Cellular Classes in the Human Brain Revealed In Vivo by Heartbeat-Related Modulation of the Extracellular Action Potential Waveform

Cell-class-specific electric field entrainment of neural activity

Electric fields affect the activity of neurons and brain circuits, yet how this happens at the cellular level remains enigmatic. Lack of understanding of how to stimulate the brain to promote or suppress specific activity significantly limits basic research and clinical applications. Here, we study how electric fields impact subthreshold and spiking properties of major cortical neuronal classes. We find that neurons in the rodent and human cortex exhibit strong, cell-class-dependent entrainment that depends on stimulation frequency. Excitatory pyramidal neurons, with their slower spike rate, entrain to both slow and fast electric fields, while inhibitory classes like Pvalb and Sst (with their fast spiking) predominantly phase-lock to fast fields. We show that this spike-field entrainment is the result of two effects: non-specific membrane polarization occurring across classes and class-specific excitability properties. Importantly, these properties are present across cortical areas and species. These findings allow for the design of selective and class-specific neuromodulation.

Control of working memory by phase-amplitude coupling of human hippocampal neurons

Retaining information in working memory is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference1,2. However, how cognitive control regulates working memory storage is unclear. Here we show that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase-amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in their working memory. In the hippocampus, TG-PAC was indicative of working memory load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. The spike timing of these PAC neurons was coordinated with frontal theta activity when cognitive control demand was high. By introducing noise correlations with persistently active neurons in the hippocampus, PAC neurons shaped the geometry of the population code. This led to higher-fidelity representations of working memory content that were associated with improved behaviour. Our results support a multicomponent architecture of working memory1,2, with frontal control managing maintenance of working memory content in storage-related areas3-5. Within this framework, hippocampal TG-PAC integrates cognitive control and working memory storage across brain areas, thereby suggesting a potential mechanism for top-down control over sensory-driven processes.

Single neurons in the thalamus and subthalamic nucleus process cardiac and respiratory signals in humans

Visceral signals are constantly processed by our central nervous system, enable homeostatic regulation, and influence perception, emotion, and cognition. While visceral processes at the cortical level have been extensively studied using non-invasive imaging techniques, very few studies have investigated how this information is processed at the single neuron level, both in humans and animals. Subcortical regions, relaying signals from peripheral interoceptors to cortical structures, are particularly understudied and how visceral information is processed in thalamic and subthalamic structures remains largely unknown. Here, we took advantage of intraoperative microelectrode recordings in patients undergoing surgery for deep brain stimulation (DBS) to investigate the activity of single neurons related to cardiac and respiratory functions in three subcortical regions: ventral intermedius nucleus (Vim) and ventral caudalis nucleus (Vc) of the thalamus, and subthalamic nucleus (STN). We report that the activity of a large portion of the recorded neurons (about 70%) was modulated by either the heartbeat, the cardiac inter-beat interval, or the respiration. These cardiac and respiratory response patterns varied largely across neurons both in terms of timing and their kind of modulation. A substantial proportion of these visceral neurons (30%) was responsive to more than one of the tested signals, underlining specialization and integration of cardiac and respiratory signals in STN and thalamic neurons. By extensively describing single unit activity related to cardiorespiratory function in thalamic and subthalamic neurons, our results highlight the major role of these subcortical regions in the processing of visceral signals.

Simulation of background neuronal activity and noise in human intracranial microwire recordings

BACKGROUND

Human intracranial microwire recordings allow measurement of neuronal activity in human subjects at a fine temporal and spatial scale. The recorded extracellular potentials represent a mixture of action potentials from nearby neurons, local field potentials, and other noise sources. Signal processing of these recordings is used to separate the activity of putative single neurons from other background and noise. To better understand the separation of single neuron activity, one approach is to simulate the signals produced by neurons firing action potentials combined with background activity and noise.

NEW METHOD

This paper characterizes the background activity and noise in human intracranial microwire recordings and presents an accurate and efficient method of simulation using an infinite impulse response filter to color white noise.

RESULTS AND COMPARISON

This method reproduces the power spectral density of the background activity and noise over a frequency range of 1-5000 Hz and is over 200 times faster than previously used methods. It thus facilitates large scale studies of variation of noise sources, field potentials, and processing parameters. It performs equivalently in terms of spike sorting to simulation using white noise. Another advantage is that the simulated signals are known to arise from a pseudorandom number generator and cannot be the result of detecting simulated background spiking activity.

CONCLUSIONS

This approach provides a rapid and accurate method of simulating background noise and neural activity in human intracranial microwire recordings. It is suitable for use in large scale simulations to study spike sorting in this type of recording.

Dataset of human-single neuron activity during a Sternberg working memory task

We present a dataset of 1809 single neurons recorded from the human medial temporal lobe (amygdala and hippocampus) and medial frontal lobe (anterior cingulate cortex, pre-supplementary motor area, ventral medial prefrontal cortex) across 41 sessions from 21 patients that underwent seizure monitoring with depth electrodes. Subjects performed a screening task (907 neurons) to identify images for which highly selective cells were present. Subjects then performed a working memory task (902 neurons), in which they were sequentially presented with 1-3 images for which highly selective cells were present and, following a maintenance period, were asked if the probe was identical to one of the maintained images. This Neurodata Without Borders formatted dataset includes spike times, extracellular spike waveforms, stimuli presented, behavior, electrode locations, and subject demographics. As validation, we replicate previous findings on the selectivity of concept cells and their persistent activity during working memory maintenance. This large dataset of rare human single-neuron recordings and behavior enables the investigation of the neural mechanisms of working memory in humans.

Spatiotemporal dedifferentiation of the global brain signal topography along the adult lifespan

Age-related variations in many regions and/or networks of the human brain have been uncovered using resting-state functional magnetic resonance imaging. However, these findings did not account for the dynamical effect the brain's global activity (global signal [GS]) causes on local characteristics, which is measured by GS topography. To address this gap, we tested GS topography including its correlation with age using a large-scale cross-sectional adult lifespan dataset (n = 492). Both GS topography and its variation with age showed frequency-specific patterns, reflecting the spatiotemporal characteristics of the dynamic change of GS topography with age. A general trend toward dedifferentiation of GS topography with age was observed in both spatial (i.e., less differences of GS between different regions) and temporal (i.e., less differences of GS between different frequencies) dimensions. Further, methodological control analyses suggested that although most age-related dedifferentiation effects remained across different preprocessing strategies, some were triggered by neuro-vascular coupling and physiological noises. Together, these results provide the first evidence for age-related effects on global brain activity and its topographic-dynamic representation in terms of spatiotemporal dedifferentiation.

Distinct neuronal representation of small and large numbers in the human medial temporal lobe

Whether small numerical quantities are represented by a special subitizing system that is distinct from a large-number estimation system has been debated for over a century. Here we show that two separate neural mechanisms underlie the representation of small and large numbers. We performed single neuron recordings in the medial temporal lobe of neurosurgical patients judging numbers. We found a boundary in neuronal coding around number 4 that correlates with the behavioural transition from subitizing to estimation. In the subitizing range, neurons showed superior tuning selectivity accompanied by suppression effects suggestive of surround inhibition as a selectivity-increasing mechanism. In contrast, tuning selectivity decreased with increasing numbers beyond 4, characterizing a ratio-dependent number estimation system. The two systems with the coding boundary separating them were also indicated using decoding and clustering analyses. The identified small-number subitizing system could be linked to attention and working memory that show comparable capacity limitations.

Interoceptive rhythms in the brain

Sensing internal bodily signals, or interoception, is fundamental to maintain life. However, interoception should not be viewed as an isolated domain, as it interacts with exteroception, cognition and action to ensure the integrity of the organism. Focusing on cardiac, respiratory and gastric rhythms, we review evidence that interoception is anatomically and functionally intertwined with the processing of signals from the external environment. Interactions arise at all stages, from the peripheral transduction of interoceptive signals to sensory processing and cortical integration, in a network that extends beyond core interoceptive regions. Interoceptive rhythms contribute to functions ranging from perceptual detection up to sense of self, or conversely compete with external inputs. Renewed interest in interoception revives long-standing issues on how the brain integrates and coordinates information in distributed regions, by means of oscillatory synchrony, predictive coding or multisensory integration. Considering interoception and exteroception in the same framework paves the way for biological modes of information processing specific to living organisms.

Body as First Teacher: The Role of Rhythmic Visceral Dynamics in Early Cognitive Development

Embodied cognition-the idea that mental states and processes should be understood in relation to one's bodily constitution and interactions with the world-remains a controversial topic within cognitive science. Recently, however, increasing interest in predictive processing theories among proponents and critics of embodiment alike has raised hopes of a reconciliation. This article sets out to appraise the unificatory potential of predictive processing, focusing in particular on embodied formulations of active inference. Our analysis suggests that most active-inference accounts invoke weak, potentially trivial conceptions of embodiment; those making stronger claims do so independently of the theoretical commitments of the active-inference framework. We argue that a more compelling version of embodied active inference can be motivated by adopting a diachronic perspective on the way rhythmic physiological activity shapes neural development in utero. According to this visceral afferent training hypothesis, early-emerging physiological processes are essential not only for supporting the biophysical development of neural structures but also for configuring the cognitive architecture those structures entail. Focusing in particular on the cardiovascular system, we propose three candidate mechanisms through which visceral afferent training might operate: (a) activity-dependent neuronal development, (b) periodic signal modeling, and (c) oscillatory network coordination.

A Proof-of-Concept Numerical Ising Machine for Neural Spike Localization

Identifying the physical locations of neurons based on the spike waveforms captured by multiple recording channels, namely spike localization, can potentially enhance spike sorting accuracy. This study proposes a new method for spike localization, where the problem is first described as a nonconvex optimization problem and then the optimization is attempted heuristically via a numerical Ising solver. The paper first presents a quadratic unconstrained binary optimization (QUBO) formulation of spike localization. Then, a MATLAB solver simulating an Ising machine is written to solve the QUBO. The proposed method is evaluated on a 2D toy problem consisting of two electrodes and a single spike event, where the neuron location search is conducted in three different regions placed at increasing distances from the electrodes. The results indicate that the neuron can be accurately identified when in one of the nearest nodes to the electrodes, whereas the accuracy decreases to 87.5% and 75% as the search region distance increases. The study for the first time formulates the spike localization problem as a QUBO and demonstrates the feasibility of solving the resultant non-convex optimization problem heuristically using an Ising machine.Clinical Relevance- High channel-count implantable neural monitoring systems allow tracking large brain regions at the cost of increased data volumes to transmit and power dissipation. The new spike localization approach presented can potentially decrease the data volume and power consumption by enabling high accuracy spike localization at the implantable system.

Associations between in vitro, in vivo and in silico cell classes in mouse primary visual cortex

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Associations between in vitro , in vivo and in silico cell classes in mouse primary visual cortex

The brain consists of many cell classes yet in vivo electrophysiology recordings are typically unable to identify and monitor their activity in the behaving animal. Here, we employed a systematic approach to link cellular, multi-modal in vitro properties from experiments with in vivo recorded units via computational modeling and optotagging experiments. We found two one-channel and six multi-channel clusters in mouse visual cortex with distinct in vivo properties in terms of activity, cortical depth, and behavior. We used biophysical models to map the two one- and the six multi-channel clusters to specific in vitro classes with unique morphology, excitability and conductance properties that explain their distinct extracellular signatures and functional characteristics. These concepts were tested in ground-truth optotagging experiments with two inhibitory classes unveiling distinct in vivo properties. This multi-modal approach presents a powerful way to separate in vivo clusters and infer their cellular properties from first principles.

Control of working memory maintenance by theta-gamma phase amplitude coupling of human hippocampal neurons

Retaining information in working memory (WM) is a demanding process that relies on cognitive control to protect memoranda-specific persistent activity from interference. How cognitive control regulates WM storage, however, remains unknown. We hypothesized that interactions of frontal control and hippocampal persistent activity are coordinated by theta-gamma phase amplitude coupling (TG-PAC). We recorded single neurons in the human medial temporal and frontal lobe while patients maintained multiple items in WM. In the hippocampus, TG-PAC was indicative of WM load and quality. We identified cells that selectively spiked during nonlinear interactions of theta phase and gamma amplitude. These PAC neurons were more strongly coordinated with frontal theta activity when cognitive control demand was high, and they introduced information-enhancing and behaviorally relevant noise correlations with persistently active neurons in the hippocampus. We show that TG-PAC integrates cognitive control and WM storage to improve the fidelity of WM representations and facilitate behavior.

Predicting in vitro single-neuron firing rates upon pharmacological perturbation using Graph Neural Networks

Modern Graph Neural Networks (GNNs) provide opportunities to study the determinants underlying the complex activity patterns of biological neuronal networks. In this study, we applied GNNs to a large-scale electrophysiological dataset of rodent primary neuronal networks obtained by means of high-density microelectrode arrays (HD-MEAs). HD-MEAs allow for long-term recording of extracellular spiking activity of individual neurons and networks and enable the extraction of physiologically relevant features at the single-neuron and population level. We employed established GNNs to generate a combined representation of single-neuron and connectivity features obtained from HD-MEA data, with the ultimate goal of predicting changes in single-neuron firing rate induced by a pharmacological perturbation. The aim of the main prediction task was to assess whether single-neuron and functional connectivity features, inferred under baseline conditions, were informative for predicting changes in neuronal activity in response to a perturbation with Bicuculline, a GABA A receptor antagonist. Our results suggest that the joint representation of node features and functional connectivity, extracted from a baseline recording, was informative for predicting firing rate changes of individual neurons after the perturbation. Specifically, our implementation of a GNN model with inductive learning capability (GraphSAGE) outperformed other prediction models that relied only on single-neuron features. We tested the generalizability of the results on two additional datasets of HD-MEA recordings-a second dataset with cultures perturbed with Bicuculline and a dataset perturbed with the GABA A receptor antagonist Gabazine. GraphSAGE models showed improved prediction accuracy over other prediction models. Our results demonstrate the added value of taking into account the functional connectivity between neurons and the potential of GNNs to study complex interactions between neurons.

Single-neuron models linking electrophysiology, morphology, and transcriptomics across cortical cell types

Which cell types constitute brain circuits is a fundamental question, but establishing the correspondence across cellular data modalities is challenging. Bio-realistic models allow probing cause-and-effect and linking seemingly disparate modalities. Here, we introduce a computational optimization workflow to generate 9,200 single-neuron models with active conductances. These models are based on 230 in vitro electrophysiological experiments followed by morphological reconstruction from the mouse visual cortex. We show that, in contrast to current belief, the generated models are robust representations of individual experiments and cortical cell types as defined via cellular electrophysiology or transcriptomics. Next, we show that differences in specific conductances predicted from the models reflect differences in gene expression supported by single-cell transcriptomics. The differences in model conductances, in turn, explain electrophysiological differences observed between the cortical subclasses. Our computational effort reconciles single-cell modalities that define cell types and enables causal relationships to be examined.

Probing the human brain at single-neuron resolution with high-density cortical recordings

Recording in vivo from large numbers of neurons is a core neuroscience technique not typically possible in humans. In this issue of Neuron, Chung et al. (2022) show high-density acute recordings in human cortex using the Neuropixels probe.

Advances in human intracranial electroencephalography research, guidelines and good practices

Since the second-half of the twentieth century, intracranial electroencephalography (iEEG), including both electrocorticography (ECoG) and stereo-electroencephalography (sEEG), has provided an intimate view into the human brain. At the interface between fundamental research and the clinic, iEEG provides both high temporal resolution and high spatial specificity but comes with constraints, such as the individual's tailored sparsity of electrode sampling. Over the years, researchers in neuroscience developed their practices to make the most of the iEEG approach. Here we offer a critical review of iEEG research practices in a didactic framework for newcomers, as well addressing issues encountered by proficient researchers. The scope is threefold: (i) review common practices in iEEG research, (ii) suggest potential guidelines for working with iEEG data and answer frequently asked questions based on the most widespread practices, and (iii) based on current neurophysiological knowledge and methodologies, pave the way to good practice standards in iEEG research. The organization of this paper follows the steps of iEEG data processing. The first section contextualizes iEEG data collection. The second section focuses on localization of intracranial electrodes. The third section highlights the main pre-processing steps. The fourth section presents iEEG signal analysis methods. The fifth section discusses statistical approaches. The sixth section draws some unique perspectives on iEEG research. Finally, to ensure a consistent nomenclature throughout the manuscript and to align with other guidelines, e.g., Brain Imaging Data Structure (BIDS) and the OHBM Committee on Best Practices in Data Analysis and Sharing (COBIDAS), we provide a glossary to disambiguate terms related to iEEG research.

High-density single-unit human cortical recordings using the Neuropixels probe

The action potential is a fundamental unit of neural computation. Even though significant advances have been made in recording large numbers of individual neurons in animal models, translation of these methodologies to humans has been limited because of clinical constraints and electrode reliability. Here, we present a reliable method for intraoperative recording of dozens of neurons in humans using the Neuropixels probe, yielding up to ∼100 simultaneously recorded single units. Most single units were active within 1 min of reaching target depth. The motion of the electrode array had a strong inverse correlation with yield, identifying a major challenge and opportunity to further increase the probe utility. Cell pairs active close in time were spatially closer in most recordings, demonstrating the power to resolve complex cortical dynamics. Altogether, this approach provides access to population single-unit activity across the depth of human neocortex at scales previously only accessible in animal models.

Distinct neuronal types contribute to hybrid temporal encoding strategies in primate auditory cortex

Studies of the encoding of sensory stimuli by the brain often consider recorded neurons as a pool of identical units. Here, we report divergence in stimulus-encoding properties between subpopulations of cortical neurons that are classified based on spike timing and waveform features. Neurons in auditory cortex of the awake marmoset (Callithrix jacchus) encode temporal information with either stimulus-synchronized or nonsynchronized responses. When we classified single-unit recordings using either a criteria-based or an unsupervised classification method into regular-spiking, fast-spiking, and bursting units, a subset of intrinsically bursting neurons formed the most highly synchronized group, with strong phase-locking to sinusoidal amplitude modulation (SAM) that extended well above 20 Hz. In contrast with other unit types, these bursting neurons fired primarily on the rising phase of SAM or the onset of unmodulated stimuli, and preferred rapid stimulus onset rates. Such differentiating behavior has been previously reported in bursting neuron models and may reflect specializations for detection of acoustic edges. These units responded to natural stimuli (vocalizations) with brief and precise spiking at particular time points that could be decoded with high temporal stringency. Regular-spiking units better reflected the shape of slow modulations and responded more selectively to vocalizations with overall firing rate increases. Population decoding using time-binned neural activity found that decoding behavior differed substantially between regular-spiking and bursting units. A relatively small pool of bursting units was sufficient to identify the stimulus with high accuracy in a manner that relied on the temporal pattern of responses. These unit type differences may contribute to parallel and complementary neural codes.

Neurons in auditory cortex show highly diverse responses to sounds. This study suggests that neuronal type inferred from baseline firing properties accounts for much of this diversity, with a subpopulation of bursting units being specialized for precise temporal encoding.

The temporal dedifferentiation of global brain signal fluctuations during human brain ageing

The variation of brain functions as healthy ageing has been discussed widely using resting-state brain imaging. Previous conclusions may be misinterpreted without considering the effects of global signal (GS) on local brain activities. Up to now, the variation of GS with ageing has not been estimated. To fill this gap, we defined the GS as the mean signal of all voxels in the gray matter and systematically investigated correlations between age and indices of GS fluctuations. What's more, these tests were replicated with data after hemodynamic response function (HRF) de-convolution and data without noise regression as well as head motion data to verify effects of non-neural information on age. The results indicated that GS fluctuations varied as ageing in three ways. First, GS fluctuations were reduced with age. Second, the GS power transferred from lower frequencies to higher frequencies with age. Third, the GS power was more evenly distributed across frequencies in ageing brain. These trends were partly influenced by HRF and physiological noise, indicating that the age effects of GS fluctuations are associated with a variety of physiological activities. These results may indicate the temporal dedifferentiation hypothesis of brain ageing from the global perspective.

Somatovisceral Convergence in Sleep-Wake Cycle: Transmitting Different Types of Information via the Same Pathway

Convergence of somatic and visceral inputs occurs at the levels of nervous system ranging from spinal cord to cerebral cortex. This anatomical organization gave explanation to a referred pain phenomenon. However, it also presents a problem: How does the brain know what information is coming for processing-somatic or visceral - if both are transferred by the same spinal cord fibers by means of the standard neuronal spikes? Recent studies provided evidence for cortical processing of interoceptive information largely occurring in sleep, when somatosensation is suppressed, and for the corresponding functional brain networks rearrangement. We suggest that convergent units of the spinal cord would be able to collectively provide mainly somatosensory information in wakefulness and mainly visceral in sleep, solving the puzzle of somatovisceral convergence. We recorded spiking activity from the spinal cord lemniscus pathway during multiple sleep-wake cycles in freely behaving rabbits. In wakefulness high increased spiking corresponded to movements. When animals stopped moving this activity ceased, the fibers remained silent during passive wakefulness. However, upon transition to sleep fibers began firing again. Analysis of spiking patterns of individual fibers revealed that in the majority of them spiking rates recovered in slow wave sleep. Thus, despite cessation of motion and a corresponding decrease of somatic component of the convergent signal, considerable ascending signaling occurs during sleep, that is likely to be visceral. We also recorded evoked responses of the lemniscus pathway to innocuous electrostimulation of the abdominal viscera, and uncovered the existence of two groups of responses depending upon the state of vigilance. Response from an individual fiber could be detected either during wakefulness or in sleep, but not in both states. Wakefulness-responsive group had lower spiking rates in wakefulness and almost stopped spiking in sleep. Sleep-responsive retained substantial spiking during sleep. These groups also differed in spike amplitudes, indicative of fiber diameter differences; however, both had somatic responses during wakefulness. We suggest a mechanism that utilizes differences in somatic and visceral activities to extract both types of information by varying transmission thresholds, and discuss the implications of this mechanism on functional networks under normal and pathological conditions.

Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex

Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, current approaches restrict recordings to a few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here we describe a new probe variant and set of techniques that enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single-unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread and modulation by LFP events such as inter-ictal discharges and burst suppression. Although some challenges remain in creating a turnkey recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution.

Pandemic Teaching: Using the Allen Cell Types Database for Final Semester Projects in an Undergraduate Neurophysiology Lab Course

We designed a final semester research project that allowed students to apply the electrophysiological concepts they learned in a lab course to propose and answer experimental questions without access to laboratory equipment. We created the activity based on lesson plans from Ashley Juavinett and the Allen Institute for Brain Science (AIBS) Allen SDK online examples. An interactive graphic interface was added for students to explore and easily quantify subtle neuronal voltage changes. Before starting the final project, students had experience with conventional extracellular and intracellular recording techniques to record and analyze extracellular action potential firing patterns and intracellular resting, action, and synaptic potentials. They demonstrated their understanding of neural signal transmission in required lab reports using data they gathered before the pandemic shutdown. After students left campus, they continued to analyze data and write lab reports focused on neuronal excitability in snail and fly neurons with data supplied by the instructors. For their final project, students were challenged to answer questions addressing neuronal excitability at both the single neuron and neuronal population level by analyzing and interpreting the open-access, patch clamp recording data from the Allen Cell Types Database using code we provided (Python/Jupyter Notebook). This virtual final semester project allowed students to ask real-world medical and scientific questions from "start to end". Through this project, students developed skills to navigate an extensive online database and gained experience with coding-based data analysis. They chose neuronal populations from human and mouse brains to compare passive properties and neuronal excitability between and within brain areas and across different species and disease states. Additionally, students learned to do simple manipulations of Python code, work remotely in teams, and polish their written scientific presentation skills. This activity could complement other remote learning options such as neuronal simulations. Few online sources offer such a wealth of neuroscience data that students can use for class assignments, and even for research and keystone projects. The activity extends the traditional material often taught in upper-level neuroscience courses, with or without a laboratory section, providing a deeper understanding of the range of excitability properties that neurons express.

Myogenic contraction of a somatic muscle powers rhythmic flow of hemolymph through Drosophila antennae and generates brain pulsations

Hemolymph is driven through the antennae of Drosophila melanogaster by the rhythmic contraction of muscle 16 (m16), which runs through the brain. Contraction of m16 results in the expansion of an elastic ampulla, opening ostia and filling the ampulla. Relaxation of the ampullary membrane forces hemolymph through vessels into the antennae. We show that m16 is an auto-active rhythmic somatic muscle. The activity of m16 leads to the rapid perfusion of the antenna by hemolymph. In addition, it leads to the rhythmic agitation of the brain, which could be important for clearing the interstitial space.

Non-linear dimensionality reduction on extracellular waveforms reveals cell type diversity in premotor cortex

Cortical circuits are thought to contain a large number of cell types that coordinate to produce behavior. Current in vivo methods rely on clustering of specified features of extracellular waveforms to identify putative cell types, but these capture only a small amount of variation. Here, we develop a new method (WaveMAP) that combines non-linear dimensionality reduction with graph clustering to identify putative cell types. We apply WaveMAP to extracellular waveforms recorded from dorsal premotor cortex of macaque monkeys performing a decision-making task. Using WaveMAP, we robustly establish eight waveform clusters and show that these clusters recapitulate previously identified narrow- and broad-spiking types while revealing previously unknown diversity within these subtypes. The eight clusters exhibited distinct laminar distributions, characteristic firing rate patterns, and decision-related dynamics. Such insights were weaker when using feature-based approaches. WaveMAP therefore provides a more nuanced understanding of the dynamics of cell types in cortical circuits.

Distinct roles of dorsal and ventral subthalamic neurons in action selection and cancellation

The subthalamic nucleus (STN) supports action selection by inhibiting all motor programs except the desired one. Recent evidence suggests that STN can also cancel an already selected action when goals change, a key aspect of cognitive control. However, there is little neurophysiological evidence for dissociation between selecting and cancelling actions in the human STN. We recorded single neurons in the STN of humans performing a stop-signal task. Movement-related neurons suppressed their activity during successful stopping, whereas stop-signal neurons activated at low-latencies near the stop-signal reaction time. In contrast, STN and motor-cortical beta-bursting occurred only later in the stopping process. Task-related neuronal properties varied by recording location from dorsolateral movement to ventromedial stop-signal tuning. Therefore, action selection and cancellation coexist in STN but are anatomically segregated. These results show that human ventromedial STN neurons carry fast stop-related signals suitable for implementing cognitive control.

Computational Models of Interoception and Body Regulation

To survive, organisms must effectively respond to the challenge of maintaining their physiological integrity in the face of an ever-changing environment. Preserving this homeostasis critically relies on adaptive behavior. In this review, we consider recent frameworks that extend classical homeostatic control via reflex arcs to include more flexible forms of adaptive behavior that take interoceptive context, experiences, and expectations into account. Specifically, we define a landscape for computational models of interoception, body regulation, and forecasting, address these models' unique challenges in relation to translational research efforts, and discuss what they can teach us about cognition as well as physical and mental health.

Brain Tissue Pulsation in Healthy Volunteers

It is well known that the brain pulses with each cardiac cycle, but interest in measuring cardiac-induced brain tissue pulsations (BTPs) is relatively recent. This study was aimed at generating BTP reference data from healthy patients for future clinical comparisons and modelling. BTPs were measured through the forehead and temporal positions as a function of age, sex, heart rate, mean arterial pressure and pulse pressure. A multivariate regression model was developed based on transcranial tissue Doppler BTP measurements from 107 healthy adults (56 male) aged from 20-81 y. A subset of 5 participants (aged 20-49 y) underwent a brain magnetic resonance imaging scan to relate the position of the ultrasound beam to anatomy. BTP amplitudes were found to vary widely between patients (from ∼4 to ∼150 µm) and were strongly associated with pulse pressure. Comparison with magnetic resonance images confirmed regional variations in BTP with depth and probe position.

Neuronal Firing and Waveform Alterations through Ictal Recruitment in Humans

Analyzing neuronal activity during human seizures is pivotal to understanding mechanisms of seizure onset and propagation. These analyses, however, invariably using extracellular recordings, are greatly hindered by various phenomena that are well established in animal studies: changes in local ionic concentration, changes in ionic conductance, and intense, hypersynchronous firing. The first two alter the action potential waveform, whereas the third increases the "noise"; all three factors confound attempts to detect and classify single neurons. To address these analytical difficulties, we developed a novel template-matching-based spike sorting method, which enabled identification of 1239 single neurons in 27 patients (13 female) with intractable focal epilepsy, that were tracked throughout multiple seizures. These new analyses showed continued neuronal firing with widespread intense activation and stereotyped action potential alterations in tissue that was invaded by the seizure: neurons displayed increased waveform duration (p < 0.001) and reduced amplitude (p < 0.001), consistent with prior animal studies. By contrast, neurons in "penumbral" regions (those receiving intense local synaptic drive from the seizure but without neuronal evidence of local seizure invasion) showed stable waveforms. All neurons returned to their preictal waveforms after seizure termination. We conclude that the distinction between "core" territories invaded by the seizure versus "penumbral" territories is evident at the level of single neurons. Furthermore, the increased waveform duration and decreased waveform amplitude are neuron-intrinsic hallmarks of seizure invasion that impede traditional spike sorting and could be used as defining characteristics of local recruitment.SIGNIFICANCE STATEMENT Animal studies consistently show marked changes in action potential waveform during epileptic discharges, but acquiring similar evidence in humans has proven difficult. Assessing neuronal involvement in ictal events is pivotal to understanding seizure dynamics and in defining clinical localization of epileptic pathology. Using a novel method to track neuronal firing, we analyzed microelectrode array recordings of spontaneously occurring human seizures, and here report two dichotomous activity patterns. In cortex that is recruited to the seizure, neuronal firing rates increase and waveforms become longer in duration and shorter in amplitude as the neurons are recruited to the seizure, while penumbral tissue shows stable action potentials, in keeping with the "dual territory" model of seizure dynamics.

Multidimensional processing in the amygdala

Brain-wide circuits that coordinate affective and social behaviours intersect in the amygdala. Consequently, amygdala lesions cause a heterogeneous array of social and non-social deficits. Social behaviours are not localized to subdivisions of the amygdala even though the inputs and outputs that carry social signals are anatomically restricted to distinct subnuclear regions. This observation may be explained by the multidimensional response properties of the component neurons. Indeed, the multitudes of circuits that converge in the amygdala enlist the same subset of neurons into different ensembles that combine social and non-social elements into high-dimensional representations. These representations may enable flexible, context-dependent social decisions. As such, multidimensional processing may operate in parallel with subcircuits of genetically identical neurons that serve specialized and functionally dissociable functions. When combined, the activity of specialized circuits may grant specificity to social behaviours, whereas multidimensional processing facilitates the flexibility and nuance needed for complex social behaviour.

Microelectrode recordings in human epilepsy: a case for clinical translation

With their 'all-or-none' action potential responses, single neurons (or units) are accepted as the basic computational unit of the brain. There is extensive animal literature to support the mechanistic importance of studying neuronal firing as a way to understand neuronal microcircuits and brain function. Although most studies have emphasized physiology, there is increasing recognition that studying single units provides novel insight into system-level mechanisms of disease. Microelectrode recordings are becoming more common in humans, paralleling the increasing use of intracranial electroencephalography recordings in the context of presurgical evaluation in focal epilepsy. In addition to single-unit data, microelectrode recordings also record local field potentials and high-frequency oscillations, some of which may be different to that recorded by clinical macroelectrodes. However, microelectrodes are being used almost exclusively in research contexts and there are currently no indications for incorporating microelectrode recordings into routine clinical care. In this review, we summarize the lessons learnt from 65 years of microelectrode recordings in human epilepsy patients. We cover the electrode constructs that can be utilized, principles of how to record and process microelectrode data and insights into ictal dynamics, interictal dynamics and cognition. We end with a critique on the possibilities of incorporating single-unit recordings into clinical care, with a focus on potential clinical indications, each with their specific evidence base and challenges.