Cell class-specific electric field entrainment of neural activity

Electric fields affect the activity of neurons and brain circuits, yet how this interaction happens at the cellular level remains enigmatic. Lack of understanding on how to stimulate the human brain to promote or suppress specific activity patterns significantly limits basic research and clinical applications. Here we study how electric fields impact the subthreshold and spiking properties of major cortical neuronal classes. We find that cortical neurons in rodent neocortex and hippocampus as well as human cortex exhibit strong and cell class-dependent entrainment that depends on the stimulation frequency. Excitatory pyramidal neurons with their typically slower spike rate entrain to slow and fast electric fields, while inhibitory classes like Pvalb and SST with their fast spiking predominantly phase lock to fast fields. We show 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 of spike-field and class-specific entrainment are present in cells across cortical areas and species (mouse and human). These findings open the door to the design of selective and class-specific neuromodulation technologies.

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.

Multi-modal characterization and simulation of human epileptic circuitry

Temporal lobe epilepsy is the fourth most common neurological disorder, with about 40% of patients not responding to pharmacological treatment. Increased cellular loss is linked to disease severity and pathological phenotypes such as heightened seizure propensity. While the hippocampus is the target of therapeutic interventions, the impact of the disease at the cellular level remains unclear. Here, we show that hippocampal granule cells change with disease progression as measured in living, resected hippocampal tissue excised from patients with epilepsy. We show that granule cells increase excitability and shorten response latency while also enlarging in cellular volume and spine density. Single-nucleus RNA sequencing combined with simulations ascribes the changes to three conductances: BK, Cav2.2, and Kir2.1. In a network model, we show that these changes related to disease progression bring the circuit into a more excitable state, while reversing them produces a less excitable, "early-disease-like" state.

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.

Structure and function of axo-axonic inhibition

Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.

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

Determining cell types is critical for understanding neural circuits but remains elusive in the living human brain. Current approaches discriminate units into putative cell classes using features of the extracellular action potential (EAP); in absence of ground truth data, this remains a problematic procedure. We find that EAPs in deep structures of the brain exhibit robust and systematic variability during the cardiac cycle. These cardiac-related features refine neural classification. We use these features to link bio-realistic models generated from in vitro human whole-cell recordings of morphologically classified neurons to in vivo recordings. We differentiate aspiny inhibitory and spiny excitatory human hippocampal neurons and, in a second stage, demonstrate that cardiac-motion features reveal two types of spiny neurons with distinct intrinsic electrophysiological properties and phase-locking characteristics to endogenous oscillations. This multi-modal approach markedly improves cell classification in humans, offers interpretable cell classes, and is applicable to other brain areas and species.

Classification of electrophysiological and morphological neuron types in the mouse visual cortex

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To systematically profile morpho-electric properties of mammalian neurons, we established a single-cell characterization pipeline using standardized patch-clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly accessible online database, the Allen Cell Types Database, to display these datasets. Intrinsic physiological properties were measured from 1,938 neurons from the adult laboratory mouse visual cortex, morphological properties were measured from 461 reconstructed neurons, and 452 neurons had both measurements available. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We established a taxonomy of morphologically and electrophysiologically defined cell types for this region of the cortex, with 17 electrophysiological types, 38 morphological types and 46 morpho-electric types. There was good correspondence with previously defined transcriptomic cell types and subclasses using the same transgenic mouse lines.

h-Channels Contribute to Divergent Intrinsic Membrane Properties of Supragranular Pyramidal Neurons in Human versus Mouse Cerebral Cortex

Gene expression studies suggest that differential ion channel expression contributes to differences in rodent versus human neuronal physiology. We tested whether h-channels more prominently contribute to the physiological properties of human compared to mouse supragranular pyramidal neurons. Single-cell/nucleus RNA sequencing revealed ubiquitous HCN1-subunit expression in excitatory neurons in human, but not mouse, supragranular layers. Using patch-clamp recordings, we found stronger h-channel-related membrane properties in supragranular pyramidal neurons in human temporal cortex, compared to mouse supragranular pyramidal neurons in temporal association area. The magnitude of these differences depended upon cortical depth and was largest in pyramidal neurons in deep L3. Additionally, pharmacologically blocking h-channels produced a larger change in membrane properties in human compared to mouse neurons. Finally, using biophysical modeling, we provide evidence that h-channels promote the transfer of theta frequencies from dendrite-to-soma in human L3 pyramidal neurons. Thus, h-channels contribute to between-species differences in a fundamental neuronal property.

BioNet: A Python interface to NEURON for modeling large-scale networks

There is a significant interest in the neuroscience community in the development of large-scale network models that would integrate diverse sets of experimental data to help elucidate mechanisms underlying neuronal activity and computations. Although powerful numerical simulators (e.g., NEURON, NEST) exist, data-driven large-scale modeling remains challenging due to difficulties involved in setting up and running network simulations. We developed a high-level application programming interface (API) in Python that facilitates building large-scale biophysically detailed networks and simulating them with NEURON on parallel computer architecture. This tool, termed "BioNet", is designed to support a modular workflow whereby the description of a constructed model is saved as files that could be subsequently loaded for further refinement and/or simulation. The API supports both NEURON's built-in as well as user-defined models of cells and synapses. It is capable of simulating a variety of observables directly supported by NEURON (e.g., spikes, membrane voltage, intracellular [Ca++]), as well as plugging in modules for computing additional observables (e.g. extracellular potential). The high-level API platform obviates the time-consuming development of custom code for implementing individual models, and enables easy model sharing via standardized files. This tool will help refocus neuroscientists on addressing outstanding scientific questions rather than developing narrow-purpose modeling code.

Fully integrated silicon probes for high-density recording of neural activity

Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca2+ imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 × 20-μm cross-section shank. The 6 × 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.

From Maxwell's equations to the theory of current-source density analysis

Despite the widespread use of current‐source density (CSD) analysis of extracellular potential recordings in the brain, the physical mechanisms responsible for the generation of the signal are still debated. While the extracellular potential is thought to be exclusively generated by the transmembrane currents, recent studies suggest that extracellular diffusive, advective and displacement currents—traditionally neglected—may also contribute considerably toward extracellular potential recordings. Here, we first justify the application of the electro‐quasistatic approximation of Maxwell's equations to describe the electromagnetic field of physiological origin. Subsequently, we perform spatial averaging of currents in neural tissue to arrive at the notion of the CSD and derive an equation relating it to the extracellular potential. We show that, in general, the extracellular potential is determined by the CSD of membrane currents as well as the gradients of the putative extracellular diffusion current. The diffusion current can contribute significantly to the extracellular potential at frequencies less than a few Hertz; in which case it must be subtracted to obtain correct CSD estimates. We also show that the advective and displacement currents in the extracellular space are negligible for physiological frequencies while, within cellular membrane, displacement current contributes toward the CSD as a capacitive current. Taken together, these findings elucidate the relationship between electric currents and the extracellular potential in brain tissue and form the necessary foundation for the analysis of extracellular recordings.

Emergence of Slow-Switching Assemblies in Structured Neuronal Networks

Unraveling the interplay between connectivity and spatio-temporal dynamics in neuronal networks is a key step to advance our understanding of neuronal information processing. Here we investigate how particular features of network connectivity underpin the propensity of neural networks to generate slow-switching assembly (SSA) dynamics, i.e., sustained epochs of increased firing within assemblies of neurons which transition slowly between different assemblies throughout the network. We show that the emergence of SSA activity is linked to spectral properties of the asymmetric synaptic weight matrix. In particular, the leading eigenvalues that dictate the slow dynamics exhibit a gap with respect to the bulk of the spectrum, and the associated Schur vectors exhibit a measure of block-localization on groups of neurons, thus resulting in coherent dynamical activity on those groups. Through simple rate models, we gain analytical understanding of the origin and importance of the spectral gap, and use these insights to develop new network topologies with alternative connectivity paradigms which also display SSA activity. Specifically, SSA dynamics involving excitatory and inhibitory neurons can be achieved by modifying the connectivity patterns between both types of neurons. We also show that SSA activity can occur at multiple timescales reflecting a hierarchy in the connectivity, and demonstrate the emergence of SSA in small-world like networks. Our work provides a step towards understanding how network structure (uncovered through advancements in neuroanatomy and connectomics) can impact on spatio-temporal neural activity and constrain the resulting dynamics.

Cell type- and activity-dependent extracellular correlates of intracellular spiking

Despite decades of extracellular action potential (EAP) recordings monitoring brain activity, the biophysical origin and inherent variability of these signals remain enigmatic. We performed whole cell patch recordings of excitatory and inhibitory neurons in rat somatosensory cortex slice while positioning a silicon probe in their vicinity to concurrently record intra- and extracellular voltages for spike frequencies under 20 Hz. We characterize biophysical events and properties (intracellular spiking, extracellular resistivity, temporal jitter, etc.) related to EAP recordings at the single-neuron level in a layer-specific manner. Notably, EAP amplitude was found to decay as the inverse of distance between the soma and the recording electrode with similar (but not identical) resistivity across layers. Furthermore, we assessed a number of EAP features and their variability with spike activity: amplitude (but not temporal) features varied substantially (∼ 30-50% compared with mean) and nonmonotonically as a function of spike frequency and spike order. Such EAP variation only partly reflects intracellular somatic spike variability and points to the plethora of processes contributing to the EAP. Also, we show that the shape of the EAP waveform is qualitatively similar to the negative of the temporal derivative to the intracellular somatic voltage, as expected from theory. Finally, we tested to what extent EAPs can impact the lowpass-filtered part of extracellular recordings, the local field potential (LFP), typically associated with synaptic activity. We found that spiking of excitatory neurons can significantly impact the LFP at frequencies as low as 20 Hz. Our results question the common assertion that the LFP acts as proxy for synaptic activity.

Physiology of layer 5 pyramidal neurons in mouse primary visual cortex: coincidence detection through bursting

L5 pyramidal neurons are the only neocortical cell type with dendrites reaching all six layers of cortex, casting them as one of the main integrators in the cortical column. What is the nature and mode of computation performed in mouse primary visual cortex (V1) given the physiology of L5 pyramidal neurons? First, we experimentally establish active properties of the dendrites of L5 pyramidal neurons of mouse V1 using patch-clamp recordings. Using a detailed multi-compartmental model, we show this physiological setup to be well suited for coincidence detection between basal and apical tuft inputs by controlling the frequency of spike output. We further show how direct inhibition of calcium channels in the dendrites modulates such coincidence detection. To establish the singe-cell computation that this biophysics supports, we show that the combination of frequency-modulation of somatic output by tuft input and (simulated) calcium-channel blockage functionally acts as a composite sigmoidal function. Finally, we explore how this computation provides a mechanism whereby dendritic spiking contributes to orientation tuning in pyramidal neurons.

Neurons in the brain have elaborate dendritic morphologies, hosting a variety of nonlinear channels that give way to single cell computation. In this study, we perform patch clamp recordings in the apical dendrites to establish the spatial distribution of nonlinear channels and the signals they support in the dendrites of layer 5 pyramidal neurons of the mouse primary visual cortex. Using this data, we create a detailed single cell model and simulate synaptic input. We then summarize the results of the simulations using a simple abstracted model, that ultimately describes the computation layer 5 pyramidal neurons perform on synaptic input. We find that this computation is a form of nonlinear frequency-modulation that works in a dendritic-spike dependent manner. Finally, we show how this computation allows dendritic spikes to contribute to the orientation tuning of pyramidal neurons in the visual cortex.

Theta phase segregation of input-specific gamma patterns in entorhinal-hippocampal networks

Precisely how rhythms support neuronal communication remains obscure. We investigated interregional coordination of gamma oscillations using high-density electrophysiological recordings in the rat hippocampus and entorhinal cortex. We found that 30-80 Hz gamma dominated CA1 local field potentials (LFPs) on the descending phase of CA1 theta waves during navigation, with 60-120 Hz gamma at the theta peak. These signals corresponded to CA3 and entorhinal input, respectively. Above 50 Hz, interregional phase-synchronization of principal cell spikes occurred mostly for LFPs in the axonal target domain. CA1 pyramidal cells were phase-locked mainly to fast gamma (>100 Hz) LFP patterns restricted to CA1, which were strongest at the theta trough. While theta phase coordination of spiking across entorhinal-hippocampal regions depended on memory demands, LFP gamma patterns below 100 Hz in the hippocampus were consistently layer specific and largely reflected afferent activity. Gamma synchronization as a mechanism for interregional communication thus rapidly loses efficacy at higher frequencies.

Spike-timing control by dendritic plateau potentials in the presence of synaptic barrages

Apical and tuft dendrites of pyramidal neurons support regenerative electrical potentials, giving rise to long-lasting (approximately hundreds of milliseconds) and strong (~50 mV from rest) depolarizations. Such plateau events rely on clustered glutamatergic input, can be mediated by calcium or by NMDA currents, and often generate somatic depolarizations that last for the time course of the dendritic plateau event. We address the computational significance of such single-neuron processing via reduced but biophysically realistic modeling. We introduce a model based on two discrete integration zones, a somatic and a dendritic one, that communicate from the dendritic to the somatic compartment via a long plateau-conductance. We show principled differences in the way dendritic vs. somatic inhibition controls spike timing, and demonstrate how this could implement spike time control in the face of barrages of synaptic inputs.

A biophysically detailed model of neocortical local field potentials predicts the critical role of active membrane currents

Brain activity generates extracellular voltage fluctuations recorded as local field potentials (LFPs). It is known that the relevant microvariables, the ionic currents across membranes, jointly generate the macrovariables, the extracellular voltage, but neither the detailed biophysical knowledge nor the required computational power have been available to model these processes. We simulated the LFP in a model of the rodent neocortical column composed of >12,000 reconstructed, multicompartmental, and spiking cortical layer 4 and 5 pyramidal neurons and basket cells, including five million dendritic and somatic compartments with voltage- and ion-dependent currents, realistic connectivity, and probabilistic AMPA, NMDA, and GABA synapses. We found that, depending on a number of factors, the LFP reflects local and cross-layer processing. Active currents dominate the generation of LFPs, not synaptic ones. Spike-related currents impact the LFP not only at higher frequencies but below 50 Hz. This work calls for re-evaluating the genesis of LFPs.

The origin of extracellular fields and currents--EEG, ECoG, LFP and spikes

Neuronal activity in the brain gives rise to transmembrane currents that can be measured in the extracellular medium. Although the major contributor of the extracellular signal is the synaptic transmembrane current, other sources--including Na(+) and Ca(2+) spikes, ionic fluxes through voltage- and ligand-gated channels, and intrinsic membrane oscillations--can substantially shape the extracellular field. High-density recordings of field activity in animals and subdural grid recordings in humans, combined with recently developed data processing tools and computational modelling, can provide insight into the cooperative behaviour of neurons, their average synaptic input and their spiking output, and can increase our understanding of how these processes contribute to the extracellular signal.

Characterization of ac voltammetric reaction-diffusion dynamics: from patterns to physical parameters

Despite the widespread use of electrochemical sensing techniques, the determination of the physical parameters from the current response of rapid voltammetric measurements has been difficult for two reasons: large capacitance contributions overwhelm the current response of transient measurements and the reaction dynamics are inherently nonlinear and nonstationary. In this work, we present a signal processing methodology that in combination with a large-amplitude/high-frequency voltage waveform method, ac voltammetry, is able to determine the underlying physical parameters in heterogeneous electrochemical reaction-diffusion processes. Through a large number of numerical calculations, we explore the effect of kinetic, thermodynamic, and mass transport parameters on two components of the current response, the even and the odd. We study the even component directly whereas for the odd component, which is considerably influenced by capacitance, we use the Hilbert transform, which is suitable for the analysis of nonstationary and nonlinear data sets, to minimize the capacitance contribution. The theoretical analysis is applied to measurements of well-characterized electrochemical reactions, Ru(NH3)6(2+/3+) and Fe(CN)6(4-/3-), using two different electrode materials, glassy carbon and platinum, and the physical parameters deduced are in excellent agreement with published results.

Subsecond Voltammetric Separation between Dopamine and Serotonin in the Presence of Ascorbate

Although voltammetry has proved an important tool for unraveling the dynamics of specific neurotransmitter molecules during the past decade, it has been very difficult to monitor more than one neurotransmitter simultaneously. In this work, we present a voltammetric methodology that allows discrimination between dopamine and serotonin, two important neurotransmitter molecules with very similar electrochemical properties, in the presence of high concentrations of ascorbate. We combined the application of a novel large-amplitude/high-frequency voltage excitation with signal processing techniques valid for the analysis of nonstationary and nonlinear phenomena. This allows us to minimize the contribution from capacitance and preserve the faradaic features of the voltammetric response providing us with excellent voltammetric detail. Using appropriate voltage excitation parameters and defining specific regions in the voltage space, so-called voltage windows, we can measure the concentrations of dopamine and serotonin separately or independently in mixed solutions even in the presence of high concentrations of ascorbate. Because of the enhanced voltammetric detail of this new technique, it is also possible to explore effects attributed to interfacial phenomena such as adsorption/desorption and electrode fouling.

Determination of Kinetic and Thermodynamic Parameters of Surface Confined Species through ac Voltammetry and a Nonstationary Signal Processing Technique: The Hilbert Transform

Data analysis of voltammetric responses has usually been done through application of the fast Fourier transform although it is widely accepted that electrochemical signals are intrinsically nonlinear and nonstationary. In this work, we present a time-series analysis based on the Hilbert transform (HT), a nonstationary signal processing technique, as an alternative tool that can overcome many of the difficulties associated with Fourier techniques. We use the HT to study the behavior of thin-film processes when the excitation perturbation is ac voltammetry. From the analysis of simulated data, we propose simple relations that enable species-specific kinetic and thermodynamic parameters to be estimated, without prior utilization of baseline subtraction even when double layer capacitance significantly influences the current response. We also propose a method to determine whether the characteristics of the applied voltage perturbation are adequate for the accurate estimation of these parameters. The methodology developed here will be applied to previously published experimental time series data (Guo, S. X.; Zhang, J.; Elton, D. M.; Bond, A. M. Anal. Chem. 2004, 76, 166-177.) obtained with ac voltammetry to show how a number of physical parameters can be directly extracted from the processed data.