Journal of Neuroscience Methods

Currently, implantation of electroencephalogram (EEG) electrodes in laboratory animals is time-consuming and requires specialized equipment. We present a novel method for EEG recordings in mice that utilizes thin needle electrodes. These electrodes are inserted into the skull at predetermined locations by gently pressing them against the bone surface. To ensure stable fixation of the implant, hook-shaped needles are positioned along the lateral aspects of the skull. The electrodes are connected to a multipin connector and secured to the skull using dental composite, after which the animal is allowed to recover from anesthesia. Importantly, procedures such as skull drilling and screw placement are not required, allowing the entire surgery to be completed in less than 15 minutes. Consequently, this EEG implantation approach is rapid and minimally invasive. Results of our studies indicate that EEG recordings obtained with needle electrodes are not inferior to those obtained with screw electrodes. Overall, the method is designed to enhance the accuracy and efficiency of EEG recording studies while improving animal welfare. O_LISimplifies the placement of EEG electrodes. C_LIO_LIReduces the time required for electrode implantation. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/715731v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@e5608org.highwire.dtl.DTLVardef@1325ea4org.highwire.dtl.DTLVardef@1e37202org.highwire.dtl.DTLVardef@1521bb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

Although commonly known as rapid and easy to use methodology, Golgi staining requires a range of staining solutions, impregnation periods, concentrations and slicing variables. The use of this methodology can help researchers identify and label individual neuronal components within the extended circuitry. The original Golgi stain technique, developed by Camillo Golgi in 1873, is a silver staining method that enabled scientists to visualize individual neurons in their entirety within nervous tissue for the first time. publications featuring the Golgi staining technique utilise cryostat or microtome slicing, with the combination of a readily purchased kit which comes with a cost and limited morphological detail. Here, we describe an optimised Golgi staining methodology that specifically targets the major drawbacks of traditional protocols; prolonged and inconsistent impregnation, slice fragility during sectioning, and variable visualization of fine dendritic structures. Through modest adjustments to impregnation duration and temperature, fixation, and vibratome sectioning conditions, this low-cost and simple protocol improves staining reliability, facilitates robust slicing without specialized embedding, and supports detailed analysis of neuronal morphology throughout the central nervous system. We validate our optimised protocol using tissue from on-going animal studies of pain and treatment. Representative images illustrate typical staining patterns, characterised by sparse background and high signal-to-noise ratio, facilitating unbiased neuronal tracing and analysis.

Investigating the neurophysiology of nociception is aided by electrophysiological recording from dorsal root ganglion (DRG) neurons. Because DRG neurons are heterogeneous with overlapping electrophysiological properties, methods to distinguish neuron subtypes are valuable for properly interpreting the measurements and drawing conclusions. Automated patch clamp recording offers an approach for conducting these experiments at higher throughput than conventional recording methods, but identification of neuron subtypes is challenging. We developed a method for recording from acutely isolated mouse DRG neurons using automated patch clamp recording coupled to optogenetic stimulation that was capable of discerning NaV1.8 and TRPV1 expressing neuron subpopulations. This approach can facilitate physiological and pharmacological studies of DRG neurons with potential value in developing and testing targeted analgesic agents.

Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear due to the vast number of available techniques, their combinatorial use in pipelines, and adjustable parameters. Consequently, most studies adopt ad hoc preprocessing strategies based on dataset characteristics, study goals, and researcher expertise, with little justification for their choices. Such variability can influence downstream results, potentially determining whether effects are detected, and introduces risks of questionable analytical practices. Here, we present a method to objectively evaluate and compare preprocessing pipelines. Our approach uses realistically simulated signals injected into real EEG data as "ground truth", enabling the assessment of a pipelines ability to remove noise without distorting neuronal signals. This evaluation is independent of the studys main analyses, ensuring that pipeline selection does not bias results. By applying this procedure, researchers can select preprocessing strategies that maximize signal-to-noise ratio while maintaining the integrity of the neural signal, improving both reproducibility and interpretability of EEG studies. Although the data presented here focuses on processing and analysis most relevant for ERP research, the method can be flexibly expanded to other types of analyses or signals.

Longitudinal brain imaging is essential for understanding neural mechanisms. Here, we present a saline-free, chronic preparation for repeated neural recording in adult Drosophila over multiple days. We describe steps for mounting flies, performing manual surgery on the head cuticle without external saline, and resealing the opening to create a transparent optical window. We demonstrate the utility of this approach by tracking single-neuron spiking and neuronal calcium dynamics over 7-10 days. This protocol is potentially applicable to other insect species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=173 SRC="FIGDIR/small/706199v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@abeb34org.highwire.dtl.DTLVardef@deaf93org.highwire.dtl.DTLVardef@1d8fc24org.highwire.dtl.DTLVardef@91a696_HPS_FORMAT_FIGEXP M_FIG C_FIG

Interest in broadband aperiodic brain activity (1/f phenomenon) has increased exponentially over recent years, partly fueled by the development of tools to parameterize it (i.e., estimate its offset/intercept and exponent/slope) using the M/EEG power spectrum. Broadband aperiodic activity needs to be separated from narrowband periodic activity before its parameters are computed. A popular method, the fooof toolbox (Donoghue et al., 2020), is based on the data-driven detection of narrowband-periodic peaks, whose maximum number is set by the user. While increasing analytic flexibility, variability in the number of detected peaks may increase sensitivity to noise and reduce the reliability of aperiodic parameter estimates and the power of analytic pipelines. Here, we present an investigation of the effects of analytic choices (e.g., number of peaks, spectral estimation method) on metrics indicating the adequacy of spectral parametrization. These include the internal consistency (odd-even reliability) of aperiodic estimates, the number of outliers generated, and their ability to detect effects. Across two different data sets (resting state and task-based) we found a decrease in the reliability of intercept and slope estimates as more peaks were allowed to be extracted. To ameliorate this problem, we propose a theory-driven modification of fooof labelled censored regression, whereby a theory-driven range of frequencies expected to contain periodic activity is removed from all spectra, and the remaining power values are regressed on the remaining frequencies to obtain parameter estimates. This method shows more reliable and robust estimates compared to fooof, while avoiding overfitting.

BackgroundSomatosensory evoked potentials (SEPs) measured with electroencephalography (EEG) are widely used to study cortical responses to touch but most research has limited the focus on few body parts, typically a finger, and applied time-consuming testing protocols. Multivariate pattern analysis (MVPA) provides a complementary approach that may increase sensitivity and allow faster stimulation, yet its relationship to classical SEP analysis in somatosensory research remains largely unexplored. MethodsFifteen participants received vibrotactile stimulation on the finger, hand, cheek, and foot while EEG was recorded. We compared a traditional "slow" stimulation protocol (800-1200 ms inter-stimulus intervals) with a "fast" protocol (300-500 ms). We compared temporal and topographical aspects between SEP and MVPA. ResultsBoth stimulation protocols produced highly similar SEP components (P100, N140, P200), topographies, and classification results, while the fast protocol reduced testing time by about 60%. SEPs revealed systematic body-part differences, with earlier components for cheek stimulation and delayed responses for the foot. Multivariate classification distinguished body parts with accuracies up to [~]50-55% (chance: 25%), peaking around 100 ms after stimulus onset. Classifier weight maps closely matched SEP topographies over centroparietal electrodes, indicating that classification relied on physiologically meaningful somatosensory signals. Classification accuracy peaked around 100 ms after stimulus onset, coinciding with the SEP P100 component, but declined gradually thereafter, suggesting that early somatosensory responses contain particularly informative multivariate patterns that generalize over time. ConclusionsFaster stimulation protocols substantially increase efficiency without compromising interpretability. Combining classical SEP analysis with multivariate classification provides complementary insights and offers a powerful framework for mapping somatosensory representations across the body.

Octopus vulgaris and other cephalopods are of increasing interest as neurobiological model organisms. This protocol describes a method to record calcium activity from individual cells in acute brain slices from Octopus vulgaris hatchlings during exogenous application of neurotransmitters. Using this protocol, we characterized single-cell responses to specific neurotransmitters in the optic lobes, which process visual information. The approach is readily adaptable to other cephalopods and small invertebrate species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/711860v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1564eaeorg.highwire.dtl.DTLVardef@147b682org.highwire.dtl.DTLVardef@11f3b85org.highwire.dtl.DTLVardef@17c9d70_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectiveA simplified headset to measure electroencephalography (EEG) from sensorimotor areas is necessary to monitor motor-related neural responses accurately in real-world environments. The aim of the present study was to design and validate a novel, easy-to-use, and reliable EEG electrode holder that enables positioning of the EEG electrodes directly over the sensorimotor cortex, while enabling flexibility in relation to varying head size. ApproachThe spatial distribution of motor-related EEG activity was estimated using a dataset of high-density EEG (HD-EEG) from 82 participants. International databases of head shape were analyzed to determine dimensional requirements for a headphone-shaped holder. The proposed headset was validated by comparing its recordings with those obtained with a commercially available HD-EEG (Experiment 1) and by computing the actual position and recorded EEG motor-related activity obtained using the proposed holder (Experiment 2). Main resultsThe estimated distance between the measurement electrode and the top of the head, used as a design requirement for the proposed headset, ranged from 56.2 to 80.0 mm. Experiment 1 showed that the center frequencies of alpha-band recorded by the proposed and by the HD-EEG headsets were highly correlated (r=0.97). Experiment 2 showed that, by using the proposed headset, the actual placement of electrodes was within 8 mm from the ideal positions. Moreover, the experiment showed consistent results in terms of task-, location-, and frequency-specific modulation of sensorimotor activities in the alpha-band. For example, significant contralateral motor-related event-related desynchronization in the alpha-band, and significant alpha-band power increase during the eyes closed condition, namely event-related synchronization. SignificanceThe proposed electrode holder is easy to use and adjustable to compensate for varying head size and it may enable reliable measurement of motor-related EEG. It could support practical application of motor-related EEG acquisition in real-world contexts in several applications including sports, rehabilitation, and artistic performance.

1ObjectiveThe complexity of neural data changes as the brain processes information during events. Universal lossless compression algorithms, which are broadly applicable and grounded in information theory, identify and exploit redundancies in data in order to compress it to essentially-optimal sizes regardless of underlying statistics. These algorithms may be used to conveniently and efficiently estimate a given signals Shannon entropy rate, a biologically relevant measure of the complexity of a signal. It is therefore natural to explore their effectiveness in the analysis of spiking neural data. ApproachThis work focuses on using compression to analyze recordings (96-channel Utah arrays) taken from motor cortex of animals performing reaching tasks for three days before and three days after administering electrolytic lesions (Subject U: 4 lesions, H: 3). In particular, we use the inverse compression ratio (ICR), which compares the sizes of compressed and uncompressed data to estimate the amount of statistically unique information. We calculate ICR with temporally-independent lossless compression (gzip) and temporally-dependent lossy compression (H.264, MPEG-2). Compression-based ICR was compared to single-neuron measures used to understand spiking data, such as average firing rates and Fano factor. Compression is also compared to common dimensionality reduction techniques, principal component analysis (PCA) and factor analysis (FA). Main ResultsStatistical tests on aggregate data comparing each metric before and after lesioning reveal that ICR is able to significantly (Mann-Whitney U test, p < 0.01) detect lesions with higher accuracy than single-neuron metrics, but not dimensionality reduction (ICR methods: 85.7%, single-neuron methods: 78.6%, dimensionality reduction: 100%). Additionally, statistical results on the same data show that ICR metrics remain more stable than single-neuron methods after lesion. The bitrate parameter of lossy compression algorithms is swept to better understand the effect of information rates and "optimal" compression on lesion detection performance. Our conclusions are confirmed by the same analyses performed on several different simulated neural datasets. SignificanceThese results suggest that compression algorithms may be a useful tool to detect and better understand perturbations to the underlying structure of neural data. Information-theoretic analyses may complement techniques like dimensionality reduction and firing rate tuning as a convenient and useful tool to characterize neural data.

Studies employing optogenetic approaches in rodent models have highlighted the important contribution of ventral tegmental area (VTA) dopamine (DA) neurons to reward, learning, and motivation. Selective manipulation of VTA DA neurons is generally achieved in these studies using transgenic mouse or rat lines that express Cre recombinase under the control of a promoter active in DA neurons, combined with intra-VTA infusion of adeno-associated virus (AAV) vectors harboring Cre recombinase-dependent expression cassettes. Reliance on transgenic Cre driver lines is expensive and decreases study efficiency, and available driver lines have unique limitations. Here, we report the development of an AAV-only approach that permits genetic access to VTA DA neurons and can support optogenetic self-stimulation in mice. We used a 2.5 kb fragment of the mouse tyrosine hydroxylase promoter (mTH) to drive Cre expression in VTA DA neurons. Intra-VTA co-infusion of AAV8-mTH-Cre with an AAV vector harboring a Cre-dependent yellow fluorescent protein expression cassette yielded high efficiency (82%) and high fidelity (73%) targeting of tyrosine hydroxylase-positive VTA neurons in C57BL/6J mice. Co-infusion of AAV8-mTH-Cre with a vector harboring a Cre-dependent channelrhodopsin (ChR2) expression cassette permitted optical regulation of VTA neurons with electrophysiological features consistent with VTA DA neurons. Moreover, C57BL/6J mice expressing ChR2 in VTA DA neurons rapidly acquired optical self-stimulation behavior. Thus, this AAV-only approach should facilitate investigation of VTA DA neuron contributions to reward-related behaviors and permit comparative assessments in reward circuit function in inbred and mutant mouse strains.

Bad channels in electroencephalography (EEG) recordings can substantially degrade downstream analyses, particularly in high-density datasets where localized hardware or motionrelated artifacts may affect groups of electrodes in a structured manner. We introduce a MATLAB Module for badchannel quality control that emphasizes interpretability, relational structure, and human-in-the-loop validation rather than fully automated rejection. The method operates on multichannel EEG data and combines complementary channel-level features, including time-dependent neighbor dissimilarity and amplitude- and variance-based statistics to score and pre-label channels as good, suspicious, or bad. To expose shared artifactual structure, channels are additionally grouped using a similarity measure derived from the co-occurrence of robustly detected high-amplitude transients, allowing channels to be reviewed together. Importantly, clustering is used as an exploratory tool to reveal co-artifactual patterns rather than to impose final class labels, which are confirmed through an interactive review interface supported by summary visualizations and grouped channel displays. This Module is released as a publicly available codebase with documentation, example workflows, and a supporting dataset. This Module is designed as a quality-control step preceding ICA and does not replace end-toend data cleaning pipelines, which typically involve additional steps such as interpolation of known bad channels.

Online processing and visualization of large-scale neural data is critical for neuroscientific discovery and advancements in neural engineering. However, with the development of technologies like Neuropixels (NP) probes, which enable simultaneous streaming from hundreds of recording electrodes, handling such data in real-time has become an ongoing challenge. Moreover, keeping pace with recording hardware has required most existing software, such as SpikeGLX for NP probes, to prioritize acquisition stability, leaving data processing and visualization to primarily be performed offline. Thus, we created OP-GLX, a MATLAB-based toolbox designed to operate in tandem with SpikeGLX to enhance the fetching, processing, and visualization of incoming neural data. The OP-GLX toolbox features several processing capabilities, including spike detection, computing time-binned firing rates, plotting spike waveforms, and conducting principal component analysis (PCA). The processed neural data is displayed on a native graphical user interface (GUI) for intuitive and customizable interaction with the experiment. The performance testing of OP-GLX showed that it supports real-time operation, confirmed by the absence of SpikeGLX stream buffer fetch errors across multiple acquisition settings. By complementing current neural data acquisition methods and providing stable online functionality, we envision that OP-GLX will enable researchers to visualize and interpret their data more effectively during ongoing neuroscience experiments.

Chronic low back pain (CLBP) is persistent and refractory, affecting 20-30% of population worldwide. Neurofeedback has been explored as a potential non-pharmacological intervention for chronic pain, although evidence in CLBP remains limited. This study evaluated PainWaive, a consumer-grade digitally-delivered neurofeedback intervention targeting multiple pain-related frequency bands recorded over the sensorimotor cortex in individuals with CLBP. In a multiple-baseline experimental design, four participants completed daily assessments of pain severity and pain interference during randomly-assigned baseline phases of 7, 10, 14, and 20 days, followed by 20 sessions of the PainWaive intervention over four weeks. Daily pain assessments continued during the post-intervention and follow-up phases. Participants rated PainWaive's usability and acceptability at post-intervention. Anxiety, depression, wellbeing, and sleep disturbance were assessed at three timepoints. Aggregated Tau-U analyses indicated a large effect (-0.67) on pain severity from baseline to intervention and very large from baseline to post-intervention (-0.92) and follow-up (-0.92) phases. Large effects (-0.63, -0.62, and -0.70) were also observed for pain interference. Individual-level analyses showed significant reductions across all participants, with visual inspection confirming progressive decreases over time. The intervention was rated usable and acceptable by all participants, while psychological outcomes were mixed and varied across participants. The findings provide promising evidence that the PainWaive neurofeedback intervention may reduce pain severity and pain interference in some individuals with CLBP. By prioritising accessibility, usability, and self-administration, PainWaive supports a foundation for more patient-centred, technology-enabled approaches to chronic pain management. Further evaluation of this approach in randomised trials is required to establish efficacy.

Affordable home-based electroencephalography (EEG) headsets could widen access to EEG assessment, but require rigorous validation before research or clinical use. Here, we evaluated a custom-developed 2-channel sensorimotor headset (PainWaive) intended for remote neuro-feedback and longitudinal monitoring in chronic pain. Eighty participants (47 female; mean age 24.0 years, SD 7.9) completed two resting-state sessions with PainWaive and a research-grade 64-channel EEG system (LiveAmp), under eyes-open (EO) and eyes-closed (EC) conditions. Alpha, beta and theta power and peak alpha frequency (PAF) were derived from homologous sensorimotor channels (C1/C2). Relative reliability was quantified with intraclass correlation coefficients (ICCs), absolute reliability with SEM%, and cross-device consistency with between-device ICCs and Pearson correlations of overall spectral shape. ICCs/correlations were interpreted using pre-specified thresholds: fair 0.20-0.39, moderate 0.40-0.59, good 0.60-0.79, excellent [&ge;]0.80. PainWaive and LiveAmp showed comparable absolute reliability across metrics (similar SEM%). Under EC, PainWaive reliability was excellent for alpha (0.81), theta (0.85) and PAF (0.94), and good for beta (0.72). Under EO, reliability was excellent for alpha (0.82), good for beta and PAF (0.61-0.72), and moderate for theta (0.59). Spectral-shape correlations between devices were excellent (r>0.90). Cross-device ICCs were good under EC for alpha/theta/PAF (ICC=0.66-0.77) though fair for beta (0.35). Under EO, ICCs were good for alpha (0.62), moderate for PAF (0.53), and fair for beta/theta (0.26-0.32). To assess performance under real-world use, we additionally analysed 2 clinical samples of individuals (total n = 8) with chronic pain who each completed 20 home-based neurofeedback sessions using PainWaive (160 sessions total). Within-session stability was good-to-excellent across metrics (ICCs>0.72). Overall, our findings suggest PainWaive is a reliable tool for the assessment of EEG metrics, supporting its use in research and clinical applications.

The mosquito Aedes aegypti is an important vector of viral pathogens and serves as a model for other vector species. Pathogens are transmitted when a mosquito bites a host animal, but the neural circuits that control seeking and biting behavior are not known. Here, we detail methods and protocols for the manipulation of neural activity in the mosquito using optogenetics, a key technique to determine the causal relationship between neural circuits and behavior. These methods include rearing mosquitoes for optogenetics and three assays that are designed to measure different steps in the sequence of arousal, attraction, proboscis probing, and engorgement on the blood of host animals. These behaviors occur at different spatial scales and in response to different sensory stimuli. Each behavioral assay is outfitted with red (625 nm) LEDs for optogenetic activation. To detect arousal in response to olfactory stimuli, flight and walking are measured in all three assays. To assay attraction or landing, mosquitoes are presented with a heated blood meal in a large arena. Proboscis probing and engorgement are assayed with video resolution that enables measurement of appendages and abdomen size. The protocol describes machine vision models to enable high-resolution temporal quantification of behavior as well as endpoint measurements of feeding. These methods can be used to test the function of any population of neurons in mosquito biting behavior and can be extended to additional behaviors.

BackgroundPrimary astrocyte cultures derived from neonatal rodent cortices provide a controlled system for investigating astrocyte-specific mechanisms. However, mixed glial preparations frequently contain contaminating microglia and oligodendrocyte progenitor cells, and most existing protocols require pooling tissue from multiple mouse pups to obtain sufficient astrocyte yields. This approach is impractical as it obscures sex and genotype, limits investigations of sex dependent astrocyte phenotypes, and precludes studies in certain transgenic models. To address this gap, our protocol achieves a high astrocyte yield from a single neonatal mouse brain, enabling sex- and genotype-specific cultures without the need for pooling. Mechanical removal of oligodendrocyte progenitors combined with pharmacological depletion of microglia using a Colony Stimulating Factor 1 Receptor (CSF1R) inhibitor produces highly enriched astrocytes suitable for functional assays, including those focused on sex-specific biology. MethodsCortical tissue was isolated from a single mouse pup is mechanically dissociated in astrocyte media. Cell suspensions are transferred to poly-D-lysine-coated flasks in astrocyte media. After 10-15 days in culture, OPCs are mechanically removed by horizontal shaking and microglia are selectively depleted by incubating cultures with CSF1R inhibitor PLX5622 for 24, 48, 72 and 96 hours. After PLX treatment, media is replaced and enriched astrocytes were maintained or passaged for experimentation. The sex of the pups is determined by PCR performed on DNA extracted from tail biopsies. ResultsImmunocytochemical analysis for astrocyte and microglia markers (GFAP and Iba1, respectively) showed that 24 hours of PLX5622 treatment did not fully eliminate microglia from mixed glial cultures. Extending treatment to 48 hours effectively depleted microglia while minimizing cytotoxicity and astrocyte loss and produced a pure, high-yield, sex-specific primary astrocyte culture. PCR reliably enabled the sex identification of pups used in culture using DNA extracted from tail biopsies. DiscussionThis protocol provides an efficient and reproducible method for generating high-purity, sex-specific primary astrocyte cultures from a single mouse brain. It improves consistency and purity while eliminating the need to pool tissue, preserving sex and genotype and enabling studies in transgenic mouse lines of both sexes.

Long-term storage of aldehyde-fixed brain tissue is commonly performed in the fluid state. This has the potential to maintain morphology for many decades, but has been found to cause progressive loss of antigenicity over time for some biomolecules. While cryoprotection and subzero storage has been successfully used for brain tissue sections or blocks, methods for preserving whole brains using this approach have not been widely characterized. Here we present a protocol for the preservation of fixed whole brains using graded immersion cryoprotection and subzero temperature storage, which is one type of a more general approach that we refer to as aldehyde-based cryopreservation (ABC). Our method uses a gradual ramp-up of the osmotic concentration of cryoprotectants, leading to a final solution containing 50% (v/v) ethylene glycol and 30% (w/v) sucrose. We used CT imaging to track cryoprotectant penetration, finding that with the use of our protocol, approximately 10 months is required to reach equilibration throughout whole human brains. In our initial histological validation, we found that insufficient equilibration time prior to freezing led to apparent ice crystal artifacts seen on ultrastructural imaging of the white matter. After refining the protocol to allow adequate diffusion time, histologic data at both the light and electron microscopic levels showed preserved cellular architecture and ultrastructure after the process of cryoprotectant loading, freezer storage, and unloading. This protocol can be implemented using laboratory freezers or freezer rooms and provides a degree of resilience against freezer failures because the morphology of the fixed tissue is expected to remain preserved long-term in the fluid state even if rewarmed. Our approach may be valuable for laboratories seeking to enhance the long-term preservation of antigenicity in large brain tissue specimens for future research applications.

We propose a quality measure for spatio-temporal spike patterns (STPs) in multiple-neuron recordings. In such recordings, repeating STPs or pattern repetitions (PRs) are often found, with many of these generated by chance. To rule those out, statistical tests have been developed to discriminate the unlikely from the more likely PRs. This statistical problem is complicated by the fact that there are several obvious quality criteria for a PR, such as the size (the number of spikes) of the pattern and the number of its occurrences. Here, we propose a canonical way of combining several criteria (which we collect in the so-called signature of the pattern) into a single quality measure, based on the unlikeliness of the pattern. This measure is defined mathematically, and a formula for its computation is derived for stationary spike trains. It can be used to compare PRs. Since spike trains are not stationary in practice, we discuss, for two experimental data sets, how well the stationary formula correlates with the defined quality measure as determined from simulations. The results encourage the use of the stationary formula or also some simpler, related formulas as proxies for the quality, for the comparison of PRs and also for statistical tests that avoid the multiple testing problem incurred by using several quality criteria. Based on our results, we propose a few test statistics, i.e., random variables on the space of multi-unit spike trains with an appropriate null-hypothesis distribution, to evaluate STPs with less computational and sampling efforts.

BackgroundHippocampal place cells (PCs) undergo representational drift, i.e., a gradual change in their place fields despite unaltered behavior. While Ca2+ imaging enables long-term tracking of PC populations, distinct PC detection methods have been shown to yield different subpopulations of PCs, with only a few systematic comparisons between methods, especially in open arenas. New MethodWe provide an analysis protocol for one-photon PC data obtained during free foraging in two-dimensional arenas that allows us to compare two widely used PC detection methods, significance of spatial information (SI), and split-half correlation (SHC), and their effect on representational drift. The analysis is demonstrated on previously published Ca2+ data from dorsal CA1 of freely foraging mice, with cells tracked for 10 consecutive days. ResultsBoth criteria, SI and SHC, yielded proportions of approx. 17% PCs with only 40% overlap. SI-identified PCs demonstrated higher stability, higher rate map correlations, and a slower rate of representational drift than SHC-PCs. Comparison with existing methodsPrevious studies comparing SI and SHC PC detection methods in Ca2+ data did not focus on either open field behavior or representational drift. ConclusionOur results indicate that the choice of PC detection method significantly affects the estimate of representational drift in Ca2+ imaging studies.