Accurate detection of spontaneous seizures using a generalized linear model with external validation

Objective Seizure detection is a major facet of electroencephalography (EEG) analysis in neurocritical care, epilepsy diagnosis and management, and the instantiation of novel therapies such as closed-loop stimulation or optogenetic control of seizures. It is also of increased importance in high-throughput, robust, and reproducible pre-clinical research. However, seizure detectors are not widely relied upon in either clinical or research settings due to limited validation. In this study, we create a high-performance seizure-detection approach, validated in multiple data sets, with the intention that such a system could be available to users for multiple purposes. Methods We introduce a generalized linear model trained on 141 EEG signal features for classification of seizures in continuous EEG for two data sets. In the first (Focal Epilepsy) data set consisting of 16 rats with focal epilepsy, we collected 1012 spontaneous seizures over 3 months of 24/7 recording. We trained a generalized linear model on the 141 features representing 20 feature classes, including univariate and multivariate, linear and nonlinear, time, and frequency domains. We tested performance on multiple hold-out test data sets. We then used the trained model in a second (Multifocal Epilepsy) data set consisting of 96 rats with 2883 spontaneous multifocal seizures. Results From the Focal Epilepsy data set, we built a pooled classifier with an Area Under the Receiver Operating Characteristic (AUROC) of 0.995 and leave-one-out classifiers with an AUROC of 0.962. We validated our method within the independently constructed Multifocal Epilepsy data set, resulting in a pooled AUROC of 0.963. We separately validated a model trained exclusively on the Focal Epilepsy data set and tested on the held-out Multifocal Epilepsy data set with an AUROC of 0.890. Latency to detection was under 5 seconds for over 80% of seizures and under 12 seconds for over 99% of seizures. Significance This method achieves the highest performance published for seizure detection on multiple independent data sets. This method of seizure detection can be applied to automated EEG analysis pipelines as well as closed loop interventional approaches, and can be especially useful in the setting of research using animals in which there is an increased need for standardization and high-throughput analysis of large number of seizures

Amygdala inputs to prefrontal cortex guide behavior amid conflicting cues of reward and punishment

Orchestrating appropriate behavioral responses in the face of competing signals that predict either rewards or threats in the environment is crucial for survival. The basolateral nucleus of the amygdala (BLA) and prelimbic (PL) medial prefrontal cortex have been implicated in reward-seeking and fear-related responses, but how information flows between these reciprocally connected structures to coordinate behavior is unknown. We recorded neuronal activity from the BLA and PL while rats performed a task wherein competing shock- and sucrose-predictive cues were simultaneously presented. The correlated firing primarily displayed a BLA→PL directionality during the shock-associated cue. Furthermore, BLA neurons optogenetically identified as projecting to PL more accurately predicted behavioral responses during competition than unidentified BLA neurons. Finally photostimulation of the BLA→PL projection increased freezing, whereas both chemogenetic and optogenetic inhibition reduced freezing. Therefore, the BLA→PL circuit is critical in governing the selection of behavioral responses in the face of competing signals.National Institutes of Health (U.S.) (Award 1R25-MH092912-01)National Institute of Mental Health (U.S.) (Grant R01- MH102441-01)National Institutes of Health (U.S.) (Award DP2- DK-102256-01

Molecular mechanisms of cell death: recommendations of the Nomenclature Committee on Cell Death 2018.

Over the past decade, the Nomenclature Committee on Cell Death (NCCD) has formulated guidelines for the definition and interpretation of cell death from morphological, biochemical, and functional perspectives. Since the field continues to expand and novel mechanisms that orchestrate multiple cell death pathways are unveiled, we propose an updated classification of cell death subroutines focusing on mechanistic and essential (as opposed to correlative and dispensable) aspects of the process. As we provide molecularly oriented definitions of terms including intrinsic apoptosis, extrinsic apoptosis, mitochondrial permeability transition (MPT)-driven necrosis, necroptosis, ferroptosis, pyroptosis, parthanatos, entotic cell death, NETotic cell death, lysosome-dependent cell death, autophagy-dependent cell death, immunogenic cell death, cellular senescence, and mitotic catastrophe, we discuss the utility of neologisms that refer to highly specialized instances of these processes. The mission of the NCCD is to provide a widely accepted nomenclature on cell death in support of the continued development of the field

Neuronal Correlates of Instrumental Learning in the Dorsal Striatum

We recorded neuronal activity simultaneously in the medial and lateral regions of the dorsal striatum as rats learned an operant task. The task involved making head entries into a response port followed by movements to collect rewards at an adjacent reward port. The availability of sucrose reward was signaled by an acoustic stimulus. During training, animals showed increased rates of responding and came to move rapidly and selectively, following the stimulus, from the response port to the reward port. Behavioral “devaluation” studies, pairing sucrose with lithium chloride, established that entries into the response port were habitual (insensitive to devaluation of sucrose) from early in training and entries into the reward port remained goal-directed (sensitive to devaluation) throughout training. Learning-related changes in behavior were paralleled by changes in neuronal activity in the dorsal striatum, with an increasing number of neurons showing task-related firing over the training period. Throughout training, we observed more task-related neurons in the lateral striatum compared with those in the medial striatum. Many of these neurons fired at higher rates during initiation of movements in the presence of the stimulus, compared with similar movements in the absence of the stimulus. Learning was also accompanied by progressive increases in movement-related potentials and transiently increased theta-band oscillations (5–8 Hz) in simultaneously recorded field potentials. Together, these data suggest that representations of task-relevant stimuli and movements develop in the dorsal striatum during instrumental learning

Streamlined, Inexpensive 3D Printing of the Brain and Skull.

Neuroimaging technologies such as Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) collect three-dimensional data (3D) that is typically viewed on two-dimensional (2D) screens. Actual 3D models, however, allow interaction with real objects such as implantable electrode grids, potentially improving patient specific neurosurgical planning and personalized clinical education. Desktop 3D printers can now produce relatively inexpensive, good quality prints. We describe our process for reliably generating life-sized 3D brain prints from MRIs and 3D skull prints from CTs. We have integrated a standardized, primarily open-source process for 3D printing brains and skulls. We describe how to convert clinical neuroimaging Digital Imaging and Communications in Medicine (DICOM) images to stereolithography (STL) files, a common 3D object file format that can be sent to 3D printing services. We additionally share how to convert these STL files to machine instruction gcode files, for reliable in-house printing on desktop, open-source 3D printers. We have successfully printed over 19 patient brain hemispheres from 7 patients on two different open-source desktop 3D printers. Each brain hemisphere costs approximately $3-4 in consumable plastic filament as described, and the total process takes 14-17 hours, almost all of which is unsupervised (preprocessing = 4-6 hr; printing = 9-11 hr, post-processing = <30 min). Printing a matching portion of a skull costs$1-5 in consumable plastic filament and takes less than 14 hr, in total. We have developed a streamlined, cost-effective process for 3D printing brain and skull models. We surveyed healthcare providers and patients who confirmed that rapid-prototype patient specific 3D models may help interdisciplinary surgical planning and patient education. The methods we describe can be applied for other clinical, research, and educational purposes

Suggestions on how to choose printing parameters for fused filament fabrication (FFF)/fused deposition modeling (FDM) 3D desktop printers.

<p>Different 3D printers and print environments may have subtle effects on the values of parameters needed in the workflow (step <i>B1</i>. <i>Generate gcode for 3D printing</i>). We therefore present the results of systematic parameter exploration (over 50 prints), to assist in empirically choosing and adjusting 3D printing parameters. Where noted, parameters may involve a tradeoff between speed and smoothness of the print, the choice of which may depend on the use of printed object. In parentheses we include the parameters that we have found to produce usable objects in the time needed (<1 day) reliably.</p

Brain Print Process.

<p>A) Sagital view of patient’s MRI (step A1). B) Brain surface rendered in ReplicatorG at the time of gcode generation (step B1). C) 3D printed brain after manually removing the support material (step B3). D) 3D printed brain overlaid with a 64-contact electrode grid to highlight possible electrode coverage during neurosurgical planning.</p

Overall modular workflow described in the text for <i>A</i>. <i>Transforming 2D images to 3D models</i> and <i>B</i>. <i>Desktop 3D Printing</i> brains and skulls.

<p>We highlight, in each table cell, software or hardware that we have found produces reliable prints, noting the slight variations in our workflow process for MRI based brain models compared to CT based skull models. The modular nature of the workflow process allows for alternative methods to be used at each step. Each step and potential alternatives are described more thoroughly in the text. Step <i>A4</i>. <i>Crop 3D model</i> is italicized because this step is optional, primarily for use when one only needs to print a subset of the organ of interest.</p

Measurements of brain hemispheres from three different patients (in mm).

<p>The size of the final cleaned print is given along three axes: anterior to posterior (A-P), medial to lateral (M-L), and superior-to-inferior (S-I), measured as defined in the Methods. Across all prints, the difference between the print and MRI measurements are typically less than 1mm (mean difference 0.5mm, standard deviation 0.3mm, maximum difference 1.2mm). For subject C, we printed the same hemisphere five different times in order to determine the consistency of prints. Across repeated prints of the same hemisphere (Subject C), prints were within 1mm of each other (standard deviation of 0.36–0.55mm).</p