Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel gating to circuit connectomics. Computational models are regularly employed to understand how multiple parameters contribute synergistically to circuit behavior. However, traditional models of anatomically and biophysically realistic neurons are computationally demanding, especially when scaled to model local circuits. To overcome this limitation, we trained several artificial neural network (ANN) architectures to model the activity of realistic multicompartmental cortical neurons. We identified an ANN architecture that accurately predicted subthreshold activity and action potential firing. The ANN could correctly generalize to previously unobserved synaptic input, including in models containing nonlinear dendritic properties. When scaled, processing times were orders of magnitude faster compared with traditional approaches, allowing for rapid parameter-space mapping in a circuit model of Rett syndrome. Thus, we present a novel ANN approach allowing for rapid, detailed network experiments using inexpensive and commonly available computational resources.

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Independent automated scoring of sleep-wake and seizures have recently been achieved; however, the combined scoring of both states has yet to be reported. Mouse models of epilepsy typically demonstrate an abnormal electroencephalographic (EEG) background with significant variability between mice, making combined scoring a more difficult classification problem for manual and automated scoring. Given the extensive EEG variability between epileptic mice, large group sizes are needed for most studies. As large datasets are unwieldy and impractical to score manually, automatic seizure and sleep-wake classification are warranted. To this end, we developed an accurate automated classifier of sleep-wake states, seizures, and the post-ictal state. Our benchmark was a classification accuracy at or above the 93% level of human inter-rater agreement. Given the failure of parametric scoring in the setting of altered baseline EEGs, we adopted a machine-learning approach. We created several multi-layer neural network architectures that were trained on human-scored training data from an extensive repository of continuous recordings of electrocorticogram (ECoG), left and right hippocampal local field potential (HPC-L and HPC-R), and electromyogram (EMG) in the murine intra-amygdala kainic acid model of medial temporal lobe epilepsy. We then compared different network models, finding a bidirectional long short-term memory (BiLSTM) design to show the best performance with validation and test portions of the dataset. The SWISC (sleep-wake and the ictal state classifier) achieved >93% scoring accuracy in all categories for epileptic and non-epileptic mice. Classification performance was principally dependent on hippocampal signals and performed well without EMG. Additionally, performance is within desirable limits for recording montages featuring only ECoG channels, expanding its potential scope. This accurate classifier will allow for rapid combined sleep-wake and seizure scoring in mouse models of epilepsy and other neurologic diseases with varying EEG abnormalities, thereby facilitating rigorous experiments with larger numbers of mice.

Purpose:Corticocortical evoked potentials (CCEPs) resulting from single pulse electrical stimulation are increasingly used to understand seizure networks, as well as normal brain connectivity. However, we observed that when using depth electrodes, traditional measures of CCEPs amplitude using a referential montage can be falsely localizing, often to white matter.Methods:We pooled 27 linear electrode arrays targeting the amygdala, hippocampus, or cingulate cortex from eight participants. Using postoperative imaging, we classified contacts as being in gray matter, white matter, or bordering each and measured the amplitude using the root-mean-squared deviation from baseline in a referential, common average, bipolar, or Laplacian montage.Results:Of 27 electrode contacts, 25 (93%) had a significantly higher mean amplitude when in gray matter than in white matter using a Laplacian montage, which was significantly more than the 12 of 27 electrodes (44%) when using a referential montage (P = 0.0003, Fisher exact test). The area under the curve for a receiver operating characteristic classifying contacts as gray or white matter was significantly higher for either the Laplacian (0.79) or the bipolar (0.72) montage when compared with either the common average (0.56) or the referential (0.51) montage (P ≤ 0.005, bootstrap).Conclusions:Both the Laplacian and bipolar montages were superior to the common average or referential montage in localizing CCEPs to gray matter. These montages may be more appropriate for interpreting CCEPs when using depth electrodes than the referential montage, which has typically been used in prior studies of CCEPs with subdural grids.

Study Objective: Validate a novel method for sleep-wake staging in mice using noninvasive electric field (EF) sensors. Methods: Mice were implanted with electroencephalogram (EEG) and electromyogram (EMG) electrodes and housed individually. Noninvasive EF sensors were attached to the exterior of each chamber to record respiration and other movement simultaneously with EEG, EMG, and video. A sleep-wake scoring method based on EF sensor data was developed with reference to EEG/EMG and then validated by three expert scorers. Additionally, novice scorers without sleep-wake scoring experience were self-trained to score sleep using only the EF sensor data, and results were compared to those from expert scorers. Lastly, ability to capture three-state sleep-wake staging with EF sensors attached to traditional mouse home-cages was tested. Results: EF sensors quantified wake, rapid eye movement (REM) sleep, and non-REM sleep with high agreement (>93%) and comparable inter- and intra-scorer error as EEG/EMG. Novice scorers successfully learned sleep-wake scoring using only EF sensor data and scoring criteria, and achieved high agreement with expert scorers (>91%). When applied to traditional home-cages, EF sensors enabled classification of three-state (wake, NREM and REM) sleep-wake independent of EEG/EMG. Conclusions: EF sensors score three-state sleep-wake architecture with high agreement to conventional EEG/EMG sleep-wake scoring 1) without invasive surgery, 2) from outside the home-cage, and 3) and without requiring specialized training or equipment. EF sensors provide an alternative method to assess rodent sleep for animal models and research laboratories in which EEG/EMG is not possible or where noninvasive approaches are preferred.

Studies of epilepsy surgery outcomes are often small and thus underpowered to reach statistically valid conclusions. We hypothesized that ordinal logistic regression would have greater statistical power than binary logistic regression when analyzing epilepsy surgery outcomes. We reviewed 10 manuscripts included in a recent meta-analysis which found that mesial temporal sclerosis (MTS) predicted better surgical outcomes after a stereotactic laser amygdalohippocampectomy (SLAH). We extracted data from 239 patients from eight studies that reported four discrete Engel surgical outcomes after SLAH, stratified by the presence or absence of MTS. The rate of freedom from disabling seizures (Engel I) was 64.3% (110/171) for patients with MTS compared to 44.1% (30/68) without MTS. The statistical power to detect MTS as a predictor for better surgical outcome after a SLAH was 29% using ordinal regression, which was significantly more than the 13% power using binary logistic regression (paired t-test, P <.001). Only 120 patients are needed for this example to achieve 80% power to detect MTS as a predictor using ordinal regression, compared to 210 patients that are needed to achieve 80% power using binary logistic regression. Ordinal regression should be considered when analyzing ordinal outcomes (such as Engel surgical outcomes), especially for datasets with small sample sizes.

Roughly two-thirds of all people report having experienced déjà vu—the odd feeling that a current experience is both novel and a repeat or replay of a previous, unrecalled experience. Reports of an association between déjà vu and seizure aura symptomatology have accumulated for over a century, and frequent déjà vu is also now known to be associated with focal seizures, particularly those of a medial temporal lobe (MTL) origin. A longstanding question is whether seizure-related déjà vu has the same basis and is the same subjective experience as non-seizure déjà vu. Survey research suggests that people who experience both seizure-related and non-seizure déjà vu can often subjectively distinguish between the two. We present a case of a person with a history of focal MTL seizures who reports having experienced both seizure-related and non-seizure common déjà vu, though the non-seizure type was more frequent during this person's youth than it is currently. The patient was studied with a virtual tour paradigm that has previously been shown to elicit déjà vu among non-clinical, young adult participants. The patient reported experiencing déjà vu of the common non-seizure type during the virtual tour paradigm, without associated abnormalities of the intracranial EEG. We situate this work in the context of broader ongoing projects examining the subjective correlates of seizures. The importance for memory research of virtual scenes, spatial tasks, virtual reality (VR), and this paradigm for isolating familiarity in the context of recall failure are discussed.

Rules derived from standard Rechtschaffen and Kales criteria were developed to accurately score rodent sleep into wake, rapid eye movement (REM) sleep, and non-REM sleep using movements detected by non-contact electric field (EF) sensors. • Using this method, rodent sleep can be scored using only respiratory and gross body movements as a validated, non-invasive alternative to electrode techniques. • The methodology and rules established for EF sensor-based sleep scoring were easily learned and implemented. • Examples of expert-scored files are included here to help novice scorers self-train to score sleep. Though validated in mice, sleep scoring using respiratory movements has the potential for application in other species and through other movement-based technologies beyond EF sensors.

Temporal lobe epilepsy is associated with MRI findings reflecting underlying mesial temporal sclerosis. Identifying these MRI features is critical for the diagnosis and management of temporal lobe epilepsy. To date, this process relies on visual assessment by highly trained human experts (e.g. neuroradiologists, epileptologists). Artificial intelligence is increasingly recognized as a promising aid in the radiological evaluation of neurological diseases, yet its applications in temporal lobe epilepsy have been limited. Here, we applied a convolutional neural network to assess the classification accuracy of temporal lobe epilepsy based on structural MRI. We demonstrate that convoluted neural networks can achieve high accuracy in the identification of unilateral temporal lobe epilepsy cases even when the MRI had been originally interpreted as normal by experts. We show that accuracy can be potentiated by employing smoothed grey matter maps and a direct acyclic graphs approach. We further discuss the foundations for the development of computer-aided tools to assist with the diagnosis of epilepsy.

Background: Mouse models are beneficial to understanding neural networks given a wide array of transgenic mice and cell-selective techniques. However, instrumentation of mice for neurophysiological studies is difficult. Often surgery is prolonged with experimental error arising from non-concurrent and variable implantations. New method: We describe a method for the rapid, reproducible and customizable instrumentation of mice. We constructed a headplate that conforms to the mouse skull surface using script-based computer aided design. This headplate was then modified to enable the friction-fit assembly prior to surgery and printed with a high-resolution resin-based 3D printer. Using this approach, we describe an easily customized headplate with dural screws for electrocorticography (ECoG), electromyogram (EMG) electrodes, cannula hole and two microdrives for local field potential (LFP) electrodes. Results: Implantation of the headplate reliably takes less than 40 min, enabling a cohort of eight mice to be implanted in one day. Good quality recordings were obtained after surgical recovery and the headplate was stable for at least four weeks. LFP electrode placement was found to be accurate. Comparison with existing methods: While similar approaches with microelectrodes have been used in rats before, and related approaches exist for targeting one brain region with tetrodes, we do not know of similar head-plates for mice, nor a strictly source-code and easily reconfigurable approach. Conclusions: 3D printing and friction-fit pre-assembly of mouse headplates offers a rapid, easily reconfigurable, consistent, and cost-effective way to implant larger numbers of mice in a highly reproducible way, reducing surgical time and mitigating experimental error.