Neuroinformatics

Three-dimensional (3D) whole-organ imaging and analysis at cellular resolution (termed 3D histology) provide profound insights into the organization and interactions of cells throughout organs. However, the quantitative analysis of these massive datasets remains a significant bottleneck due to the lack of integrated, user-friendly tools. Here, we present 3DBrainOne, an end-to-end ImageJ plugin that streamlines the essential 3D histological analysis of the mouse brain--from raw image preprocessing to region-wise quantification--within a single platform. 3DBrainOne features a robust whole-brain cell-counting module that uses a Difference-of-Gaussians (DoG) blob detection algorithm followed by a ResNet18-based deep learning classifier, enabling high-fidelity automatic whole-brain cell counting with a graphical user interface (GUI) for visual inspection and manual curation of analysis results. 3DBrainOne also supports multi-channel colocalization analysis. Furthermore, this platform includes modules for atlas alignment and brain-region-wise volumetric quantification, enabling brain region-resolved cell counting and structural analyses. As an ImageJ plugin, 3DBrainOne is compatible with a range of operating systems and hardware. In summary, 3DBrainOne is an integrated, versatile, and easy-to-use platform that will facilitate 3D histological analyses in experimental neuroscience.

Background: The rapid expansion of large-scale neuroscience datasets has increased the need for automated, accurate, and standardized quality control (QC). Manual proofreading of 3-dimensional neural morphology (SWC files) remains labor-intensive, error-prone, and non-scalable. We developed and evaluated a fully automated, machine-learning driven QC pipeline to standardize neural reconstructions, detect and correct structural anomalies, and rectify dendritic labeling in pyramidal neurons. Methods: We developed an end-to-end, cloud-deployed pipeline for automated QC, correction, and standardization of SWC-formatted neural morphologies. The framework integrates deterministic structural normalization, topology repair, geometric correction, quantitative morphometric analysis, and graph-based dendritic relabeling within a containerized React/Flask architecture deployed on Amazon Web Services. Rule-based algorithms systematically detect, classify, and correct structural irregularities including overlapping nodes, spurious side branches, non-positive radii, disconnected components, and anomalously long parent-child connections. A graph convolutional network, trained on Sholl-derived features from 20,500 pyramidal neurons, performs dendritic relabeling. Model training employed an 80/10/10 train-validation-test split with adaptive learning-rate scheduling and distributed execution across ten runs to evaluate stability and reproducibility. The pipeline generates images of the final product and computes quantitative morphometrics using L-Measure. Results: All neuronal reconstructions were processed without manual intervention. Automated normalization and topology repair restored structurally coherent and biologically accurate morphologies suitable for quantitative analysis and visualization without data loss. Dendritic relabeling achieved a mean accuracy of 99.51%, consistent between validation and test sets, with class-weighted precision of 0.978, recall of 0.977, and F1-score of 0.977. Enforcing a single apical dendritic tree per neuron improved anatomical consistency without reducing classification performance. Distributed training completed all runs in approximately 25 hours, demonstrating scalability and reproducibility for large datasets. Conclusions: We present a fully automated and cloud-scalable open-source pipeline for standardizing neural reconstructions and performing biologically consistent dendritic classification with near-perfect accuracy. The automated correction and relabeling procedures do not alter or compromise the size or unaffected morphological detail of the original SWC files, ensuring geometric fidelity and compatibility with downstream analysis tools. This open-access framework provides a robust foundation for high-throughput neural morphology curation and large-scale neuroanatomical analysis.

Hippocampal area CA2 has recently emerged as a critical region for social recognition memory. Furthermore, this understudied region has been implicated in psychiatric diseases and neurodegenerative diseases. There has been accumulating evidence indicating that the pyramidal neurons (PNs) in area CA2 exhibit functional specializations that correlate with somatic position in stratum pyramidale (sp). In this study, we investigated the morphological differences in dendritic architecture of CA2 PNs with a focus on the radial gradient, i.e., along the deep-superficial axis of the sp. We conducted a comprehensive morphological analysis including Sholl intersection profiles, branching order distributions, root angle distributions, and dendritic cable lengths. We found that CA2 PNs have fewer oblique dendrites and a larger number of tuft-like dendrites as compared to CA1 PNs. Furthermore, within the CA2 population, we found that many of the dendritic structural features gradually changed along the radial axis from deep to superficial somatic location, indicating a continuum of dendritic morphology rather than two sharply defined subtypes of pyramidal neurons. This morphological characterization may serve as a starting point to better understand the corresponding functional organization of CA2. The gradual difference between deeper and superficial CA2 PNs suggests a continuum of their computational capabilities beyond two binary functional classes. In briefUsing several methods, we examine the dendritic morphology of over 130 CA2 and CA1 pyramidal neurons and find that many properties such as the cable length and terminal numbers of the dendritic arbors vary as a with the location of the soma in the pyramidal layer. HighlightsO_LIWe use scholl analysis, graph theory and machine learning techniques to quantify the different dendritic morphologies of CA2 pyramidal neurons. C_LIO_LIMany properties of CA2 pyramidal neuron apical dendrites vary as a function of somatic location in the pyramidal layer. C_LIO_LIMore superficial CA2 pyramidal neurons have longer oblique apical dendrites, and shorter tuft dendrites. C_LI

Lick microstructure is a term used in behavioural neuroscience to describe the information that can be obtained from a detailed examination of rodent drinking behaviour. Rather than simply recording total intake (volume consumed), lick microstructure examines how licks are grouped, and the spacing of these groups of licks. This type of analysis can provide important insights into why an animal is drinking, for example, whether it is influenced by taste or affected by consequences of consumption (e.g., feeling "full"). Here we present a software package, lickcalc, that allows detailed microstructural analysis of licking patterns. The software is browser-based and is hosted at https://lickcalc.uit.no or the repository can be downloaded and installed locally. Lick timestamps can be loaded from a variety of formats and different analysis and plotting options allow quality control of data and determining critical parameters for microstructural analysis number and size of lick bursts. Data can be divided into epochs for detailed examination of changes across session. Batch processing and custom configurations are supported. In this manuscript, we demonstrate use of the functions exposed by lickcalc by analysing data comparing lick patterns between mice on a protein-restricted and control (non-restricted diet). We show that lickcalc allows quality control of the data and uncovering of subtle differences in lick behaviour that are not apparent when just considering the total number of licks. This software makes microstructural analysis accessible to any researchers who wish to employ it while providing sophisticated analyses with high scientific value.

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

We present a free and open-source, semi-automated, topologically robust pipeline for fitting cable models to 3D surface mesh morphology data of neuronal membranes, particularly suited to structures with complex shapes and topological holes. The motivation for this work is the discovery of morphologically complex neural spines on the auditory space-specific neurons of the barn owl (Tyto alba, Tyto furcata), dubbed "toric spines", notable for their high curvature, branching density, and holes/loops. Multicompartmental simulation software requires morphology to be represented as cable models (e.g., SWC format), yet existing software tools for fitting cable models to complex 3D surface meshes have not produced satisfactory results for toric spines, and loops are generally unsupported. We present the Mesh and Skeleton Cable Fitting (MASCAF) pipeline and software, which fits a cable model (e.g., SWC format) to a surface mesh using mean-curvature flow skeletonization. In this paper, we demonstrate how MASCAF is applied to fit cable models, how loops can be reconstructed in simulations with the Arbor and NEURON simulation software, and how the results can be validated using geometry and simulator-based methods. While non-tree morphologies such as toric spines are neuroanatomically special, our software pipeline provides a cable-model fitting approach for surface mesh data that is topologically robust, deterministic, open-source, and applicable to general morphologies, thereby closing a crucial gap between neuronal imaging and high-resolution simulation.

Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come from soft-science backgrounds (like medicine, biology, or psychology) and do not have enough quantitative skills to understand the inner workings of A/CNNs. To avoid undesirable black boxes, neuroscientists should acquire some rudiments of computational neuroscience and machine learning. However, most researchers do not have the time nor the resources to make big learning investments, and self-study materials are hardly tailored to people with little mathematical background. This paper aims to fill this gap by providing a concise but accurate introduction to CNNs and their use in neuroscience -- using the minimum required mathematics, neuroscientific analogies, and Python code examples. A companion Jupyter Notebook guides readers through code examples, translating theory into practice and providing visual outputs. The paper is organised in three sections: The Concepts, The Implementation, and The Biological Plausibility of A/CNNs. The three sections are largely independent, so readers can either go through the entire paper or select a section of interest.

Anatomically and biophysically detailed models of neurons have been widely used to study information processing in these cells. Most studies focused on understanding specific phenomena, while more general models that aim to capture various cellular processes simultaneously remain rare even though such models are required to predict neuronal behavior under more complex, natural conditions. In this study, we aimed to develop a detailed, data-driven, general-purpose biophysical model of hippocampal CA1 pyramidal neurons. We leveraged extensive morphological, biophysical and physiological data available for this cell type, and established a systematic workflow for model construction and validation that relies on our recently developed software tools. The model is based on a high-quality morphological reconstruction and includes a diverse curated set of ion channel models. After incorporating the available constraints on the distribution of ion channels, the remaining free parameters were optimized using the Neuroptimus tool to fit a variety of electrophysiological features extracted from somatic whole-cell recordings. Validation using HippoUnit confirmed the models ability to replicate key electrophysiological features, including somatic voltage responses to current input, the attenuation of synaptic potentials and backpropagating action potentials, and nonlinear synaptic integration in oblique dendrites. Our model also included active dendritic spines, modeled either explicitly or by merging their biophysical mechanisms into those of the parent dendrite. We found that many aspects of neuronal behavior were unaffected by the level of detail in modeling spines, but modeling nonlinear synaptic integration accurately required the explicit modeling of spines. Our data-driven model of CA1 pyramidal cells matching diverse experimental constraints is a general tool for the investigation of the activity and plasticity of these cells and can also be a reliable component of detailed models of the hippocampal network. Our systematic approach to building and validating general-purpose models should apply to other cell types as well. Author SummaryThe brain processes information through the activity of billions of individual neurons. To understand how these cells work, scientists build detailed computer models that reproduce their electrical behavior. These models make it possible to explore situations that are difficult or impossible to test experimentally. However, many existing neuron models were designed to explain only a few specific phenomena, which limits their usefulness in more complex settings. In this study, we developed a comprehensive computer model of a hippocampal CA1 pyramidal neuron, a cell type that plays a central role in learning and memory. We built the model using extensive experimental data and applied automated methods to ensure that it reproduces a broad range of observed neuronal behaviors. We also examined how small structures called dendritic spines--tiny protrusions where most synaptic communication occurs--affect how neurons combine incoming signals. We found that even simplified models without individual spines can capture many aspects of neuronal activity, but understanding more complex forms of signal integration requires modeling spines explicitly. Our work also supports the development of more realistic simulations of brain circuits.

Neural information flow describes the movement of activity between neurons or brain areas. Advances in experimental methods have allowed production of large amounts of observational data related to neuronal activity from the single-neuron to population level. Most current methods for analysing these data are based on pairwise comparison of activity, and fall short of reliably extracting neural information flow network structure. Dynamic Bayesian networks may overcome some of these limitations. Here we evaluate the performance of a range of Bayesian network scoring metrics against the performance of multivariate Granger causality and LASSO regression for their ability to learn the connectivity underlying simulated single-neuron and neuronal population data. We find that discrete dynamic Bayesian networks are the best performing method for single-neuron data, and perform consistently for neural-population data. Continuous dynamic Bayesian networks have a tenancy to learn overly dense structures for both data types, but may have utility in scoping studies on single-neuron data. Multivariate Granger causality is the most robust method for learning structure of neural information flow between neural-populations, but performs poorly on single-neuron data. Significance testing within multivariate Granger causality produces variable results between data types. Overall, this work highlights how the analysis of neural information flow can vary depending on they type and structure of underlying data, and promotes discrete dynamic Bayesian networks as a useful and consistent tool for neural information flow analysis.

One of the prominent methods in neuroimaging data processing is SSM-PCA, which is based on principal component analysis and allows for the identification of diagnostically significant patterns in the form of statistical maps. We developed software, PIE Toolbox, employs SSM-PCA and classification based on the obtained diagnostic patterns revealed from functional and structural tomographic brain imaging. The program supports the entire analysis pipeline including preprocessing of brain images, diagnostic patterns extraction, building classification models, and prediction based on them. The resulting diagnostic patterns are weighted principal components obtained through SSM-PCA, or their linear combinations. PIE Toolbox allows selection of relevant structural and functional brain patterns, computation of their expression values in regions of interest, classification using support vector machines, and evaluation of model performance via cross-validation. This approach enables the use of patterns as features of intergroup differences for individual diagnosis. The software has been validated on both simulated and ADNI datasets.

Human brain tissue preserved in biorepositories is foundational for the structural, cellular, and biomolecular research necessary for a mechanistic understanding of neurological diseases. Realizing the research potential of these valuable resources requires well-characterized research-relevant tissue that can be efficiently identified by investigators and incorporated into the conceptual and computational frameworks of interdisciplinary research. Several large-scale efforts to improve research reliability and reproducibility have sought to characterize and annotate the processes by which these samples are collected, yet limited progress has been made on standardizing spatial information for these samples. Biorepositories systematically collect brain tissue according to a brain sampling protocol (BSP) that differs between institutions, yet explicit spatial information regarding the samples may not be documented in standard operating procedures (SOPs). The amount of anatomical location details available to investigators are inconsistent across biorepositories and typically lack sufficient anatomical precision to ensure correspondence with samples from other biorepositories or research relevant brain regions specified by neuroimaging, functional, or disease-susceptibility criteria. Here, we introduce a pipeline for developing a Spatial Atlas for Mapping Protocol Locations of Ex vivo Samples (SAMPLES), which uses a neuroimaging framework to create a 3D representation of a BSP through a metrically precise digital instantiation of the procedures for brain extraction, segmentation, slicing, and sampling on a modern digital brain template. SAMPLES incorporates modern neuroinformatics conventions to create explicit 3D labels of BSP-defined samples that can be interactively visualized with freely available neuroimaging software. We illustrate the pipeline by developing an atlas for the protocol from the University of Washington BioRepository and Integrated Neuropathology laboratory (UW BRaIN SAMPLES). By providing an explicit, computable reference, SAMPLES atlases can support the efficient identification, referencing, and utilization of postmortem samples for interdisciplinary research. These capabilities enable biorepository workflows, data harmonization across biorepositories, and integration with antemortem neuroimaging.

Electroencephalography (EEG) preprocessing is a critical yet time-consuming step that often relies on expert-driven, semi-automatic pipelines, limiting scalability and reproducibility across large datasets. In this work, we present sEEGnal, a fully automated and modular pipeline for EEG preprocessing designed to produce outputs comparable to expert-driven analyses while ensuring consistency and computational efficiency. The pipeline integrates three main modules: data standardization following the EEG extension of the Brain Imaging Data Structure (BIDS), bad channel detection, and artifact identification, combining physiologically grounded criteria with independent component analysis and ICLabel-based classification. Performance was evaluated against manual preprocessing performed by EEG experts at two complementary levels: preprocessing metadata (bad channels, artifact duration, and rejected components) and EEG-derived measures. In addition, test-retest analyses were conducted to assess the stability of the pipeline across repeated recordings. Results show that sEEGnal achieves performance comparable to expert-driven preprocessing while preserving key neurophysiological features. Furthermore, the pipeline demonstrates reduced variability and increased consistency compared to human experts. These findings support sEEGnal as a robust and scalable solution for automated EEG preprocessing in both research and large-scale applications. HighlightsFully automated and modular EEG preprocessing pipeline. Benchmarked against expert-driven preprocessing. Comparable performance in metadata and EEG-derived measures. Demonstrates stable performance in test-retest recordings. BIDS-based framework for reproducible EEG data handling.

Despite growing understanding of the benefits of having Findable, Accessible, Interoperable, and Reusable (FAIR) data, many datasets still cannot be shared. Federated analysis methods can enable multisite studies that do not require the sharing of participant-level information. However, there are many practical hurdles that prevent the large-scale adoption of federated methods. We discuss challenges related to cross-site data preparation for federated learning, present solutions offered by recent neuroinformatics projects, and showcase an example of tool integration applied to neurodegenerative disease data.

Currently, there is no consensus about how investigators should format their NIBS data for sharing. This presents a barrier to the advancement of big data analyses because it requires time-consuming operations to generate consistent formats across different shared datasets. Recently, we launched Big non-invasive brain stimulation data (Big NIBS data), an open-access platform and repository for NIBS data (https://www.bignibsdata.com/), providing a structured mechanism for researchers to share NIBS data. However, the reusability and interoperability of data uploaded to Big NIBS data is restricted by the absence of a common data structure. The current paper addresses this problem by creating the NIBS data analysis structure (NIBS-DAS), a template pipeline for the layout, management, and analysis of collated NIBS outcome data. While its primary purpose is to provide a template layout for uploading collated data to the Big NIBS data repository, NIBS-DAS also offers guidelines for the management and analysis of collated NIBS data, thereby forming a data analysis pipeline that can be freely used by the NIBS field in general. We anticipate that NIBS-DAS will serve to facilitate data sharing on the Big NIBS data platform and promote greater standardisation of data management and analytical practices in the NIBS field.

BackgroundThe relevance of spontaneous activity has been unlocked thanks to recent large scale recordings that revealed, via Shared Variance Component Analysis (SVCA), the high-dimensional nature of the ongoing activity. A fundamental problem is how the dimension modifies when more neurons are included in the analysis. Contradictory results have been reported on this subject based on SVCA and Principal Component Analysis (PCA). New MethodWe investigate pro et contra of SVCA and PCA for the identification of reliable responses encoding underlying state variables. We focus on common features of the spectra of the reliable variances (RVs) and on their dimensionality. The analysis is demonstrated on previously published Ca2+ data from the visual and the dorsal cortex in head fixed mice during spontaneous behavior. ResultsRVs grow proportionally to the number N of neurons and show a power-law decay k- with the k-th SVC dimension over a range bounded by a maximal dimension kc, initially diverging as N 1/ and then saturating at sufficiently large N. The reliable dimensionality, estimated with different methodologies, also shows a clear saturation to an asymptotic value for large N. Furthermore, its value decreases when becomes larger, as demonstrated by employing experimental data as well as theoretical predictions. ConclusionWe have shown that SVCA is an extremely effective tool to extract reliable features from the neural signals, and that the exponent represents a biomarker able to reveal the level of correlation of the neurons as well as the dimensionality of the reliable space. HighlightsO_LIAdvantages and drawbacks of Shared Variance Component Analysis to extract reliable signals from neural data C_LIO_LIComparison of different methods to estimate reliable neural dimensionality associated to spontaneous activity C_LIO_LIAnalytical expressions of embedding dimensionality for power-law decaying reliable variances C_LIO_LIBounded growth of the dimensionality with the number of neurons C_LI

SHORT ABSTRACTA key barrier to developing effective drugs for disorders of the central nervous system (CNS) is understanding their impact on neural circuits. This protocol demonstrates how physics-based neural simulations can be used to interpret electrophysiological biomarkers of neurotherapeutics, providing a mechanistically grounded approach to the development of neurotherapeutics. LONG ABSTRACTElectroencephalography (EEG) and electrophysiology methods provide millisecond resolution biomarkers for central nervous system disorders and are used to assess treatment-related effects. However, lack of understanding about the neural mechanisms generating such biomarkers impedes the development of diagnostics and therapeutics based on these signals. The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling software that connects localized EEG biomarkers to their multi-scale neural generators. This protocol demonstrates a hypothesis-driven workflow using HNN to test possible neural mechanisms of neurotherapy-induced EEG biomarkers by optimizing parameters to achieve a fit between simulated and empirical current source waveforms. Corresponding multi-scale cell- and circuit-level activity can then be visualized and quantified, providing validation targets for model predictions in follow up empirical studies. An example is provided which shows how to examine the generating mechanisms of the early event-related potential (ERP) components of an auditory evoked response (P1, N1 and P2) and to assess changes following neural circuit modification due to neurotherapeutic administration. This protocol demonstration enables scientists to design simulation experiments to develop testable predictions on how EEG biomarkers reflect neural circuit mechanisms of example therapeutics. A similar protocol can be applied to study disease mechanisms or other therapies.

1.Quantitative neuropathology has advanced through whole-slide imaging and digital histology platforms. Yet, these measurements rarely align with neuroimaging coordinate frameworks that may be useful for spatial modeling and other applications. QNPtoVox, short for quantitative neuropathology to voxels, is a reproducible, modular pipeline that transforms quantitative metrics generated by digital pathology software (HALO) into voxel-based maps registered to a standard common coordinate (MNI) template. The workflow integrates digital histopathology, gross tissue photography, ex-vivo MRI, and nonlinear registration to generate spatially standardized 3D pathology representations. This Methods article provides a complete procedural description, including required materials, step-wise instructions, operator-dependent checkpoints, expected outputs, reproducibility evaluation, and troubleshooting. QNPtoVox enables voxel-level integration of neuropathology with neuroimaging tools, unlocking existing histopathology datasets for computational modeling and cross-cohort harmonization.

A persistent bottleneck in post-Independent Component Analysis (ICA) Electroencephalogram (EEG) preprocessing is the manual identification of artifact components for removal. In practice, this step can be slow, subjective, and difficult to standardize, particularly for cardiac contamination and trigger-related leakage, where artifact structure may be distributed across multiple components or appear outside the highest-variance Independent Components (ICs). We developed the SENSI-EEG-Preproc-ICA-EKG-Trigger Module to make this stage faster and more reproducible without removing the user from the decision process. The Module is a semi-automated MATLAB framework for post-ICA screening of cardiac and trigger-related artifact components using spectral characteristics. EKG candidates are prioritized by detecting harmonic structure around a physiologically plausible heart-rate fundamental, whereas trigger-related candidates are prioritized by measuring harmonic concentration at frequencies determined by the known repetition period of the trigger sequence. The resulting candidates are then reviewed in dedicated interfaces that present scalp topography, time-domain activity, and frequency-domain structure together, allowing the final classification to be confirmed or corrected by the user. In this way, the Module narrows the search space while preserving interpretability and explicit human control over the final keep/remove decision. The release includes a public codebase, a user manual, example workflows, and an accompanying example dataset. This paper presents the Module as a practical methods-and-software contribution for post-ICA EEG cleaning.

Neurodegenerative diseases such as Mild Cognitive Impairment (MCI), Multiple Sclerosis (MS), Parkinsons Disease (PD), and Amyotrophic Lateral Sclerosis (ALS) are becoming more prevalent. Each of these diseases, despite its specific pathophysiological mechanisms, leads to widespread reorganization of brain activity. However, the corresponding neurophysiological signatures of these changes have been elusive. As a consequence, to date, it is not possible to effectively distinguish these diseases from neurophysiological data alone. This work uses Magnetoencephalography (MEG) resting-state data, combined with interpretable machine learning techniques, to support differential diagnosis. We expand on previous work and design a Riemannian geometry-based classification pipeline. The pipeline is fed with typical connectivity metrics, such as covariance or correlation matrices. To maintain interpretability while reducing feature dimensionality, we introduce a classifier-independent feature selection procedure that uses effect-sizes derived from the Kruskal-Wallis test. The ensemble classification pipeline, called REDDI, achieved a mean balanced accuracy of 0.81 ({+/-}0.04) across five folds, representing a 13% improvement over the state-of-the-art, while remaining clinically transparent. As such, our approach achieves reliable, interpretable, data-driven, operator-independent decision-support tools in Neurology.

Over the past two decades, open-source research software such as SPM, AFNI and FSL formed the substrate for advancements in the brain functional magnetic resonance imaging (fMRI) field. The spinal cord fMRI field has matured substantially over the past decade, yet there is limited research software tailored for processing cord fMRI data that has distinct noise sources, unique challenges, niche processing requirements and special needs. Spinal cord fMRI data analysis is a different beast, involving specialized pre- and post-processing steps due to the cords unique anatomy and higher distortions/physiological noise, thus requiring extensive and careful quality assessment. Building upon 10+ years of research and development, we present Neptune - a user-interface-based MATLAB toolbox. With 30,000+ lines of in-house code, it is designed to be easy to use and does not require programming knowledge. Neptune builds on our previously published 15-step pre-processing pipeline (Barry et al., 2016) and presents a 19-step pipeline with new processing steps, and enhancements to existing steps. Neptune has a 4-step post-processing pipeline aimed at fMRI connectivity modeling. It generates extensive and novel quality control visuals to enable a thorough assessment of data quality, and displays them in an elegant webpage format. We demonstrate the utility of Neptune on our 7T data. Certain features of the popular Spinal Cord Toolbox (SCT) are integrated into Neptune, and users can import/export between Neptune and other software such as FSL and SPM. The availability of this open-source, easy-to-use software will benefit the spinal cord fMRI community, and also tip the cost-benefit balance for brain fMRI researchers to invest in learning new software to conduct important neuroscientific and clinical research using spinal cord fMRI.