Multisite Recordings Research Articles

In Vivo Injection of Anti-LGI1 Antibodies into the Rodent M1 Cortex and Hippocampus Is Ineffective in Inducing Seizures.

Autoimmune encephalitis (AIE) associated with antibodies directed against the leucine-rich glioma inactivated 1 (LGI1) protein is the second most common AIE and is responsible for deleterious neocortical and limbic epileptic seizures. Previous studies demonstrated a pathogenic role of anti-LGI1 antibodies via alterations in the expression and function of Kv1 channels and AMPA receptors. However, the causal link between antibodies and epileptic seizures has never been demonstrated. Here, we attempted to determine the role of human anti-LGI1 autoantibodies in the genesis of seizures by analyzing the impact of their intracerebral injection in rodents. Acute and chronic injections were performed in rats and mice in the hippocampus and primary motor cortex, the two main brain regions affected by the disease. Acute infusion of CSF or serum IgG of anti-LGI1 AIE patients did not lead to the emergence of epileptic activities, as assessed by multisite electrophysiological recordings over a 10 h period after injection. A chronic 14 d injection, coupled with continuous video-EEG monitoring, was not more effective. Overall, these results demonstrate that acute and chronic injections of CSF or purified IgG from LGI1 patients are not able to generate epileptic activity by themselves in the different animal models tested.

How our understanding of memory replay evolves.

Memory reactivations and replay, widely reported in the hippocampus and cortex across species, have been implicated in memory consolidation, planning, and spatial and skill learning. Technological advances in electrophysiology, calcium imaging, and human neuroimaging techniques have enabled neuroscientists to measure large-scale neural activity with increasing spatiotemporal resolution and have provided opportunities for developing robust analytic methods to identify memory replay. In this article, we first review a large body of historically important and representative memory replay studies from the animal and human literature. We then discuss our current understanding of memory replay functions in learning, planning, and memory consolidation and further discuss the progress in computational modeling that has contributed to these improvements. Next, we review past and present analytic methods for replay analyses and discuss their limitations and challenges. Finally, looking ahead, we discuss some promising analytic methods for detecting nonstereotypical, behaviorally nondecodable structures from large-scale neural recordings. We argue that seamless integration of multisite recordings, real-time replay decoding, and closed-loop manipulation experiments will be essential for delineating the role of memory replay in a wide range of cognitive and motor functions.

Coupling between the prelimbic cortex, nucleus reuniens, and hippocampus during NREM sleep remains stable under cognitive and homeostatic demands.

The interplay between the medial prefrontal cortex and hippocampus during non-rapid eye movement (NREM) sleep contributes to the consolidation of contextual memories. To assess the role of the thalamic nucleus reuniens (Nre) in this interaction, we investigated the coupling of neuro-oscillatory activities among prelimbic cortex, Nre, and hippocampus across sleep states and their role in the consolidation of contextual memories using multi-site electrophysiological recordings and optogenetic manipulations. We showed that ripples are time-locked to the Up state of cortical slow waves, the transition from UP to DOWN state in thalamic slow waves, the troughs of cortical spindles, and the peaks of thalamic spindles during spontaneous sleep, rebound sleep and sleep following a fear conditioning task. In addition, spiking activity in Nre increased before hippocampal ripples, and the phase-locking of hippocampal ripples and thalamic spindles during NREM sleep was stronger after acquisition of a fear memory. We showed that optogenetic inhibition of Nre neurons reduced phase-locking of ripples to cortical slow waves in the ventral hippocampus whilst their activation altered the preferred phase of ripples to slow waves in ventral and dorsal hippocampi. However, none of these optogenetic manipulations of Nre during sleep after acquisition of fear conditioning did alter sleep-dependent memory consolidation. Collectively, these results showed that Nre is central in modulating hippocampus and cortical rhythms during NREM sleep.

Abstract 12092: Natural Language Processing to Identify Reasons for Gender Disparities in Statin Use

Introduction: Statins are life-saving medications for patients with atherosclerotic cardiovascular disease (ASCVD), but women with ASCVD are persistently less likely to be prescribed statins than men. This study aims to use Natural Language Processing (NLP) to further elucidate patient and provider factors contributing to this disparity. Methods: The study cohort included patients with >2 ASCVD encounters between 2014 and 2021 within a multisite electronic health record (EHR) in Northern California. Data from a random sample of our cohort (N = 942) was manually annotated to develop a benchmark deep learning natural language processing (NLP) approach, Clinical Bidirectional Encoder Representations from Transformers (BERT); 80% of these were used for model training and 20% for testing. After reviewing structured EHR data (e.g. prescriptions, allergies), BERT was used to identify and interpret discussions of statins in clinical notes. Results: Of 88,913 patients with ASCVD (mean age 67.8 ± 13.1 years), 35,901 (40.4%) were women. Women were less likely to be prescribed statins (56.6% vs. 67.6%, p < 0.001). Only 18.6% of nonallergic patients without a statin prescription had a mention of statins in unstructured EHR text. Statin use through unstructured text was less likely to be identified among women than men (32.8% vs. 42.6%, p < 0.001). Reasons for statin nonuse did not significantly differ by gender (Figure). Conclusions: Women with ASCVD were less likely to be use statins, and this disparity became more pronounced with the inclusion of unstructured data. An NLP approach revealed actionable reasons for statin nonuse. Future studies should leverage these approaches to monitor and track statin adherence by combining structured and unstructured data in real-world populations.

Team is brain: leveraging EHR audit log data for new insights into acute care processes.

To determine whether novel measures of contextual factors from multi-site electronic health record (EHR) audit log data can explain variation in clinical process outcomes. We selected one widely-used process outcome: emergency department (ED)-based team time to deliver tissue plasminogen activator (tPA) to patients with acute ischemic stroke (AIS). We evaluated Epic audit log data (that tracks EHR user-interactions) for 3052 AIS patients aged 18+ who received tPA after presenting to an ED at three Northern California health systems (Stanford Health Care, UCSF Health, and Kaiser Permanente Northern California). Our primary outcome was door-to-needle time (DNT) and we assessed bivariate and multivariate relationships with six audit log-derived measures of treatment team busyness and prior team experience. Prior team experience was consistently associated with shorter DNT; teams with greater prior experience specifically on AIS cases had shorter DNT (minutes) across all sites: (Site 1: -94.73, 95% CI: -129.53 to 59.92; Site 2: -80.93, 95% CI: -130.43 to 31.43; Site 3: -42.95, 95% CI: -62.73 to 23.17). Teams with greater prior experience across all types of cases also had shorter DNT at two sites: (Site 1: -6.96, 95% CI: -14.56 to 0.65; Site 2: -19.16, 95% CI: -36.15 to 2.16; Site 3: -11.07, 95% CI: -17.39 to 4.74). Team busyness was not consistently associated with DNT across study sites. EHR audit log data offers a novel, scalable approach to measure key contextual factors relevant to clinical process outcomes across multiple sites. Audit log-based measures of team experience were associated with better process outcomes for AIS care, suggesting opportunities to study underlying mechanisms and improve care through deliberate training, team-building, and scheduling to maximize team experience.

PO033 / #999 INVESTIGATION OF MULTI UNIT MULTI SITE MICRO ELECTRODE RECORDINGS OF SUBTHALAMIC NUCLEUS NEURONS USING MACHINE LEARNING TECHNIQUES: STIMULATION`S WITH DBS IN PARKINSON`S: E-POSTER VIEWING

We proposed here a method of multineural spike classification based on multisite electrode recording, signal analysis, and hierarchical clustering for studying correlated activities of adjacent neurons in nervous systems of Parkinson`s diseased conditions, i.e., subjects suffering with the Parkinson disease.

Running speed and REM sleep control two distinct modes of rapid interhemispheric communication.

SUMMARYRhythmic gamma-band communication within and across cortical hemispheres is critical for optimal perception, navigation, and memory. Here, using multisite recordings in both rats and mice, we show that even faster ~140 Hz rhythms are robustly anti-phase across cortical hemispheres, visually resembling splines, the interlocking teeth on mechanical gears. Splines are strongest in superficial granular retrosplenial cortex, a region important for spatial navigation and memory. Spline-frequency interhemispheric communication becomes more coherent and more precisely anti-phase at faster running speeds. Anti-phase splines also demarcate high-activity frames during REM sleep. While splines and associated neuronal spiking are anti-phase across retrosplenial hemispheres during navigation and REM sleep, gamma-rhythmic interhemispheric communication is precisely in-phase. Gamma and splines occur at distinct points of a theta cycle and thus highlight the ability of interhemispheric cortical communication to rapidly switch between in-phase (gamma) and anti-phase (spline) modes within individual theta cycles during both navigation and REM sleep.

Multi-site medical record review for validation of intentional self-harm coding in emergency departments

BackgroundCodes in the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), are used for injury surveillance, including surveillance of intentional self-harm, as they appear in administrative billing records. This study estimated the positive predictive value of ICD-10-CM codes for intentional self-harm in emergency department (ED) billing records for patients aged 10 years and older who did not die and were not admitted to an inpatient medical service.MethodsThe study team in Maryland, Colorado, and Massachusetts selected all or a random sample of ED billing records with an ICD-10-CM code for intentional self-harm (specific codes that began with X71-X83, T36-T65, T71, T14.91). Positive predictive value (PPV) was determined by the number and percentage of records with a physician diagnosis of intentional self-harm, based on a retrospective review of the original medical record.ResultsThe estimated PPV for the codes’ capture of intentional self-harm based on physician diagnosis in the original medical record was 89.8% (95% CI 85.0–93.4) for Maryland records, 91.9% (95% CI 87.7–95.0) for Colorado records, and 97.3% (95% CI 95.1–98.7) for Massachusetts records.ConclusionGiven the high PPV of the codes, epidemiologists can use the codes for public health surveillance of intentional self-harm treated in the ED using ICD-10-CM coded administrative billing records. However, these codes and related variables in the billing database cannot definitively distinguish between suicidal and non-suicidal intentional self-harm.

1262-P: Developing a Computable Phenotype Algorithm for Identification of Children and Adolescents with Diabetes Using Electronic Health Records

The Assessing the Burden of Diabetes by Type in Children, Adolescents, and Young Adults (DiCAYA) Network, a CDC/NIDDK-funded collaborative, aims to create a multi-site electronic health record (EHR) -based diabetes surveillance system. Foundational to the network's efforts is the development of a computable phenotype (CP) algorithm that can identify cases of diabetes. To advance the mission of the DiCAYA network, University of Florida (UF) Health system researchers developed a pilot CP algorithm for identifying diabetes cases in youth. The CP algorithm was iteratively derived based on structured data from EHRs (UF Health system 2012-2020) . We randomly selected 500 presumed cases among individuals < 18 years old who has (1) HbA1c ≥ 6.5%; or (2) fasting glucose ≥ 126 mg/dL; or (3) random plasma glucose ≥ 200 mg/dL; or (4) diabetes-related diagnosis code from an inpatient or outpatient encounter; or (5) prescribed, administered, or dispensed diabetes-related medication. Four reviewers independently reviewed the patient charts to determine diabetes status and type. Presumed cases without type 1 (T1D) or type 2 (T2D) diabetes diagnosis codes were categorized as nondiabetes or other types. The rest were categorized as T1D if the ratio of T1D codes to the sum of T1D and T2D codes was ≥ 0.5, or otherwise categorized as T2D. Next, we applied a list of diagnoses and procedures that can determine diabetes type (e.g., steroid use suggests induced diabetes) to correct misclassifications from step 1. Among the 500 reviewed cases, 159 and 64 had T1D and T2D. The sensitivity, specificity, and positive predictive values of the CP algorithm were 94%, 98%, and 96% for T1D; 95%, 95%, and 73% for T2D. We developed a highly accurate EHR-based CP for diabetes in youth based on EHR data from UF Health. Consistent with prior studies, T2D was more difficult to identify using these methods. A DiCAYA-wide validation and algorithm refinement process will be conducted. Disclosure P.Li: None. M.Prosperi: None. B.E.Dixon: Advisory Panel; Merck Sharp & Dohme Corp. D.Dabelea: None. L.H.Utidjian: None. T.L.Crume: None. L.Thorpe: None. A.D.Liese: None. D.Schatz: Advisory Panel; Abbott Diabetes, Medtronic. M.A.Atkinson: None. M.J.Haller: Advisory Panel; SAB Biotherapeutics , Consultant; MannKind Corporation, Sanofi. E.Spector: None. E.Shenkman: None. J.Bian: None. Y.Guo: None. H.Shao: Board Member; BRAVO4HEALTH, LLC. M.A.Atkinson: None. K.Alkhuzam: None. R.S.Patel: None. W.T.Donahoo: None. S.Bost: None. T.Lyu: None. Y.Wu: None. W.Hogan: None. Funding CDC/NIDDK U18DP006512

Modular Electrode Array for Multi-site Extracellular Recordings from Brains of Freely Moving Rodents.

Multi-site extracellular recordings from awake, freely moving rodents are an insightful technique that allows deduction of the dynamics of neural activity within a network of brain regions. Multiple advances in the design and materials of recording setups are available in the literature. However, most of these designs require several skill sets to assemble the electrodes and are expensive. Here, we explain in detail a custom design to build a multi-site (16 sites) electrode array (EA) and record extracellular electrical signals (local field potential and multi-unit spiking activity) at variable depths in freely behaving rodents. This EA weighs ∼3.0 g and costs less than $30. It provides mesoscopic neural activity maps (at millimeter scale) at low spatial resolution, thus enabling the experimenting group to further target specific regions with more expensive high-density probes at the resolution of an individual neuron. The article outlines the processes of building and implanting the array and recording neural activity during a behavior task. We also highlight the limitations of our design and the necessary steps to troubleshoot common issues faced during the initial implementation of the protocols. Finally, we explain the specific data one would obtain while using the probes during social interactions between rodents. © 2022 Wiley Periodicals LLC. Basic Protocol 1: Preparation of the electrode array Basic Protocol 2: Surgical implantation of the electrode array Basic Protocol 3: Recording of neural activity from the electrode array while a mouse performs social investigation of a novel conspecific Basic Protocol 4: Histology and electrode registration.

Multivariate autoregressive model estimation for high-dimensional intracranial electrophysiological data

Fundamental to elucidating the functional organization of the brain is the assessment of causal interactions between different brain regions. Multivariate autoregressive (MVAR) modeling techniques applied to multisite electrophysiological recordings are a promising avenue for identifying such causal links. They estimate the degree to which past activity in one or more brain regions is predictive of another region's present activity, while simultaneously accounting for the mediating effects of other regions. Including as many mediating variables as possible in the model has the benefit of drastically reducing the odds of detecting spurious causal connectivity. However, effective bounds on the number of MVAR model coefficients that can be estimated reliably from limited data make exploiting the potential of MVAR models challenging for even modest numbers of recording sites. Here, we utilize well-established dimensionality-reduction techniques to fit MVAR models to human intracranial data from ∼100 – 200 recording sites spanning dozens of anatomically and functionally distinct cortical regions. First, we show that high-dimensional MVAR models can be successfully estimated from long segments of data and yield plausible connectivity profiles. Next, we use these models to generate synthetic data with known ground-truth connectivity to explore the utility of applying principal component analysis and group least absolute shrinkage and selection operator (gLASSO) to reduce the number of parameters (connections) during model fitting to shorter data segments. We show that gLASSO is highly effective for recovering ground-truth connectivity in the limited data regime, capturing important features of connectivity for high-dimensional models with as little as 10 seconds of data. The methods presented here have broad applicability to the analysis of high-dimensional time series data in neuroscience, facilitating the elucidation of the neural basis of sensation, cognition, and arousal.

205 Optimization of DBS Electrode Position With Multisite Microelectrode Recordings, Macrostimulation, and Atlas-Based Prediction Maps Visualized in 3D

INTRODUCTION: Deep brain stimulation (DBS) at our institution is performed with a custom-made miniature stereotactic frame (STarFix, FHC) with the patient kept awake during electrode implantation to perform microelectrode recordings (MER) and macrostimulation testing. Pre-operative targets are selected with guidance from atlas-based efficacy prediction maps. Multisite intraoperative microelectrode recordings (MER) and macrostimulation data are collected, visualized in 3D in real-time, and integrated into selection of a final target. METHODS: A sample of 490 patients treated with DBS at Vanderbilt from 2011-2021 with 892 individual leads were included. During surgery, FHC microelectrodes are implanted in 2-3 test tracks on each side and microelectrode recordings are collected. Atlas-based prediction maps based on position and efficacy of previous cases help guide targeting. Macrostimulation data is collected at multiple depths along the same tracks, with efficacy quantified in quartiles and side effects noted. Preoperative maps and intraoperative data are visualized in 3D with color and integrated in real time to guide optimal FEP selection. Basic statistics were used to calculate percentages and means. RESULTS: Of 892 individual leads, 467 (52%) had FEP in the center track and 225 of those had FEP within 1mm of planned target depth. MER was collected in 435 (49%) of test tracks with 380 (43%) recordings collected in the FEP track. Stimulation data was collected for all leads and 867 (97%) of FEP tracks. CONCLUSION: Although planned targets were carefully selected preoperatively based on anatomy, coordinates, and guidance from atlas-based efficacy maps, FEP differed from anticipated target in 75% of cases. This discrepancy supports use of multisite MER and stimulation testing and the value of keeping patients awake to complete testing, optimizing their FEP in real-time based on maximal efficacy and minimal side effects. Finally, atlas-based prediction maps help guide initial targeting.

Delta Oscillations Coordinate Intracerebellar and Cerebello-Hippocampal Network Dynamics during Sleep.

During sleep, the widespread coordination of neuronal oscillations across both cortical and subcortical brain regions is thought to support various physiological functions. However, how sleep-related activity within the brain's largest sensorimotor structure, the cerebellum, is multiplexed with well-described sleep-related mechanisms in regions such as the hippocampus remains unknown. We therefore simultaneously recorded from the dorsal hippocampus and three distinct regions of the cerebellum (Crus I, lobule VI, and lobules II/III) in male mice during natural sleep. Local field potential (LFP) oscillations were found to be coordinated between these structures in a sleep stage-specific manner. During non-REM sleep, prominent δ frequency coherence was observed between lobule VI and hippocampus, whereas non-REM-associated hippocampal sharp-wave ripple activity evoked discrete LFP modulation in all recorded cerebellar regions, with the shortest latency effects in lobule VI. We also describe discrete phasic sharp potentials (PSPs), which synchronize across cerebellar regions and trigger sharp-wave ripple suppression. During REM, cerebellar δ phase significantly modulated hippocampal theta frequency, and this effect was greatest when PSPs were abundant. PSPs were phase-locked to cerebellar δ oscillation peak and hippocampal theta oscillation trough, respectively. Within all three cerebellar regions, prominent LFP oscillations were observed at both low (δ, <4 Hz) and very high frequencies (∼250 Hz) during non-REM and REM sleep. Intracerebellar cross-frequency analysis revealed that δ oscillations modulate those in the very high-frequency range. Together, these results reveal multiple candidate physiological mechanisms to support "offline," bidirectional interaction within distributed cerebello-hippocampal networks.SIGNIFICANCE STATEMENT Sleep is associated with widespread coordination of activity across a range of brain regions. However, little is known about how activity within the largest sensorimotor region of the brain, the cerebellum, is both intrinsically organized and links with higher-order structures, such as the hippocampus, during sleep. By making multisite local field potential recordings in naturally sleeping mice, we reveal and characterize multiple sleep stage-specific physiological mechanisms linking three distinct cerebellar regions with the hippocampus. Central to these physiological mechanisms is a prominent δ (<4 Hz) oscillation, which temporally coordinates both intracerebellar and cerebello-hippocampal network dynamics. Understanding this distributed network activity is important for gaining insight into cerebellar contributions to sleep-dependent processes, such as memory consolidation.

Investigation of multi-site micro recordings of subthalamic nucleus neurons using machine learning MER with DBS in Parkinson`s – A simulation study

Multineural spikes were acquired with a multisite electrode placed in the hippocampus pyramidal cell layer of non-primate anesthetized snitch animals. If the impedance of each electrode-site is relatively low and the distance amongst electrode sites is appropriately miniatured, a spike generated by a neuron is parallelly recorded at multielectrode sites with different amplitudes. The covariance between the spike of the at each electrode-point and a template was computed as a damping-factor due to the volume conduction of the spike from the neuron to electrode-site. Computed damping factors were vectorized and analyzed by simple but elegant hierarchical-clustering using a multidimensional statistical-test. Since a cluster of damping vectors was shown to correspond to an antidromically identified neuron, spikes of distinct neurons are classified by suggesting to the scatterings of damping vectors. Errors in damping vector computing due to partially overlapping spikes were minimized by successively subtracting preceding spikes from raw data. Clustering errors due to complex-spike-bursts (i.e., spikes with variable-amplitudes) were prevented by detecting such bursts and using only the first spike of a burst for clustering.

Single and Multisite Graphene‐Based Electroretinography Recording Electrodes: A Benchmarking Study

AbstractElectroretinography (ERG) is a clinical test employed to understand and diagnose many retinopathies. ERG is usually performed by placing a macroscopic ring gold wire electrode on the cornea while flashing light onto the eye to measure changes in the transretinal potential. However, macroscopic gold electrodes are severely limiting since they do not provide a flexible interface to contact the sensitive corneal tissue, making this technique highly uncomfortable for the patient. Another major drawback is the opacity of gold electrodes, which only allows them to record the ERG signal on the corneal periphery, preventing central ERG recordings. To overcome the limitations of metal‐based macroscopic ERG electrodes, flexible electrodes are fabricated using graphene as a transparent, flexible, and sensitive material. The transparency of the graphene is exploited to fabricate microelectrode arrays (MEAs) that are able to perform multisite recording on the cornea. The graphene‐based ERG electrodes are benchmarked against the widely used gold electrodes in a P23H rat model with photoreceptor degeneration. This study shows that the graphene‐based ERG electrodes can faithfully record ERGs under a wide range of conditions (light intensity, stage of photoreceptor degeneration, etc.) while offering additional benefits for ERG recordings such as transparency and flexibility.

Cortico-subcortical β burst dynamics underlying movement cancellation in humans.

Dominant neuroanatomical models hold that humans regulate their movements via loop-like cortico-subcortical networks, which include the subthalamic nucleus (STN), motor thalamus, and sensorimotor cortex (SMC). Inhibitory commands across these networks are purportedly sent via transient, burst-like signals in the β frequency (15-29 Hz). However, since human depth-recording studies are typically limited to one recording site, direct evidence for this proposition is hitherto lacking. Here, we present simultaneous multi-site recordings from SMC and either STN or motor thalamus in humans performing the stop-signal task. In line with their purported function as inhibitory signals, subcortical β-bursts were increased on successful stop-trials. STN bursts in particular were followed within 50 ms by increased β-bursting over SMC. Moreover, between-site comparisons (including in a patient with simultaneous recordings from SMC, thalamus, and STN) confirmed that β-bursts in STN temporally precede thalamic β-bursts. This highly unique set of recordings provides empirical evidence for the role of β-bursts in conveying inhibitory commands along long-proposed cortico-subcortical networks underlying movement regulation in humans.

Resting-State EEG Signal for Major Depressive Disorder Detection: A Systematic Validation on a Large and Diverse Dataset.

Major depressive disorder (MDD) is a global healthcare issue and one of the leading causes of disability. Machine learning combined with non-invasive electroencephalography (EEG) has recently been shown to have the potential to diagnose MDD. However, most of these studies analyzed small samples of participants recruited from a single source, raising serious concerns about the generalizability of these results in clinical practice. Thus, it has become critical to re-evaluate the efficacy of various common EEG features for MDD detection across large and diverse datasets. To address this issue, we collected resting-state EEG data from 400 participants across four medical centers and tested classification performance of four common EEG features: band power (BP), coherence, Higuchi’s fractal dimension, and Katz’s fractal dimension. Then, a sequential backward selection (SBS) method was used to determine the optimal subset. To overcome the large data variability due to an increased data size and multi-site EEG recordings, we introduced the conformal kernel (CK) transformation to further improve the MDD as compared with the healthy control (HC) classification performance of support vector machine (SVM). The results show that (1) coherence features account for 98% of the optimal feature subset; (2) the CK-SVM outperforms other classifiers such as K-nearest neighbors (K-NN), linear discriminant analysis (LDA), and SVM; (3) the combination of the optimal feature subset and CK-SVM achieves a high five-fold cross-validation accuracy of 91.07% on the training set (140 MDD and 140 HC) and 84.16% on the independent test set (60 MDD and 60 HC). The current results suggest that the coherence-based connectivity is a more reliable feature for achieving high and generalizable MDD detection performance in real-life clinical practice.

Multisite silicon probes enable simultaneous recording of spontaneous and evoked activity in multiple isolated C-fibres in rat saphenous nerve

BackgroundRecordings of electrical activity in nerves have provided valuable insights into normal function and pathological behaviours of the nervous system. Current high-resolution techniques (e.g. teased fibre recordings) typically utilise electrodes with a single recording site, capturing the activity of a single isolated neuron per recording. New methodWe conducted proof-of-principle C-fibre recordings in the saphenous nerve of urethane-anaesthetised adult Wistar rats using 32-channel multisite silicon electrodes. Data was acquired using the OpenEphys recording system and clustered offline with Kilosort 2.5. ResultsIn single recordings in 5 rats, 32 units with conduction velocities in the C-fibre range (< 1 m/s) were identified via constant latency responses and classified using activity dependent slowing. In two animals, 6 C-fibres (5 classified as nociceptors) were well isolated after clustering. Their activity could be tracked throughout the recording – including during periods of spontaneous activity. Axonal conduction velocities were calculated from spontaneous activity and/or low frequency electrical stimulation using only the differences in action potential latency as it propagated past multiple probe sites. Comparison with existing methodsSingle electrode approaches have a low data yield and generating group data for specific fibre types is challenging as it requires multiple experimental subjects and recording sessions. This is particularly true when the experimental targets are the small, unmyelinated C-fibres carrying nociceptive information. ConclusionsWe demonstrate that multisite recordings can greatly increase experimental yields and enhance fibre identification. The approach is of particular utility when coupled with clustering analysis. Multisite probes and analysis approaches constitute a valuable new toolbox for researchers studying the peripheral nervous system.

Uncovering spatial representations from spatiotemporal patterns of rodent hippocampal field potentials

SummarySpatiotemporal patterns of large-scale spiking and field potentials of the rodent hippocampus encode spatial representations during maze runs, immobility, and sleep. Here, we show that multisite hippocampal field potential amplitude at ultra-high-frequency band (FPAuhf), a generalized form of multiunit activity, provides not only a fast and reliable reconstruction of the rodent's position when awake, but also a readout of replay content during sharp-wave ripples. This FPAuhf feature may serve as a robust real-time decoding strategy from large-scale recordings in closed-loop experiments. Furthermore, we develop unsupervised learning approaches to extract low-dimensional spatiotemporal FPAuhf features during run and ripple periods and to infer latent dynamical structures from lower-rank FPAuhf features. We also develop an optical flow-based method to identify propagating spatiotemporal LFP patterns from multisite array recordings, which can be used as a decoding application. Finally, we develop a prospective decoding strategy to predict an animal's future decision in goal-directed navigation.

Unsupervised Methods for Detection of Neural States: Case Study of Hippocampal-Amygdala Interactions.

The hippocampus and amygdala are functionally coupled brain regions that play a crucial role in processes involving memory and learning. Because interareal communication has been reported both during specific sleep stages and in awake, behaving animals, these brain regions can serve as an archetype to establish that measuring functional interactions is important for comprehending neural systems. To this end, we analyze here a public dataset of local field potentials (LFPs) recorded in rats simultaneously from the hippocampus and amygdala during different behaviors. Employing a specific, time-lagged embedding technique, named topological causality (TC), we infer directed interactions between the LFP band powers of the two regions across six frequency bands in a time-resolved manner. The combined power and interaction signals are processed with our own unsupervised tools developed originally for the analysis of molecular dynamics simulations to effectively visualize and identify putative, neural states that are visited by the animals repeatedly. Our proposed methodology minimizes impositions onto the data, such as isolating specific epochs, or averaging across externally annotated behavioral stages, and succeeds in separating internal states by external labels such as sleep or stimulus events. We show that this works better for two of the three rats we analyzed, and highlight the need to acknowledge individuality in analyses of this type. Importantly, we demonstrate that the quantification of functional interactions is a significant factor in discriminating these external labels, and we suggest our methodology as a general tool for large, multisite recordings.