Neuroinformatics

Electrophysiology data acquisition of single neurons represents a key factor for the understanding of neuronal dynamics. However, the traditional method to acquire this data is through patch-clamp technology, which presents serious scalability flaws due to its slowness and complexity to record at fine-grained spatial precision (dendrites and axon). In silico biophysical models are therefore created for simulating hundreds of experiments that would be impractical to recreate in vitro. The optimal way to create these models is based on the knowledge of the morphological and electrical features for each neuron. Since large-scale data acquisition is often unfeasible for electrical data, previous expert knowledge can be used but it must have an acceptable degree of similarity with the type of neurons that we are trying to model. Here, we present a data-driven machine learning approach to predict the electrophysiological features of single neurons in case of only having their morphology available. To solve this multi-output regression problem, we use an artificial neural network that has the particularity of providing a probability distribution for every output feature, to incorporate uncertainty. Input data to train the model is obtained from from the Allen Cell Types database. The electrical properties can depend on the morphology, whose acquisition technology is highly automated and scalable so there exist large data sets of them. We also provide integrations with the BluePyOpt library to create a biophysical model using the original morphology and the predicted electrical features. Finally, we connect the resulting biophysical model with the Geppetto UI software to run all the simulations in a sophisticated user interface.

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.

MotivationRecent advances in reconstructing 3D neuron morphologies at the whole brain level offer exciting opportunities to study single cell genotyping and phenotyping. However, it remains challenging to define cell types and sub-types properly. ResultsAs morphological feature spaces are often too complicated to classify neurons, we introduce a method to detect the optimal subspace of features so that neurons can be well clustered. We have applied this method to one of the largest curated databases of morphological reconstructions that contains more than 9,400 mouse neurons of 19 cell types. Our method is able to detect the distinctive feature subspaces for each cell type. Our approach also outperforms prevailing cell typing approaches in terms of its ability to identify key morphological indicators for each neuron type and separate superclasses of these neuron types. the subclasses of neuronal types could supply information for brain connectivity and modeling, also promote other analysis including feature spaces. AvailabilityAll datasets used in this study are publicly available. All analyses were conducted with python package Scikitlearn 0.23.1 version. Source code used for data processing, analysis and figure generation is available as an open-source Python package, on https://github.com/SEU-ALLEN-codebase/ManifoldAnalysis Contactljliu@braintell.org

The recent development of high-resolution three-dimensional (3D) digital brain atlases and high-throughput brain wide imaging techniques has fueled the generation of large datasets that can be registered to a common reference frame. This registration facilitates integrating data from different sources and resolutions to assemble rich multidimensional datasets. Generating insights from these new types of datasets depends critically on the ability to easily visualize and explore the data in an interactive manner. This is, however, a challenging task. Currently available software is dedicated to single atlases, model species or data types, and generating 3D renderings that merge anatomically registered data from diverse sources requires extensive development and programming skills. To address this challenge, we have developed brainrender: a generic, open-source Python package for simultaneous and interactive visualization of multidimensional datasets registered to brain atlases. Brainrender has been designed to facilitate the creation of complex custom renderings and can be used programmatically or through a graphical user interface. It can easily render different data types in the same visualization, including user-generated data, and enables seamless use of different brain atlases using the same code base. In addition, brainrender generates high-quality visualizations that can be used interactively and exported as high-resolution figures and animated videos. By facilitating the visualization of anatomically registered data, brainrender should accelerate the analysis, interpretation, and dissemination of brain-wide multidimensional data.

The Neocortical Computational Microscale model (NeoCoMM) is a unique neurophysiologically-inspired software. It offers a friendly graphical user interface that allows for the simulation of the intracellular and extracellular neural activity of a neocortical column. This software provides a realistic framework that can portray the neural activity and underlying cellular mechanisms related to different brain pathologies such as epilepsy. NeoCoMM is capable of (1) simulating the cortical tissue of three different species, (2) visualizing individual cell responses to external stimulation, (3) visualizing the corresponding local field potential, (4) studying the impact of the recording electrode features on simulated signals, and (5) testing various physiological and pathological hypotheses. While NeoCoMM was primarily developed for simulating epileptiform activity, it can also generate healthy brain rhythms or be adapted to other brain disorders.

The shape of neuronal morphologies plays a critical role in determining their dynamical properties and the functionality of the brain. With an abundance of neuronal morphology reconstructions, a robust definition of cell types is important to understand their role in brain functionality. However, an objective morphology classification scheme is hard to establish due to disagreements on the definition of cell types, on which subjective views of field experts show significant differences. The robust grouping of neurons based on their morphological shapes is important for generative models and for establishing a link between anatomical properties and other modalities, such as biophysical and transcriptomic information. We combine deep learning techniques with a variety of mathematical descriptions of neurons and evaluate the classification accuracy of different methods. We demonstrate that various methodologies, including graph neural networks, topological morphology descriptors, and morphometrics, consistently perform with the highest accuracy for a variety of datasets. Based on these methods, we present a robust classification of both inhibitory and excitatory cell types in the rodent cortex and propose a generalized scheme for a consistent classification of neurons into classes.

Brain imaging is an increasingly inter-disciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis, and ultimately displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well, but are not necessarily designed to be interoperable. Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a way to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is our labs internal Python toolkit that has been continuously developed and maintained by the labs trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the lab: functions and routines that we often use but that are not part of any established pipeline or package. Since its inception, the toolkit has been open and welcomes contribution from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a lab, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community.

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.

Autism is a neurodevelopmental condition affecting [~]1% of the population. Recently, machine learning models have been trained to classify participants with autism using their neuroimaging features, though the performance of these models varies in the literature. Differences in experimental setup hamper the direct comparison of different machine-learning approaches. In this paper, five of the most widely used and best-performing machine learning models in the field were trained to classify participants with autism and typically developing (TD) participants, using functional connectivity matrices, structural volumetric measures and phenotypic information from the Autism Brain Imaging Data Exchange (ABIDE) dataset. Their performance was compared under the same evaluation standard. The models implemented included: graph convolutional networks (GCN), edge-variational graph convolutional networks (EV-GCN), fully connected networks (FCN), auto-encoder followed by a fully connected network (AE-FCN) and support vector machine (SVM). Our results show that all models performed similarly, achieving a classification accuracy around 70%. Our results suggest that different inclusion criteria, data modalities and evaluation pipelines rather than different machine learning models may explain variations in accuracy in published literature. The highest accuracy in our framework was obtained by an ensemble of GCN models trained on combination of functional MRI and structural MRI features, reaching classification accuracy of 72.2% and AUC = 0.78 on the test set. The combined structural and functional modalities exhibited higher predictive ability compared to using single modality features alone. Ensemble methods were found to be helpful to improve the performance of the models. Furthermore, we also investigated the stability of features identified by the different machine learning models using the SmoothGrad interpretation method. The FCN model demonstrated the highest stability selecting relevant features contributing to model decision making. Code available at: https://github.com/YilanDong19/Machine-learning-with-ABIDE.

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.

Neuroimaging data is complex and high-dimensional that poses challenges for machine learning (ML) applications. Of varieties of reasons contributing on accuracy decoding, variable feature selection is one of crucial steps for determining target feature in data analysis, especially in the context of neuroimaging studies where the number of features is often much larger than the number of observations. Therefore, optimization of feature selection from such high-dimensional neuroimaging data has been challenging using conventional ML algorithms. Here, we introduce an efficient ML package incorporating a forward variable selection (FVS) algorithm that optimizes the identification of features for both classification and regression models. In our framework, the best ML model and feature pairs that explain the inputs can be automatically determined. Moreover, the toolbox can be executed in a parallel environment for efficient computation. The parallelized FVS algorithm iteratively selects the best feature pair compared against the previous steps to maximize the predictive performance. The FVS algorithm evaluates the goodness-of-fit across different models using the k-fold cross validation and identifies the best subset of features based on a pre-defined criterion for each model. Furthermore, the hyperparameters of each ML model are optimized at each forward iteration. A final outcome highlights an optimized number of selected features (brain regions of interest) with decoding accuracies. Using our pipeline, we examined the effectiveness of our toolbox on an existing neuroimaging (structural MRI) dataset. Compared ML models with and without FVS approach, we demonstrate that the FVS significantly improved the accuracy of the ML algorithm over the counterpart model without FVS. Furthermore, we confirmed the use of parallel computation considerably reduced the computational burden for the high-dimensional MRI data. This oFVSD toolbox efficiently and effectively improves the performance of both classification and regression models on neuroimaging data and should be applicable to many other neuroimaging data and more. This Python package is open-source and freely available, making it a useful toolbox for neuroimaging communities seeking improvement of decoding accuracy for their datasets.

AO_SCPLOWBSTRACTC_SCPLOWThe immense scale and complexity of neuronal electron microscopy (EM) datasets pose significant challenges in data processing, validation, and interpretation, necessitating the development of efficient, automated, and scalable error-detection methodologies. This paper proposes a novel approach that employs mesh processing techniques to identify potential error locations near neuronal tips. Error detection at tips is a particularly important challenge since these errors usually indicate that many synapses are falsely split from their parent neuron, injuring the integrity of the connectomic reconstruction. Additionally, we draw implications and results from an implementation of this error detection in a semi-automated proofreading pipeline. Manual proofreading is a laborious, costly, and currently necessary method for identifying the errors in the machine learning based segmentation of neural tissue. This approach streamlines the process of proofreading by systematically highlighting areas likely to contain inaccuracies and guiding proofreaders towards potential continuations, accelerating the rate at which errors are corrected.

Neural processes in the hippocampus and entorhinal cortex are thought to be crucial for spatial cognition. A growing variety of theoretical models have been proposed to capture the rich neural and behavioral phenomena associated with these circuits. However, systematic comparison of these theories, both against each other and against empirical data, remains challenging. To address this gap, we present NeuralPlayground, an open-source standardised software framework for comparisons between theory and experiment in the domain of spatial cognition. This Python software package offers a reproducible way to compare models against a centralised library of published experimental results, including neural recordings and animal behavior. The framework implements three Agents embodying different computational models; three Experiments comprising publicly available neural and behavioral datasets; a customisable 2-dimensional Arena (continuous and discrete) able to generate common and novel spatial layouts; and a Comparison tool that facilitates systematic comparisons between models and data. Each module can also be used separately, allowing standardised and flexible access to influential models and data sets. We hope NeuralPlayground, available on GitHub3, provides a starting point for a shared, standardized, open, and reproducible computational understanding of the role of the hippocampus and entorhinal cortex in spatial cognition.

Accurate decoding of neural signals often requires assigning extracellular waveforms acquired on the same electrode to their originating neurons, a process known as spike sorting. While many offline sorters are available, accurate online sorting of spikes with many channels is still a challenging problem. Existing online sorters either use simple algorithms with low accuracy, can only process a handful of channels, or depend on a complex runtime environment that is difficult to set up. We have developed a state-of-the-art online spike sorting platform in Python that enables large-scale, fully automatic real-time spike sorting and decoding on hundreds of channels. Our system is cross-platform and works seamlessly with the Open Ephys suite of open-source hardware and software widely used in many neuroscience laboratories worldwide. It also comes with a user-friendly graphical user interface to monitor the cluster quality, spike waveforms and neuronal firing rate. Our platform has comparable accuracy to offline sorters and can achieve an end-to-end sorting latency of around 160 ms for 128-channel signals. It will be useful for research in fundamental neuroscience, closed-loop feedback neuromodulation and brain-computer interfaces.

--Neuroimaging study results can vary significantly depending on the datasets and processing pipelines utilized by researchers to run their analyses, contributing to reproducibility issues. These issues are compounded by the fact that there are a large variety of seemingly equivalent tools and methodologies available to researchers for processing neuroimaging data. Here we present NeuroCI, a novel software framework that allows users to evaluate the variability of their results across multiple pipelines and datasets. NeuroCI makes use of Continuous Integration (CI), a software engineering technique, to facilitate the reproducibility of computational experiments by launching a series of automated tests when code or data is added to a repository. However, unlike regular CI services, our CI-based framework uses distributed computation and storage to meet the large memory and storage requirements of neuroimaging pipelines and datasets. Moreover, the frameworks modular design enables it to continuously ingest pipelines and datasets provided by the user, and to compute and visualize results across the multiple different pipelines and datasets. This allows researchers and practitioners to quantify the variability and reliability of results in their domain across a large range of computational methods.

BackgroundTranscranial magnetic stimulation (TMS) is a painless non-invasive method that allows focal activation or deactivation of a human brain region in order to assess effects on other brain regions. As such, it has a unique role in elucidating brain connectivity during behavior and at rest. Information regarding brain connectivity derived from TMS experiments has been published in hundreds of papers but is not accessible in aggregate. ObjectiveOur objective was to identify, extract, and represent TMS-connectivity data in a graph database. This approach uses nodes connected by edges to capture the directed nature of interregional communication in the brain while also being flexible enough to contain other information about the connections, such as the source of information and details about the experiments that produced them. MethodsData related to interregional brain connectivity is first extracted from full-text publications, with creation of a table-like structure that list data of multiple types, principally the source and target brain regions, sign (excitatory/inhibitory) and latency. While machine-reading methods were explored, so far human experts have had to extract and verify data. These data are used to populate a neo4j graph database. A graphical user interface coupled with a query system allows users to search for networks and display information about connections between any two brain regions of interest. ResultsExperiments involving two TMS stimulating coils, in which one is over a putative source region and the other is over another region with a measurable effect in the body (such as the primary motor cortex) are the most straightforward to represent in the database. Even in those experiments, differing conventions for naming regions, and differing experimental parameters such as stimulation intensity and coil position, create issues for representing data from multiple studies in the same database. Other types of experiments, such a neuromodulatory stimulation such as rTMS, can provide information regarding connectivity, but are harder to represent. But we have, thus far, stored information about 100 connections in the database and demonstrated its utility in exploring direct and indirect connections between brain regions. We have also explored adding a causal inference method to those connections, allowing information about latency to shape the connections retrieved given latency limits. ConclusionA graph database can flexibly store information about interregional brain connectivity and is particularly useful for exploring the temporal aspects of brain networks.

With increasing neuroimaging modalities and data diversity, mapping brain regions to a standard atlas template has become a challenging problem. Machine learning in general and deep learning, in particular, have been providing robust solutions for several neuroimaging tasks, including brain image registration and segmentation. However, these methods require a large amount of data for groundtruth labels, annotated by human experts, which is time-consuming. In this work, we introduce NeuroAtlas, an AI-based framework for atlas generation and brain region segmentation. We showcase an end-to-end solution for brain registration and segmentation by providing i) a deep learning modeling suite with a variety of high-performing model architectures to map a brain atlas onto the input brain section and ii) a Graphical User Interface (GUI)-based plugin for large-scale data annotation with a feature of modifying the predicted labels for active learning. We demonstrate a robust performance of our framework on the human brains, captured through various imaging modalities and age groups, and demonstrate its application for mouse brains as well. NeuroAtlas tool will be open-sourced and entirely compatible with both local as well as cloud-based computing so that users can easily adapt to their neuroimaging custom datasets.

We present NiftyTorch a Deep Learning Framework for NeuroImaging. The motivation behind the development of such a library is that there are scant amount of centralized tool for deploying 3D deep learning for NeuroImaging. In addition, most of the existing tools require expert technical knowledge in Deep Learning or programming, creating a barrier for entry. The goal is to provide a one stop package using which the users can perform classification tasks, Segmentation tasks and Image Transformation tasks. The intended audience are the members of NeuroImaging who would like to explore deep learning but have no background in coding. In this article we explore the capabilities of the framework, the performance of the framework and the future work for the framework.

Large-scale computational models of the brain are necessary to accurately represent anatomical and functional variability in neuronal biophysics across brain regions and also to capture and study local and global interactions between neuronal populations on a behaviorally-relevant temporal scale. We present the methodology behind and an initial implementation of a novel open-source computational framework for construction, simulation, and analysis of models consisting of millions of neurons on high-performance computing systems, based on the NEURON and CoreNEURON simulators (Carnevale and Hines, 2006, Kumbhar et al., 2019). This framework uses the HDF5 data format and software library (HDF Group, 2021) and includes a data format for storing morphological, synaptic, and connectivity information of large neuronal network models, and an accompanying open-source software library that provides efficient, scalable parallel storage and MPI-based data movement capabilities. We outline our approaches for constructing detailed large-scale biophysical models with topographical connectivity and input stimuli, and present simulation results obtained with a full-scale model of the dentate gyrus constructed with our framework. The model generates sparse and spatially selective population activity that fits well with in-vivo experimental data. Moreover, our approach is fully general and can be applied to modeling other regions of the hippocampal formation in order to rapidly evaluate specific hypotheses about large-scale neural architectural features.

Collaborative neuroimaging research is often hindered by technological, policy, administrative, and methodological barriers, despite the abundance of available data. COINSTAC is a platform that successfully tackles these challenges through federated analysis, allowing researchers to analyze datasets without publicly sharing their data. This paper presents a significant enhancement to the COINSTAC platform: COINSTAC Vaults (CVs). CVs are designed to further reduce barriers by hosting standardized, persistent, and highly-available datasets, while seamlessly integrating with COINSTACs federated analysis capabilities. CVs offer a user-friendly interface for self-service analysis, streamlining collaboration and eliminating the need for manual coordination with data owners. Importantly, CVs can also be used in conjunction with open data as well, by simply creating a CV hosting the open data one would like to include in the analysis, thus filling an important gap in the data sharing ecosystem. We demonstrate the impact of CVs through several functional and structural neuroimaging studies utilizing federated analysis showcasing their potential to improve the reproducibility of research and increase sample sizes in neuroimaging studies.