Journal of Neuroscience Methods

High-precision behavior tracking and closed-loop intervention are essential for studying the neural basis of cognition and behavior. Existing commercial systems are costly and inflexible for customization, while current open-source tools are often lack of real-time functionality and suffer from steep learning curve. To address these issues, we developed RpiBeh, an open-source, cost-effective, and versatile software tailored for rodent neuroethological research. The software features an intuitive interface with extensive customization options. RpiBeh leverages a Raspberry Pi and camera for video streaming, enabling behavior-driven closed-loop control. Additionally, it provides frame-by-frame video timestamp output for precise synchronization with external devices. For real-time tracking and locomotion pattern analysis, RpiBeh utilizes several novel algorithms and integrated newly developed deep-learning method. Specifically, we introduced two algorithms: a Background Subtraction Method (BSM) for real-time position tracking and a Frame Difference (FD) algorithm for freezing behavior detection. RpiBeh was validated in single animal real-time tracking and locomotion pattern detection, demonstrating flexibility and effectiveness in configurating behavior-triggered closed-loop reinforcement experiments including passive place avoidance task and social fear conditioning tasks. It achieved the same level of performance in tracking and locomotion pattern detection comparing to benchmark software including ANY-maze and DeepLabCut, with superior customization and expandability. Consequently, RpiBeh offers an efficient, affordable, and open-source solution for video tracking and behavior-driven closed-loop experiments.

Determining the localization of intracerebral implants in rodent brain stands as a critical final step in most physiological and behaviroral studies, especially when targeting deep brain nuclei. Conventional histological approaches, reliant on manual estimation through sectioning and slice examination, are error-prone, potentially complicating data interpretation. Leveraging recent advances in tissue-clearing techniques and light-sheet fluorescence microscopy, we introduce a method enabling virtual brain slicing in any orientation, offering precise implant localization without the limitations of traditional tissue sectioning. To illustrate the methods utility, we present findings from the implantation of linear silicon probes into the midbrain interpeduncular nucleus (IPN) of anesthetized transgenic mice expressing chanelrhodopsin-2 and enhanced yellow fluorescent protein under the choline acetyltransferase (ChAT) promoter/enhancer regions (ChAT-Chr2-EYFP mice). Utilizing a fluorescent dye applied to the electrode surface, we visualized both the targeted area and the precise localization, enabling enhanced inter-subject comparisons. Three dimensional (3D) brain renderings, presented effortlessly in video format across various orientations, showcase the versatility of this approach.

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.

Spatial cognition research requires behavioral paradigms that can distinguish between different navigational elements, such as allocentric (map-like) navigation and egocentric (e.g., body centered) navigation. To fill this need, we developed a flexible experimental platform that can be quickly modified without the need for significant changes to software and hardware. In this paper, we present this inexpensive and flexible behavioral platform paired with software which we are making freely available. Our behavioral platform serves as the foundation for a range of experiments, and though developed for assessing spatial cognition, it also has applications in the non-spatial domain of behavioral testing. There are two components of the software platform, Maze and Stim Trigger. Both programs can work in conjunction with electrophysiology acquisition systems, allowing for precise time stamping of neural events with behavior. The Maze program includes functionality for automatic reward delivery based on user defined zones. Stim Trigger permits control of brain stimulation via any equipment that can be paired with an Arduino board. We seek to share our software and leverage the potential by expanding functionality in the future to meet the needs of a larger community of researchers. Significance StatementThis paper presents an innovative and cost-effective behavioral platform designed to distinguish between different navigational elements, addressing the crucial need for better spatial cognition research paradigms. The platforms flexibility allows for quick modifications without major software or hardware changes. Additionally, the freely available software, comprising Maze and Stim Trigger components, enables precise time stamping of neural events with behavior, while facilitating automatic reward delivery and brain stimulation control. Beyond spatial cognition assessment, the platforms adaptability extends to non-spatial behavioral testing. By openly sharing this software, the authors aim to foster collaboration and encourage future developments, promoting its application to a broader community of researchers. This platform represents a significant advancement in spatial cognition research and behavioral experimentation 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

PyRodentTracks (PRT) is a scalable and customizable computer vision and RFID- based system for multiple rodent tracking and behavior assessment that can be set up within minutes in any user-defined arena at minimal cost. PRT is composed of the online Raspberry Pi-based video and RFID acquisition and the subsequent offline analysis tools. The system is capable of tracking up to 6 mice in experiments ranging from minutes to days. PRT maintained a minimum of 88% detections tracked with an overall accuracy >85% when compared to manual validation of videos containing 1-4 mice in a modified home-cage. As expected, chronic recording in home-cage revealed diurnal activity patterns. Moreover, it was observed that novel non-cagemate mice pairs exhibit more similarity in travel trajectory patterns over a 10-minute period in the openfield than cagemates. Therefore, shared features within travel trajectories between animals may be a measure of sociability that has not been previously reported. Moreover, PRT can interface with open-source packages such as Deeplabcut and Traja for pose estimation and travel trajectory analysis, respectively. In combination with Traja, PRT resolved motor deficits exhibited in stroke animals. Overall, we present an affordable, open-sourced, and customizable/scalable rodent-specific behavior recording and analysis system. Statement of SignificanceAn affordable, customizable, and easy-to-use open-source rodent tracking system is described. To tackle the increasingly complex questions in neuroscience, researchers need a flexible system to track rodents of different coat colors in various complex experimental paradigms. The majority of current tools, commercial or otherwise, can only be fully automated to track multiple animals of the same type in a single defined environment and are not easily setup within custom arenas or cages. Moreover, many tools are not only expensive but are also difficult to set up and use, often requiring users to have extensive hardware and software knowledge. In contrast, PRT is easy to install and can be adapted to track rodents of any coat color in any user-defined environment with few restrictions. We believe that PRT will be an invaluable tool for researchers that are quantifying behavior in identified animals.

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.

Approaches of optogenetic manipulation of neuronal activity have boosted our understanding of the functional architecture of brain circuits underlying various behaviors. In the meantime, rapid development in computer vision greatly accelerates the automation of behavioral analysis. Real-time and event-triggered interference is often necessary for establishing a tight correlation between neuronal activity and behavioral outcome. However, it is time consuming and easily causes variations when performed manually by experimenters. Here, we describe our Track-Control toolbox, a fully automated system with real-time object detection and low latency closed-loop hardware feedback. We demonstrate that the toolbox can be applied in a broad spectrum of behavioral assays commonly used in the neuroscience field, including open field, plus maze, Morris water maze, real-time place preference, social interaction, and sensory-induced defensive behavior tests. The Track-Control toolbox has proved an efficient and easy-to-use method with excellent flexibility for functional extension. Moreover, the toolbox is free, open source, graphic processing unit (GPU)-independent, and compatible across operating system (OS) platforms. Each lab can easily integrate Track-Control into their existing systems to achieve automation.

String-pulling in rodents (rats and mice) is a task in which animals make hand-over-hand movements to spontaneously reel in a string with or without a food reward attached to its end. The task elicits bilateral skilled hand movements for which rodents require little training. The task is suitable for phenotyping physiology and pathophysiology of sensorimotor integration in rodent models of neurological and motor disorders. Because a rodent stands in the same location and its movements are repetitive, the task lends itself to quantification of topographical and kinematic parameters for on-line tactile tracking of the string, skilled hand movements for grasping, and rhythmical bilateral forearm movements to advance the string. Here we describe a Matlab(R) based software with a graphical user interface to assist researchers in analyzing the video record of string pulling. The software allows global characterization of position and motion using optical flow estimation, descriptive statistics, principal component, and independent component analyses as well as temporal measures of Fano factor, entropy, and Higuchi fractal dimension. Based on image segmentation and object tracking heuristic algorithms, the software also allows independent tracking of the body, ears, nose, and forehands for estimation of kinematic parameters such as body length, body angle, head roll, head yaw, head pitch, movement paths and speed of hand movement. The utility of the task and that of the software is presented by describing mouse strain characteristics in string-pulling behavior of two strains of mice, C57BL/6 and Swiss Webster. Postural and skilled hand kinematic differences that characterize the strains highlight the utility of the task and assessment methods for phenotypic and neurological analysis of healthy and rodent models of diseases such as Parkinsons, Huntingtons, Alzheimers and other neurological and motor disorders. Significance statementMouse models are used to investigate the physiology and pathophysiology of motor deficits observed in human neurological conditions, for testing substances for therapeutic drug development, and to investigate the role of neural systems and their genetic basis in the expression of behavior. Behavioral tasks involving unconditioned and natural behavior can provide rich insights into motor performance in animal models and analyses can be aided by the automated processing of video data for reliable quantification and high throughput.

1Investigators conducting behavioral experiments often need precise control over the timing of the delivery of stimuli to subjects and to collect the precise times of the subsequent behavioral responses. Furthermore, investigators want fine-tuned control over how various multi-modal cues are presented. behaviorMate takes an "Intranet of Things" approach, using a networked system of hardware and software components for achieving these goals. The system outputs a file with integrated timestamp-event pairs that investigators can then format and process using their own analysis pipelines. We present an overview of the electronic components and GUI application that make up behaviorMate as well as mechanical designs for compatible experimental rigs to provide the reader with the ability to set up their own system. A wide variety of paradigms are supported, including goal-oriented learning, random foraging, and context switching. We demonstrate behaviorMates utility and reliability with a range of use cases from several published studies and benchmark tests. Finally, we present experimental validation demonstrating different modalities of hippocampal place field studies. Both treadmill with burlap belt and virtual reality with running wheel paradigms were performed to confirm the efficacy and flexibility of the approach. Previous solutions rely on proprietary systems that may have large upfront costs or present frameworks that require customized software to be developed. behaviorMate uses open-source software and a flexible configuration system to mitigate both concerns. behaviorMate has a proven record for head-fixed imaging experiments and could be easily adopted for task control in a variety of experimental situations.

The advent of whole brain clearing and imaging methods extends the breadth and depth at which brain-wide neural populations and structures can be studied. However, these methods have yet to be applied to larger brains, such as the brains of the common laboratory rat, despite the importance of these models in behavioral neuroscience research. Here we introduce AdipoClear+, an optimized immunolabeling and clearing methodology for application to adult rat brain hemispheres, and validate its application through the testing of common antibodies and electrode tract visualization. In order to extend the accessibility of this methodology for general use, we have developed an open source platform for the registration of rat brain volumes to standard brain atlases for high throughput analysis.

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.

Low-cost and open-source neural recording systems are increasingly important for expanding access to electrophysiological research. However, many existing platforms still rely on specialized hardware or limited modularity, restricting flexibility for laboratories seeking customizable solutions. Here, we developed and evaluated a modular neural recording platform constructed entirely from commercially available components. Recordings were compared against the ground truth. The platform successfully recovered local field potential (LFP)-like waveforms in most conditions and detected spike-like activity during direct connection recordings. Principal component analysis and k-means clustering further demonstrated the ability to distinguish multiple simulated spike waveforms. Signal quality varied across configurations, with saline recordings and preamplifier integration introducing increased noise and reduced detectability. These findings demonstrate the feasibility of building affordable and modular electrophysiology systems using widely accessible hardware. Although the current implementation has limitations in sampling rate, noise performance, and in vivo validation, the presented framework provides a practical foundation for future customizable open-source neural recording.

Nonhuman primates (NHPs) are pivotal for unlocking the complexities of human cognition, yet traditional cognitive studies remain constrained to specialized laboratories. To revolutionize this paradigm, we present CalliCog: an open-source, scalable in-cage platform tailored for freely behaving experiments in small primate species such as the common marmoset (Callithrix jacchus). CalliCog includes modular operant chambers that operate autonomously and integrate seamlessly with home cages, eliminating human intervention. Our results showcase the power of CalliCog to train experimentally naive marmosets in touchscreen-based cognitive tasks. Remarkably, across two independent facilities, marmosets achieved touchscreen proficiency within two weeks and successfully completed tasks probing behavioral flexibility and working memory. Moreover, CalliCog enabled precise synchronization of behavioral data with electrocorticography (ECoG) recordings from freely moving animals, opening new frontiers for neurobehavioral research. By making CalliCog openly accessible, we aim to democratize cognitive experimentation with small NHPs, narrowing the translational gap between preclinical models and human cognition. MotivationCognitive neuroscience research involving nonhuman primates (NHPs) has traditionally been confined to a few highly specialized laboratories equipped with advanced infrastructure, expert knowledge, and specialized resources for housing and testing these animals. The common marmoset (Callithrix jacchus), a small NHP species, has gained popularity in cognitive research due to its ability to address some of these challenges. However, behavioral studies in marmosets remain labor-intensive and restricted mainly to experts in the field, making them less accessible to the broader scientific community. To address these barriers, we introduce an open and accessible platform designed for automated cognitive experiments in home cage settings with marmosets. This system supports the integration of cognitive behavioral analysis with wireless neural recordings, is cost-effective, and requires minimal technical expertise to build and operate.

MotivationProfiling neurons by their electrophysiological phenotype is essential for understanding their roles in information coding within and beyond the nervous systems. Technological development has unleashed our power to record neurons more than ever before, yet the booming size of the dataset poses new challenges for data analysis. Current software tools require users to have either significant programming knowledge or to devote great time and effort, which impedes their prevalence and adoption among experimentalists. To address this problem, here we present ElecFeX, a MATLAB-based graphical user interface designed for a more accessible and efficient analysis of single-cell electrophysiological recordings. ElecFeX has a simple and succinct graphical layout to enable effortless handling of large datasets. This tool includes a set of customizable methods for most common electrophysiological features, and these methods can process multiple files all at once in a reliable and reproducible manner. The output is assembled in a properly formatted file which is exportable for further analysis such as statistical comparison and clustering. By providing such a streamlined and user-friendly open-sourced interface, we hope ElecFeX can benefit broader users for their studies associated with neural activity. SummaryCharacterizing neurons by their electrophysiological phenotypes is essential for understanding the neural basis of behavioral and cognitive functions. Recent developments in electrode technologies have enabled the collection of hundreds of neural recordings; that necessitated the development of new toolkits capable of performing feature extraction efficiently. To address this urgent need for a powerful and accessible tool, we present ElecFeX, an open-source MATLAB-based toolbox that (1) has a succinct and intuitive graphical user interface, (2) provides generalized methods for wide-ranging electrophysiological features, (3) processes large-size dataset effortlessly, and (4) yields formatted output for further analysis such as neuronal characterization and classification. We implemented the toolbox on a diverse set of neural recordings and demonstrated its functionality, efficiency, and versatility in capturing features that can well-distinguish neuronal subgroups across brain regions and species. ElecFeX is thus presented as a powerful tool to significantly promote future studies on neuronal electrical activity.

Magnetic resonance imaging (MRI) is a critical tool for translational neuroscience, offering cross-species insights into brain structure and function; however, its application in preclinical research is constrained by routine anesthesia use or sedation, which alters neural activity and limits comparisons to awake human imaging. Awake rodent functional MRI (fMRI) provides a powerful platform for investigating brain function under physiologically relevant conditions, but implementation is limited by technical challenges, particularly head motion and stress during scanning. Most restraint systems employ initial anesthesia, compromising translatability of findings, and highlighting the need for improved designs. We developed a novel restraint system optimized for awake rat fMRI. The system consists of modular 3D-printed components and can be assembled in under five minutes. It is accompanied by a protocol that includes head-post implantation followed by an 11-day habituation period post-surgical recovery. The system eliminates the need for isoflurane anesthesia, ear bars, and bite bars, reducing stress and improving animal comfort. It supports integration with behavioral paradigms such as pupil tracking and licking responses. High-resolution T2-weighted anatomical images and functional scans obtained using the system showed excellent spatial clarity and minimal motion artifacts. Quality control metrics, including head motion parameters and temporal signal-to-noise ratio, confirmed the systems stability and suitability for awake imaging. Functional connectivity analysis revealed robust positive correlations between functionally relevant regions. This system offers a scalable, reproducible, and animal-friendly solution for awake rat fMRI. While the current design limits direct cranial access for multimodal recordings, it enables high-quality, behaviorally enriched imaging without anesthesia. Significance Statement: Most rodent fMRI studies, including awake studies, rely on anesthesia, which profoundly alters brain activity and limits the interpretation of the data. This study presents a novel restraint system that enables high-quality fMRI in fully awake rats, eliminating the need for anesthesia, ear bars, and bite bars. By reducing stress and motion, this simple restraint system allows for investigation of neural activity and connectivity without confounds from sedation or anesthesia. Its open-source, modular design supports behavioral tasks and broad accessibility, making it a valuable tool for neuroscience research seeking to bridge the gap between preclinical imaging and real-world brain function.

Biomedical research studies, specifically regarding human neurodegenerative diseases, are bound by ethical challenges, and have limited diagnostic and treatment options. Transgenic mouse models offer an incredible research advantage to conduct feasible and practical research with the ability to precisely define the progression of neurodegenerative disease over a wellcontrolled dosage and timeline. The use of transgenic mouse models has been extensive and is critical to advancing research in many ways, including understanding brain morphology and general tissue changes caused by neurological diseases. Often, these studies require specific brain regions or other neurological tissues which may be difficult to obtain. Unfortunately, specific extraction and dissection protocols are few and far between, leading to inconsistent results and a lack of reproducibility. A well-defined protocol, such as this, is instrumental in overcoming these obstacles and acquiring better experimental results. Five mouse-specific protocols are described: brain extraction, brain microdissection, spinal cord extrusion, cerebral spinal fluid (CSF) collection, and sciatic nerve dissection. Each protocol was completed under biosafety level 2 (BSL-2) guidelines, similar to the sterility precautions required in human surgery. Each protocol also includes a collective materials list that defines proper instruments and usage. The protocol was refined based on feedback from numerous research studies in transcriptomics and pharmaceutical development. These applications require minimizing tissue damage, dissection accuracy, and the ability to reproduce the results--skills that are also directly transferable to clinical settings. The proper implementation of these protocols will allow for more accurate and precise results with reduced variability. This study provides well-defined, succinct, accessible protocols that are more ethical and improve the overall quality of the conducted research. By addressing this need, it supports greater advancements in many cross disciplinary areas.

Although many methods for automated fluorescent-labeled cell detection have been proposed, not all of them assume a highly inhomogeneous background arising from complex biological structures. Here, we propose an automated cell detection algorithm that accounts for and subtracts the inhomogeneous background by avoiding high-intensity pixels in the blur filtering calculation. Cells were detected by intensity thresholding in the background-subtracted image, and the algorithms performance was tested on NeuN- and c-Fos-stained images in the mouse prefrontal cortex and hippocampal dentate gyrus. In addition, applications in c-Fos positive cell counting and the quantification for the expression level in double-labeled cells were demonstrated. Our method of automated detection after background assumption (ADABA) offers the advantage of high-throughput and unbiased analysis in regions with complex biological structures that produce inhomogeneous background. Highlights- We proposed a method to assume and subtract inhomogeneous background pattern. (79/85) - Cells were automatically detected in the background-subtracted image. (71/85) - The automated detection results corresponded with the manual detection. (73/85) - Detection of IEG positive cells and overlapping with neural marker were demonstrated. (85/85)

We report an automated, low-cost, and open-source methodology for rodent object recognition tasks (ORT), which are widely used behavior paradigms. The system was designed with an effort of behavior standardization and improvement. When it combined with easily accessible 3-D manufacturing technology, this proximity capacitive sensor based method provides an opportunity to explore new, and high throughput hardware for NORT. The systems software allows behavior data to be instantaneously monitored and its output can be plotted promptly immediately upon the completion of the behavior test, thus saving labor and time. The performance of this package is cross-verified with a widely used video-based automated software scoring method and examined its sensitivity to chemogenetic behavior manipulations. This integrated package is an affordable and accurate tool for translational recognition memory research and other type of research with frequent use of NORT paradigms. Visual Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC="FIGDIR/small/518044v1_ufig1.gif" ALT="Figure 1"> View larger version (18K): org.highwire.dtl.DTLVardef@1205ca6org.highwire.dtl.DTLVardef@1a2d156org.highwire.dtl.DTLVardef@5d8ef9org.highwire.dtl.DTLVardef@5589ee_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectiveThe acquisition of electroencephalogram (EEG) data during neurostimulation, particularly concurrent transcutaneous electrical stimulation of the auricular vagus nerve, introduces unique challenges for data preprocessing and analysis due to the presence of significant stimulation artifacts. This study evaluates various denoising techniques to address these challenges effectively. MethodsA variety of denoising techniques were investigated, including interpolation methods, spectral filtering, and spatial filtering techniques. The techniques evaluated included low-pass and notch filtering, spectrum interpolation, average artifact subtraction, the Zapline algorithm, and advanced methods such as independent component analysis (ICA), signal-space projection (SSP), and generalized eigendecomposition with stimulation artifact source separation (GED/SASS). The efficacy of these algorithms was evaluated across three distinct datasets: simulated data, data from a gelatin phantom model, and real human subject data. ResultsOur findings indicate that GED (SASS) and SSP significantly outperformed other methods in reducing artifacts while preserving the integrity of the EEG signal. ICA and Zapline were effective too, but came with important limitations. These methods demonstrated robustness across different data types and conditions, providing effective artifact mitigation with minimal disruption to other essential signal components. ConclusionThis comprehensive analysis demonstrates the efficacy of advanced spatial filtering techniques in the preprocessing of EEG data during auricular vagus nerve stimulation. These methods offer promising avenues for enhancing the quality and reliability of neurostimulation-associated EEG data, facilitating a deeper understanding and wider applications in clinical and research settings.