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.

Stereotaxic brain surgery is a foundational neurosurgical technique used to deliver chemical, pharmacological, or genetic material to specific brain regions, or to precisely implant electrodes and stimulators. Accurate targeting depends on reliable identification of skull landmarks, particularly bregma and lambda. Here we present a photogrammetry-based automated small animal stereotaxic platform that uses a single, freely handheld camera, such as a standard smartphone, to generate high-resolution, polychromatic 3D skull reconstructions. Because photogrammetry requires no fixed overhead hardware, the surgical field remains fully accessible for instruments, microscopes, and other equipment. The resulting color reconstructions substantially improve identification of bregma and lambda compared to monochromatic approaches, and the higher spatial resolution translates directly into improved targeting accuracy and surgical speed. The system operates by a user moving a handheld camera around the exposed skull. The platform automatically produces a detailed 3D mesh and computes stereotaxic coordinates without manual measurement. Together, these properties yield a practical, accessible yet accurate platform for automated small animal neurosurgery. We previously described a structured illumination approach in combination with a Steward platform; however, that system required a fixed projector and camera array that occupied critical surgical workspace and produced monochromatic meshes that complicated landmark identification. The photogrammetry-based method described here overcomes both limitations while retaining full compatibility with the Steward platform as a stereotaxic base.

Vision is traditionally studied using simplified stimuli presented briefly near the center of a flat display while subjects maintain visual fixation. This paradigm contrasts sharply with real-world vision, which is immersive, dynamic and strongly entrained to self-initiated actions. Traditional approaches have allowed researchers to systematically study the neural encoding of visual features and the influence of cognitive operations such as attention. However, other methods are needed to study more holistic and first-person perspectives on vision, such as those related to physical space, continuous time, and self-movement. To enable the study of these and other aspects of real-world vision, we developed hemispherical ("dome") display systems for macaque visual neuroscience. For functional MRI experiments, a compact rear-projection dome display fits within the bore of a clinical MRI scanner. For electrophysiological recordings, a larger front-projection dome display is illuminated from above using a spherical mirror. Both setups enable complete and dynamic stimulation of approximately 180{degrees} of the subjects field of view, thus facilitating studies requiring visual immersion. To ensure accurate angular geometry across the hemispherical display, we present unified rendering and calibration software that supports natural fisheye videos, conventional visual stimuli, and virtual 3D environments. The calibration procedure automatically compensates for projector, mirror, and dome distortions through geometric pre-warping, ensuring correct visual-angle representation across the display. Pilot fMRI experiments demonstrate robust activation of peripheral visual cortex during both conventional pattern stimulation and naturalistic self-movement. Together, these dome systems provide a flexible platform for investigating aspects of vision that are difficult to study with conventional displays, including peripheral processing, visual immersion, optic flow, self-motion, and holistic scene perception.

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.

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.

Rodent brain matrices that produce coronal or sagittal brain sections for histology confer reproducibility and enable high throughput processing of tissues. However, a stainless steel or acrylic brain matrix that produces tissue sections in a transversal (or horizontal) orientation is currently unavailable as a standard tool. This limits the direct comparison of bilateral brain hemispheres within a single histological section, as freehand trimming to obtain horizontal planes is not easily replicable across samples. To mitigate this challenge, we designed a low-cost (USD 7 per unit), 3D-printed resin-based transverse brain matrix that accommodates mouse brains ranging from 12 to 16 mm in length from the olfactory bulb to the brainstem. Our matrix reproducibly generates horizontal tissue sections with a minimum of 1-mm-thickness without causing visible tissue deformation, which is comparable to the performance of commercial rodent brain matrices. Users may adapt the accompanying CAD code using our video tutorials to customize the transverse brain matrix for their specific needs, including alternative brain size, shape, and tissue thickness.

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.

Correcting for multiple comparisons is a fundamental challenge throughout the biological sciences, particularly for data sampled over ordered continua such as time, space, or frequency. Existing approaches, including cluster-based permutation tests and threshold-free cluster enhancement (TFCE), leverage spatial or temporal contiguity but remain dependent on predefined statistical frameworks or thresholding procedures. Here we introduce the All Window-Size Search (AWSS) method, a permutation-based procedure that formally controls the family-wise error rate while adaptively searching across all contiguous window sizes and locations. For each permutation, test statistics are summed across every possible window, generating null distributions of maximal statistics at every window size. A second stage estimates the null distribution of the most significant uncorrected p-value that would arise from searching across all window sizes, allowing final p-values to be corrected for the adaptive search process itself. This procedure statistically formalizes the implicit multiscale search that investigators naturally perform when visually inspecting ordered data. Simulations with known ground-truth effects demonstrate that AWSS can provide substantially greater statistical power than conventional cluster-based permutation methods for broad, low-amplitude effects while maintaining appropriate family-wise error control. Because the framework is independent of any particular statistical test, it is readily applicable to diverse forms of one-dimensional ordered data. Here we test this application with simulations as well as using real human sEEG neural recording data. Future extensions will generalize the method to multidimensional spatial and spatiotemporal datasets, including neuroimaging and other high-dimensional biological data.

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

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)

Head-fixed behavior in rodents is a foundational technique in systems neuroscience which enables use of sophisticated imaging techniques in combination with animal behavior. However, accessibility of head-fixed behavior techniques is limited. Animal training consumes a large amount of experimenter labor and commercial setups, when available, are largely inflexible and financially burdensome. Here, we present a low-cost, modular, and open-source hardware and software implementation for head-fixed rodent decision-making tasks. Our design lowers experimenter labor and enables large teams of researchers to participate in animal training with minimal experimenter error using a simple touchscreen GUI and automated training progression. We demonstrate the efficacy of the platform by training a cohort of animals in a two-choice probabilistic rapid-reversal task in which mice continuously update action choices based on recent reward history. The presented design lowers the barrier to entry for laboratories seeking to conduct head-fixed rodent behavior and provides modular solutions for developing custom rigs based on experimental demands.