Journal of Neuroscience Methods

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.

A major challenge in behavioral neuroscience is the lack of a unified software framework capable of implementing diverse paradigms across species and experimental setups. Researchers currently face a trade-off: they must either spend significant time developing custom, siloed solutions that hinder reproducibility, or incur substantial costs purchasing inflexible, closed systems. Here, we present Neurokraken, an open-source, Python-native platform designed to overcome these limitations. Neurokraken allows writing experiment progression entirely in standard python, while its core architecture automatically sets up a microcontroller for the connected hardware components and enables python side access with millisecond-precision timing and automatic logging. The system prioritizes ease of use and flexibility, enabling advanced series of events and conditions, the usage of python ecosystem code and packages within experiments, and the addition of any arduino-compatible electronic devices for custom experiments. As a result, users can easily create interactive virtual and real environments to engage, monitor, and record subjects. We present Neurokraken's versatility across a wide range of paradigms, for human and non-human primate psychophysics, and complex rodent behavior in both head-fixed and freely moving paradigms. Its modular design allows for rapid hardware reconfiguration, while a fully customizable user interface enables real-time monitoring and interactive experimental control without compromising timing precision. By uniting laboratory-grade precision with an accessible and flexible open-source philosophy, Neurokraken provides a single, powerful solution to design and execute next-generation behavioral experiments. We hope Neurokraken helps accelerate research, improve reproducibility throughout the neuroscience community, and make advanced behavioral experimentation more accessible through its substantial cost-efficiency.

This step-by-step protocol provides a cross-species, non-surgical approach that enables prenatal gene delivery to the developing nervous system in rats and marmosets. Under transabdominal ultrasound guidance, intracerebroventricular injection of recombinant adeno-associated virus vectors into the fetal brain achieves robust and long-term transduction from prenatal stages into adulthood. This approach can be adapted to other species and target sites outside nervous system, enabling safe and selective intrauterine manipulation and the generation of diverse experimental models for basic and preclinical research. For complete details on the use and execution of this protocol, please refer to Ribeiro Gomes et al (2026)1. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=181 SRC="FIGDIR/small/737050v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@696364org.highwire.dtl.DTLVardef@fc3c7forg.highwire.dtl.DTLVardef@1e7c7caorg.highwire.dtl.DTLVardef@1edcef0_HPS_FORMAT_FIGEXP M_FIG C_FIG Before you beginExperimental procedures during gestation allow researchers to study developmental processes, including how manipulations of the fetus and its intrauterine environment influence biological outcomes. Ultrasound imaging guidance greatly facilitates such interventions by providing safe and targeted access to fetal compartments, including for prenatal gene delivery to developing neural cell populations. Critically, delivery of recombinant adeno-associated viruses (rAAVs) into the cerebrospinal fluid (CSF) of developing animals enables widespread gene transfer across the brain. The efficiency and distribution of transduction are strongly influenced by developmental stage, making the timing of delivery an important experimental variable. In altricial species such as mice, major developmental processes, including cortical lamination and the establishment of long-range connections, begin prenatally but continue throughout early postnatal life. In primates, however, development is more advanced at birth, and many equivalent developmental events are shifted to the prenatal period. Consequently, developmental stages that can be targeted postnatally in mice require prenatal access in primates. Here, we present a step-by-step protocol for ultrasound-guided fetal intracerebroventricular viral injection (FIVI) of rAAV in marmosets (Callithrix jacchus) and rats (Rattus norvegicus). The procedure was initially developed and optimized in rats before being translated to marmosets, small New World primates that share key developmental, anatomical, and functional characteristics with humans. Together, these models illustrate the cross-species applicability of the approach, while providing gene delivery strategies for both a genetically tractable rodent model and a translationally relevant nonhuman primate. FIVI enables broad gene transfer and stable, long-term transgene expression in wild type animals, facilitating the generation of complementary quasi-transgenic models for research and translational applications from prenatal development through adulthood.

SignificanceDigital Holographic Microscopy (DHM) provides label-free quantitative phase images (QPIs) of living cells and has become a powerful tool for studying cellular morphology and dynamics. While most DHM studies have focused on cell-level analysis, the quantitative characterization of neuronal network organization and maturation from DHM images remains largely unexplored, highlighting the need for dedicated computational approaches. AimWe aimed to develop an automated framework combining deep-learning-based image analysis and graph theory to quantitatively characterize the organization, connectivity, and maturation of neuronal networks in primary rat cortical cultures imaged by DHM. ApproachTwo U-Net convolutional neural networks were trained on manually annotated DHM phase images to segment neuronal cell bodies and neurites. The resulting segmentation maps were used to infer putative morphological connections between neurons and generate graph representations of neuronal networks, referred to as graph fingerprints. A panel of 18 connectomics-inspired graph features was then computed to characterize local and global properties of network organization across four stages of culture maturation. ResultsThe mean area under the receiver operating characteristic curves was 0.98 for cell-body and 0.91 for neurite segmentation, indicating near-perfect identification. Graph-theoretical analysis revealed reproducible topological changes during network maturation in vitro, including increased density, reduced modularity, and progressive network integration. Correlation analysis showed that the 18 graph features grouped into two highly correlated families. A Random Forest classifier identified density and modularity as the most informative descriptors, achieving an accuracy of 87% in classifying maturation stages of neuronal cultures. ConclusionsOur results demonstrate that combining DHM, deep-learning-based segmentation, and graphtheoretical analysis enables quantitative characterization of neuronal network organization and maturation from label-free phase images. This framework provides a foundation for future studies of pharmacological experiments, neuronal network phenotyping, and human induced pluripotent stem cell (hiPSC)-derived neuronal cultures, where quantitative assessment of network organization remains a major challenge.

Optical recording from large numbers of neurons is an indispensable technique for studying neuronal ensembles. We use optical sectioning through pinhole illumination to reduce the background fluorescence (F0) and increase the optical signal ({Delta}F/F0) in ex vivo brain slices densely labeled with GCaMP6f, allowing an ordinary fluorescence microscope to capture calcium transients from over 300 individual CA1 neurons - a marked increase compared to ordinary wide field fluorescence illumination. Multiple layers of overlapping neurons can be identified by their locations and the shape in space of their {Delta}F/F0 images. A single pinhole mask was placed at the field stop of a wide field illuminator, and the image of the pinhole was projected onto the tissue by a 20X NA 0.95 water immersion objective (Olympus). This created an illuminated disk with a diameter of [~]200 m and optical sections of hippocampal CA1 pyramidal layer tissue [~]100 m thick. This illumination blocked a large fraction of the F0, which in turn increased the {Delta}F/F0 5-10-fold compared to that of wide field illumination. When putative pyramidal neurons fire sparsely in the brain slice, up to 300 partially superimposed neurons can be identified by their shape and spatial location in the thick ([~]480 m) ex vivo slice in the CA1 area surrounding the pinhole image. The signal-to-noise ratio was adequate even at a low excitation light level of [~]20k photoelectrons per pixel well on the camera, allowing for 3,000 seconds of total recording time without significant bleaching. This pinhole "half confocal" method has created a useful way to sample calcium transient signals in thick tissue with a large population of neurons densely labeled with GCaMP-6f.

Virtual reality (VR) interaction remains largely dependent on explicit motor input, limiting seamless and adaptive interaction. This study investigated whether electroencephalography (EEG)-based passive brain-computer interfaces (BCIs), combined with eye gaze, can decode interaction intent directly from its underlying neurophysiological correlates during dynamic VR gameplay. We operationalized interaction intent as comprising two components: affordance-related evaluation, indicating whether an attended object affords interaction, and approach-avoidance evaluation, indicating the directional tendency of interaction toward desirable or undesirable outcomes. Twenty-three participants completed a VR game with two calibration sessions and one online BCI session. Offline analyses showed above-chance decoding of the binary approach-avoidance decision classification across all actionable trials, with a grand-average accuracy of 66.28% across participants. This decoding transferred to online closed-loop gameplay, where grand-average accuracy remained above chance at 69.64%. Category-level analyses further revealed substantial variability in classification separability. For approach-avoidance-related classifications, accuracy reached 80.84% for the most distinct pairing between clearly valenced reward and punishment categories, but dropped to near chance at 59.03% for the more context-dependent pairing with ambiguous motivational valence. Affordance-related classifications between non-actionable and actionable item categories were consistently high, ranging from 77.76% to 83.50%. User Experience questionnaire results showed that, despite limitations leading to perceived loss of control and reduced ease of use, participants found the BCI-based interaction paradigm itself more fun than the controller baseline. To our knowledge, this is the first demonstration of real-time EEG decoding of interaction intent during dynamic VR gameplay, contributing toward intuitive user-adapted interfaces driven by physiological signals in immersive environments.

BackgroundConventional rodent models for the study of corneal pain commonly evoke eye blink reflex using methods that indiscriminately activate polymodal nociceptors, mechanoreceptors, and thermoreceptors at temporal resolutions that dont closely match the sub-second timescale of underlying neural dynamics. New methodWe introduce a novel automated behavioral paradigm for detecting blink reflexes in transgenic TRPV1-ChR2-EYFP mice, enabled by cell-type-specific, millisecond-precision optogenetic stimulation of corneal nociceptors (490 nm light). Using multi-feature quantification, we achieve robust automated detection using univariate and multivariate classifiers. ResultsTRPV1-ChR2-EYFP mice exhibited blink reflexes to high-intensity blue light (490 nm, 10 ms pulses) in a threshold-dependent manner (N=3). Blink probability was 77.1 {+/-} 17.1% at high intensity (2.77 mW/mm2) versus 4.2 {+/-} 4.2% at low intensity (0.46 mW/mm2). Red light (638 nm) produced no intensity-dependent change. Noxious air puff evoked blinks in >95% of trials under all conditions. DeepLabCut-based pose estimation extracted six features quantifying the blink reflex, enabling automated detection with [≥]98% accuracy using univariate and multivariate classifiers. Comparison with existing methodsUnlike conventional air puff paradigms, this optogenetic approach enables precise, cell-type-specific stimulation of corneal nociceptors, supporting automated analysis of blink responses at sub-second resolution. ConclusionsThis video tracking behavioral method using machine learning algorithms that accurately classify blink versus no-blink enables high-throughput and observer-independent empirical assessment of blink reflex, suggestive of corneal pain. Moreover, inducing blink reflex in TRPV1-ChR2 mice using high-intensity blue light also demonstrates nociceptive-specific behavioral responses analogous to somatosensory optogenetically-evoked hindpaw pain in the same animal genotype.

A test technique for measuring high-frequency transient current components in deep brain tissue is presented. The technique applies a voltage pulse with a high value in dV/dt, generating a corresponding current pulse with high dI/dt that can elicit measurable transient current responses from the electrode/tissue interface and adjacent brain tissue; responses are analyzed in the frequency domain by Fast Fourier Transform at a 200 kHz sampling frequency. The method was motivated by prior evidence that ferritin and neuromelanin in catecholaminergic tissue may support high-frequency conduction properties that have not previously been characterized in vivo. The protocol was applied in 277 measurements across five Sprague Dawley rats at cortical and basal ganglia locations in different locations in the brain. Preliminary spectral results show differences between catecholaminergic regions and cortical tissue that support further development and validation of the method.

Ex vivo diffusion weighted imaging (DWI) enables high-resolution characterization of brain connectivity and is increasingly applied in comparative and evolutionary neuroscience. However, variability in tissue preparation and contrast agent exposure can substantially affect relaxation properties and compromise reproducibility, particularly in non-mammalian species. Here, we systematically assess the impact of different gadolinium-based contrast agent exposure protocols on relaxation stability and DWI compatibility in fixed pigeon brains. Brains were perfusion-fixed with 2% paraformaldehyde and assigned to four preparation protocols: (i) contrast agent exposure during perfusion, post-fixation, and rehydration; (ii) post-fixation and rehydration only; (iii) rehydration only; (iv) no contrast agent. Quantitative T1, T2, T2*, and DWI data were acquired at five time points over 70 days using a 7T MRI system. Protocols involving contrast agent during perfusion or post-fixation produced comparable relaxation trajectories, with T1, T2, and T2* stabilizing by Day 13. On day 13 the T1 values of tissue that was exposed to contrast agent, regardless of the application protocol were between 230.86 ms and 266.89 ms, while the T1 values of the control group were over 1100 ms at this point in time. T2 values of the experimental groups were between 39.97 ms and 56.17 ms while T2 values of the control group were between 58.68 ms and 77.82 ms. T2* values of the experimental groups were between 27.27 ms and 43.33 ms while T2* values of the control group were between 46.16 ms and 65.93 ms. Importantly, contrast agent exposure during rehydration alone resulted in equivalent stabilization after two weeks, reflecting gradual contrast agent diffusion into the tissue. In contrast, control samples without contrast agent exhibited significantly elevated T2 and T2* at later time points. These results demonstrate that post-fixation contrast agent exposure during rehydration is sufficient to achieve stable relaxation parameters and DWI compatibility, assessed via fractional anisotropy (FA) and mean diffusivity (MD) in ex vivo avian brain tissue. This minimal preparation protocol enhances reproducibility, reduces handling complexity, and supports standardized cross-species neuroimaging of brain connectivity.

The primary motor cortex has a prominent role in transcranial magnetic stimulation (TMS). It is one of few locations that provide directly observable responses, and its physiology serves as model or reference for almost all other TMS targets, e.g., through the motor threshold and spatial targeting relative to its position. It furthermore sets the safety limits for the entire brain. Its easily detectable responses have led to closed-loop methods for a range of aspects, e.g., for automated thresholding, amplitude tracking, and targeting. The high variability of brain stimulation methods would substantially benefit from fast unbiased closed-loop methods. However, the development of more potent methods would early on in the design phase require proper models that allowed tuning and testing with sufficient conditions but without a high number of experiments, which are time-consuming and expensive or even impossible at the needed scale. On the one hand, theoretical researchers without access to experiments miss realistic spatial response models of brain stimulation to develop better methods. On the other hand, subjects should potentially not be exposed to early closed-loop-methods without sufficient prior testing. After all, initial, poorly tuned feed-back as needed for closed-loop operation is known to demonstrate erratic behavior. To bridge this gap, we developed a digital-twin-style population model that generates motor evoked potentials in response to virtual stimuli and includes statistical information on spatial (coil position and orientation) as well as pulse-strength-dependent neural recruitment in the population to represent inter- and intra-individual variability. The population data were extracted from a combination of datasets of healthy and depressed subjects to reach large numbers and wide coverage. The model allows users to simulate different subjects and millions of runs for software-in-the loop testing. The model provides all code open-source to stimulate further development.

Precise prognostication in acute brain injury is limited by a lack of reliable biomarkers of consciousness available to clinicians at the bedside. The ABCD framework is a method of classifying resting-state clinical EEG into categories that reflect levels of thalamocortical network function. ABCD classifications in the intensive care unit (ICU) have been shown to provide diagnostic and prognostic utility for patients with severe brain injuries, but the current gold standard for ABCD classification is visual inspection of power spectra, which is labor-intensive and requires expertise in spectral analysis. Using 4,611 manually classified EEG power spectra, we developed an automated, highly accurate, and well-calibrated convolutional neural net-based classifier of EEG into ABCD categories. The classifier has performance comparable to that of the current gold standard and that outperforms an alternative method of automated spectral analysis. As proof-of-principle for clinical implementation, we apply the classifier to a continuous EEG record from a patient with acute severe traumatic brain injury in the ICU, demonstrating its ability to yield continuous ABCD classifications that capture state fluctuations with high temporal and spatial resolution. The automated ABCD classifier allows for efficient analysis of continuous EEG records, facilitating the translation of the ABCD framework to the bedside for patients with acute severe brain injuries. The ABCD classifier also creates new opportunities to efficiently analyze large EEG datasets and generate new insights into the electrophysiological properties of human consciousness.

Classification of motor imagery (MI) tasks through EEG is valuable in brain-computer interfacing and rehabilitation engineering. EEG channels selection for MI task classification is well discussed problem and is challenging due to its combinatorial nature. Most of the existing methods are subject and task-dependent. This paper introduces a subject-independent EEG channel selection. The proposed approach consists of two stages. First, we rank channels based on their divergence from a reference channel Cz. We hypothesize that channels less divergent from Cz are more relevant for MI task classification. In the second stage, we employ a three-stage feature selection and classification model to evaluate the selected channels. It consists of a bandpass filter, followed by common spatial pattern (CSP) filter and three classifiers viz. SVM, 1-NN and 5-NN. Two publicly available datasets viz. PhysioNet and BCI Competition III IVa datasets have been used to assess the method. It performs 15.21\% more than 3Cs and just 2.91\% less than all-channels accuracy with as few as 20/118 channels on BCI Competition data and 19.64\% more than 3Cs on the PhysioNet dataset with 16/64 channels. Empirical comparison implies that the method performs better than classical models such as CSP Rank, fishers rank, and normalized mutual information, significantly. Results support that our hypothesis that divergence between channels and a reference channel Cz can be used as a ranking measure for channel selection.

Traumatic brain injury produces widespread axonal damage can be assessed histologically using amyloid precursor protein (APP) immunohistochemistry, which labels injured axonal profiles at cellular resolution [1, 2]. However, quantification of APP pathology remains a major bottleneck: annotation is manual, time-consuming, spatially localized, and variable across raters, limiting scalability and reproducibility. This limitation is particularly important in studies that use histology as a reference for neuroimaging or other tissue-level measurements, where cellular APP pathology must be quantified in a spatial form that can be aligned with imaging abnormalities. Here, we introduce PIGMENT, an annotation-efficient deep-learning framework for automated segmentation and quantification of APP-positive pathology in porcine white matter histology. PIGMENT uses a compact SegFormer-B0 architecture trained on 525 expert-annotated 512 x 512-pixel tiles from four APP-stained sections across three pigs. Because APP-positive profiles are sparse, fragmented, stain-variable, and morphologically diverse, PIGMENT combines limited expert labels with APP-specific augmentation designed to model variation in APP-positive intensity, size, continuity, fragmentation, and local tissue context. We evaluated PIGMENT using an instance-level detection rate that measures whether discrete APP-positive components are localized. Across held-out APP-stained data, PIGMENT achieved a mean instance-level detection rate of 0.86. Across the configurations tested, the highest mean detection rate was achieved by a training set that included sections from different animals, suggesting that annotation diversity may be an important factor under limited-label conditions. By extending limited high-confidence expert annotations into whole-section APP burden maps, PIGMENT provides a scalable framework for characterizing the extent and spatial distribution of traumatic axonal injury. These maps may support future studies that align histological injury burden with imaging-derived measures.

The behavioural activity of freely moving animals is a confounding factor that affects the recording, analysis, and final results of animal EEG experiments. Along with the lack of standardisation in animal in vivo electrophysiology experiments, this could lead to huge inconsistencies, especially in the analysis of centrally acting drugs. Therefore, the main aim of this paper is to investigate the effects of behavioural activity versus inactivity on the multichannel EEG in freely moving rats. In a large sample (n = 116) of waking recordings from 12 cortical electrodes (ECoG) in Wistar rats, we evaluated behavioural activity-related changes in the power spectrum, current source density, and power-based global functional connectivity (GFC) in a 3D rat brain model, according to the TOHOKU Rat Brain Atlas. The main findings were that behavioural activity induced 1) a robust power increase in 6-8 Hz, peaking at 7 Hz with maximum changes over the parietal and temporal cortex, 2) an increase in gamma power (30-80 Hz) across the whole brain, 3) a decrease in delta (1-4 Hz) and beta (12-30 Hz) power across the whole cortex. Changes were also localised in subcortical regions, particularly in the diencephalon/thalamus. The GFC analysis showed a similar pattern of power changes across the 6-8 Hz, delta, and beta bands; however, GFC in the gamma band decreased. Again, the GFC analysis revealed changes in connectivity within subcortical structures, primarily in the thalamus. None of the measures was affected in the alpha band (8-12 Hz). These findings emphasise behavioural state as a critical factor influencing EEG outcomes, with important implications for the standardisation and translational validity of preclinical neurophysiological studies.

We propose a study protocol for routine clinical electroencephalograms (EEGs) from public hospitals, which represents a vast resource for neuroscience research. These non-invasive measures of brain function, paired with rich clinical annotations from large and diverse patient populations, are critical for developing robust artificial intelligence (AI) models and conducting population-level studies. This protocol presents a scalable methodology for curating and harmonizing extensive clinical EEG datasets, encompassing over 40,000 individual studies, to facilitate research applications. Key steps include: (i) integration of raw EEG recordings with corresponding clinical records, including neurological reports, diagnostic codes, and potentially medication data; and (ii) spatial standardization of EEG signals by mapping them to a common brain space defined by functional and anatomical landmarks. The resulting harmonized datasets enable the development of large-scale EEG foundation models, the discovery of novel EEG waveform representations, and the creation of normative "brain charts" for electrophysiological assessment across the lifespan. By enabling standardised, large-scale analyses of real-world clinical EEG data, this protocol supports data-intensive solutions for EEG applications and addresses the challenge of generalising AI models. Our approach promotes the translation of AI tools from research to diverse patient populations, advancing population neuroscience.

Joint music making offers an ecologically powerful framework for investigating human social interaction and synchronization. Yet, experimental paradigms often rely on traditional instruments that limit accessibility, reproducibility, and experimental control. In parallel, the use of music for therapy and rehabilitation is expanding, motivating the development of digital musical instruments that can serve research, educational, and clinical purposes. Here, we introduce the e-Music Box Roma (eMB Roma), an open, reproducible digital musical instrument designed to study music making behavior regardless of musical training. The eMB Roma plays preregistered music with tempo controlled by hand rotary movements. Building on the original e-Music Box (Novembre et al., 2015), the eMB Roma retains its intuitive rotary hand control while introducing major innovations: a fully open and 3D-printable design, modular hardware with integrated slider and button controls, polyphonic output with multiple simultaneous instruments, and MIDI compatibility. Additionally, a dedicated graphical user interface allows real-time monitoring, experiment control, device synchronization (like neuroimaging or motion capture devices), and both solo and joint music-making paradigms. The eMB Roma provides a flexible and accessible platform for research contexts, allowing experimental control, reproducibility, and future extensions. Its open design and modularity make it suitable not only for research but also for therapeutic, rehabilitation, and educational applications, where it can support personalized interventions and quantitative assessment of motor performance.

Cortico-thalamo-cortical circuits entail extensive trans-thalamic connectivity between cortical areas, yet their structural organization and function remain poorly understood. Here, the thalamocortical projections of several higher-order thalamic nuclei were characterized by retrograde tracing from two cortical areas, the primary somatosensory (S1) and motor (M1) cortices. Cholera toxin B conjugated with different fluorophores allowed for simultaneous detection of projection neurons targeting S1 and M1. A cell detection pipeline based on neural networks was developed to allow semi-automated analysis of large thalamic imaging volumes to quantitatively infer the spatial distribution of projection neurons in the posterior complex (PO) and the adjacent ethmoid nucleus (Eth), nucleus centrolateralis (CL), nucleus paracentralis (PCN), and the nucleus parafascicularis (PF). The arrangement of neurons projecting to both, primary somatosensory and motor cortices, occurs at different connection strengths and was topographically organized in all nuclei studied. Co-injections into both cortical areas revealed projection neurons with axons branching into both S1 and M1 cortices. Our work introduces a pipeline for semi-automated quantitative analysis of thalamic projection patterns that could be useful for connectivity analyses in general. This approach revealed repetitive anatomical patterns in different thalamic nuclei with regard to projection strength, spatial organization and fraction of projection neurons targeting two cortical areas simultaneously.

Neurophysiology datasets are becoming increasingly complex, combining behavioral measurements with high-dimensional neuronal activity recordings coming from optical and/or electrophysiological measurements. The Neurodata Without Borders (NWB) standard has emerged in the community as the format of record. While standardized and widely used preprocessing tools generating NWB files have been developed, extensible frameworks for scientific analysis downstream of the NWB ecosystem are still under-represented. We present CICADA, a Python framework dedicated to analysis of neurophysiological data in the standardized NWB format. The toolbox is built as three hierarchically-organized packages: cicada-nwb (NWB access layer), cicada-analysis (plugin-based analysis engine and tool library), and cicada-gui (PyQt5 desktop application at the head of the pipeline). Beyond this architectural separation, CICADA is built around a central design principle: supporting a continuum from turnkey use to full modularity. Researchers can use the complete GUI-driven cicada-gui workflow without writing code, programmatically use existing analysis plugins from cicada-analysis, contribute to new analysis plugins, reuse utilities from cicada-tools, or build entirely custom pipelines on top of the cicada-nwb access layer alone. The same analysis plugin runs identically in interactive GUI and parameter-configured headless modes, enabling reproducible multi-session, multi-animal group analyses. We illustrate the versatility of CICADA with example analyses of behavioral, calcium imaging (two-photon and widefield) and extracellular electrophysiology datasets from rodent laboratories. CICADA is open source, actively maintained, and designed so that any laboratory can contribute at any level of the stack without modifying the core framework.

The corpus callosum is a major white matter bundle responsible for connecting both hemispheres. In mammals, due to a variety of causes, the development of the corpus callosum can be impaired - this brain malformation is known as corpus callosum dysgenesis (CCD). The clinical presentation of CCD varies, with patients exhibiting three morphological phenotypes: agenesis, partial dysgenesis, and hypoplasia. Although the first two presentations are easily detectable on MRI scans, the latter is more challenging, as the structure is fully formed but has a reduced area. In this study, we develop (1) a pipeline to generate synthetic MRI scans with apparent anatomical variation and (2) train a U-Net-based tool to automatically segment the corpus callosum of marmosets in both healthy and disease contexts. Methodologically, a custom script was devised to apply rotation and translation to T1-weighted MRI scans at the volume level. Because the slicing grid remains unchanged, these rigid transformations translate into apparent anatomical variations at the slice level. We compared corpus callosum measurements obtained from automatically segmented masks with those from manually delineated masks. The average Dice score was above 0.90, and the Hausdorff distance was below 0.4 mm. We also stratified our cohort according to phenotype (healthy controls and hypoplastic animals). The magnitude of the effect and the significance level observed between the voxel counts of healthy and hypoplastic animals using manually delineated masks were comparable to those obtained via automatic segmentations. These results show that our pipeline can generate a sufficiently varied training pool to build an accurate U-Net segmentation model with high diagnostic capability.

Neural stem cell (NSC) transplantation is a promising strategy for repairing the injured spinal cord, but transplanted cells typically require immunosuppressive therapy to prevent rejection, even for induced pluripotent stem cell (iPSC)-derived autologous grafts. However, the effects of immunosuppressive drugs on neurite outgrowth and axonal regeneration, processes critical for neural circuit reconstruction, have not been fully characterized. In this study, we tested nine clinically relevant immunosuppressants on human iPSC-derived neurons and primary human spinal cord NSCs in vitro at concentrations approximating clinical exposure levels. The drug panel included FK-506 (tacrolimus), cyclosporine A (CsA), rapamycin, belatacept (Nulojix), etanercept (Enbrel), mycophenolate mofetil (CellCept), cyclophosphamide (Cytoxan), prednisone, and azathioprine (Imuran). Neurite outgrowth was quantified via automated high-content imaging. Multiple agents, including CsA, Imuran, Nulojix, and CellCept, induced significant reductions in neurite outgrowth in a cell type- and dose-dependent manner, with CsA producing the most robust and consistent inhibition across both cell lines. In contrast, FK-506 showed no significant effect on neurite extension at clinically relevant concentrations. Consistent with the in vitro results, human neural progenitor cell grafts in a rodent spinal cord injury model exhibited significantly reduced graft-derived axon extension in the host spinal cord when hosts were treated with CsA rather than FK-506. These findings demonstrate that immunosuppressant choice can profoundly influence neural graft integration and axonal regeneration. Our study underscores the importance of preclinical evaluation of immunosuppressive regimens and suggests that selecting agents such as FK-506 over CsA may improve outcomes in future stem cell-based therapeutic trials for spinal cord injury and related disorders of the central nervous system.