Journal of Neuroscience Methods

Background: Transcardial perfusion fixation is widely used to preserve rodent brains for histological and ultrastructural analysis, but protocols can vary in the use of pre-fixation washout, fixative formulation, and osmotic additives. Relatively few studies have directly compared how the chemical composition of the perfusate affects tissue preservation. Methods: Male Sprague-Dawley rats underwent transcardial perfusion using a series of aldehyde-based fixative formulations that varied in fixative composition, use of a phosphate buffered saline washout, and the addition of mannitol or polyethylene glycol 35 kDa (PEG35). Perfusion outcomes were assessed using gross brain morphology, semi-quantitative grading of vascular blood clearance, quantitative detection of residual erythrocytes in whole-slide histological images, and light microscopic measures of cellular visualization and morphology. Perfusate osmolality was measured, and selected specimens were examined by electron microscopy. Results: Substantial vascular blood clearance was frequently achieved with fixative-only perfusion, indicating that a pre-fixation washout was not required to achieve high levels of blood clearance under the conditions tested. Routine light microscopy-based measures of cellular visibility and morphology were broadly similar across treatment groups. Adding mannitol and PEG35 to the fixative solution both led to concentration-dependent gross tissue shrinkage, particularly with PEG35, without correspondingly large or consistent changes on light microscopy. A substantial degree of case-to-case variation in perfusion quality was also observed among animals undergoing nominally similar procedures. Conclusions: Substantial variation in perfusate composition produced relatively modest differences in the light microscopy outcomes examined, despite pronounced effects of osmotic additives on gross brain morphology. There were some cases where perfusion with fixative alone led to near-complete blood vessel clearance, and the use of a preceding phosphate buffered saline washout was not associated with higher vascular clearance scores. The degree of case-to-case variability observed within treatment conditions suggests that procedural factors can play an important role on perfusion quality in addition to perfusate composition.

Suspension mass cytometry can measure 50 or more markers per cell and is not impacted by cellular autofluorescence, making it an appealing platform for comprehensive phenotyping of single cells in tissue with high innate fluorescence, such as brain. However, this organ is challenging to reduce to a single cell suspension: it is a deeply interconnected tissue rich in extracellular material and contains a wide variety of cell types including neurons, their supporting glia, and hematopoietic cells from peripheral circulation, any or all of which may have competing requirements for survival through the dissociation process. This study compares approaches for producing single-cell preparations from white and gray matter enriched regions of adult mouse brain, testing simple mechanical dissociation, Accutase, and three gentle enzymes plus DNase: papain, collagenase II, and collagenase plus dispase, all with or without Percoll gradient centrifugation to separate intact cells from debris. Live cells were counted by hemocytometer and Trypan blue exclusion to assess yield and compare across enzyme conditions. Mass cytometry was used to assess cellular composition after representative dissociation, debris removal, and viable cryopreservation. Key proteins measured included beta III-tubulin for neurons, glial fibrillary acidic protein (GFAP) for astrocytes, oligodendrocyte transcription factor (OLIG2) for oligodendrocytes, CD31 for endothelial cells, CD24 for ependymal cells, purinergic receptor P2Y12 (P2RY12) for microglia, and CD45 for leukocytes. Mechanical disaggregation paired with enzymatic dissociation by papain, Collagenase/Disapse, or Collagenase II plus DNase produced approximately 400,000 live cells per 100 grams of tissue. Cell yield was not significantly different between conditions and yields were similar between white and grey matter. Addition of Percoll gradient centrifugation substantially reduced the number of viable cells recovered (average decrease of 88%) and resulted in viable cell numbers that were insufficient for robust analysis of less common cell subsets. In addition to neurons, mass cytometry identified six major populations of non-neuronal cells: oligodendrocytes, astrocytes, endothelial cells, ependymal cells, microglia, and leukocytes. The relative abundance of these six populations was generally unchanged by cryopreservation in both grey matter (r = 0.95), and white matter (r = 0.90), though loss of oligodendrocytes did occur in both tissues. Proliferation, as measured by Ki67 positivity, was non-existent; consistent with this, levels of phosphorylated STAT1 (p-STAT1) and p-STAT3 proteins were low in all cell populations and unchanged by cryopreservation. A modest increase in p-S6(S235/236) after cryopreservation was consistently observed only in microglia. Together, these results establish standardized procedures for generating viable single cell suspensions for cytometry that preserve the cellular diversity of the adult murine brain with minimal perturbation of intracellular signaling cascades.

From a statistical point of view, clustering spike time-windows from multi-electrode arrays (MEAs) recordings is a challenging high-dimensional, unsupervised clustering task where stationarity, commonly assumed in standard time-series analysis, is often violated and for which a gold standard is currently unavailable. Here we aim at providing practical guidance on how to cluster this type of data by systematically comparing 108 clustering pipelines differing along three main dimensions: (i) the data feature space; (ii) the metrics used to quantify distance between data points; and (iii) the clustering algorithm. For our benchmark we used both labeled synthetic data, mimicking four physiologically-inspired classes of spike time-windows, and a set of real MEAs recordings from a two-dimensional in-vitro neural culture. The performance of the competing pipelines was evaluated in terms of balanced accuracy and computational time when analyzing the synthetic datasets, while the Silhouette score was used for the real dataset, where no ground-truth is available. Overall, our analysis shows that the best combination is formed by k-means with Euclidean distance applied after Principal Component Analysis (PCA) of the spike time-windows. Conversely, hierarchical clustering showed the highest computational burden, while Independent Component Analysis and kernel PCA provided less effective noise suppression.

Independent component analysis (ICA) is widely used to remove artefacts from EEG and TMS-EEG recordings, but the components that correspond to artefacts are usually identified by manual inspection, which is subjective and difficult to reproduce. Existing automated classifiers are either designed for ordinary EEG and do not target the artefacts specific to concurrent transcranial magnetic stimulation, or are built on EEGLAB, so none is available for FieldTrip-based pipelines. We present TEFAR, a lightweight, configurable framework for semi-automatic artefact-component classification that applies the same detectors to ordinary EEG and TMS-EEG. TEFAR is built on FieldTrip and requires no additional MATLAB toolboxes. Each artefact class (line noise, blinks, lateral eye movements, cranial muscle, cardiac activity, and the TMS decay and recharge artefacts) is identified from the spectral, spatial, or temporal signature that its components are known to express. Detection thresholds use robust statistics (the median and the median absolute deviation rather than the mean and standard deviation), so that a single dominant artefact component cannot raise the threshold that is meant to detect it. In 30 ground-truth simulations, TEFAR reached a specificity of 1.000 and a sensitivity of 0.96-0.97 for both profiles. An application of TEFAR to real EEG and TMS-EEG recordings is the next step and will be reported separately.

With the rapid advancement of clinical neuroscience and Brain-Computer Interfaces (BCIs), there is an increasing demand for convenient, user-friendly EEG recording methods suitable for diverse environments, including mobile and home-based settings. Traditional gel-based EEG systems, while reliable, are inconvenient and time-consuming, whereas dry electrode systems tend to suffer from elevated noise levels. In this study, we investigated a novel methodological manipulation aimed at enhancing dry electrode signal quality: wetting the hair directly with tap water to improve scalp electrode conductivity. To this end, we recruited 22 healthy participants and compared their resting-state (RS) EEG activity across three experimental conditions (dry, semi-dry, and gel) within a single-session design. Specifically, we analyzed electrode impedance, spectral power (SP), and functional connectivity (FC). Our results show that basic hair-wetting substantially improved mean electrode impedance by 75.7% relative to the dry condition, reduced the proportion of bad channels from approximately 40% to 15%, and increased spectral-power similarity with gel recordings by 7.7% during eyes-closed recordings, and improved FC similarity by approximately 56-70% across frequency bands. Although this method does not fully match the signal quality of traditional gel-based systems, it represents a promising compromise, enhancing EEG data quality under suboptimal conditions. This approach offers a practical and non-invasive means to improve EEG signal quality, ultimately expanding real-world applications of EEG.

Maximizing the morphological and molecular fidelity of preserved mammalian brain tissue is essential for basic neuroscience and brain banking, where tissue quality determines the reliability of downstream analyses. Aldehyde fixation and cryogenic storage are the most powerful preservation techniques available. However, how to best combine them is incompletely characterized. Here, we tested twelve different approaches to preserve the murine brain, including standard aldehyde perfusion fixation, variants of aldehyde-stabilized cryopreservation (ASC) using ethylene glycol (EG), aldehyde fixation followed by unprotected freezing, and several cryopreservation protocols without preceding aldehyde fixation, such as interleaved equilibration with vitrification solution. Preservation quality was assessed using light microscopy, transmission electron microscopy, and patch-level feature analysis with the DINOv2 vision foundation model. We found that ASC with sodium dodecyl sulfate (SDS)-mediated blood-brain barrier permeabilization preserved nuclear morphology, myelin periodicity, and neuropil texture comparable to standard aldehyde perfusion fixation, providing the first independent replication of ASC. Omitting SDS caused severe parenchymal dehydration, due to a mismatch between water and cryoprotectant transport. Aldehyde fixation followed by unprotected slow freezing confined ice damage primarily to perivascular zones, while fast freezing led to intranuclear clefts and cavities. Among non-fixation protocols, interleaved equilibration with vitrification solution preserved myelinated axon profiles and chromatin patterns, but induced perivascular edema. High concentration glycerol perfusion caused significant osmotic dehydration. Straight freezing of unfixed tissue without cryoprotectant was followed by membrane disruption, but resulted in the visualization of more structure than expected, likely as a result of structural restitution upon thawing. Taken together, our results provide a framework for matching preservation strategies to the needs of different types of brain banking and neuroscience research.

Microglial morphology is widely used as an indicator of cellular state, yet existing analytical approaches are often labor-intensive, require proprietary software, or depend on high-resolution imaging platforms. Here, SkeleSketch is presented as an open-source Fiji/ImageJ plugin that automates microglial skeletonization, morphological quantification, and data organization while allowing optional user-guided correction. Using spinal cord microglia from PBS- and LPS-treated mice, SkeleSketch accurately reconstructed cellular morphology and captured expected LPS-induced morphological remodeling, including enlarged soma size and reduced branching complexity. Comparable measurements were obtained from both CX3CR1-GFP reporter mice and Iba1 immunostaining, demonstrating robustness across labeling strategies. Additionally, SkeleSketch morphometrics were positively correlated with those generated by the commercial software Imaris. Our pipeline provides an accessible, reproducible, and flexible platform for quantitative microglial morphology analysis using standard epifluorescence microscopy.

Black participants remain underrepresented in electroencephalography (EEG) research, partly because current EEG devices work poorly with dense, coily Type 4 Afro-textured hair. Research-grade EEG typically wires electrodes on a cap, and therefore the electrodes often fail to reach the scalp through dense, Afro-textured hair. Protocols that ask Black participants to unbraid or restyle protective hairstyles add burdens that discourage enrollment in research studies. To address this, we designed and tested 3D-printed attachments that fit onto an EEG electrode and anchored it directly in the hair, using the hairstyle itself as the mounting structure. We fabricated three design configurations for natural Afro-textured hair, cornrows/braids, and twists/microlocs. Seventeen healthy adults with Type 4a-4c hair used electrode attachments that matched their hairstyle. During a single visit, EEG was recorded with 16 conventional cap electrodes defined by the international 10-20 system and with 16 electrodes using novel electrode attachments anchored directly to their hair. Participants completed resting and walking trials with each system. Electrode-to-scalp distance and gel volume were measured during setup. Electrode disconnection, impedance, signal quality (i.e. peak alpha power and aperiodic (1/f) component), and comfort level were measured throughout the data collection. Compared with the conventional cap, the novel attachments reduced electrode-to-scalp distance, gel use, and impedance. Twenty percent of the conventional cap electrodes failed to connect to the scalp compared with 0.1% of novel electrodes. Impedance rose across trials for the conventional cap, whereas the novel attachments showed a smaller increase and maintained lower impedance throughout. Signal quality was significantly better using the novel attachments, while comfort level did not differ between systems. In conclusion, hair-anchored electrode attachment may present a feasible alternative to conventional cap-based EEG for people with Type 4 hair, yielding more usable channels and better signal quality, and supporting more inclusive EEG acquisition.

Background and Objectives: Code-modulated visual evoked potentials (c-VEPs) provide fast and reliable control signals for reactive brain-computer interfaces (rBCIs). However, deployment beyond laboratories remains limited by visually intrusive stimulation, reliance on wet electroencephalography (EEG), and false activations during non-control periods. We introduce a usability-oriented c-VEP rBCI combining low-salience textured stimulation, slow flashing codes, asynchronous evidence accumulation, and semi-dry EEG acquisition. Methods: Thirty participants evaluated the system on a standard screen using an 11-class keypad typing task. Two identical online typing sessions were separated by a 2-min video-viewing period serving as an explicit idle condition. Feasibility was then assessed in virtual reality (VR) through a proof-of-concept shell-game task involving head movements and immersive rendering constraints. Visual comfort, peripheral distraction, and fatigue were assessed subjectively in both conditions. Results: In the keypad task, the system achieved 99.8% accuracy with 33 s of calibration data and a mean decoding time of 4.3 s. No spurious commands occurred during the tested 2-min idle condition. In VR, online accuracy reached 100%, with a mean decoding time of 5.6 s. Participants reported high visual comfort and minimal peripheral distraction in both conditions, although fatigue was higher in VR. Conclusion: The proposed c-VEP rBCI combines short calibration, high accuracy, comfortable stimulation, semi-dry EEG, and asynchronous decision-making. Performance during the explicit idle condition and VR implementation support the feasibility of wearable and practical c-VEP systems for self-paced interaction beyond laboratory environments. These findings encourage evaluation in longer idle periods and real-world tasks.

Rhythmic visual stimulation offers a non-invasive means of modulating oscillatory brain activity, but conventional flicker may induce considerable visual discomfort. We developed an individualized Gabor flicker stimulation designed to balance neural entrainment and tolerability. In Experiment 1, thirty-four participants passively viewed Plain and Gabor flickers presented at their individual alpha frequency (IAF), IAF - 2 Hz, or IAF + 2 Hz, and at threshold-adjusted or full intensity. Steady-state visual evoked potential signal-to-noise ratio (SNR) was highest at IAF and increased with intensity. Plain and Gabor flickers produced comparable overall SNR, but Gabor flicker was rated as substantially more comfortable and less mentally fatiguing, with stimulation at IAF providing the most favorable balance between neural entrainment and tolerability. In Experiment 2, the optimized stimulation parameters were applied during a retro-cue working memory task using individualized theta and alpha frequency flickers. Stimulation increased SNR and inter-trial phase coherence relative to a non rhythmic Natural condition. No behavioral benefit was observed in the full sample. However, an exploratory subgroup analysis indicated higher accuracy with theta stimulation of cued representations and alpha stimulation of uncued representations than in the Natural condition among participants who showed limited baseline retro-cue benefits. These findings show that individualized Gabor flicker can provide robust and well-tolerated neural entrainment during passive viewing and working memory retention, although its behavioral effects may vary across individuals according to their cognitive performance.

Stereotaxic intracranial microinjection in mice is a critical step in neuroscience workflows, enabled by robust hardware design, precise operation, and neuroanatomical expertise. Manual injections are prone to error and stereotax-mountable commercial automated injectors are extremely expensive. Further, the knowledge needed for performing accurate intracranial injections and surgeries is heterogeneous and disparate. Here we demonstrate the design and characterization of the UD Neuroinjector software-hardware-knowledge architecture, which serves to both reduce the cost of performing precisely controlled stereotaxic microinjections and provide a web platform for planning accurate targeting of specific mouse brain regions. We designed the UD Neuroinjector using low-cost hardware and 3D-printed parts, with a total build-cost of $300 --- less than 0.1x the cost of a commercial injector. Arduino-based firmware enables programmable flow rates, automated injection and extraction, and manual joystick control. Capillary-fluid measurements show stable, highly linear displacement (R2 > 0.93) at commonly used injection rates. In a head-to-head comparison with a commercial injector, in vivo DAPI injections into the mouse thalamus showed comparable spread and DAPI+ cell counts. For effective experimental design of intracranial injections, the web-based components of our architecture provide AI-enabled assistance and atlas-guided navigation. We used a hybrid retrieval-augmented generation (RAG) approach to ground AI-assisted protocol guidance for mouse intracranial surgeries using a corpus of 3,738 papers. Using the Common Coordinate Framework version 3 (CCFv3), we designed an interactive tool for identifying target coordinates. Together, the UD Neuroinjector architecture integrates and enables efficient knowledge discovery, experimental design, planning, and implementation of mouse intracranial surgeries.

Mobile EEG can link neuroimaging to natural behavior, but effects recorded during movement, and sport in particular, are difficult to interpret because neural activity overlaps with signals from movement, muscle activity, and cable movement artifacts. We analyzed the OpenNeuro Real World Table Tennis dataset to test whether peri-hit spectral activity could be analyzed and how it differed between human-opponent and ball-machine play. Data from 25 participants comprising EEG, noise-layers, EMG and motion recordings was analyzed with pre-hit, impact, and post-hit windows. EEG power bands were residualized using trial-level root mean square from noise-layer, EMG, and motion/miscellaneous channels. Spectral parameterization modeled spectra as an aperiodic component plus periodic peaks. The principal short-window model was resolution-aware; sensitivity analyses reduced model complexity, used a 2.0-s peri-hit subwindow (-1.0 to +1.0 s), or disabled periodic fitting. Results showed residualized alpha and beta power was lower during ball-machine play, including pre-hit posterior alpha power. The clearest context difference was a higher ball-machine aperiodic exponent over posterior and parietal sensors, corresponding to a steeper broadband spectral slope. Posterior post-hit means were 0.451 and 0.606 for human-opponent and ball-machine play, respectively. A focal parietal effect persisted across specifications. The results support artifact-informed, resolution-aware spectral analysis as an effective approach for detecting robust spectral differences between naturalistic visuomotor contexts and suggest that EEG may provide useful information about the processes engaged by different practice environments, without establishing a single cognitive or physiological mechanism or demonstrating a training outcome.

Microstate analysis studies dynamic brain activity through a discrete representation of electroencephalographic (EEG) signals and their topography. While the body of literature describing findings in diverse experimental and clinical settings is growing rapidly, the effects of several core components of the microstate algorithm are still insufficiently understood. This study systematically investigates how two common temporal smoothing algorithms, parametric smoothing and GFP peak interpolation, influence a wide range of microstate sequence parameters, and how they shape the integrated picture of brain dynamics inferred from these metrics. We study eyes-closed resting-state EEG recordings from N=201 healthy subjects from a public EEG database. For the elementary temporal metrics, we find that widening smoothing windows markedly increase microstate duration, reduce occurrence rates, but leave coverage mostly unaffected. In terms of simple syntax properties, smoothing windows above 30 ms yield an inconsistent picture with a memoryless, first-order Markov structure at short time scales, but a positive Hurst phenomenon suggestive of long-range correlations at long time scales. Evidence of asymmetry and non-equilibrium dynamics are suppressed by smoothing. At intermediate time scales, microstate periodicity was only observed up to a window size of 28 ms. Smoothing had a strong impact on the complexity metrics entropy rate and excess entropy, decreasing the former and increasing the latter. For most metrics, the effects of GFP peak interpolation were comparable to those of smoothing windows in the range between 28 ms and 44 ms. In conclusion, elementary and higher-order microstate parameters must be interpreted in the context of preprocessing, as many parameters change continuously with smoothing intensity. This limits the comparability of microstate parameters obtained with different preprocessing settings. Strong smoothing with a window size above 30 ms or GFP peak interpolation yields a fundamentally different picture of resting state brain dynamics, i.e. that of a memoryless, aperiodic, time-reversible first-order Markov chain. Since insights from other neurophysiological modalities suggest that time series memory and time-irreversibility are markers of normal brain activity, our recommendation is to avoid intense smoothing to minimize alterations of the initial sequences.

Animal pose estimation typically requires manual annotation of body parts to train neural networks for each new recording system or environmental condition. Here we use a general-purpose vision-language model (VLM) to generate annotations in multi-animal recordings of fruit flies (Drosophila melanogaster). Initial annotations follow written anatomical descriptions and are reviewed by an independent VLM session using neighboring video frames. On 120 held-out fly images, AI annotations had a median distance of 2.28 pixels from human labels, with 84.0% agreement within eight pixels, averaged across landmark types. The comparison included 10 landmarks, with head and eyes analyzed separately because of systematic differences in placement. Review improved agreement primarily at the feet, with the largest remaining discrepancies at positions with limited visual evidence. Visual inspection revealed cases in which the AI annotations were more anatomically accurate than those of an experienced human annotator, particularly at the feet. A convolutional network trained using SLEAP on 300 AI-annotated fly images showed similar agreement with human labels and closely reproduced the AI annotations on held-out images, with a median distance of 1.56 pixels. We also applied the procedure to recordings from another laboratory, using new AI annotations to train a separate pose network. Although VLM annotation required minutes per fly, the trained networks enabled pose estimation throughout more than 700,000 frames. These results demonstrate that VLM annotations can support fast animal pose estimation without manually labeled training images.

Many studies using mouse models of human brain disorders rely on extended recordings of brain electrical activity through electroencephalogram, or EEG. The features that researchers measure in these recordings, such as seizures and epileptiform activity, are deeply influenced by whether the animal is awake, in non-rapid eye movement (NREM) sleep, or in rapid eye movement (REM) sleep. Because total sleep duration varies naturally across animals, rigorous experimental comparisons require classifying the sleep state of every few seconds of a recording. Manual annotation is highly labor-intensive, often requiring hours per subject. While automated scoring tools accelerate this process, they are typically trained exclusively on healthy cohorts and frequently fail when applied to disease models that exhibit atypical or fragmented sleep architecture. Somnus is an open-source Python package designed to automate rodent sleep staging using EEG, muscle activity (EMG), and optional video-tracking data. It includes a robust base model trained on diverse recordings from six independent laboratories, enabling out-of-the-box classification across various equipment setups without requiring prior manual labeling. A central feature of Somnus is its capacity for rapid adaptation to pathological sleep structure. The software provides a graphical interface that directs researchers to the specific epochs where the base model is least certain. Users can manually correct these targeted epochs in a built-in viewer by comparing the model's classification to the raw EEG signal and video, and then use their corrections to fine-tune the model. This workflow empowers researchers to generate accurate custom scorers for complex disease models or novel treatments with just a few minutes of manual correction, bypassing the need to hand-score entire experimental cohorts or develop their own models from scratch.

Two-photon calcium imaging enables measurements of neural activity from individual neurons and their subcellular processes in awake animals. Axial brain motion can change measured fluorescence independently of neural activity and move small structures out of the imaging plane, confounding two-photon calcium measurements in behaving animals. We developed two complementary post-acquisition methods, selected according to whether the imaged structure remains detectable across the encountered depth range. When it does, z-stack correction estimates imaging depth from a separately acquired reference z-stack and uses an axial fluorescence profile specific to each neural structure to rescale fluorescence to a common reference depth. When a structure can leave a single imaging plane, volume-based z-registration reconstructs a common-depth movie from repeated acquisitions of closely spaced planes. In awake mice, both methods tracked experimentally imposed axial displacements within the sampled range, with depth-estimation root mean squared errors (RMSEs) below 1 m for z-stack correction and 0.69-1.03 m for volume-based z-registration. Correction changed fluorescence substantially for a subset of somata, with larger effects in axonal boutons. Both methods are implemented in suite2p-in-depth, a Python package that integrates with Suite2p and provides diagnostics for depth estimation, correction quality and data reliability. Together, these methods enable axial-motion artifacts to be assessed and corrected within established Suite2p workflows.

Mapping brain connections is paramount in neuroscience. Lacking has been a method that can systematically map mesoscale (<1mm) connections in the brain in vivo at brainwide scale. Mesoscale mapping is particularly important for NHPs in which information is represented in submillimeter clusters (e.g. cortical columns, striosomes, thalamic rods). Here, we present a method called INS-fMRI, which comprises optical stimulation of single sites in the brain using pulsed Infrared Neural Stimulation (INS), coupled with mapping of functionally connected sites in the brain using ultrahigh field fMRI. It is the only method available for mapping mesoscale connections rapidly (multiple networks mapped within single MRI sessions), systematically (allowing comparison of networks within single individuals), in vivo (no animal sacrifice needed), at whole brain scale, and without viral transfection. This method has broad applicability to a wide range of neuroscience questions. It can be used at any brain site, on different animal models, and on different MRI platforms. The ability to probe circuits repeatedly within single individuals also opens doors for studying changes in mesoscale circuits over time (e.g. for development, aging, disease studies). In addition, as this method circumvents the need for viral transfection, there is exciting potential for human clinical application.

Electroencephalography (EEG) preprocessing determines signal quality and interpretation, particularly for transcranial magnetic stimulation combined with EEG (TMS-EEG), where recordings are affected by large and complex stimulation-related artifacts. Several preprocessing approaches have been developed, but substantial variability remains in how pipelines are implemented, documented, and evaluated across recordings, limiting reproducibility and comparability across studies. We developed NESTApp, a MATLAB-based graphical interface built on EEGLAB, to provide an accessible and reproducible framework for implementing and evaluating TMS-EEG preprocessing pipelines. Rather than prescribing a specific pipeline, NESTApp allows users to construct and consistently apply customisable workflows incorporating established EEGLAB and TMS-EEG processing tools, while automatically documenting processing decisions and parameters. Integrated quality-control procedures track data removal and flag recordings based on user-defined criteria, facilitating systematic evaluation across participants. The application also supports group-level visualisation and export of processed TMS-EEG measures for subsequent analyses. Applied to 29 recordings from 16 participants, NESTApp processed 27 and identified those requiring further inspection. By integrating pipeline implementation, documentation, quality control, and group-level evaluation within a single interface, NESTApp provides a transparent framework for TMS-EEG preprocessing that may improve reproducibility, facilitate consistent processing across cohorts, and lower technical barriers to the implementation of established preprocessing approaches.

Estimating receptive fields in large neural populations is often limited by experimental time: stimuli with theoretically favorable statistics for receptive-field estimation may, in practice, evoke too few informative spikes in neurons with high specificity for structured features, requiring impracticably long recordings. Therefore, we asked whether a structured stimulus could broadly engage neural populations across stages of the early visual system while permitting a tractable and interpretable reverse-correlation analysis. To address this question, we developed "reverse correlation against stimulus elements (RCASE)", an analysis framework for stimuli that can be represented frame by frame through the presence or absence of specific stimulus elements. We then designed such a stimulus consisting of random moving objects (RMO) and compared RMO/RCASE with conventional spike-triggered averaging using a dense binary white noise (WN/STA) stimulus across different stages of the early visual system of the mouse, namely the retina, the nucleus of the optic tract, the superior colliculus and the primary visual cortex, and in the primate retina. To distinguish the contributions of spatial and temporal scales, motion, stimulus structure, and analysis choice, we additionally compared against WN/STA with different square sizes and frame rates, spike-triggered covariance analysis, as well as random static objects and spatially correlated cloud stimuli. RMO evoked stronger and more reliable responses than WN in the mouse retina and across the investigated mouse visual areas. RMO/RCASE yielded discernible receptive fields for a substantially larger proportion of neurons and in a fraction of the recording time. Although the stimulus objects featured localized moving contrast edges, this advantage was not restricted to direction-selective cell populations. In the primate retina, in contrast, WN/STA approached the performance of RMO/RCASE. RMO/RCASE also supported simultaneous estimation of stimulus direction, speed, and color tuning. Our approach enables rapid and interpretable functional characterization of large populations of visual neurons, enabling to simultaneously recover receptive fields and feature tuning, whenever a low-dimensional feature space can be specified beforehand.

1. For many animals, vision represents the primary sensory modality governing a vast diversity of behaviours. Creating 'virtual twins', or visually realistic digital reconstructions of real individuals interacting within their environments, offers a promising approach for understanding how visual information guides an animal's decisions, behaviours, and interactions. Such systems enable the creation of video scenes from any view, including the viewpoint of the animals themselves. This allows receiver information to then be analysed from the perspective of species-specific visual systems, or for the accurate reconstruction of a real animal's movement to provide social stimuli for behavioural experiments. 2. Despite advances in 3D modelling, photorealistic scene rendering, and animal tracking, efficiently creating realistic digital reconstructions of movement remains challenging for many species. In fishes, a continuously-deforming body due to the absence of discrete joints make it particularly difficult to map tracked body keypoints onto articulated digital models, often resulting in unnatural or erratic animations. 3. Here, we introduce DigiFish, an add-on for the open-source animating software Blender, that generates realistic fish animations from tracking data. By integrating anatomical keypoint tracking with spline-based body curvatures, DigiFish reconstructs biologically-realistic swimming postures and movements of fishes within customisable virtual aquatic environments. The system also supports virtual 'point-of-view' cameras that can be placed arbitrarily within a scene, or dynamically driven by eye-tracking data, enabling unlimited perspectives on how individuals, conspecifics, or landmarks appear against complex backgrounds and how these views change with realistic swimming and eye movements. 4. We provide the Blender add-on and user guide and use DigiFish to reconstruct two different contexts: staged predator-prey interactions in the laboratory and natural schooling of wild fish in the field. Using a further reconstruction of fish swimming in an arena, we also use an Elementary Motion Detector model to show that our virtual reconstructions successfully capture perceptually-realistic movements and postures of individual fish within a digitally reconstructed scene. 5. By integrating photo-realistic scenes with biologically-realistic, simulated motion, DigiFish offers a flexible and accessible platform for investigating visually guided behaviour, perception, and cognition in complex and dynamic environments.