Neuroinformatics

Mean-field models are extensively used in large-scale brain simulations because they provide a wieldy description of population dynamics while preserving key features of neural activity. Despite their widespread adoption, no common and reproducible methodology currently exists to systematically derive and validate mean-field models starting from biologically grounded single neuron dynamics. As a result, implementations are often ad hoc, difficult to reproduce and rarely reusable. Here we introduce BRIDGE, a modular, open-source Python pipeline that enables the bottom-up reconstruction, analysis, validation, and simulation of mean-field models from single neurons. The framework integrates single neurons modelling, network simulations, extraction of population statistics, parameters analysis, quantitative comparisons between spiking neural networks and corresponding mean-field representations, and simulation of network of mean-fields. Its flexible architecture allows users to incorporate different neuron models and to generate region-specific or state-dependent mean-field formulations. BRIDGE provides a reproducible foundation for developing biologically informed mean-field models suitable for large-scale and whole-brain simulations, supporting the transition from generic homogeneous population models toward region-specific ones. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=91 SRC="FIGDIR/small/742067v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@1124404org.highwire.dtl.DTLVardef@2f8b2aorg.highwire.dtl.DTLVardef@1598f37org.highwire.dtl.DTLVardef@c9814b_HPS_FORMAT_FIGEXP M_FIG C_FIG

A central goal of neuroscience is to establish how similar (or different) brain networks are across individuals, development, psychiatric conditions, and even between biological and artificial brains. Whenever one such comparison is made, it relies on the assumption that we have a measure that produces an accurate and plausible similarity score. Yet, the field lacks one such measure, and the wide variety of existing measures often produces conflicting results. To address this problem, we perform a systematic comparison of 16 established similarity measures. First, we show that different measures consistently disagree on which brain networks are most similar. Second, we rank all measures according to multiple criteria such as biological plausibility, computational efficiency, and sensitivity. We show that DeltaCon is the most accurate measure in terms of parameter recovery, and that it ranks among the fastest and most noise-tolerant. For this reason, we conclude that DeltaCon is the best general-purpose similarity measure. However, no measure ranks best on more than two of the five criteria we assessed, and different measures should be preferred depending on the exact research question. Measure selection can now be made on evidence and should be a crucial decision in any study that compares brain networks.

A major goal of the BRAIN Initiative Cell Atlas Network (BICAN) is to create a suite of foundational reference cell atlases and associated standards for human and non-human primate brains. Central to this goal is the creation of cross-species harmonized cellular taxonomies and structural parcellations with formal ontologies that can be mapped into 3D reference frameworks bridging neuroimaging and cellular and histological resolutions. We describe here an iterative approach, focused initially on the basal ganglia, to co-create structural and cellular ontologies in human, macaque and marmoset brains, including a Harmonized Ontology of Mammalian Brain Anatomy (HOMBA), and to map and refine structural parcellations into neuroimaging-based common coordinate frameworks. These references provide the framework for documenting and mapping all experimental sampling in BICAN, allowing analyses of cellular and molecular variation as a function of topographic position, and enabling comparisons of cellular, molecular and neuroimaging-based functional variation within and between primate species. HighlightsO_LIA hierarchical Harmonized Ontology of Mammalian Brain Anatomy (HOMBA) covering 2348 structures C_LIO_LIHOMBA-annotated 3D common coordinate frameworks (CCFs) of the basal ganglia across species C_LIO_LIHistologically informed 3D parcellation/atlas of 280 human subcortical structures indexed by HOMBA C_LIO_LIMapping and integration of structural, cellular and functional data with HOMBA and CCFs C_LI

Neuroimaging research focusing on subcortical brain regions showed that they play a role in a wide range of cognitive functions and associated their structural alterations to various psychiatric disorders. Methods to analyse such structures were developed to observe changes, not simply in terms of volumes, but in terms of surfaces, which are able to detect subtle and local changes in shape and geometrical configuration. Such advanced analyses have however been largely restricted to command line environments with relatively limited access to Python users or researchers with low technical expertise. We present SubCortexMesh as a user-friendly toolbox which covers automated surface estimation from popular subcortical volume segmentations (FreeSurfer and the Functional Magnetic Resonance Imaging of the Brain Software Library (FSL)), computes shape-related vertex-wise metrics (thickness, surface area and curvature) for whole cohorts and includes statistical analyses. This is all done inside Python, automatically computing all subjects of a given preprocessed directory, with the explicit intent to minimise steps and manual coding required from the users. SubCortexMesh includes statistical tools which allow conventional random field theory-based cluster analyses on native subject metrics, standardised within common surface templates, thus containing a whole workflow necessary to run an up-to-date shape-wise subcortical analyses in one package.

Recurrent neural networks (RNNs) provide a central tool in neuroscience to optimize cognitive tasks and reconstruct dynamical systems. Around these two paradigms, many RNN model variants have been tailored to capture specific computations of the brain. In practice, however, this methodological diversity remains difficult to exploit, because different paradigms and model variants are mostly implemented in idiosyncratic, study-specific code. Here we introduce NeuralRNN, an open-source Python framework that unifies these paradigms and model variants in a general-purpose pipeline. NeuralRNN brings task optimization, dynamical system reconstruction, and biological constraints into a single objective interface. It also provides an integrated and extensible model library spanning generic, structure-constrained, gain-modulated, and gated RNNs. Furthermore, data, models, and training are constructed through a shared automated pipeline and are equipped with a suite of tools for dynamical systems analysis. Together, NeuralRNN lowers the barrier to implementing, comparing, and extending RNN methods in neuroscience.

Simplified spiking neuron models such as the Adaptive Exponential Integrate-and-Fire (AdEx) model reproduce the firing behaviour of diverse neuron types at a fraction of the cost of biophysically detailed models, making them attractive for large-scale brain simulation. Fitting their parameters to recordings, however, still relies on derivative-free search such as grid search or evolutionary algorithms, because the discrete spike-and-reset mechanism renders these models non-differentiable. Surrogate gradients, used in deep spiking networks, replace the spike derivative with a smooth approximation on the backward pass. Whether they also enable efficient gradient-based parameter estimation for single simplified neurons is untested. We implement a differentiable AdEx model with a surrogate-gradient variant in the Jaxley framework. The implementation is on par with target-compiled C++ Brian2 code while remaining end-to-end differentiable, vmap-batchable and GPU/TPU-portable. It is 200 times faster than Brian2's default runtime mode. Across a recovery-radius benchmark of 2,700 paired runs, gradient-based fitting never matched a gradient-free Nelder-Mead baseline. At sub- threshold levels, gradient descent instead beats Nelder-Mead. The obstacle is therefore the spike mechanism and the loss geometry it induces, not gradient descent itself.

Large-scale biophysically-detailed models of the brain are instrumental in unraveling how cellular, synaptic, and circuit-level mechanisms contribute to neural dynamics. Such models are built around large populations of single-neuron models whose morphologies must preserve physiological variability to reproduce integrative properties and firing patterns observed experimentally. Experimental reconstruction of morphologies is a time-consuming and resource-intensive process, unlikely to supply them at the required scale. Several data-driven methods for morphology synthesis have been developed, which nonetheless rely on extensive datasets for parameter estimation. To address this limitation, we developed NeuWalk, to our knowledge the first public Python library for morphology synthesis based on branching-and-annihilating, biased-and-correlated random walks. It estimates branching and annihilation rates from Sholl intersections and, optionally, bifurcation counts, while also ensuring their replication in synthetic morphologies; user-specified combinations of biases, implementing somatofugality, self-avoidance, spatial competition, and branching-angle control, sculpt morphological patterns characteristic of each neuron class. We then validated the method on mitral and middle tufted cells of the olfactory bulb, semilunar and pyramidal cells of the anterior piriform cortex, and neocortical pyramidal neurons: means and variances of Sholl intersections, bifurcation counts, and total dendritic length were statistically indistinguishable between synthetic and natural morphologies, with the single exception of its variance in apical dendrites of piriform cortex pyramidal neurons. As Sholl intersections and bifurcation counts are commonly reported in the literature, and biases can be inferred from published images or qualitative descriptions, NeuWalk requires no immediate access to experimental reconstructions, striking a trade-off between data-driven parameterization and empiricism without sacrificing morphological accuracy.

Biophysically detailed models of human cortical microcircuit provide key insights into brain function and disease biomarkers, yet their computational cost lengthens simulation runtime and limits scalability. To address this, we systematically evaluated recent simplification methods for morphological reduction and synapse merging in terms of preserving spiking and electroencephalographic (EEG) properties of human cortical microcircuit models. At the single- cell level, reduced compartmental models accurately reproduced spiking behaviors. However, at the microcircuit level, while achieving a 5-fold speed-up, morphological reduction failed to preserve spectral dynamics of spiking and EEG despite maintaining average baseline and response firing rates. Conversely, applying synapse merging alone on the full morphology successfully reproduced both spiking activity and EEG dynamics, when constrained to [≤] 50 m spatial intervals, and yielded a 2-fold speed-up. These results were consistent across both cortical layer 2/3 and layer 5 models, and in depression models with reduced somatostatin interneuron inhibition. Our study identifies model simplification methods that speed up cortical microcircuit simulations while preserving the accuracy of the simulated brain signals.

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

EegFun.jl is an open-source package for electroencephalography (EEG) analysis implemented in the Julia programming language. EegFun.jl provides a flexible framework for EEG research, covering data import from standard file formats, filtering and re-referencing, Independent Component Analysis (ICA) for artifact detection/correction, epoch extraction, and ERP averaging and visualisation. The Julia language provides the readability of a high-level scripting environment together with execution speeds comparable to compiled code. EegFun.jl combines interactive data visualization with high-performance execution, making large-scale analyses both efficient and easy. Here, we provide a brief overview and introductory tutorial of the core stages of the EEG analysis workflow to illustrate the packages capabilities. The package is freely available under the MIT license.

Subcortical brain alterations are a key feature of dementia disease progression. Machine learning (ML) has been applied widely to MRI-based brain features in dementia, where performance depends on the model choice, training data size, and input feature characteristics. Most studies compare ML models using a single training sample size. Here, we evaluate the sample-size efficiency of ML models based on subcortical gross volume and vertex-wise shape features for dementia stage classification using 2,511 samples in the Alzheimer's Disease Neuroimaging Initiative (ADNI). Learning curves were generated for dementia vs. cognitively normal controls (CN), dementia vs. mild cognitive impairment (MCI), and MCI vs. CN across increasing training sample sizes. Classification performance improved when increasing sample size for all models, with late- fusion models consistently achieving the highest performance, and Logit-TVL1 outperforming the other shape-based models. Learning curve analysis showed that classification performance was driven by the training sample size and the magnitude of anatomical group differences, and can be used to optimize future model selection tasks in dementia and other brain disorders.

Brain cell morphology plays a crucial role in function and pathology. Biophysical models of diffusion MRI (dMRI) quantify cell morphology in vivo, enabling the design of novel biomarkers. These models represent cells by simplified geometries, such as spheres and randomly oriented cylinders, for which analytical signal expressions exist. However, dMRI signals are sensitive to morphological features, such as branching, tapering, undulation, beading, and protrusions, unaccounted for in most models, as they often render analytical solutions impossible. Simulations of the dMRI signal in synthetically generated cells offer a powerful tool to explore how microstructural morphology impacts the dMRI signal. Nevertheless, no tool to generate digital replicas of brain cells is available openly. To address this gap, we introduce the OCTOPUS toolbox, which generates cells featuring all geometrical features described above. OCTOPUS, provided via the Python interface OCTOpool, enables accessible, efficient creation of cells with complex geometries. We recreated histologically reconstructed neuronal and glial cells, including pyramidal, GABAergic and glutamatergic neurons, and astro- and microglia. To illustrate the plausibility of OCTOPUS-generated cells, we reproduced established properties of dMRI signals from brain tissue, such as the signatures of short-range disorder, branching and protrusions, and a high-b-value power law. By comparing the geometries and dMRI signals of generated and original cells, we found different growth strategies adequate for more isotropic and more anisotropic cells. We anticipate that realistic cell substrates created by OCTOPUS will help validate biophysical models, design dMRI sequences sensitive to fine-grained cell morphology beyond analytical models, and generate realistic numerical substrates of brain tissue.

Deep learning models for electroencephalography (EEG) pathology detection achieve competitive accuracy but offer limited interpretability. In this paper, we introduce concept bottleneck models (CBMs) for EEG pathology detection which use clinical EEG biomarkers computed analytically from the signal as a bottleneck to be used for prediction. Those concepts include band power, spectral ratios, alpha peak frequency and amplitude, hemispheric asymmetry and phase locking values. With the backbone based on shallow convolutional neural network (CNN), we compare a black box baseline with two variants of CBMs. The hybrid variant is a classifier receives a residual projection of the backbone features. The pure variant is a CBM where the classifier receives only the predicted concepts. Models are evaluated on abnormality detection and dementia classification followed by concept prediction quality and leakage analysis. Both CBMs approach the baseline on TUAB (82.7-82.9% vs 83.6%) and are 2-5 percentage points lower on CAUEEG (57.9-60.5% vs 62.7%). The pure CBM exhibits concept fidelity concern where the predicted concepts deviate from their ground truth values. Replacing them with their true values causes severe cost on accuracy by 26 and 22 points on TUAB and CAUEEG. The concept fidelity concern is mitigated by increasing the weight of the concept loss to match the ground truth concepts. the mitigation comes with small accuracy cost on TUAB but a relatively larger cost on CAUEEG. The code of the experiments and full logs are available at https://www.github.com/mhmdrdwn/cbm.

The cynomolgus macaque is a widely used primate model in neuroscience, yet existing brain atlas resources remain limited in resolution and lack a standardized framework for integrating structural, molecular, and connectivity data within a common reference space. Here we present CynoBrain, a high-resolution multi-modal brain atlas constructed from population-averaged MRI data at 9.4 Tesla, yielding a 75 micron isotropic template with significant improvement in resolution. A co-registered whole-brain autofluorescence template resolves cytoarchitectural boundaries not visible in MRI template. Three major parcellation schemes, D99 143-area, M132 105-area, and SARM are incorporated in CynoBrain space. Spatial transcriptomic based laminar definition, reconstructed from 2D sections into volumetric common space, provides a six-layer cortical segmentation spanning the whole cortex. As a demonstration of the integrative capability of this framework, 2D retrograde tracing data is reconstructed and mapped to the Cynobrain space, enabling volumetric compilation of 2D macaque brain mapping data across subjects. An online interactive platform was constructed to allow full access to Cynobrain, establishing it as a scalable, open platform for cross-modal data integration for primate neuroscience. Overall, CynoBrain is a unified framework for cross-modal data integration for macaque brain mapping with high-resolution, multiple atlases and transcriptomic based laminar definition.

The choroid plexus (ChP) is a highly vascularized structure located in the brain ventricles that plays important roles in multiple central nervous system functions. ChP enlargement has been implicated in many neuropsychiatric conditions such as multiple sclerosis, depression, schizophrenia, and Alzheimer's disease. However, limitation in the accuracy of the current segmentation tools highlight the need for more reliable methods to quantify ChP volume in healthy and clinical human populations. The shape and size of the ChP vary drastically from person to person, and currently there are no large ChP datasets which allow comprehensive training of segmentation models. Here we introduce DataPlex, a collection of 700 structural MRI brain images where the lateral ventricle ChP has been manually labeled and checked by expert human raters. DataPlex is balanced and representative across a wide range of scanning parameters, demographics, and clinical profiles, and subject-level information will be available for each labeled image. DataPlex will be open source to support the creation of reliable segmentation tools for the human lateral ventricle ChP.

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

Accurate alignment of hippocampal anatomy across individuals remains challenging due to complex and highly variable folding patterns that are not well captured by conventional volumetric approaches. HippUnfold introduced a surface-based representation of the hippocampus, but key components--including coordinate estimation and inter-subject correspondence--were defined in the volumetric domain, making them susceptible to topological errors and interpolation artifacts. Here, we introduce a surface-intrinsic formulation of hippocampal unfolding in which geometry, intrinsic coordinates, and correspondence are defined directly on subject-specific surface manifolds. Intrinsic anterior-posterior and proximal-distal coordinates are computed by solving Laplace equations on the surface, and correspondence is established through surface-based resampling in unfolded space, replacing inverse volumetric warping. Relative to the original HippUnfold approach, this formulation improves test-retest consistency, subject identifiability, and mesh quality, while better preserving subject-specific gyral and sulcal morphology. Surface representations show reduced distortion between folded and unfolded spaces and eliminate misplaced or outlier vertices associated with volumetric warping. These improvements translate to enhanced sensitivity in a clinical application, improving lateralization of temporal lobe epilepsy. These results demonstrate that a surface-intrinsic formulation provides a principled and robust foundation for hippocampal unfolding, enabling topology-preserving alignment and more accurate characterization of inter-individual variability in health and disease.

Background. Automated segmentation of glioma subregions on multiparametric MRI can be improved by changing either the network architecture or the information presented to the network. These two design levers are usually studied together, which makes their individual contributions difficult to see. We present an integrated analysis of two completed, independent studies, each of which isolates one lever. Methods. In the architecture-level study (Slice), SIBA-UNet - a 2D U-Net combining depthwise separable convolution, inverted-bottleneck blocks and attention-guided skip fusion - was trained on 128 x 128 multimodal slices from BraTS 2020 with a patient-level split, and a sigmoid decision threshold was selected on validation data. In the representation-level study (Subtract), CMSR-3D augmented the four standard MRI sequences with four voxel-wise cross-modal subtraction maps (T1-T1CE, T2-FLAIR, FLAIR-T1CE, T2-T1CE) and evaluated 4-, 6- and 8-channel inputs with an identical 3D U-Net on 1,251 BraTS-GLI 2023 cases (1,001/125/125 split). Results. SIBA-UNet reached an overall validation Dice of 0.8601 at a validation-selected threshold of 0.51, with reported subregion Dice of 0.8795 (necrotic core), 0.8501 (edema) and 0.9125 (enhancing tumor); the source ablation was only partially reported. With the architecture held fixed, the 8-channel CMSR-3D configuration gave the highest mean Dice among the evaluated inputs (0.8699 vs. 0.8534 for the 4-channel baseline) and the lowest mean HD95 (5.9859 vs. 8.2308), whereas either two-map subset alone changed mean Dice by at most 0.0037. Conclusions. Architecture design and input-representation design are separable levers for brain tumor segmentation. The controlled representation ablation indicates that the complete set of subtraction maps was more informative than either subset under a fixed 3D U-Net. Both studies were evaluated internally on their respective datasets, and their numerical results are not directly compared because the datasets, dimensionality and evaluation protocols differ.

Behavioral annotation of synchronized video recordings is an essential step in developmental electroencephalography (EEG) research, supporting both the identification of behavior-related artifacts and the investigation of brain-behavior relationships. Existing annotation workflows, however, are often fragmented: proprietary EEG software provides limited flexibility for behavioral coding, whereas dedicated behavioral annotation platforms typically lack native integration with EEG data. We developed AVOCODO (Audio/VideO CODing Optimization), an open-source MATLAB-based software platform that integrates synchronized behavioral annotation directly into the EEG workflow. AVOCODO reads native EGI MFF recordings, synchronizes embedded video with EEG, visualizes the audio spectrogram to facilitate precise annotation of vocalizations, and writes user-defined behavioral events directly back into the original MFF recording as native EEG event markers while simultaneously exporting annotations as CSV files. The software supports fully customizable behavioral coding schemes, optional EEG visualization for quality control, and reloading of previously annotated recordings for review and inter-rater verification. Since its initial development in 2024, AVOCODO has been applied internally across five developmental EEG studies involving approximately 500 pediatric participants and more than 3,000 EEG recordings. By bridging behavioral annotation and EEG preprocessing within a unified open-source workflow, AVOCODO has the potential to improve the efficiency, reproducibility, and scalability of behavioral annotation in developmental EEG research.

Micro-electrode array (MEA) recordings are widely used to characterize functional connectivity in neural cultures and have gained traction for the analysis of human brain slices. However, the impact of graph construction methodology on the resulting network topology has not been systematically quantified. Here, we benchmark three methods - shared spiking activity, Pearson cross-correlation, and the spike time tiling coefficient (STTC) - across 37 recordings from human cortical slice cultures classified into low, moderate, and high activity groups. We show that method choice alone produces large topological differences (Cohens d = 0.86-1.14 for clustering coefficient, d > 1.0 for node count), while higher-order features such as modularity remain stable. Each method exhibits a distinct sensitivity profile: shared spiking detects activity-dependent changes primarily through network size, correlation uniquely captures clustering differences, and STTC combines strong biological sensitivity with negligible parameter dependence across lag windows (all d < 0.1). Within shared spiking, z-score normalization dominates all other parameter choices (d > 1.0 versus bin size effects of d < 0.23), functioning as an implicit analytical null model that fundamentally reshapes the edge set rather than merely rescaling weights. Inter-method edge overlap is low (Jaccard index 0.08-0.45) and activity dependent, demonstrating that these methods identify substantially different connections from identical data. Our results reveal that methodological choices including construction method, threshold, and normalization introduce hidden degrees of freedom with effect sizes comparable to the biological signals being measured. We provide practical recommendations for parameter selection, reporting, and cross-method validation in MEA-based network neuroscience. Author SummaryWhen we record electrical activity from brain tissue using grids of electrodes, we can ask how different sites influence one another and map the tissue as a network of connections. Thanks to novel culturing methods, this approach is increasingly used to study human brain slices. However, deciding what is "connected" is not well defined. Researchers use several different methods, and it has never been clear how much this choice shapes the network they end up describing. Here we compared three widely used methods on 37 recordings from human cortical slices spanning a range of activity levels. We found that the method alone can change the apparent structure of the network as much as real biological differences do. The methods frequently disagreed about which connections exist and some technical choices, including normalization techniques, had surprisingly large effects. Because these hidden choices can rival the biological signal, we provide this benchmarking work with practical recommendations for selecting, reporting, and cross-checking methods, so that network studies of brain tissue become more transparent, comparable, and reproducible.