Chloride deregulation and GABA depolarization in MTOR related malformations of cortical development.

Focal Cortical Dysplasia, Hemimegalencephaly and Cortical Tuber are pediatric epileptogenic malformations of cortical development (MCDs) frequently pharmaco-resistant and mostly surgically treated by the resection of epileptic cortex. Availability of cortical resection samples allowed significant mechanistic discoveries directly from human material. Causal brain somatic or germline mutations in the AKT/PI3K/DEPDC5/MTOR genes were identified. GABAa mediated paradoxical depolarization, related to altered chloride (Cl-) homeostasis, was shown to participate to ictogenesis in human pediatric MCDs. However, the link between genomic alterations and neuronal hyperexcitability is still unclear. Here we studied the post translational interactions between the mTOR pathway and the regulation of cation-chloride cotransporters (CCC), KCC2 and NKCC1, that are largely responsible for controlling intracellular Cl- and ultimately GABAergic transmission. For this study, 35 children (25 MTORopathies and 10 pseudo controls, diagnosed by histology plus genetic profiling) were operated for drug resistant epilepsy. Postoperative cortical tissues were recorded on multielectrode array (MEA) to map epileptic activities. CCC expression level and phosphorylation status of the WNK1/SPAK-OSR1 pathway was measured during basal conditions and after pharmacological modulation. Direct interactions between mTOR and WNK1 pathway components were investigated by immunoprecipitation. Membranous incorporation of MCD samples in Xenopus laevis oocytes enabled Cl- conductance and equilibrium potential (EGABA) for GABA measurement. Of the 25 clinical cases, half harbored a somatic mutation in the mTOR pathway, while pS6 expression was increased in all MCD samples. Spontaneous interictal discharges were recorded in 65% of the slices. CCC expression was altered in MCDs, with a reduced KCC2/NKCC1 ratio and decreased KCC2 membranous expression. CCC expression was regulated by the WNK1/SPAK-OSR1 kinases through direct phosphorylation of Thr906 on KCC2, that was reversed by WNK1 and SPAK antagonists (NEM and Staurosporine). mSIN1 subunit of MTORC2 was found to interact with SPAK-OSR1 and WNK1. Interactions between these key epileptogenic pathways could be reversed by the mTOR specific antagonist Rapamycin, leading to a dephosphorylation of CCCs and recovery of the KCC2/NKCC1 ratio. The functional effect of such recovery was validated by the restoration of the depolarizing shift in EGABA by rapamycin, measured after incorporation of MCD membranes to X. laevis oocytes, in line with a reestablishment of normal ECl-. Our study deciphers a protein interaction network through a phosphorylation cascade between MTOR and WNK1/SPAK-OSR1 leading to chloride cotransporters deregulation, increased neuronal chloride levels and GABAa dysfunction in malformations of Cortical Development, linking genomic defects and functional effects and paving the way to target epilepsy therapy.

Sharp detection of oscillation packets in rich time-frequency representations of neural signals.

Brain oscillations most often occur in bursts, called oscillation packets, which span a finite extent in time and frequency. Recent studies have shown that these packets portray a much more dynamic picture of synchronization and transient communication between sites than previously thought. To understand their nature and statistical properties, techniques are needed to objectively detect oscillation packets and to quantify their temporal and frequency extent, as well as their magnitude. There are various methods to detect bursts of oscillations. The simplest ones divide the signal into band limited sub-components, quantifying the strength of the resulting components. These methods cannot by themselves cope with broadband transients that look like genuine oscillations when restricted to a narrow band. The most successful detection methods rely on time-frequency representations, which can readily show broadband transients and harmonics. However, the performance of such methods is conditioned by the ability of the representation to localize packets simultaneously in time and frequency, and by the capabilities of packet detection techniques, whose current state of the art is limited to extraction of bounding boxes. Here, we focus on the second problem, introducing two detection methods that use concepts derived from clustering and topographic prominence. These methods are able to delineate the packets' precise contour in the time-frequency plane. We validate the new approaches using both synthetic and real data recorded in humans and animals and rely on a super-resolution time-frequency representation, namely the superlets, as input to the detection algorithms. In addition, we define robust tests for benchmarking and compare the new methods to previous techniques. Results indicate that the two methods we introduce shine in low signal-to-noise ratio conditions, where they only miss a fraction of packets undetected by previous methods. Finally, algorithms that delineate precisely the border of spectral features and their subcomponents offer far more valuable information than simple rectangular bounding boxes (time and frequency span) and can provide a solid foundation to investigate neural oscillations' dynamics.

Time-Frequency Representations of Brain Oscillations: Which One Is Better?

Brain oscillations are thought to subserve important functions by organizing the dynamical landscape of neural circuits. The expression of such oscillations in neural signals is usually evaluated using time-frequency representations (TFR), which resolve oscillatory processes in both time and frequency. While a vast number of methods exist to compute TFRs, there is often no objective criterion to decide which one is better. In feature-rich data, such as that recorded from the brain, sources of noise and unrelated processes abound and contaminate results. The impact of these distractor sources is especially problematic, such that TFRs that are more robust to contaminants are expected to provide more useful representations. In addition, the minutiae of the techniques themselves impart better or worse time and frequency resolutions, which also influence the usefulness of the TFRs. Here, we introduce a methodology to evaluate the "quality" of TFRs of neural signals by quantifying how much information they retain about the experimental condition during visual stimulation and recognition tasks, in mice and humans, respectively. We used machine learning to discriminate between various experimental conditions based on TFRs computed with different methods. We found that various methods provide more or less informative TFRs depending on the characteristics of the data. In general, however, more advanced techniques, such as the superlet transform, seem to provide better results for complex time-frequency landscapes, such as those extracted from electroencephalography signals. Finally, we introduce a method based on feature perturbation that is able to quantify how much time-frequency components contribute to the correct discrimination among experimental conditions. The methodology introduced in the present study may be extended to other analyses of neural data, enabling the discovery of data features that are modulated by the experimental manipulation.