Further characterisation of late somatosensory evoked potentials using electroencephalogram and magnetoencephalogram source imaging.

Beside the well-documented involvement of secondary somatosensory area, the cortical network underlying late somatosensory evoked potentials (P60/N60 and P100/N100) is still unknown. Electroencephalogram and magnetoencephalogram source imaging were performed to further investigate the origin of the brain cortical areas involved in late somatosensory evoked potentials, using sensory inputs of different strengths and by testing the correlation between cortical sources. Simultaneous high-density electroencephalograms and magnetoencephalograms were performed in 19 participants, and electrical stimulation was applied to the median nerve (wrist level) at intensity between 1.5 and 9 times the perceptual threshold. Source imaging was undertaken to map the stimulus-induced brain cortical activity according to each individual brain magnetic resonance imaging, during three windows of analysis covering early and late somatosensory evoked potentials. Results for P60/N60 and P100/N100 were compared with those for P20/N20 (early response). According to literature, maximal activity during P20/N20 was found in central sulcus contralateral to stimulation site. During P60/N60 and P100/N100, activity was observed in contralateral primary sensorimotor area, secondary somatosensory area (on both hemispheres) and premotor and multisensory associative cortices. Late responses exhibited similar characteristics but different from P20/N20, and no significant correlation was found between early and late generated activities. Specific clusters of cortical activities were activated with specific input/output relationships underlying early and late somatosensory evoked potentials. Cortical networks, partly common to and distinct from early somatosensory responses, contribute to late responses, all participating in the complex somatosensory brain processing.

A neural mass model with direct and indirect excitatory feedback loops: identification of bifurcations and temporal dynamics.

Neural mass modeling is a part of computational neuroscience that was developed to study the general behavior of a neuronal population. This type of mesoscopic model is able to generate output signals that are comparable to experimental data, such as electroencephalograms. Classically, neural mass models consider two interconnected populations: excitatory pyramidal cells and inhibitory interneurons. However, many authors have included an excitatory feedback on the pyramidal cell population. Two distinct approaches have been developed: a direct feedback on the main pyramidal cell population and an indirect feedback via a secondary pyramidal cell population. In this letter, we propose a new neural mass model that couples these two approaches. We perform a detailed bifurcation analysis and present a glossary of dynamical behaviors and associated time series. Our study reveals that the model is able to generate particular realistic time series that were never pointed out in either simulated or experimental data. Finally, we aim to evaluate the effect of balance between both excitatory feedbacks on the dynamical behavior of the model. For this purpose, we compute the codimension 2 bifurcation diagrams of the system to establish a map of the repartition of dynamical behaviors in a direct versus indirect feedback parameter space. A perspective of this work is, from a given temporal series, to estimate the parameter value range, especially in terms of direct versus indirect excitatory feedback.

Glutamatergic neuron-targeted loss of LGI1 epilepsy gene results in seizures.

Leucin-rich, glioma inactivated 1 (LGI1) is a secreted protein linked to human seizures of both genetic and autoimmune aetiology. Mutations in the LGI1 gene are responsible for autosomal dominant temporal lobe epilepsy with auditory features, whereas LGI1 autoantibodies are involved in limbic encephalitis, an acquired epileptic disorder associated with cognitive impairment. We and others previously reported that Lgi1-deficient mice have early-onset spontaneous seizures leading to premature death at 2-3 weeks of age. Yet, where and when Lgi1 deficiency causes epilepsy remains unknown. To address these questions, we generated Lgi1 conditional knockout (cKO) mice using a set of universal Cre-driver mouse lines. Selective deletion of Lgi1 was achieved in glutamatergic pyramidal neurons during embryonic (Emx1-Lgi1cKO) or late postnatal (CaMKIIα-Lgi1cKO) developmental stages, or in gamma amino butyric acidergic (GABAergic) parvalbumin interneurons (PV-Lgi1cKO). Emx1-Lgi1cKO mice displayed early-onset and lethal seizures, whereas CaMKIIα-Lgi1cKO mice presented late-onset occasional seizures associated with variable reduced lifespan. In contrast, neither spontaneous seizures nor increased seizure susceptibility to convulsant were observed when Lgi1 was deleted in parvalbumin interneurons. Together, these data showed that LGI1 depletion restricted to pyramidal cells is sufficient to generate seizures, whereas seizure thresholds were unchanged after depletion in gamma amino butyric acidergic parvalbumin interneurons. We suggest that LGI1 secreted from excitatory neurons, but not parvalbumin inhibitory neurons, makes a major contribution to the pathogenesis of LGI1-related epilepsies. Our data further indicate that LGI1 is required from embryogenesis to adulthood to achieve proper circuit functioning.

Shape features of epileptic spikes are a marker of epileptogenesis in mice.

PURPOSE: To identify reliable biomarkers for quantitatively assessing the development of epilepsy in brain. METHODS: In a kainate mouse model of temporal lobe epilepsy, we performed long-term video-electroencephalography (EEG) monitoring (several weeks) of freely moving animals, from kainic acid injection to chronic epileptic stage. Using signal processing techniques, we automatically detected single epileptic spikes (ESs), and we quantified the evolution of shape features during the epileptogenesis process. Using a computational model of hippocampal activity (neuronal population level), we investigated excitatory-related and inhibitory-related parameters involved in morphologic changes of ESs. KEY FINDINGS: The frequency of ESs increases during epileptogenesis. Regarding shape features, we found that both the initial spike component and the wave component of opposite polarity of ESs gradually increase during epileptogenesis. These very specific alterations of the shape of ESs were reproduced in a computational physiologically relevant neuronal population model. Using this model, we disclosed some key parameters (related to glutamatergic and γ-aminobutyric acid [GABA]ergic synaptic transmission) that explain the shape features of simulated ESs. Of interest, the model predicted that the decrease of GABAergic inhibition is responsible for the increase of the wave component of ESs. This prediction (at first sight counterintuitive) was verified in both in vivo and in vitro experiments. Finally, from aforementioned electrophysiologic features, we devised a novel and easily computable index (wave area/spike amplitude ratio) indicative of the progression of the disease (early vs. late stage). SIGNIFICANCE: Results suggest that dendritic inhibition in hippocampal circuits undertake dramatic changes over the latent period. These changes are responsible for observed modifications in the shape of ESs recorded in local field potential (LFP) signals. The proposed index may constitute a biomarker of epileptogenesis.

Interictal spikes, fast ripples and seizures in partial epilepsies--combining multi-level computational models with experimental data.

Epileptic seizures, epileptic spikes and high-frequency oscillations (HFOs) are recognized as three electrophysiological markers of epileptogenic neuronal systems. It can be reasonably hypothesized that distinct (hyper)excitability mechanisms underlie these electrophysiological signatures. The question is 'What are these mechanisms?'. Solving this difficult question would considerably help our understanding of epileptogenic processes and would also advance our interpretation of electrophysiological signals. In this paper, we show how computational models of brain epileptic activity can be used to address this issue. With a special emphasis on the hippocampal activity recorded in various experimental models (in vivo and in vitro) as well as in epileptic patients, we confront results and insights we can get from computational models lying at two different levels of description, namely macroscopic (neural mass) and microscopic (detailed network of neurons). At each level, we show how spikes, seizures and HFOs can (or cannot) be generated depending on the model features. The replication of observed signals, the prediction of possible mechanisms as well as their experimental validation are described and discussed; as are the advantages and limitations of the two modelling approaches.

Altered dynamics of neurovascular coupling in CADASIL.

BACKGROUND AND OBJECTIVE: Neurovascular coupling is the complex biological process that underlies use-dependent increases in blood flow in response to neural activation. Neurovascular coupling was investigated at the early stage of CADASIL, a genetic paradigm of ischemic small vessel disease. METHODS: Functional hyperemia and evoked potentials during 20- and 40-sec visual and motor stimulations were monitored simultaneously using arterial spin labeling-functional magnetic resonance imaging (ASL-fMRI) and electroencephalography. RESULTS: Cortical functional hyperemia differed significantly between 19 patients and 19 healthy individuals, whereas evoked potentials were unaltered. Functional hyperemia dynamics, assessed using the difference in the slope of the response curve between 15 and 30 sec, showed a time-shifted decrease in the response to 40-sec neural stimulations in CADASIL patients. These results were replicated in a second cohort of 10 patients and 10 controls and confirmed in the whole population. INTERPRETATION: Alterations of neurovascular coupling occur early in CADASIL and can be assessed by ASL-fMRI using a simple marker of vascular dysfunction.

Mechanistic insights into a TIMP3-sensitive pathway constitutively engaged in the regulation of cerebral hemodynamics.

Cerebral small vessel disease (SVD) is a leading cause of stroke and dementia. CADASIL, an inherited SVD, alters cerebral artery function, compromising blood flow to the working brain. TIMP3 (tissue inhibitor of metalloproteinase 3) accumulation in the vascular extracellular matrix in CADASIL is a key contributor to cerebrovascular dysfunction. However, the linkage between elevated TIMP3 and compromised cerebral blood flow (CBF) remains unknown. Here, we show that TIMP3 acts through inhibition of the metalloprotease ADAM17 and HB-EGF to regulate cerebral arterial tone and blood flow responses. In a clinically relevant CADASIL mouse model, we show that exogenous ADAM17 or HB-EGF restores cerebral arterial tone and blood flow responses, and identify upregulated voltage-dependent potassium channel (KV) number in cerebral arterial myocytes as a heretofore-unrecognized downstream effector of TIMP3-induced deficits. These results support the concept that the balance of TIMP3 and ADAM17 activity modulates CBF through regulation of myocyte KV channel number.

Investigating Human Neurovascular Coupling Using Functional Neuroimaging: A Critical Review of Dynamic Models.

The mechanisms that link a transient neural activity to the corresponding increase of cerebral blood flow (CBF) are termed neurovascular coupling (NVC). They are possibly impaired at early stages of small vessel or neurodegenerative diseases. Investigation of NVC in humans has been made possible with the development of various neuroimaging techniques based on variations of local hemodynamics during neural activity. Specific dynamic models are currently used for interpreting these data that can include biophysical parameters related to NVC. After a brief review of the current knowledge about possible mechanisms acting in NVC we selected seven models with explicit integration of NVC found in the literature. All these models were described using the same procedure. We compared their physiological assumptions, mathematical formalism, and validation. In particular, we pointed out their strong differences in terms of complexity. Finally, we discussed their validity and their potential applications. These models may provide key information to investigate various aspects of NVC in human pathology.

Mutations in STX1B, encoding a presynaptic protein, cause fever-associated epilepsy syndromes.

Febrile seizures affect 2-4% of all children and have a strong genetic component. Recurrent mutations in three main genes (SCN1A, SCN1B and GABRG2) have been identified that cause febrile seizures with or without epilepsy. Here we report the identification of mutations in STX1B, encoding syntaxin-1B, that are associated with both febrile seizures and epilepsy. Whole-exome sequencing in independent large pedigrees identified cosegregating STX1B mutations predicted to cause an early truncation or an in-frame insertion or deletion. Three additional nonsense or missense mutations and a de novo microdeletion encompassing STX1B were then identified in 449 familial or sporadic cases. Video and local field potential analyses of zebrafish larvae with antisense knockdown of stx1b showed seizure-like behavior and epileptiform discharges that were highly sensitive to increased temperature. Wild-type human syntaxin-1B but not a mutated protein rescued the effects of stx1b knockdown in zebrafish. Our results thus implicate STX1B and the presynaptic release machinery in fever-associated epilepsy syndromes.

Time-domain features of epileptic spikes as potential bio-markers of the epileptogenesis process.

Epilepsy is a neurological disorder characterized by recurrent seizures which affects about 1% people worldwide. During the past decades, some mechanisms involved in ictogenesis (generation of seizures) have been identified and, to some extent, partially understood. However, regarding epileptogenesis (process by which a neuronal system becomes epileptic), underlying mechanisms remain elusive. This difficulty is mostly related to the fact that epileptogenesis can only be addressed using experimental models. In this study, we have analyzed the shape of a specific electrophysiological pattern, referred to as "epileptic spike", encountered during the epileptogenesis process in an in vivo model of temporal lobe epilepsy (mouse, kainate). Results show that the features of these transient events (duration and amplitude) change as a function of time as the brain evolves towards the chronic epileptic state characterized by the appearance of spontaneous seizures. Using a detailed computational model of the hippocampus (CA1 sub-field), an interpretation of observed modifications is provided, in relationship with possible alterations that take place in underlying neuronal circuits.