focus in a Macaca fascicularis presenting focal motor epilepsy.

METHODS

We developed a model of motor seizures after intracortical injection of penicillin G in the primary motor cortex of a Macaca fascicularis. We performed electric epidural cortical stimulation at low, medium, and high frequency using continuous or short-term stimulation. Short-term stimulation was triggered on seizure onset, either visually or automatically with a seizure detection algorithm connected to a programmable stimulator.

RESULTS

Automated detection could detect 100% of the seizures, but ensuing cortical electric stimulation failed to abort seizures.

CONCLUSION

This study demonstrates the inefficacy of the stimulation of the cortical focus to prevent seizures induced by local injection of penicillin G. Because this model may be too severe to allow comparison to human epilepsies, further work is required in other monkey models of focal epilepsy.

Concepts of connectivity and human epileptic activity

This review attempts to place the concept of connectivity from increasingly sophisticated neuroimaging data analysis methodologies within the field of epilepsy research. We introduce the more principled connectivity terminology developed recently in neuroimaging and review some of the key concepts related to the characterization of propagation of epileptic activity using what may be called traditional correlation-based studies based on EEG. We then show how essentially similar methodologies, and more recently models addressing causality, have been used to characterize whole-brain and regional networks using functional MRI data. Following a discussion of our current understanding of the neuronal system aspects of the onset and propagation of epileptic discharges and seizures, we discuss the most advanced and ambitious framework to attempt to fully characterize epileptic networks based on neuroimaging data.

Dynamic causal modeling of cortical activity from the acute to the chronic stage after stroke

Functional neuroimaging studies frequently demonstrated that stroke patients show bilateral activity in motor and premotor areas during movements of the paretic hand in contrast to a more lateralized activation observed in healthy subjects. Moreover, a few studies modeling functional or effective connectivity reported performance-related changes in the motor network after stroke. Here, we investigated the temporal evolution of intra- and interhemispheric (dys-) connectivity during motor recovery from the acute to the early chronic phase post-stroke. Twelve patients performed hand movements in an fMRI task in the acute (≤72 hours) and subacute stage (2 weeks) post-stroke. A subgroup of 10 patients participated in a third assessment in the early chronic stage (3-6 months). Twelve healthy subjects served as reference for brain connectivity. Changes in effective connectivity within a bilateral network comprising M1, premotor cortex (PMC), and supplementary motor area (SMA) were estimated by dynamic causal modeling. Motor performance was assessed by the Action Research Arm Test and maximum grip force. Results showed reduced positive coupling of ipsilesional SMA and PMC with ipsilesional M1 in the acute stage. Coupling parameters among these areas increased with recovery and predicted a better outcome. Likewise, negative influences from ipsilesional areas to contralesional M1 were attenuated in the acute stage. In the subacute stage, contralesional M1 exerted a positive influence on ipsilesional M1. Negative influences from ipsilesional areas on contralesional M1 subsequently normalized, but patients with poorer outcome in the chronic stage now showed enhanced negative coupling from contralesional upon ipsilesional M1. These findings show that the reinstatement of effective connectivity in the ipsilesional hemisphere is an important feature of motor recovery after stroke. The shift of an early, supportive role of contralesional M1 into enhanced inhibitory coupling might indicate maladaptive processes which could be a target of non-invasive brain stimulation techniques.

Directed differential connectivity graph of interictal epileptiform discharges

In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic regions from intracerebral EEG (iEEG). We aim to assess whether quantitative epileptic graph analysis during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the connections that change significantly between epileptic and nonepileptic states as defined by the interictal events. Postprocessings based on mutual information and multiobjective optimization are proposed to localize the leading epileptic regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy. Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using electrically stimulated seizures, reveal good performance of the present method.

Studying network mechanisms using intracranial stimulation in epileptic patients

Patients suffering from focal drug-resistant epilepsy who are explored using intracranial electrodes allow to obtain data of exceptional value for studying brain dynamics in correlation with pathophysiological and cognitive processes. Direct electrical stimulation (DES) of cortical regions and axonal tracts in those patients elicits a number of very specific perceptual or behavioral responses, but also abnormal responses due to specific configurations of epileptic networks. Here, we review how anatomo-functional brain connectivity and epilepsy network mechanisms can be assessed from DES responses measured in patients. After a brief summary of mechanisms of action of brain electrical stimulation, we recall the conceptual framework for interpreting DES results in the context of brain connectivity and review how DES can be used for the characterization of functional networks, the identification of the seizure onset zone, the study of brain plasticity mechanisms, and the anticipation of epileptic seizures. This pool of exceptional data may be underexploited by fundamental research on brain connectivity and leaves much to be learned.

Repetition suppression and plasticity in the human brain

The suppression of neuronal responses to a repeated event is a ubiquitous phenomenon in neuroscience. However, the underlying mechanisms remain largely unexplored. The aim of this study was to examine the temporal evolution of experience-dependent changes in connectivity induced by repeated stimuli. We recorded event-related potentials (ERPs) during frequency changes of a repeating tone. Bayesian inversion of dynamic causal models (DCM) of ERPs revealed systematic repetition-dependent changes in both intrinsic and extrinsic connections, within a hierarchical cortical network. Critically, these changes occurred very quickly, over inter-stimulus intervals that implicate short-term synaptic plasticity. Furthermore, intrinsic (within-source) connections showed biphasic changes that were much faster than changes in extrinsic (between-source) connections, which decreased monotonically with repetition. This study shows that auditory perceptual learning is associated with repetition-dependent plasticity in the human brain. It is remarkable that distinct changes in intrinsic and extrinsic connections could be quantified so reliably and non-invasively using EEG.