Electrophysiological Events Research Articles

Low-dose quinine targets KCNH6 to potentiate glucose-induced insulin secretion.

Insulin secretion is mainly regulated by two electrophysiological events, depolarization initiated by the closure of ATP-sensitive K+ (KATP) channels and repolarization mediated by K+ efflux. Quinine, a natural component commonly used for the treatment of malaria, has been reported to directly stimulate insulin release and lead to hypoglycemia in patients during treatment through inhibiting KATP channels. In this study, we verified the insulinotropic effect of quinine on the isolated mouse pancreatic islets. We also revealed that low-dose quinine (<20 µM) did not directly provoke Ca2+ spikes or insulin secretion under low-glucose conditions but potentiated Ca2+ influx and insulin secretion induced by high glucose, which cannot be explained by KATP inhibition. KCNH6 (hERG2) is a voltage-dependent K+ (Kv) channel that plays a critical role in the repolarization of pancreatic β cells. Patch clamp experiments showed that quinine inhibited hERG channels at low micromolar concentrations. However, whether quinine can target KCNH6 to potentiate glucose-induced insulin secretion remains unclear. Here, we showed that in vivo administration of low-dose quinine (25mg/kg) improved glucose tolerance and increased glucose-induced insulin release in wild-type control mice but not in Kcnh6-β-cell-specific knockout (βKO) mice. Consistently, in vitro treatment of primary islet β cells with low-dose quinine (10 µM) prolonged action potential duration and augmented glucose-induced Ca2+ influx in the wild-type control group but not in the Kcnh6-βKO group. Our results demonstrate that KCNH6 plays an important role in low-dose quinine-potentiated insulin secretion and provide new insights into KCNH6-targeted drug development.

Non-Invasive Vagal Nerve Stimulation Pre-Treatment Reduces Neurological Dysfunction After Closed Head Injury in Mice.

Non-invasive vagus nerve stimulation (nVNS) has recently been suggested as a potential therapy for traumatic brain injury (TBI). We previously demonstrated that nVNS inhibits cortical spreading depolarization, the electrophysiological event underlying migraine aura, and is relevant to TBI. Our past work also suggests a role for interleukin-1 beta (IL-1β) in cognitive deficits after closed head injury (CHI) in mice. We show that nVNS pre-treatment suppresses CHI-associated spatial learning and memory impairment and prevents IL-1β activation in injured neurons, but not endothelial cells. In contrast, nVNS administered 10 min after CHI was ineffective. These data suggest that nVNS prophylaxis might ameliorate neuronal dysfunction associated with CHI in populations at high risk for concussive TBI.

Detecting unitary synaptic events with machine learning

Spontaneously occurring miniature excitatory postsynaptic currents (mEPSCs) are fundamental electrophysiological events produced by quantal vesicular transmitter release at synapses. Their analysis can provide important information regarding pre- and postsynaptic function. However, the small signal relative to recording noise requires expertise and considerable time for their identification. Furthermore, many mEPSCs smaller than ~8 pA are not well resolved (e.g., those produced at distant synapses or synapses with few receptor channels). Here, we describe an automated approach to detect mEPSCs using a machine learning-based tool. This method, which can be easily generalized to other one-dimensional signals, eliminates inter-observer bias, provides an estimate of its sensitivity and specificity and permits reliable detection of small (e.g., 5 pA) spontaneous unitary synaptic events.

V2IED: Dual-view learning framework for detecting events of interictal epileptiform discharges

Interictal epileptiform discharges (IED) as large intermittent electrophysiological events are associated with various severe brain disorders. Automated IED detection has long been a challenging task, and mainstream methods largely focus on singling out IEDs from backgrounds from the perspective of waveform, leaving normal sharp transients/artifacts with similar waveforms almost unattended. An open issue still remains to accurately detect IED events that directly reflect the abnormalities in brain electrophysiological activities, minimizing the interference from irrelevant sharp transients with similar waveforms only. This study then proposes a dual-view learning framework (namely V2IED) to detect IED events from multi-channel EEG via aggregating features from the two phases: (1) Morphological Feature Learning: directly treating the EEG as a sequence with multiple channels, a 1D-CNN (Convolutional Neural Network) is applied to explicitly learning the deep morphological features; and (2) Spatial Feature Learning: viewing the EEG as a 3D tensor embedding channel topology, a CNN captures the spatial features at each sampling point followed by an LSTM (Long Short-Term Memories) to learn the evolution of these features. Experimental results from a public EEG dataset against the state-of-the-art counterparts indicate that: (1) compared with the existing optimal models, V2IED achieves a larger area under the receiver operating characteristic (ROC) curve in detecting IEDs from normal sharp transients with a 5.25% improvement in accuracy; (2) the introduction of spatial features improves performance by 2.4% in accuracy; and (3) V2IED also performs excellently in distinguishing IEDs from background signals especially benign variants.

All–potassium channel CRISPR screening reveals a lysine-specific pathway of insulin secretion

ObjectiveGenome-scale CRISPR–Cas9 knockout coupled with single-cell RNA sequencing (scRNA-seq) has been used to identify function-related genes. However, this method may knock out too many genes, leading to low efficiency in finding genes of interest. Insulin secretion is controlled by several electrophysiological events, including fluxes of KATP depolarization and K+ repolarization. It is well known that glucose stimulates insulin secretion from pancreatic β-cells, mainly via the KATP depolarization channel, but whether other nutrients directly regulate the repolarization K+ channel to promote insulin secretion is unknown. MethodsWe used a system involving CRISPR–Cas9-mediated knockout of all 83 K+ channels and scRNA-seq in a pancreatic β cell line to identify genes associated with insulin secretion. ResultsThe expression levels of insulin genes were significantly increased after all–K+ channel knockout. Furthermore, Kcnb1 and Kcnh6 were the two most important repolarization K+ channels for the increase in high-glucose-dependent insulin secretion that occurred upon application of specific inhibitors of the channels. Kcnh6 currents, but not Kcnb1 currents, were reduced by one of the amino acids, lysine, in both transfected cells, primary cells and mice with β-cell-specific deletion of Kcnh6. ConclusionsOur function-related CRISPR screen with scRNA-seq identifies Kcnh6 as a lysine-specific channel.

Propagating population activity patterns during spontaneous slow waves in the thalamus of rodents

Slow waves (SWs) represent the most prominent electrophysiological events in the thalamocortical system under anesthesia and during deep sleep. Recent studies have revealed that SWs have complex spatiotemporal dynamics and propagate across neocortical regions. However, it is still unclear whether neuronal activity in the thalamus exhibits similar propagation properties during SWs. Here, we report propagating population activity in the thalamus of ketamine/xylazine-anesthetized rats and mice visualized by high-density silicon probe recordings. In both rodent species, propagation of spontaneous thalamic activity during up-states was most frequently observed in dorsal thalamic nuclei such as the higher order posterior (Po), lateral posterior (LP) or laterodorsal (LD) nuclei. The preferred direction of thalamic activity spreading was along the dorsoventral axis, with over half of the up-states exhibiting a gradual propagation in the ventral-to-dorsal direction. Furthermore, simultaneous neocortical and thalamic recordings collected under anesthesia demonstrated that there is a weak but noticeable interrelation between propagation patterns observed during cortical up-states and those displayed by thalamic population activity. In addition, using chronically implanted silicon probes, we detected propagating activity patterns in the thalamus of naturally sleeping rats during slow-wave sleep. However, in comparison to propagating up-states observed under anesthesia, these propagating patterns were characterized by a reduced rate of occurrence and a faster propagation speed. Our findings suggest that the propagation of spontaneous population activity is an intrinsic property of the thalamocortical network during synchronized brain states such as deep sleep or anesthesia. Additionally, our data implies that the neocortex may have partial control over the formation of propagation patterns within the dorsal thalamus under anesthesia.

Chaotic resonance in an astrocyte-coupled excitable neuron

We study the chaotic resonance phenomenon whereby the response of a neuron to a weak signal is amplified with the help of chaotic current stemming from background activity in the brain. This resonance behavior exhibits a bell-shaped curve in terms of detection quality due to increasing chaotic current intensity. Recent experimental studies have shown that astrocytes, which are the most abundant types of glial cells, may be responsible for the regulation of electrophysiological events in neuronal medium. Hence, we consider here a realistic neuronal system which is constituted by a bipartite network consisting of an excitable neuron and an astrocyte. Our analysis reveals that signal detection quality can be greatly enhanced with the astrocyte contribution obtained by appropriate neuronal and astrocytic cell dynamics. We find that depolarization-induced astrocytic glutamate release is able to improve chaotic resonance performance considerably in the presence of an adequately strong interaction between the astrocyte and the excitable neuron receiving a weak signal with a relatively higher frequency. We also show that a moderate production rate of gliotransmitters is required for the astrocyte to affect resonance performance of the neuron. Except for those conditions where the facilitating effect of astrocyte is observed, it can also reduce signal detection performance in the neuron. Furthermore, we demonstrated that intrinsic neuronal excitability is regulated by the astrocyte, via a comparison of resonance behaviors under effects of bias and astrocytic current separately. Taken together, our findings provide a novel insight into the functioning of astrocyte-neuron circuits, in particular the encoding weak signals via chaotic resonance, and suggest that astrocytes play a key role in intrinsic regulation and selectivity in neuronal information processing.

Estimation of cAMP binding in hippocampus CA1 field by a fluorescent probe.

The hippocampus is an allocortex structure involved in many complex processes, from memory formation to spatial navigation. It starts developing during prenatal life but acquires its adult functional properties around the peripubertal age, in both humans and mice. Such prolonged maturation is accompanied by structural changes in microcircuitry and functional changes involving biochemical and electrophysiological events. Moreover, hippocampus undergoes plasticity phenomena throughout life. In murine rodents, the most relevant maturation steps in Cornu Ammonis 1 (CA1) hippocampal subfield occur during the third-fourth weeks of life. During this period, also the expression and localization of cAMP-dependent protein kinases (PKA) refines: many regulatory (R1A) PKA clusters appear, bound to the cytoskeleton. Here the binding characteristics of R1A are determined in CA1 by using confocal microscopy. Apparently, two binding sites are present with no evidence of cooperativity. Equilibrium dissociation constant is estimated around 22.9nM. This value is lower from that estimated for R1A in soluble form, suggesting a different binding site conformation or accessibility in the tissue. The method described here may be useful to track the developmental changes in binding activity, which affects cAMP availability at selected intracellular microzones. Possible relations with functional consequences are discussed.

Vibration-reduced anxiety-like behavior relies on ameliorating abnormalities of the somatosensory cortex and medial prefrontal cortex.

Abstract JOURNAL/nrgr/04.03/01300535-202406000-00040/inline-graphic1/v/2023-10-30T152229Z/r/image-tiff Tibetan singing bowls emit low-frequency sounds and produce perceptible harmonic tones and vibrations through manual tapping. The sounds the singing bowls produce have been shown to enhance relaxation and reduce anxiety. However, the underlying mechanism remains unclear. In this study, we used chronic restraint stress or sleep deprivation to establish mouse models of anxiety that exhibit anxiety-like behaviors. We then supplied treatment with singing bowls in a bottomless cage placed on the top of a cushion. We found that unlike in humans, the combination of harmonic tones and vibrations did not improve anxiety-like behaviors in mice, while individual vibration components did. Additionally, the vibration of singing bowls increased the level of N-methyl-D-aspartate receptor 1 in the somatosensory cortex and prefrontal cortex of the mice, decreased the level of γ-aminobutyric acid A (GABA) receptor α 1 subtype, reduced the level of CaMKII in the prefrontal cortex, and increased the number of GABAergic interneurons. At the same time, electrophysiological tests showed that the vibration of singing bowls significantly reduced the abnormal low-frequency gamma oscillation peak frequency in the medial prefrontal cortex caused by stress restraint pressure and sleep deprivation. Results from this study indicate that the vibration of singing bowls can alleviate anxiety-like behaviors by reducing abnormal molecular and electrophysiological events in somatosensory and medial prefrontal cortex.

Normative Temporal Dynamics of Resting EEG Microstates.

The large-scale electrophysiological events known as electroencephalographic microstates provide an important window into the intrinsic activity of whole-brain neuronal networks. The spontaneous activity of coordinated brain networks, including the ongoing temporal dynamics expressed by microstates, are thought to reflect individuals' neurocognitive functioning, and predict development, disease progression, and psychological differences among varied populations. A comprehensive understanding of human brain function therefore requires characterizing typical and atypical patterns in the temporal dynamics of microstates. But population-level estimates of normative microstate temporal dynamics are still unknown. To address this gap, I conducted a systematic search of the literature and accompanying meta-analysis of the average dynamics of microstates obtained from studies investigating spontaneous brain activity in individuals during periods of eyes-closed and eyes-open rest. Meta-analyses provided estimates of the average temporal dynamics of microstates across 93 studies totaling 6583 unique individual participants drawn from diverse populations. Results quantified the expected range of plausible estimates of average microstate dynamics across study samples, as well as characterized heterogeneity resulting from sampling variability and systematic differences in development, clinical diagnoses, or other study methodological factors. Specifically, microstate dynamics significantly differed for samples with specific developmental differences or clinical diagnoses, relative to healthy, typically developing samples. This research supports the notion that microstates and their dynamics reflect functionally relevant properties of large-scale brain networks, encoding typical and atypical neurocognitive functioning.

The Joint Effect of Social Comparison and Social Distance on Evaluation of Intertemporal Choice Outcomes in Event-related Potential Studies.

Intertemporal choice plays a crucial role in our daily lives, influencing decisions related to education, health, consumption, and investment. This research proposes an innovative experimental protocol that examines how social comparison and social distance jointly affect the neural processes involved in outcome assessment for intertemporal choices. The study is based on the theoretical framework of cognitive resource competition. This protocol enables researchers to dynamically establish an indifference point for each participant, effectively eliminating the influence of any biased indifference points on the assessment of intertemporal choices. Consequently, the study solely measures the combined impact of social comparison and social distance on how participants evaluate intertemporal choice outcomes. The findings reveal that individuals are more inclined to opt for immediate outcomes under negative unfair conditions. Moreover, compared to the fair and positive unfair conditions, people tend to undervalue delayed outcomes in the negative unfair condition. The strength of this approach lies in its dynamic indifference point setting, making it an effective method to investigate the influence of various external factors (such as social status and power level) on intertemporal decision-making. While the protocol is designed to measure electrophysiological events like event-related potentials, it can also be tailored for use with fMRI.

Interictal epileptiform discharges affect memory in an Alzheimer’s disease mouse model

Interictal epileptiform discharges (IEDs) are transient abnormal electrophysiological events commonly observed in epilepsy patients but are also present in other neurological diseases, such as Alzheimer's disease (AD). Understanding the role IEDs have on the hippocampal circuit is important for our understanding of the cognitive deficits seen in epilepsy and AD. We characterize and compare the IEDs of human epilepsy patients from microwire hippocampal recording with those of AD transgenic mice with implanted multilayer hippocampal silicon probes. Both the local field potential features and firing patterns of pyramidal cells and interneurons were similar in the mouse and human. We found that as IEDs emerged from the CA3-1 circuits, they recruited pyramidal cells and silenced interneurons, followed by post-IED suppression. IEDs suppressed the incidence and altered the properties of physiological sharp-wave ripples, altered their physiological properties, and interfered with the replay of place field sequences in a maze. In addition, IEDs in AD mice inversely correlated with daily memory performance. Together, our work implies that IEDs may present a common and epilepsy-independent phenomenon in neurodegenerative diseases that perturbs hippocampal-cortical communication and interferes with memory.

Interictal Epileptiform Discharge Detection Using Multi-Head Deep Convolutional Neural Network.

Interictal epileptiform discharges (IEDs) are intermittent electrophysiological events that occur in patients with epilepsy between seizures. Automated detection of IEDs helps clinician to identify cortical irritations and relations to seizure recurrence. It also reduces the necessity of visual inspection by physicians interpreting the EEG. This paper presents a novel deep learning-based approach that combines one-dimensional local binary pattern symbolization method with a regularized multi-head one-dimensional convolutional neural network to learn unique morphological patterns from different EEG sub-bands for IED detection. Experimentation using the Temple University Events corpus scalp EEG data shows promising performance, e.g. F1-score of 87.18%.

Estrogen modulation of cortical spreading depression

Background and aimsCortical spreading depression (CSD), a transient neuronal and glial depolarization that propagates slowly across the cerebral cortex, is the putative electrophysiological event underlying migraine aura and a headache trigger. Migraine is three times more prevalent in women than men, linked to circulating female hormones. High estrogen levels or estrogen withdrawal may be a migraine trigger for many women. We, therefore, aimed to examine whether sex, gonadectomy, and female hormone supplementation and withdrawal affect the susceptibility to CSD.MethodsTo determine CSD susceptibility, we recorded the frequency of CSDs triggered during 2-h topical KCl application in intact or gonadectomized female and male rats, without or with estradiol or progesterone supplementation via daily intraperitoneal injections. Estrogen or progesterone treatment followed by withdrawal was studied in a separate cohort. To take the first step towards identifying potential mechanisms, we studied glutamate and GABAA receptor binding using autoradiography.ResultsThe CSD frequency in intact female rats was higher than intact male and ovariectomized rats. We did not detect a change in CSD frequency during different stages of the estrous cycle in intact females. Daily estrogen injections for three weeks did not change CSD frequency. However, one-week estrogen withdrawal after two weeks of treatment significantly increased CSD frequency compared with the vehicle group in gonadectomized females. The same protocol of estrogen treatment and withdrawal was ineffective in gonadectomized males. In contrast to estrogen, daily progesterone injections for three weeks elevated CSD susceptibility, and one-week withdrawal after two weeks of treatment partially normalized this effect. Autoradiography did not reveal significant changes in glutamate or GABAA receptor binding density after estrogen treatment and withdrawal.ConclusionsThese data suggest that females are more susceptible to CSD, and sexual dimorphism is abrogated by gonadectomy. Moreover, estrogen withdrawal after prolonged daily treatment enhances CSD susceptibility. These findings may have implications for estrogen-withdrawal migraine, although the latter tends to be without aura.

Cardiomyocyte electrophysiology and its modulation: current views and future prospects.

Normal and abnormal cardiac rhythms are of key physiological and clinical interest. This introductory article begins from Sylvio Weidmann's key historic 1950s microelectrode measurements of cardiac electrophysiological activity and Singh & Vaughan Williams's classification of cardiotropic targets. It then proceeds to introduce the insights into cardiomyocyte function and its regulation that subsequently emerged and their therapeutic implications. We recapitulate the resulting view that surface membrane electrophysiological events underlying cardiac excitation and its initiation, conduction and recovery constitute the final common path for the cellular mechanisms that impinge upon this normal or abnormal cardiac electrophysiological activity. We then consider progress in the more recently characterized successive regulatory hierarchies involving Ca2+ homeostasis, excitation-contraction coupling and autonomic G-protein signalling and their often reciprocal interactions with the surface membrane events, and their circadian rhythms. Then follow accounts of longer-term upstream modulation processes involving altered channel expression, cardiomyocyte energetics and hypertrophic and fibrotic cardiac remodelling. Consideration of these developments introduces each of the articles in this Phil. Trans. B theme issue. The findings contained in these articles translate naturally into recent classifications of cardiac electrophysiological targets and drug actions, thereby encouraging future iterations of experimental cardiac electrophysiological discovery, and testing directed towards clinical management. This article is part of the theme issue 'The heartbeat: its molecular basis and physiological mechanisms'.

Cortical pain induced by optogenetic cortical spreading depression: from whole brain activity mapping

BackgroundCortical spreading depression (CSD) is an electrophysiological event underlying migraine aura. Traditional CSD models are invasive and often cause injuries. The aim of the study was to establish a minimally invasive optogenetic CSD model and identify the active networks after CSD using whole-brain activity mapping.MethodsCSD was induced in mice by light illumination, and their periorbital thresholds and behaviours in the open field, elevated plus-maze and light-aversion were recorded. Using c-fos, we mapped the brain activity after CSD. The whole brain was imaged, reconstructed and analyzed using the Volumetric Imaging with Synchronized on-the-fly-scan and Readout technique. To ensure the accuracy of the results, the immunofluorescence staining method was used to verify the imaging results.ResultsThe optogenetic CSD model showed significantly decreased periorbital thresholds, increased facial grooming and freezing behaviours and prominent light-aversion behaviours. Brain activity mapping revealed that the somatosensory, primary sensory, olfactory, basal ganglia and default mode networks were activated. However, the thalamus and trigeminal nucleus caudalis were not activated.ConclusionsOptogenetic CSD model could mimic the behaviours of headache and photophobia. Moreover, the optogenetic CSD could activate multiple sensory cortical regions without the thalamus or trigeminal nucleus caudalis to induce cortical pain.

A Model for the Propagation of Seizure Activity in Normal Brain Tissue.

Epilepsies are characterized by paroxysmal electrophysiological events and seizures, which can propagate across the brain. One of the main unsolved questions in epilepsy is how epileptic activity can invade normal tissue and thus propagate across the brain. To investigate this question, we consider three computational models at the neural network scale to study the underlying dynamics of seizure propagation, understand which specific features play a role, and relate them to clinical or experimental observations. We consider both the internal connectivity structure between neurons and the input properties in our characterization. We show that a paroxysmal input is sometimes controlled by the network while in other instances, it can lead the network activity to itself produce paroxysmal activity, and thus will further propagate to efferent networks. We further show how the details of the network architecture are essential to determine this switch to a seizure-like regime. We investigated the nature of the instability involved and in particular found a central role for the inhibitory connectivity. We propose a probabilistic approach to the propagative/non-propagative scenarios, which may serve as a guide to control the seizure by using appropriate stimuli.

Understanding the roles of central and autonomic activity during sleep in the improvement of working memory and episodic memory

The last decade has seen significant progress in identifying sleep mechanisms that support cognition. Most of these studies focus on the link between electrophysiological events of the central nervous system during sleep and improvements in different cognitive domains, while the dynamic shifts of the autonomic nervous system across sleep have been largely overlooked. Recent studies, however, have identified significant contributions of autonomic inputs during sleep to cognition. Yet, there remain considerable gaps in understanding how central and autonomic systems work together during sleep to facilitate cognitive improvement. In this article we examine the evidence for the independent and interactive roles of central and autonomic activities during sleep and wake in cognitive processing. We specifically focus on the prefrontal–subcortical structures supporting working memory and mechanisms underlying the formation of hippocampal-dependent episodic memory. Our Slow Oscillation Switch Model identifies separate and competing underlying mechanisms supporting the two memory domains at the synaptic, systems, and behavioral levels. We propose that sleep is a competitive arena in which both memory domains vie for limited resources, experimentally demonstrated when boosting one system leads to a functional trade-off in electrophysiological and behavioral outcomes. As these findings inevitably lead to further questions, we suggest areas of future research to better understand how the brain and body interact to support a wide range of cognitive domains during a single sleep episode.

Spatiotemporal patterns of sleep spindle activity in human anterior thalamus and cortex

Sleep spindles (8 - 16 Hz) are transient electrophysiological events during non-rapid eye movement sleep. While sleep spindles are routinely observed in the cortex using scalp electroencephalography (EEG), recordings of their thalamic counterparts have not been widely studied in humans. Based on a few existing studies, it has been hypothesized that spindles occur as largely local phenomena. We investigated intra-thalamic and thalamocortical spindle co-occurrence, which may underlie thalamocortical communication. We obtained scalp EEG and thalamic recordings from 7 patients that received bilateral deep brain stimulation (DBS) electrodes to the anterior thalamus for the treatment of drug resistant focal epilepsy. Spindles were categorized into subtypes based on their main frequency (i.e., slow (10±2 Hz) or fast (14±2 Hz)) and their level of thalamic involvement (spanning one channel, or spreading uni- or bilaterally within the thalamus). For the first time, we contrasted observed spindle patterns with permuted data to estimate random spindle co-occurrence. We found that multichannel spindle patterns were systematically coordinated at the thalamic and thalamocortical level. Importantly, distinct topographical patterns of thalamocortical spindle overlap were associated with slow and fast subtypes of spindles. These observations provide further evidence for coordinated spindle activity in thalamocortical networks.

Detection of spreading depolarization events and spatiotemporal analysis for advancing stroke therapy.

While the presence of spreading depolarization (SD) and associated spreading depression have been well studied and known to be associated with post-ischemic brain damage, the spatiotemporal spread of these events from the site of injury is not well understood. With the recent development of high-density micro-electrocorticographic (ECoG) electrode arrays, monitoring the spread of the depolarizing events and associated depression is possible. The goal of this work is to define the electrocorticographic features of SD and associated depression across the multichannel array and search for patterns in these features that emerge across both space and time. We present the spatial distribution of features found from chronic ECoG recordings acquired from awake behaving rats induced with a rodent model of stroke. SD events were detected with an unsupervised algorithm that searched for a stereotyped pattern in the first derivative of the ECoG. The algorithm yielded a 58% correct detection rate on average across four rats, and a 36% false positive rate. We defined key electrophysiological features and mapped them onto the physical brain regions using MATLAB, such as the peak-to-peak amplitude of each SD event, the width (or duration) of the SD event, direct current (DC) level, and average rate of decline in the signal baseline. We performed k-means clustering to the activity in this feature space which yielded three contiguous regions in physical space. The elbow optimization method was applied to a distortion metric and indicated that 3 clusters was optimal. These findings motivate us to conduct future studies that would verify whether these 3 clusters in electrode-space correspond to immunohistochemically defined regions of tissue health, namely, infarct, penumbra, and healthy tissue. Clinical Relevance- The extent and severity of damage that stroke ultimately causes is suspected to be related to the progression of spreading depolarization and associated depression. An understanding of how the features of these electrophysiological events progress across the brain and over time is an important step toward eventual development of closed-loop therapies which limit and minimize the long-term effects of stroke.