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Multi-electrode arrays (MEAs) are being more widely used by researchers as an instrument platform for monitoring prolonged, non-destructive recordings of spontaneously firing neuronsin vitrofor applications in modeling Alzheimer’s, Parkinson’s, schizophrenia, and many other diseases of the human CNS. With the more widespread use of these instruments, there is a need to examine the prior art of studies utilizing MEAs and delineate best practices for data acquisition and analysis to avoid errors in interpretation of the resultant data. Using a dataset of recordings from primary rat (Rattus norvegicus) cortical cultures, methods and statistical power for discerning changes in neuronal activity on the array level are examined. Further, a method for unsupervised spike sorting is implemented, allowing for the resolution of action potential incidents down to the single neuron level. Following implementation of spike sorting, the dynamics of firing frequency across populations of individual neurons and networks are examined longitudinally. Finally, the ability to detect a frequency independent phenotype, the change in action potential amplitude, is demonstrated through the use of pore-forming neurotoxin treatments. Taken together, this study provides guidance and tools for users wishing to incorporate multi-well MEA usage into their studies.

Damage in biological neuronal networks triggers a complex functional reorganization whose mechanisms are still poorly understood. To delineate this reorganization process, here we investigate the functional alterations of in vitro rat cortical circuits following localized laser ablation. The analysis of the functional network configuration before and after ablation allowed us to quantify the extent of functional alterations and the characteristic spatial and temporal scales along recovery. We observed that damage precipitated a fast rerouting of information flow that restored network’s communicability in about 15 min. Functional restoration was led by the immediate neighbors around trauma but was orchestrated by the entire network. Our in vitro setup exposes the ability of neuronal circuits to articulate fast responses to acute damage, and may serve as a proxy to devise recovery strategies in actual brain circuits. Moreover, this biological setup can become a benchmark to empirically test network theories about the spontaneous recovery in dynamical networks. Significance Statement Given the sheer size of the brain, in vitro models in the form of neuronal cultures have emerged as a promising tool to investigate dynamic and network alterations in detail upon physical damage. Here we present a new experimental paradigm based on the combination of laser microsurgery and calcium fluorescence imaging to analyze network functional alterations after a focal lesion. We show that the network is not only able to cope with damage but that the regions around the lesion core actively participate in recovery, restoring the initial network activity levels in just 15 min. Our approach offers interesting perspectives for modeling network functional loss and recovery in a number of damage actions, from stroke to degenerative disorders.

Two-photon fluorescence microscopy has been used extensively to probe the structure and functions of cells in living biological tissue. Two-photon excitation generates fluorescence from the focal plane, but also from outside the focal plane, with out-of-focus fluorescence increasing as the focus is pushed deeper into tissue. It has been postulated that the two-photon depth limit, beyond which results become inaccurate, is where in-focus and out-of-focus fluorescence are equal, which we term the balance depth. Calculations suggest that the balance depth should be at ∼600 µm in mouse cortex. Neither the two-photon depth limit nor the balance depth have been measured in brain tissue. We found the depth limit and balance depth of two-photon excitation in mice with GCaMP6 indicator expression in all layers of visual cortex, by comparing near-simultaneous two-photon and three-photon excitation. Two-photon and three-photon results from superficial locations were almost identical. two-photon results were inaccurate beyond the balance depth, consistent with the depth limit matching the balance depth for two-photon excitation. However, the two-photon depth limit and balance depth were at 450 µm, shallower than predicted by calculations. Our results were from tissue with a largely homogenous distribution of fluorophores. The expected balance depth is deeper in tissue with fewer fluorophores outside the focal plane and our results therefore establish a superficial bound on the two-photon depth limit in mouse visual cortex.

Visual Abstract, While BDNF is receiving considerable attention for its role in synaptic plasticity and in nervous system dysfunction, identifying brain circuits involving BDNF-expressing neurons has been challenging. BDNF levels are very low in most brain areas, except for the large mossy fiber terminals in the hippocampus where BDNF accumulates at readily detectable levels. This report describes the generation of a mouse line allowing the detection of single brain cells synthesizing BDNF. A bicistronic construct encoding BDNF tagged with a P2A sequence preceding GFP allows the translation of BDNF and GFP as separate proteins. Following its validation with transfected cells, this construct was used to replace the endogenous Bdnf gene. Viable and fertile homozygote animals were generated, with the GFP signal marking neuronal cell bodies translating the Bdnf mRNA. Importantly, the distribution of immunoreactive BDNF remained unchanged, as exemplified by its accumulation in mossy fiber terminals in the transgenic animals. GFP-labeled neurons could be readily visualized in distinct layers in the cerebral cortex where BDNF has been difficult to detect with currently available reagents. In the hippocampal formation, quantification of the GFP signal revealed that

Anatomical methods for determining cell-type specific connectivity are essential to inspire and constrain our understanding of neural circuit function. We developed genetically-encoded reagents for fluorescence-synapse labeling and connectivity analysis in brain tissue, using a fluorogen-activating protein (FAP)- or YFP-coupled, postsynaptically-localized neuroligin-1 targeting sequence (FAP/YFPpost). FAPpost expression did not alter mEPSC or mIPSC properties. Sparse AAV-mediated expression of FAP/YFPpost with the cell-filling, red fluorophore dTomato (dTom) enabled high-throughput, compartment-specific detection of putative synapses across diverse neuron types in mouse somatosensory cortex. We took advantage of the bright, far-red emission of FAPpost puncta for multichannel fluorescence alignment of dendrites, FAPpost puncta, and presynaptic neurites in transgenic mice with saturated labeling of parvalbumin (PV), somatostatin (SST) or vasoactive intestinal peptide (VIP)-expressing neurons using Cre-reporter driven expression of YFP. Subtype-specific inhibitory connectivity onto L2 neocortical pyramidal (Pyr) neurons was assessed using automated puncta detection and neurite apposition. Quantitative and compartment-specific comparisons show that PV inputs are the predominant source of inhibition at both the soma and the dendrites and were particularly concentrated at the primary apical dendrite. SST inputs were interleaved with PV inputs at all secondary- and higher-order dendritic branches. These fluorescence-based synapse labeling reagents can facilitate large-scale and cell-type specific quantitation of changes in synaptic connectivity across development, learning, and disease states. Significance Statement High-throughput quantitation of synapse number and distribution can reveal principles of circuit function and their adaptive or pathological alterations. Molecular genetic, fluorescence-based approaches targeted to discrete cell types can enable automated detection and quantification of input-specific synapses in complex brain tissues. In addition, these tools present a low barrier to use within the neuroscience community through volumetric confocal analysis of tissue specimens. Here we evaluate inhibitory synapse distribution across layer 2 (L2) pyramidal neurons using postsynaptic expression of a previously characterized, neuroligin-based construct. We find that inhibitory inputs from fluorescently-labeled parvalbumin and somatostatin neurons are intermingled across the proximal dendrites, and that inputs from vasoactive-intestinal peptide neurons are rare for L2 pyramidal neurons.

High-throughput automated experiments accelerate discovery in neuroscience research and reduce bias. To enable high-throughput behavioral experiments, we developed a user-friendly and scalable automated system that can simultaneously train hundreds of mice on behavioral tasks, with time-stamped behavioral information recorded continuously for weeks. We trained 12 cages of C57Bl6/J mice (24 mice, 2 mice per cage) to perform auditory behavioral tasks. We found that circadian rhythms modulated overall behavioral activity as expected for nocturnal animals. However, auditory detection and discrimination accuracy remained consistently high in both light and dark cycles. We also found a periodic modulation of behavioral response rates only during the discrimination task, suggesting that the mice periodically reduce task-engagement (i.e., take ‘breaks’) when task difficulty increases due to the more complex stimulus-response paradigm for discrimination vs detection. Our results highlight how automated systems for continuous high-throughput behavioral experiments enable both efficient data collection and new observations on animal behavior. Significance Statement Automated high-throughput behavioral experiments in mice promise researchers the ability to quickly and reliably asses the behavior of large animal populations, while also minimizing experimenter-induced bias. However, the technical complexities of automation have limited widespread adoption of high-throughput behavioral methods. Here, we present a new tool for behavioral research, the ToneBox, which allows both novice and expert behaviorists to automatically test hundreds of mice simultaneously on different behavioral tasks. We provide manufacturing specifications and detailed documentation of system operation. Using C57Bl6/J mice we show that the ToneBox tracks circadian cycles via behavioral response rates, and that task difficultly modulates duty-cycles of task-engagement.

Characterizing how the brain responds to stimuli has been a goal of sensory neuroscience for decades. One key approach has been to fit linear models to describe the relationship between sensory inputs and neural responses. This has included models aimed at predicting spike trains, local field potentials, BOLD responses, and EEG/MEG. In the case of EEG/MEG, one explicit use of this linear modeling approach has been the fitting of so-called temporal response functions (TRFs). TRFs have been used to study how auditory cortex tracks the amplitude envelope of acoustic stimuli, including continuous speech. However, such linear models typically assume that variations in the amplitude of the stimulus feature (i.e., the envelope) produce variations in the magnitude but not the latency or morphology of the resulting neural response. Here, we show that by amplitude binning the stimulus envelope, and then using it to fit a multivariate TRF, we can better account for these amplitude-dependent changes, and that this leads to a significant improvement in model performance for both amplitude-modulated noise and continuous speech in humans. We also show that this performance can be further improved through the inclusion of an additional envelope representation that emphasizes onsets and positive changes in the stimulus, consistent with the idea that while some neurons track the entire envelope, others respond preferentially to onsets in the stimulus. We contend that these results have practical implications for researchers interested in modeling brain responses to amplitude modulated sounds.

Network excitability is governed by synaptic efficacy, intrinsic excitability, and the circuitry in which these factors are expressed. The complex interplay between these factors determines how circuits function, and at the extreme, their susceptibility to seizure. We have developed a novel optogenetic intensity response procedure that provides a sensitive, quantitative estimate of network excitability. By combining optogenetic stimulation of the hippocampus with chronic multi-site recordings in peri-hippocampal structures of awake behaving mice, we induced abnormal network-wide epileptiform population discharges (PD) that were nearly indistinguishable from spontaneously occurring interictal spikes. By systematically varying light intensity, and therefore the magnitude of the optogenetically-mediated current, we generated intensity-response curves using the probability of PD as the dependent variable. This probability curve was well fit by a Boltzmann function, from which we calculated the intensity that produces a half-maximal probability of discharge (I50). This novel metric, the I50, is correlated with the optogenetic after-discharge threshold (oADT) in the same mice. Manipulations known to increase excitability, such as sub-convulsive doses (20 mg/kg) of the chemoconvulsant pentylenetetrazol (PTZ), produced a leftward shift in the curve compared to baseline. The anti-epileptic drug levetiracetam (40 mk/kg), in combination with PTZ, produced a rightward shift. Optogenetically-induced population discharge threshold (oPDT) baselines were stable over time, suggesting the metric is appropriate for within-subject experimental designs with multiple pharmacological manipulations. The oPDT is a sensitive measure of subconvulsive network excitability, with broad applicability to a number of areas of investigation.Significance StatementNetwork excitability is carefully regulated by homeostatic mechanisms in the brain in order to maintain optimal functional conditions. Abnormal excitability is associated with a number of neurological disorders including epilepsy. Excitability can be measured in single cellsin vitro, but it is difficult to extrapolate from these values to the functional impact on the network as a whole. Epileptiform discharges are network wide events that represent a distinct transition from normal to abnormal functional modes. We developed a new technique that uses light intensity-response curves to precisely determine the threshold for this transition as a surrogate measure of network excitability and seizure susceptibility.

Visual Abstract, Microglia are specialized brain-resident macrophages with important functions in health and disease. To improve our understanding of these cells, the research community needs genetic tools to identify and control them in a manner that distinguishes them from closely related cell types. We have targeted the recently discovered microglia-specific Tmem119 gene to generate knock-in mice expressing EGFP (JAX#031823) or CreERT2 (JAX#031820) for the identification and manipulation of microglia, respectively. Genetic characterization of the locus and qPCR-based analysis demonstrate correct positioning of the transgenes and intact expression of endogenous Tmem119 in the knock-in mouse models. Immunofluorescence analysis further shows that parenchymal microglia, but not other brain macrophages, are completely and faithfully labeled in the EGFP-line at different time points of development. Flow cytometry indicates highly selective expression of EGFP in CD11b+CD45lo microglia. Similarly, immunofluorescence and flow cytometry analyses using a Cre-dependent reporter mouse line demonstrate activity of CreERT2 primarily in microglia upon tamoxifen administration with the caveat of activity in leptomeningeal cells. Finally, flow cytometric analyses reveal absence of EGFP expression and minimal activity of CreERT2 in blood monocytes of the Tmem119-EGFP and Tmem119-CreERT2 lines, respectively. These new transgenic lines extend the microglia toolbox by providing the currently most specific genetic labeling and control over these cells in the myeloid compartment of mice.

Designer receptors exclusively activated by designer drugs (DREADD)-based chemogenetic tools are extensively used to manipulate neuronal activity in a cell type-specific manner. Whole-cell patch-clamp recordings indicate membrane depolarization, coupled with increased neuronal firing rate, following administration of the DREADD ligand, clozapine-N-oxide (CNO) to activate the Gq-coupled DREADD, hM3Dq. Although hM3Dq has been used to enhance neuronal firing in order to manipulate diverse behaviors, often within 30 min to 1 h after CNO administration, the physiological effects on excitatory neurotransmission remain poorly understood. We investigated the influence of CNO-mediated hM3Dq DREADD activation on distinct aspects of hippocampal excitatory neurotransmission at the Schaffer collateral-CA1 synapse in hippocampal slices derived from mice expressing hM3Dq in Ca2+/calmodulin-dependent protein kinase α (CamKIIα)-positive excitatory neurons. Our results indicate a clear dose-dependent effect on field EPSP (fEPSP) slope, with no change noted at the lower dose of CNO (1 µM) and a significant, long-term decline in fEPSP slope observed at higher doses (5-20 µM). Further, we noted a robust θ burst stimulus (TBS) induced long-term potentiation (LTP) in the presence of the lower CNO (1 µM) dose, which was significantly attenuated at the higher CNO (20 µM) dose. Whole-cell patch-clamp recording revealed both complex dose-dependent regulation of excitability, and spontaneous and evoked activity of CA1 pyramidal neurons in response to hM3Dq activation across CNO concentrations. Our data indicate that CNO-mediated activation of the hM3Dq DREADD results in dose-dependent regulation of excitatory hippocampal neurotransmission and highlight the importance of careful interpretation of behavioral experiments involving chemogenetic manipulation. ispartof: ENEURO vol:6 issue:6 ispartof: location:United States status: published

Visual Abstract, Transparent and flexible materials are attractive for a wide range of emerging bioelectronic applications. These include neural interfacing devices for both recording and stimulation, where low electrochemical electrode impedance is valuable. Here the conducting polymer poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is used to fabricate electrodes that are small enough to allow unencumbered optical access for imaging a large cell population with two-photon (2P) microscopy, yet provide low impedance for simultaneous high quality recordings of neural activity in vivo. To demonstrate this, pathophysiological activity was induced in the mouse cortex using 4-aminopyridine (4AP), and the resulting electrical activity was detected with the PEDOT:PSS-based probe while imaging calcium activity directly below the probe area. The induced calcium activity of the neuronal network as measured by the fluorescence change in the cells correlated well with the electrophysiological recordings from the cortical grid of PEDOT:PSS microelectrodes. Our approach provides a valuable vehicle for complementing classical high temporal resolution electrophysiological analysis with optical imaging.

The study of brain circuits depends on a clear understanding of the role played by different neuronal populations. Therefore, the unambiguous identification of different cell types is essential for the correct interpretation of experimental data. Here, we emphasize to the broader neuroscience community the importance of recognizing the persistent presence of Cajal–Retzius cells in the molecular layers of the postnatal hippocampus, and then we suggest a variety of criteria for distinguishing Cajal–Retzius cells from other neurons of the hippocampal molecular layers, such as GABAergic interneurons and semilunar granule cells. The toolbox of criteria that we have investigated (in male and female mice) can be useful both for anatomical and functional experiments, and relies on the quantitative study of neuronal somatic/nuclear morphology, location and developmental profile, expression of specific molecular markers (GAD67, reelin, COUP-TFII, calretinin, and p73), single cell anatomy, and electrophysiological properties. We conclude that Cajal–Retzius cells are small, non-GABAergic neurons that are tightly associated with the hippocampal fissure (HF), and that, within this area of interest, selectively express the proteins p73 and calretinin. We highlight the dangers of using markers such as reelin or COUP-TFII to identify Cajal–Retzius cells or GABAergic interneurons because of their poor specificity. Lastly, we examine neurons of the postnatal hippocampal molecular layers and show cell type-specific differences in their dendritic/axonal morphologies and density distributions, as well as in their membrane properties and spontaneous synaptic inputs. These parameters can be used to distinguish biocytin-filled and/or electrophysiologically recorded neurons and should be considered to avoid interpretational mistakes.

Light-sheet microscopy (LSM) has proven a useful tool in neuroscience to image whole brains with high frame rates at cellular resolution and, in combination with tissue clearing methods, is often employed to reconstruct the cyto-architecture over the intact mouse brain. Inherently to LSM, however, residual opaque objects, always present to some extent even in extremely well optically cleared samples, cause stripe artifacts, which, in the best case, severely affect image homogeneity and, in the worst case, completely obscure features of interest. Here, demonstrating two example applications in intact optically cleared mouse brains, we report how Bessel beams reduce streaking artifacts and produce high-fidelity structural data for the brain-wide morphology of neuronal and vascular networks. We found that a third of the imaged volume of the brain was affected by strong striated image intensity inhomogeneity and, furthermore, a significant amount of information content lost with Gaussian illumination was accessible when interrogated with Bessel beams. In conclusion, Bessel beams produce high-fidelity structural data of improved image homogeneity and might significantly relax demands placed on the automated tools to count, trace, or segment fluorescent features of interest.

Despite the profound influence on coding capacity of sensory neurons, the measurements of noise correlations have been inconsistent. This is, possibly, because nonstationarity, i.e., drifting baselines, engendered the spurious long-term correlations even if no actual short-term correlation existed. Although attempts to separate them have been made previously, they weread hocfor specific cases or computationally too demanding. Here we proposed an information-geometric method to unbiasedly estimate pure short-term noise correlations irrespective of the background brain activities without demanding computational resources. First, the benchmark simulations demonstrated that the proposed estimator is more accurate and computationally efficient than the conventional correlograms and the residual correlations with Kalman filters or moving averages of length three or more, while the best moving average of length two coincided with the propose method regarding correlation estimates. Next, we analyzed the cat V1 neural responses to demonstrate that the statistical test accompanying the proposed method combined with the existing nonstationarity test enabled us to dissociate short-term and long-term noise correlations. When we excluded the spurious noise correlations of purely long-term nature, only a small fraction of neuron pairs showed significant short-term correlations, possibly reconciling the previous inconsistent observations on existence of significant noise correlations. The decoding accuracy was slightly improved by the short-term correlations. Although the long-term correlations deteriorated the generalizability, the generalizability was recovered by the decoder with trend removal, suggesting that brains could overcome nonstationarity. Thus, the proposed method enables us to elucidate the impacts of short-term and long-term noise correlations in a dissociated manner.

The neurodegenerative Huntington’s disease (HD) is caused by a polyglutamine (polyQ) amplification in the huntingtin protein (HTT). Currently there is no effective therapy available for HD; however, several efforts are directed to develop and optimize HTT-lowering methods to improve HD phenotypes. To validate these approaches, there is an immediate need for reliable, sensitive, and easily accessible methods to quantify HTT expression. Using the AlphaLISA platform, we developed two novel sensitive and robust assays for quantification of HTT in biological samples using commercially available antibodies. The first, a polyQ-independent assay, measures the total pool of HTT, while the second, a polyQ-dependent assay, preferentially detects the mutant form of HTT. Using purified HTT protein standards and brain homogenates from an HD mouse model, we determine a lower limit of quantification of 1 and 3 pmand optimal reproducibility with CV values lower than 7% for intra- and 20% for interassay. In addition, we used the assays to quantify HTT in neural stem cells generated from patient-derived induced pluripotent stem cellsin vitroand in human brain tissue lysates. Finally, we could detect changes in HTT levels in a mouse model where mutant HTT was conditionally deleted in neural tissue, verifying the potential to monitor the outcome of HTT-lowering strategies. This analytical platform is ideal for high-throughput screens and thus has an added value for the HD community as a tool to optimize novel therapeutic approaches aimed at modulating HTT protein levels.

Advances in calcium imaging have made it possible to record from an increasingly larger number of neurons simultaneously. Neuroscientists can now routinely image hundreds to thousands of individual neurons. An emerging technical challenge that parallels the advancement in imaging a large number of individual neurons is the processing of correspondingly large datasets. One important step is the identification of individual neurons. Traditional methods rely mainly on manual or semimanual inspection, which cannot be scaled for processing large datasets. To address this challenge, we focused on developing an automated segmentation method, which we refer to as automated cell segmentation by adaptive thresholding (ACSAT). ACSAT works with a time-collapsed image and includes an iterative procedure that automatically calculates global and local threshold values during successive iterations based on the distribution of image pixel intensities. Thus, the algorithm is capable of handling variations in morphological details and in fluorescence intensities in different calcium imaging datasets. In this paper, we demonstrate the utility of ACSAT by testing it on 500 simulated datasets, two wide-field hippocampus datasets, a wide-field striatum dataset, a wide-field cell culture dataset, and a two-photon hippocampus dataset. For the simulated datasets with truth, ACSAT achieved >80% recall and precision when the signal-to-noise ratio was no less than ∼24 dB.

Microglia, the resident immune cells in the CNS, play multiple roles during development. In the embryonic cerebral wall, microglia modulate the functions of neural stem/progenitor cells through their distribution in regions undergoing cell proliferation and/or differentiation. Previous studies using CX3CR1-GFP transgenic mice demonstrated that microglia extensively survey these regions. To simultaneously visualize microglia and neural-lineage cells that interact with each other, we applied thein uteroelectroporation (IUE) technique, which has been widely used for gene-transfer in neurodevelopmental studies, to CX3CR1-GFP mice (males and females). However, we unexpectedly faced a technical problem: although microglia are normally distributed homogeneously throughout the mid-embryonic cortical wall with only limited luminal entry, the intraventricular presence of exogenously derived plasmid DNAs induced microglia to accumulate along the apical surface of the cortex and aggregate in the choroid plexus. This effect was independent of capillary needle puncture of the brain wall or application of electrical pulses. The microglial response occurred at plasmid DNA concentrations lower than those routinely used for IUE, and was mediated by activation of Toll-like receptor 9 (TLR9), an innate immune sensor that recognizes unmethylated cytosine-phosphate guanosine motifs abundant in microbial DNA. Administration of plasmid DNA together with oligonucleotide 2088, the antagonist of TLR9, partially restored the dispersed intramural localization of microglia and significantly decreased luminal accumulation of these cells. Thus, via TLR9, intraventricular plasmid DNA administration causes aberrant distribution of embryonic microglia, suggesting that the behavior of microglia in brain primordia subjected to IUE should be carefully interpreted.

Recent developments in CRISPR-based gene editing have provided new avenues to interrogate gene function. However, application of these tools in the central nervous system has been delayed due to difficulties in transgene expression in post-mitotic neurons. Here, we present a highly efficient, neuron-optimized dual lentiviral CRISPR-based transcriptional activation (CRISPRa) system to drive gene expression in primary neuronal cultures and the adult brain of rodent model systems. We demonstrate robust, modular, and tunable induction of endogenous target genes as well as multiplexed gene regulation necessary for investigation of complex transcriptional programs. CRISPRa targeting unique promoters in the complex multi-transcript gene Brain-derived neurotrophic factor (Bdnf) revealed both transcript- and genome-level selectivity of this approach, in addition to highlighting downstream transcriptional and physiological consequences of Bdnf regulation. Finally, we illustrate that CRISPRa is highly efficient in vivo, resulting in increased protein levels of a target gene in diverse brain structures. Taken together, these results demonstrate that CRISPRa is an efficient and selective method to study gene expression programs in brain health and disease.

Visual Abstract, Frontal eye field (FEF) in macaque monkeys contributes to visual attention, visual–motor transformations and production of eye movements. Traditionally, neurons in FEF have been classified by the magnitude of increased discharge rates following visual stimulus presentation, during a waiting period, and associated with eye movement production. However, considerable heterogeneity remains within the traditional visual, visuomovement, and movement categories. Cluster analysis is a data-driven method of identifying self-segregating groups within a dataset. Because many cluster analysis techniques exist and outcomes vary with analysis assumptions, consensus clustering aggregates over multiple analyses, identifying robust groups. To describe more comprehensively the neuronal composition of FEF, we applied a consensus clustering technique for unsupervised categorization of patterns of spike rate modulation measured during a memory-guided saccade task. We report 10 functional categories, expanding on the traditional 3 categories. Categories were distinguished by latency, magnitude, and sign of visual response; the presence of sustained activity; and the dynamics, magnitude and sign of saccade-related modulation. Consensus clustering can include other metrics and can be applied to datasets from other brain regions to provide better information guiding microcircuit models of cortical function.

Synapse formation analyses can be performed by imaging and quantifying fluorescent signals of synaptic markers. Traditionally, these analyses are done using simple or multiple thresholding and segmentation approaches or by labor-intensive manual analysis by a human observer. Here, we describeIntellicount, a high-throughput, fully-automated synapse quantification program which applies a novel machine learning (ML)-based image processing algorithm to systematically improve region of interest (ROI) identification over simple thresholding techniques. Through processing large datasets from both human and mouse neurons, we demonstrate that this approach allows image processing to proceed independently of carefully set thresholds, thus reducing the need for human intervention. As a result, this method can efficiently and accurately process large image datasets with minimal interaction by the experimenter, making it less prone to bias and less liable to human error. Furthermore,Intellicountis integrated into an intuitive graphical user interface (GUI) that provides a set of valuable features, including automated and multifunctional figure generation, routine statistical analyses, and the ability to run full datasets through nested folders, greatly expediting the data analysis process.

Visual Abstract, Speech is an ecologically essential signal, whose processing crucially involves the subcortical nuclei of the auditory brainstem, but there are few experimental options for studying these early responses in human listeners under natural conditions. While encoding of continuous natural speech has been successfully probed in the cortex with neurophysiological tools such as electroencephalography (EEG) and magnetoencephalography, the rapidity of subcortical response components combined with unfavorable signal-to-noise ratios signal-to-noise ratio has prevented application of those methods to the brainstem. Instead, experiments have used thousands of repetitions of simple stimuli such as clicks, tone-bursts, or brief spoken syllables, with deviations from those paradigms leading to ambiguity in the neural origins of measured responses. In this study we developed and tested a new way to measure the auditory brainstem response (ABR) to ongoing, naturally uttered speech, using EEG to record from human listeners. We found a high degree of morphological similarity between the speech-derived ABRs and the standard click-evoked ABR, in particular, a preserved Wave V, the most prominent voltage peak in the standard click-evoked ABR. Because this method yields distinct peaks that recapitulate the canonical ABR, at latencies too short to originate from the cortex, the responses measured can be unambiguously determined to be subcortical in origin. The use of naturally uttered speech to measure the ABR allows the design of engaging behavioral tasks, facilitating new investigations of the potential effects of cognitive processes like language and attention on brainstem processing.

We present an algorithm for detecting the location of cells from two-photon calcium imaging data. In our framework, multiple coupled active contours evolve, guided by a model-based cost function, to identify cell boundaries. An active contour seeks to partition a local region into two subregions, a cell interior and ex-terior, in which all pixels have maximally ‘similar’ time courses. This simple, local model allows contours to be evolved predominantly independently. When contours are sufficiently close, their evolution is coupled, in a manner that permits overlap. We illustrate the ability of the proposed method to demix overlapping cells on real data. The proposed framework is flexible, incorporating no prior information regarding a cell’s morphology or stereotypical temporal activity, which enables the detection of cells with diverse properties. We demonstrate algorithm performance on a challenging mouse in vitro dataset, containing synchronously spiking cells, and a manually labelled mouse in vivo dataset, on which ABLE achieves a 67.5% success rate.Significance statementTwo-photon calcium imaging enables the study of brain activity during learning and behaviour at single-cell resolution. To decode neuronal spiking activity from the data, algorithms are first required to detect the location of cells in the video. It is still common for scientists to perform this task manually, as the heterogeneity in cell shape and frequency of cellular overlap impede automatic segmentation algorithms. We developed a versatile algorithm based on a popular image segmentation approach (the Level Set Method) and demonstrated its capability to overcome these challenges. We include no assumptions on cell shape or stereotypical temporal activity. This lends our framework the flexibility to be applied to new datasets with minimal adjustment.

Visual Abstract, Establishing the molecular diversity of cell types is crucial for the study of the nervous system. We compiled a cross-laboratory database of mouse brain cell type-specific transcriptomes from 36 major cell types from across the mammalian brain using rigorously curated published data from pooled cell type microarray and single-cell RNA-sequencing (RNA-seq) studies. We used these data to identify cell type-specific marker genes, discovering a substantial number of novel markers, many of which we validated using computational and experimental approaches. We further demonstrate that summarized expression of marker gene sets (MGSs) in bulk tissue data can be used to estimate the relative cell type abundance across samples. To facilitate use of this expanding resource, we provide a user-friendly web interface at www.neuroexpresso.org.

One of the principal functions of the brain is to control movement and rapidly adapt behavior to a changing external environment. Over the last decades our ability to monitor activity in the brain, manipulate it while also manipulating the environment the animal moves through, has been tackled with increasing sophistication. However, our ability to track the movement of the animal in real time has not kept pace. Here, we use a dynamic vision sensor (DVS) based event-driven neuromorphic camera system to implement real-time, low-latency tracking of a single whisker that mice can move at ∼25 Hz. The customized DVS system described here converts whisker motion into a series of events that can be used to estimate the position of the whisker and to trigger a position-based output interactively within 2 ms. This neuromorphic chip-based closed-loop system provides feedback rapidly and flexibly. With this system, it becomes possible to use the movement of whiskers or in principal, movement of any part of the body to reward, punish, in a rapidly reconfigurable way. These methods can be used to manipulate behavior, and the neural circuits that help animals adapt to changing values of a sequence of motor actions.

Genetically modified mice have become standard tools in neuroscience research. Our understanding of the basal ganglia in particular has been greatly assisted by BAC mutants with selective transgene expression in striatal neurons forming the direct or indirect pathways. However, for more sophisticated behavioral tasks and larger intracranial implants, rat models are preferred. Furthermore, BAC lines can show variable expression patterns depending upon genomic insertion site. We therefore used CRISPR/Cas9 to generate two novel knock-in rat lines specifically encoding Cre recombinase immediately after the dopamine D1 receptor (Drd1a) or adenosine 2a receptor (Adora2a) loci. Here, we validate these lines usingin situhybridization and viral vector mediated transfection to demonstrate selective, functional Cre expression in the striatal direct and indirect pathways, respectively. We used whole-genome sequencing to confirm the lack of off-target effects and established that both rat lines have normal locomotor activity and learning in simple instrumental and Pavlovian tasks. We expect these new D1-Cre and A2a-Cre rat lines will be widely used to study both normal brain functions and neurological and psychiatric pathophysiology.

Visual Abstract, Physical activity is a critical behavioral variable in many research studies and is, therefore, important to quantify. However, existing methods for measuring physical activity have limitations which include high expense, specialized caging or equipment, and high computational overhead. To address these limitations, we present an open-source, cost-effective, device for measuring rodent activity. Our device is battery powered and designed to be placed in vivarium home cages to enable high-throughput, long-term operation with minimal investigator intervention. The primary aim of this study was to assess the feasibility of using passive infrared (PIR) sensors and microcontroller-based dataloggers in a rodent home cages to collect physical activity records. To this end, we developed an open-source PIR based data-logging device called the rodent activity detector (RAD). We publish the design files and code so others can readily build the RAD in their own labs. To demonstrate its utility, we used the RAD to collect physical activity data from 40 individually housed mice for up to 10 weeks. This dataset demonstrates the ability of the RAD to (1) operate in a high-throughput installation, (2) detect high-fat diet (HFD)-induced changes in physical activity, and (3) quantify circadian rhythms in individual animals. We further validated the data output of the RAD with simultaneous video tracking of mice in multiple caging configurations, to determine the features of physical activity that it detects. The RAD is easy to build, economical, and fits in vivarium caging. The scalability of such devices will enable high-throughput studies of physical activity in research studies.

In the parkinsonian state, the motor cortex and basal ganglia (BG) undergo dynamic remodeling of movement representation. One such change is the loss of the normal contralateral lateralized activity pattern. The increase in the number of movement-related neurons responding to ipsilateral or bilateral limb movements may cause motor problems, including impaired balance, reduced bimanual coordination, and abnormal mirror movements. However, it remains unknown how individual types of motor cortical neurons organize this reconstruction. To explore the effect of dopamine depletion on lateralized activity in the parkinsonian state, we used a partial hemiparkinsonian model [6-hydroxydopamine (6-OHDA) lesion] in Long–Evans rats performing unilateral movements in a right–left pedal task, while recording from primary (M1) and secondary motor cortex (M2). The lesion decreased contralateral preferred activity in both M1 and M2. In addition, this change differed among identified intratelencephalic (IT) and pyramidal tract (PT) cortical projection neurons, depending on the cortical area. We detected a decrease in lateralized activity only in PT neurons in M1, whereas in M2, this change was observed in IT neurons, with no change in the PT population. Our results suggest a differential effect of dopamine depletion in the lateralized activity of the motor cortex, and suggest possible compensatory changes in the contralateral hemisphere.

Microglia play a pivotal role in maintaining homeostasis of the CNS. There is growing interest in understanding how microglia influence normal brain function and disease progression. Several microglia-specific Cx3cr1-Cre lines have been developed and have become indispensable tools in many investigations of microglial function. However, some recent studies have reported that these lines may have significant leakage into neurons. Other studies have reported that Cx3cr1 is expressed in non-microglial cells, including neurons and astrocytes, in vitro or in vivo either during brain development or upon neurological insult. All these reports raise serious concerns about the trustworthiness of these Cre-lines and whether the conclusions drawn from previous studies are valid. Here, we found that a floxed fluorescent reporter mouse line which has been frequently used to verify Cre lines displayed spontaneous expression of the GFP reporter, independent of Cre recombinase, thus revealing a potential caveat in assessing cre lines. We further confirmed that two Cx3cr1-Cre mouse lines can drive fluorescent reporter expression largely restrictively in microglia. Finally, we clarified that these two mouse lines maintain microglia-specific expression even following excitatory injury. Together, our findings confirm that two previously created Cx3cr1-Cre lines remain as invaluable tools for studying microglia. Moreover, to ensure the quality of data generated and the soundness of conclusions drawn from such data, it should be compulsory to thoroughly examine reporter lines for spontaneous leakiness when labeling cells to study CNS function and diseases.

Flow cytometry and fluorescence-activated sorting are powerful techniques that hold great promise for studying heterogeneous populations of submicron particles such as synaptosomes, but many technical challenges arise in these experiments. To date, most flow cytometry studies of synaptosomes have relied on particle detection using forward scatter (FSC) measurements and size estimation with polystyrene (PS) bead standards. However, these practices have serious limitations, and special care must be taken to overcome the poor sensitivity of conventional flow cytometers in the analysis of submicron particles. Technical artifacts can confound these experiments, especially the detection of multiple particles as a single event. Here, we compared analysis of P2 crude synaptosomal preparations from murine forebrain on multiple flow cytometers using both FSC-triggered and fluorescence-triggered detection. We implemented multicolor fluorescent dye-based assays to quantify coincident particle detection and aggregation, and we assessed the false colocalization of antigens in immunostaining analyses. Our results demonstrate that fluorescence triggering and proper dilution can control for coincident particle detection, but not particle aggregation. We confirmed previous studies showing that FSC-based size estimation with PS beads underestimates biological particle size, and we identified pervasive aggregation in the FSC range analyzed in most synaptosome flow cytometry studies. We found that analyzing P2 samples in sucrose/EDTA/tris (SET) buffer reduces aggregation compared to PBS, but does not completely eliminate the presence of aggregates, especially in immunostaining experiments. Our study highlights challenges and pitfalls in synaptosome flow cytometry and provides a methodological framework for future studies.

Genome editing techniques have facilitated significant advances in our understanding of fundamental biological processes, and the Cre-Lox system has been instrumental in these achievements. Driving Cre expression specifically in injured neurons has not been previously possible: we sought to address this limitation in mice using a Cre-ERT2 construct driven by a reliable indicator of axotomy, activating transcription factor 3 (ATF3). When crossed with reporter mice, a significant amount of recombination was achieved (without tamoxifen treatment) in peripherally-projecting sensory, sympathetic, and motoneurons after peripheral nerve crush in hemizygotes (65–80% by 16 d) and was absent in uninjured neurons. Importantly, injury-induced recombination did not occur in Schwann cells distal to the injury, and with a knock-out-validated antibody we verified an absence of ATF3 expression. Functional recovery following sciatic nerve crush in ATF3-deficient mice (both hemizygotes and homozygotes) was delayed, indicating previously unreported haploinsufficiency. In a proof-of-principle experiment, we crossed the ATF3-CreERT2 line with a floxed phosphatase and tensin homolog (PTEN) line and show significantly improved axonal regeneration, as well as more complete recovery of neuromuscular function. We also demonstrate the utility of the ATF3-CreERT2 hemizygous line by characterizing recombination after lateral spinal hemisection (C8/T1), which identified specific populations of ascending spinal cord neurons (including putative spinothalamic and spinocerebellar) and descending supraspinal neurons (rubrospinal, vestibulospinal, reticulospinal and hypothalamic). We anticipate these mice will be valuable in distinguishing axotomized from uninjured neurons of several different classes (e.g., via reporter expression), and in probing the function of any number of genes as they relate to neuronal injury and regeneration.

The β-band oscillation in the subthalamic nucleus (STN) is a therapeutic target for Parkinson’s disease. Previous studies demonstrated thatl-DOPA decreases the β-band (13–30 Hz) oscillations with improvement of motor symptoms. However, it has not been elucidated whether patients with Parkinson’s disease are able to control the β-band oscillation voluntarily. Here, we hypothesized that neurofeedback training to control the β-band power in the STN induces plastic changes in the STN of individuals with Parkinson’s disease. We recorded the signals from STN deep-brain stimulation electrodes during operations to replace implantable pulse generators in eight human patients (3 male) with bilateral electrodes. Four patients were induced to decrease the β-band power during the feedback training (down-training condition), whereas the other patients were induced to increase (up-training condition). All patients were blinded to their assigned condition. Adjacent contacts that showed the highest β-band power were selected for the feedback. During the 10 min training, patients were shown a circle whose diameter was controlled by the β-band power of the selected contacts. Powers in the β-band during 5 min resting sessions recorded before and after the feedback were compared. In the down-training condition, the β-band power of the selected contacts decreased significantly after feedback in all four patients (p< 0.05). In contrast, the β-band power significantly increased after feedback in two of four patients in the up-training condition. Overall, the patients could voluntarily control the β-band power in STN in the instructed direction (p< 0.05) through neurofeedback.

Visual Abstract, A multielectrode system that can address widely separated targets at multiple sites across multiple brain regions with independent implant angling is needed to investigate neural function and signaling in systems and circuits of small animals. Here, we present the systemDrive, a novel multisite, multiregion microdrive that is capable of moving microwire electrode bundles into targets along independent and nonparallel drive trajectories. Our design decouples the stereotaxic surgical placement of individual guide cannulas for each trajectory from the placement of a flexible drive structure. This separation enables placement of many microwire multitrodes along widely spaced and independent drive axes with user-set electrode trajectories and depths from a single microdrive body, and achieves stereotaxic precision with each. The system leverages tight tube–cannula tolerances and geometric constraints on flexible drive axes to ensure concentric alignment of electrode bundles within guide cannulas. Additionally, the headmount and microdrive both have an open-center design to allow for the placement of additional sensing modalities. This design is the first, in the context of small rodent chronic research, to provide the capability to finely position microwires through multiple widely distributed cell groups, each with stereotaxic precision, along arbitrary and nonparallel trajectories that are not restricted to emanate from a single source. We demonstrate the use of the systemDrive in male Long–Evans rats to observe simultaneous single-unit and multiunit activity from multiple widely separated sleep–wake regulatory brainstem cell groups, along with cortical and hippocampal activity, during free behavior over multiple many-day continuous recording periods.

Visual Abstract, Microglia are dynamic immune cells of the central nervous system, and their morphology is commonly used as a readout of cellular function. However, current morphological analysis techniques rely on either tracing of cells or two-dimensional projection analysis, which are time-consuming, subject to bias, and may ignore important three-dimensional (3D) information. Therefore, we have created 3DMorph, a MATLAB-based script that analyzes microglial morphology from 3D data. The program initially requires input of threshold levels, cell size expectations, and preferred methods of skeletonization. This makes 3DMorph easily scalable and adaptable to different imaging parameters or cell types. After these settings are defined, the program is completely automatic and can batch process files without user input. Output data includes cell volume, territorial volume, branch length, number of endpoints and branch points, and average distance between cells. We show that 3DMorph is accurate compared to manual tracing, with significantly decreased user input time. Importantly, 3DMorph is capable of processing in vivo microglial morphology, as well as other 3D branching cell types, from mouse cranial windows or acute hippocampal slices. Therefore, we present a novel, user-friendly, scalable, and semiautomatic method of analyzing cell morphology in 3 dimensions. This method should improve the accuracy of cell measurements, remove user bias between conditions, increase reproducibility between experimenters and labs, and reduce user input time. We provide this open source code on GitHub so that it is free and accessible to all investigators.

It is widely assumed that distributed neuronal networks are fundamental to the functioning of the brain. Consistent spike timing between neurons is thought to be one of the key principles for the formation of these networks. This can involve synchronous spiking or spiking with time delays, forming spike sequences when the order of spiking is consistent. Finding networks defined by their sequence of time-shifted spikes, denoted here as spike timing networks, is a tremendous challenge. As neurons can participate in multiple spike sequences at multiple between-spike time delays, the possible complexity of networks is prohibitively large. We present a novel approach that is capable of (1) extracting spike timing networks regardless of their sequence complexity, and (2) that describes their spiking sequences with high temporal precision. We achieve this by decomposing frequency-transformed neuronal spiking into separate networks, characterizing each network’s spike sequence by a time delay per neuron, forming a spike sequence timeline. These networks provide a detailed template for an investigation of the experimental relevance of their spike sequences. Using simulated spike timing networks, we show network extraction is robust to spiking noise, spike timing jitter, and partial occurrences of the involved spike sequences. Using rat multineuron recordings, we demonstrate the approach is capable of revealing real spike timing networks with sub-millisecond temporal precision. By uncovering spike timing networks, the prevalence, structure, and function of complex spike sequences can be investigated in greater detail, allowing us to gain a better understanding of their role in neuronal functioning.

Visual Abstract, Human neurons expressing mutations associated with neurodegenerative disease are becoming more widely available. Hence, developing assays capable of accurately detecting changes that occur early in the disease process and identifying therapeutics able to slow these changes should become ever more important. Using automated live-cell imaging, we studied human motor neurons in the process of dying following neurotrophic factor withdrawal. We tracked different neuronal features, including cell body size, neurite length, and number of nodes. In particular, measuring the number of nodes in individual neurons proved to be an accurate predictor of relative health. Importantly, intermediate phenotypes were defined and could be used to distinguish between agents that could fully restore neurons and neurites and those only capable of maintaining neuronal cell bodies. Application of live-cell imaging to disease modeling has the potential to uncover new classes of therapeutic molecules that intervene early in disease progression.

Visual Abstract, Filters are widely used for the modulation, typically attenuation, of amplitudes of different frequencies within neurophysiological signals. Filters, however, also induce changes in the phases of different frequencies whose amplitude is unmodulated. These phase shifts cause time lags in the filtered signals, leading to a disruption of the timing information between different frequencies within the same signal and between different signals. The emerging time lags can be either constant in the case of linear phase (LP) filters or vary as a function of the frequency in the more common case of non-LP (NLP) filters. Since filters are used ubiquitously online in the early stages of data acquisition, the vast majority of neurophysiological signals thus suffer from distortion of the timing information even prior to their sampling. This distortion is often exacerbated by further multiple offline filtering stages of the sampled signal. The distortion of timing information may cause misinterpretation of the results and lead to erroneous conclusions. Here we present a variety of typical examples of filter-induced phase distortions and discuss the evaluation and restoration of the timing information underlying the original signal.

The dynamic clamp should be a standard part of every cellular electrophysiologist’s toolbox. That it is not, even 25 years after its introduction, comes down to three issues: money, the disruption that adding dynamic clamp to an existing electrophysiology rig entails, and the technical prowess required of experimenters. These have been valid and limiting issues in the past, but no longer. Technological advances associated with the so-called maker movement render them moot. We demonstrate this by implementing a fast (∼100 kHz) dynamic clamp system using an inexpensive microcontroller (Teensy 3.6). The overall cost of the system is less than USD$100, and assembling it requires no prior electronics experience. Modifying it—for example, to add Hodgkin–Huxley-style conductances—requires no prior programming experience. The system works together with existing electrophysiology data acquisition systems (for Macintosh, Windows, and Linux); it does not attempt to supplant them. Moreover, the process of assembling, modifying, and using the system constitutes a useful pedagogical exercise for students and researchers with no background but an interest in electronics and programming. We demonstrate the system’s utility by implementing conductances as fast as a transient sodium conductance and as complex as the Ornstein–Uhlenbeck conductances of the “point conductance” model of synaptic background activity.

Visual Abstract, Methods for resolving the three-dimensional (3D) microstructure of the brain typically start by thinly slicing and staining the brain, followed by imaging numerous individual sections with visible light photons or electrons. In contrast, X-rays can be used to image thick samples, providing a rapid approach for producing large 3D brain maps without sectioning. Here we demonstrate the use of synchrotron X-ray microtomography (µCT) for producing mesoscale (∼1 µm 3 resolution) brain maps from millimeter-scale volumes of mouse brain. We introduce a pipeline for µCT-based brain mapping that develops and integrates methods for sample preparation, imaging, and automated segmentation of cells, blood vessels, and myelinated axons, in addition to statistical analyses of these brain structures. Our results demonstrate that X-ray tomography achieves rapid quantification of large brain volumes, complementing other brain mapping and connectomics efforts.

Lesion-symptom mapping is often employed to define brain structures that are crucial for human behavior. Even though poststroke deficits result from gray matter damage as well as secondary white matter loss, the impact of structural disconnection is overlooked by conventional lesion-symptom mapping because it does not measure loss of connectivity beyond the stroke lesion. This study describes how traditional lesion mapping can be combined with structural connectome lesion symptom mapping (CLSM) and connectome dynamics lesion symptom mapping (CDLSM) to relate residual white matter networks to behavior. Using data from a large cohort of stroke survivors with aphasia, we observed improved prediction of aphasia severity when traditional lesion symptom mapping was combined with CLSM and CDLSM. Moreover, only CLSM and CDLSM disclosed the importance of temporal-parietal junction connections in aphasia severity. In summary, connectome measures can uniquely reveal brain networks that are necessary for function, improving the traditional lesion symptom mapping approach.