Interneurons contribute to the hemodynamic/metabolic response to epileptiform discharges

Function and development of interneurons involved in brain tissue oxygen regulation

The regulation of oxygen in brain tissue is one of the most important fundamental questions in neuroscience and medicine. The brain is a metabolically demanding organ, and its health directly depends on maintaining oxygen concentrations within a relatively narrow range that is both sufficiently high to prevent hypoxia, and low enough to restrict the overproduction of oxygen species. Neurovascular interactions, which are responsible for oxygen delivery, consist of neuronal and glial components. GABAergic interneurons play a particularly important role in neurovascular interactions. The involvement of interneurons extends beyond the perspective of inhibition, which prevents excessive neuronal activity and oxygen consumption, and includes direct modulation of the microvasculature depending upon their sub-type. Namely, nitric oxide synthase-expressing (NOS), vasoactive intestinal peptide-expressing (VIP), and somatostatin-expressing (SST) interneurons have shown modulatory effects on microvessels. VIP interneurons are known to elicit vasodilation, SST interneurons typically cause vasoconstriction, and NOS interneurons have to propensity to induce both effects. Given the importance and heterogeneity of interneurons in regulating local brain tissue oxygen concentrations, we review their differing functions and developmental trajectories. Importantly, VIP and SST interneurons display key developmental milestones in adolescence, while NOS interneurons mature much earlier. The implications of these findings point to different periods of critical development of the interneuron-mediated oxygen regulatory systems. Such that interference with normal maturation processes early in development may effect NOS interneuron neurovascular interactions to a greater degree, while insults later in development may be more targeted toward VIP- and SST-mediated mechanisms of oxygen regulation.

Early Development of the GABAergic System and the Associated Risks of Neonatal Anesthesia

Human and animal studies have elucidated the apparent neurodevelopmental effects resulting from neonatal anesthesia. Observations of learning and behavioral deficits in children, who were exposed to anesthesia early in development, have instigated a flurry of studies that have predominantly utilized animal models to further interrogate the mechanisms of neonatal anesthesia-induced neurotoxicity. Specifically, while neonatal anesthesia has demonstrated its propensity to affect multiple cell types in the brain, it has shown to have a particularly detrimental effect on the gamma aminobutyric acid (GABA)ergic system, which contributes to the observed learning and behavioral deficits. The damage to GABAergic neurons, resulting from neonatal anesthesia, seems to involve structure-specific changes in excitatory-inhibitory balance and neurovascular coupling, which manifest following a significant interval after neonatal anesthesia exposure. Thus, to better understand how neonatal anesthesia affects the GABAergic system, we first review the early development of the GABAergic system in various structures that have been the focus of neonatal anesthesia research. This is followed by an explanation that, due to the prolonged developmental curve of the GABAergic system, the entirety of the negative effects of neonatal anesthesia on learning and behavior in children are not immediately evident, but instead take a substantial amount of time (years) to fully develop. In order to address these concerns going forward, we subsequently offer a variety of in vivo methods which can be used to record these delayed effects.

What Triggers the Interictal Epileptic Spike? A Multimodal Multiscale Analysis of the Dynamic of Synaptic and Non-synaptic Neuronal and Vascular Compartments Using Electrical and Optical Measurements

Interictal spikes (IISs) may result from a disturbance of the intimate functional balance between various neuronal (synaptic and non-synaptic), vascular, and metabolic compartments. To better characterize the complex interactions within these compartments at different scales we developed a simultaneous multimodal-multiscale approach and measure their activity around the time of the IIS. We performed such measurements in an epileptic rat model (n = 43). We thus evaluated (1) synaptic dynamics by combining electrocorticography and multiunit activity recording in the time and time-frequency domain, (2) non-synaptic dynamics by recording modifications in light scattering induced by changes in the membrane configuration related to cell activity using the fast optical signal, and (3) vascular dynamics using functional near-infrared spectroscopy and, independently but simultaneously to the electrocorticography, the changes in cerebral blood flow using diffuse correlation spectroscopy. The first observed alterations in the measured signals occurred in the hemodynamic compartments a few seconds before the peak of the IIS. These hemodynamic changes were followed by changes in coherence and then synchronization between the deep and superficial neural networks in the 1 s preceding the IIS peaks. Finally, changes in light scattering before the epileptic spikes suggest a change in membrane configuration before the IIS. Our multimodal, multiscale approach highlights the complexity of (1) interactions between the various neuronal, vascular, and extracellular compartments, (2) neural interactions between various layers, (3) the synaptic mechanisms (coherence and synchronization), and (4) non-synaptic mechanisms that take place in the neuronal network around the time of the IISs in a very specific cerebral hemodynamic environment.

Temporal and Spatial Dynamics of Different Interictal Epileptic Discharges: A Time-Frequency EEG Approach in Pediatric Focal Refractory Epilepsy

Objective: Characterization of the spatial and temporal dynamics of interictal epileptic discharges (IED) using time-frequency analysis (TFA) and electrical-source localization (ESL).

Methods: TFA was performed on IED (spikes, spike waves, and polyspike waves) recorded by high-density-EEG (HD-EEG) in 19 refractory focal epileptic children. Temporal modulations related to IEDs were analyzed in a time window around the IED peaks [−1,000 to 1,000 ms]. Spatial modulations were analyzed by ESL in the time-frequency and time domains.

Results: IED were associated with complex power spectral modulations. We observed increases in power spectrum (IPS) patterns specific to IED type. For spikes, the TFA pattern consisted of an IPS (−100 to +100 ms, 4–50 Hz). For spike waves, the IPS was followed by a second IPS (+100 to +400 ms, 4–10 Hz), corresponding to the slow wave. IPS patterns were preceded (−400 to −100 ms, 4–40 Hz), and followed (+100 to +400 ms) by a decrease in the power spectrum (DPS) (n = 8). For 14 out of 19 patients, at least one ESL method was concordant with the epileptogenic area. For the remaining five patients, all of them had temporal epilepsies. ESL in the time-frequency domain (DPS/IPS) provided concordant (n = 6) or complementary (n = 4) information to the ESL in the time domain concerning the epileptogenic zone. ESL in time-frequency domain (DPS/IPS) was the only method to provide concordant information concerning the epileptogenic zone in three patients.

Significance: TFA demonstrates complex time-frequency modulations of the neuronal networks around IED, suggesting that the pathological mechanisms are initiated well before onset of the classical hyper-synchronization of the IED. Combining time and time-frequency analysis of the ESL provides complementary information to define the epileptogenic zone in refractory focal epilepsy.

Multimodal, noninvasive seizure network mapping software: A novel tool for preoperative epilepsy evaluation

BACKGROUND

Despite rigorous preoperative evaluation, epilepsy surgery achieves seizure freedom in only two-thirds of cases. Current preoperative evaluation does not include a detailed network analysis despite the association of network-level changes with epilepsy.

OBJECTIVE

We sought to create a software algorithm to map individualized epilepsy networks by combining noninvasive electroencephalography (EEG) source localization and nonconcurrent resting state functional magnetic resonance imaging (rsfMRI).

METHODS

Scalp EEG and rsfMRI data were acquired for three sample cases: one healthy control case, one case of right temporal lobe epilepsy, and one case of bitemporal seizure onset. Data from rsfMRI were preprocessed, and a time-series function was extracted. Connection coefficients were used to threshold out spurious connections and model global functional networks in a 3D map. Epileptic discharges were localized using a forward model of cortical mesh dipoles followed by an empirical Bayesian approach of inverse source reconstruction and co-registered with rsfMRI. Co-activating brain regions were mapped.

RESULTS

Three illustrative sample cases are presented. In the healthy control case, the software showed symmetrical global connectivity. In the right temporal lobe epilepsy case, asymmetry was found in the global connectivity metrics with a paucity of connectivity ipsilateral to the epileptogenic cortex. The superior longitudinal fasciculus, uncinate fasciculus, and commissural fibers connecting disparate and discontinuous cortical regions involved in the epilepsy network were visualized. In the case with bitemporal lobe epilepsy, global connectivity was symmetric. It showed a network of correlating cortical activity local to epileptogenic tissue in both temporal lobes. The network involved white matter tracks in a similar pattern to those seen in the right temporal case.

CONCLUSIONS

This modeling algorithm allows better definition of the global brain network and potentially demonstrates differences in connectivity between an epileptic and a non-epileptic brain. This finding may be useful for mapping cortico-cortical connections representing the putative epilepsy networks. With this methodology, we localized the epileptogenic brain and showed network asymmetry and long-distance cortical co-activation. This software tool is the first to use a multimodal, nonconcurrent, and noninvasive approach to model and visualize the epilepsy network.