The critical role of persistent sodium current in hippocampal gamma oscillations.

Gamma network oscillations in the brain are fast rhythmic network oscillations in the gamma frequency range (~30-100 Hz), playing key roles in the hippocampus for learning, memory, and spatial processing. There is evidence indicating that GABAergic interneurons, including parvalbumin-expressing basket cells (PVBCs), contribute to cortical gamma oscillations through synaptic interactions with excitatory cells. However, the molecular, cellular, and circuit underpinnings underlying generation and maintenance of cortical gamma oscillations are largely elusive. Recent studies demonstrated that intrinsic and synaptic properties of GABAergic interneurons and excitatory cells are regulated by a slowly inactivating or non-inactivating sodium current (i.e., persistent sodium current, INaP), suggesting that INaP is involved in gamma oscillations. Here, we tested whether INaP plays a role in hippocampal gamma oscillations using pharmacological, optogenetic, and electrophysiological approaches. We found that INaP blockers, phenytoin (40 µM and 100 µM) and riluzole (10 µM), reduced gamma oscillations induced by optogenetic stimulation of CaMKII-expressing cells in CA1 networks. Whole-cell patch-clamp recordings further demonstrated that phenytoin (100 µM) reduced INaP and firing frequencies in both PVBCs and pyramidal cells without altering threshold and amplitude of action potentials, but increased rheobase in both cell types. These results suggest that INaP in pyramidal cells and PVBCs is required for hippocampal gamma oscillations, supporting a pyramidal-interneuron network gamma model. Phenytoin-mediated modulation of hippocampal gamma oscillations may be a mechanism underlying its anticonvulsant efficacy, as well as its contribution to cognitive impairments in epilepsy patients.

Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity.

Interictal high-frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outcome after the epilepsy surgery. The utility of HFO in planning the surgery, though, is unclear. Reasons include the variability of the HFO across patients and brain regions and the influence of the sleep-wake cycle, which causes large fluctuations in the ratio between the HFO observed in SOZ and non-SOZ regions. To cope with these limitations, a rank-based solution is proposed to identify the SOZ by using the HFO in multichannel intracranial EEG. A time-varying index of the epileptic susceptibility of the different brain areas is derived from the HFO rate and a support vector machine is applied on this index to identify the SOZ. The solution is trained and tested on separate groups of patients to avoid the use of patient-specific information and provides optimal SOZ prediction using as little as 30 min of recordings per channel (window). Tested on 14 patients with various combinations of seizure type, epilepsy etiology, and SOZ arrangement (172.7 ± 90.1 h/channel per patient and 75.6 ± 23.5 channels/patient, mean ± S.D.), our solution identified the SOZ with 0.92 ± 0.03 accuracy and 0.91 ± 0.03 area under the ROC curve (mean ± S.D.) across patients. For each patient, the window onset time was varied over 72 continuous hours and the prediction of the SOZ remained insensitive to the onset time, thus showing potential for surgery planning.

Temporal Pattern of Ripple Events in Temporal Lobe Epilepsy: Towards a Pattern-based Localization of the Seizure Onset Zone.

Ripples (80-250 Hz) are brief high-frequency oscillations that are often detected in intracranial EEG (iEEG) and are currently investigated as a potential biomarker to facilitate the Iocalization of the seizure onset zone (SOZ) in patients with drug-resistant epilepsy. While the rate and shape of these oscillations have been positively correlated with the SOZ, the temporal pattern of these oscillations in the epileptic brain still requires investigation. In this study, we investigate the temporal pattern of ripple events in five patients with temporal lobe epilepsy (TLE), which is one of the most common forms of epilepsy. The rate of ripple events is positively correlated with the SOZ in TLE but its diagnostic utility in localizing the SOZ remains unclear, which suggests that additional ripple-related features should be investigated. By combining point process modeling and cluster analysis, we show that a recurrent, non-stationary bursting pattern characterizes the SOZ channels consistently across patients, while the non-SOZ channels have poor between-channel similarity and no consistent pattern over time nor across patients. Furthermore, the degree of separation between SOZ and non-SOZ model parameter vectors is significantly higher (ANOVA test, ${P}$-value $P\lt 0.01$) than the degree of separation between the ripple rates, which suggests that the temporal pattern more than the rate may contribute to the pre- surgical Iocalization of the SOZ.

Decoding Kinematics Using Task-Independent Movement-Phase-Specific Encoding Models.

Neural decoders of kinematic variables have largely relied on task-dependent (TD) encoding models of the neural activity. TD decoders, though, require prior knowledge of the tasks, which may be unavailable, lack scalability as the number of tasks grows, and require a large number of trials per task to reduce the effects of neuronal variability. The execution of movements involves a sequence of phases (e.g., idle, planning, and so on) whose progression contributes to the neuronal variability. We hypothesize that information about the movement phase facilitates the decoding of kinematics and compensates for the lack of prior knowledge about the task. We test this hypothesis by designing a task-independent movement-phase-specific (TI-MPS) decoding algorithm. The algorithm assumes that movements proceed through a consistent sequence of phases regardless of the specific task, and it builds one model per phase by combining data from different tasks. Phase transitions are detected online from neural data and, for each phase, a specific encoding model is used. The TI-MPS algorithm was tested on single-unit recordings from 437 neurons in the dorsal and ventral pre-motor cortices from two nonhuman primates performing 3-D multi-object reach-to-grasp tasks. The TI-MPS decoder accurately decoded kinematics from tasks it was not trained for and outperformed TD approaches (one-way ANOVA with Tukey's post-hoc test and -value <0.05). Results indicate that a TI paradigm with MPS models may help decoding kinematics when prior information about the task is unavailable and pave the way toward clinically viable prosthetics.