Neuroinformatics

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.

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.

Neurodegenerative diseases such as Mild Cognitive Impairment (MCI), Multiple Sclerosis (MS), Parkinson s 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.

Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear due to the vast number of available techniques, their combinatorial use in pipelines, and adjustable parameters. Consequently, most studies adopt ad hoc preprocessing strategies based on dataset characteristics, study goals, and researcher expertise, with little justification for their choices. Such variability can influence downstream results, potentially determining whether effects are detected, and introduces risks of questionable analytical practices. Here, we present a method to objectively evaluate and compare preprocessing pipelines. Our approach uses realistically simulated signals injected into real EEG data as "ground truth", enabling the assessment of a pipelines ability to remove noise without distorting neuronal signals. This evaluation is independent of the studys main analyses, ensuring that pipeline selection does not bias results. By applying this procedure, researchers can select preprocessing strategies that maximize signal-to-noise ratio while maintaining the integrity of the neural signal, improving both reproducibility and interpretability of EEG studies. Although the data presented here focuses on processing and analysis most relevant for ERP research, the method can be flexibly expanded to other types of analyses or signals.

Neuroscience needs observation. Observation lets us evaluate data quality, judge whether models are biologically realistic, and generate new hypotheses. However, high-dimensional behavioral and neural data are too complex to be easily displayed and eye-tested. Computational methods can reduce the dimensionality of data and reveal statistically robust dynamical structure but often yield results that are difficult to relate back to the underlying biology. In addition, the choice of what parameters to quantify may not capture unexpectedly relevant aspects of the data. To supplement quantification with enhanced qualitative observation, we developed Visualization and Sonification of NeuroData (ViSoND), an open-source approach for displaying multiple data streams using video and sonification. Sonification is nothing new to neuroscience. Scientists have sonified their physiological preparations since Lord Adrians earliest recordings. We extend this tradition by mapping multiple physiological datastreams to musical notes using MIDI. Synchronizing MIDI to video provides an opportunity to watch an animals movement while listening to physiological signals such as action potentials. Here we provide two demonstrations of this approach. First, we used ViSoND to interpret behavioral structure revealed by a computational model trained on the breathing rhythms of freely behaving mice. Second, ViSoND revealed patterns of neural activity in mouse visual cortex corresponding to eye blinks, events that were previously filtered out of analysis. These use cases show that ViSoND can supplement quantitative rigor with observational interpretability. Additionally, ViSoND provides an accessible way to display data which may broaden the audience for communication of neuroscientific findings.

Neural decoding is a powerful approach for inferring which variables are represented in the activity of a population of neurons, with broad applications ranging from basic neuroscience to clinical settings such as brain-computer interfaces. More recently, decoding has also been used as a cross-validated tool for studying the computationally relevant properties of representational geometry, revealing not only whether a variable is encoded, but also how it is encoded and which computations the collective activity of neural populations may support. However, decoding analyses present several technical challenges and common pitfalls that can lead to misleading conclusions if not handled carefully. Here, we introduce Decodanda, a Python toolbox for decoding and geometric analysis of neural population activity. Decodanda provides functions for decoding arbitrary variables and for quantifying geometric features of neural representations, including shattering dimensionality and cross-condition generalization performance (CCGP). Importantly, the package automates several essential best-practice safeguards, including trial-based cross-validation to avoid training-testing leakage from temporally correlated neural traces (a particularly important issue for calcium imaging data), null models for statistical significance, pseudo-population pooling, and cross-variable balancing to determine which of a set of correlated variables is genuinely encoded in the activity. Decodanda is agnostic to the specific classifier used for decoding, and it is designed to be both user-friendly and highly customizable, allowing researchers to assemble flexible analysis pipelines from modular building blocks. Here, we provide an overview of the design principles of Decodanda and illustrate its use cases in neuroscience research. Documentation, example notebooks, and source code are available at github.com/lposani/decodanda.

Currently, implantation of electroencephalogram (EEG) electrodes in laboratory animals is time-consuming and requires specialized equipment. We present a novel method for EEG recordings in mice that utilizes thin needle electrodes. These electrodes are inserted into the skull at predetermined locations by gently pressing them against the bone surface. To ensure stable fixation of the implant, hook-shaped needles are positioned along the lateral aspects of the skull. The electrodes are connected to a multipin connector and secured to the skull using dental composite, after which the animal is allowed to recover from anesthesia. Importantly, procedures such as skull drilling and screw placement are not required, allowing the entire surgery to be completed in less than 15 minutes. Consequently, this EEG implantation approach is rapid and minimally invasive. Results of our studies indicate that EEG recordings obtained with needle electrodes are not inferior to those obtained with screw electrodes. Overall, the method is designed to enhance the accuracy and efficiency of EEG recording studies while improving animal welfare. O_LISimplifies the placement of EEG electrodes. C_LIO_LIReduces the time required for electrode implantation. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=67 SRC="FIGDIR/small/715731v1_ufig1.gif" ALT="Figure 1"> View larger version (44K): org.highwire.dtl.DTLVardef@e5608org.highwire.dtl.DTLVardef@1325ea4org.highwire.dtl.DTLVardef@1e37202org.highwire.dtl.DTLVardef@1521bb8_HPS_FORMAT_FIGEXP M_FIG C_FIG

ObjectiveFinding indicators of early response to antidepressant treatment in EEG signals recorded from patients suffering from major depressive disorder. MethodsFunctional brain connectivity networks based on weighted imaginary coherence and weighted imaginary mean phase coherence were computed for 176 patients for 6 different EEG frequency bands. Cross-hemispheric connectivity (CH) and lateral asymmetry (LA) were estimated from these networks based on EEG signals recorded before the beginning of treatment (V is1) and one week after the start of the treatment (V is2). Repeated measures ANOVA was used to check for statistically significant changes in connectivity based on these measures at V is2 w.r.t. V is1. Post-hoc analysis was performed with multiple pairwise comparison tests to determine which group means were significantly different. ResultsIt was found that CHV is2 was significantly reduced w.r.t. CHV is1 in the {beta}1 [12.5 - 17.5 Hz] frequency band for the responders to treatment. Also, LAV is2 was significantly increased w.r.t. LAV is1 in the {beta}1 frequency band for the responders. No such significant changes were observed for the non-responders. Brain networks constructed using both weighted imaginary coherence and weighted imaginary mean phase coherence were found to exhibit these results. For the CH connectivity changes, binarized networks and for the LA connectivity changes, weighted networks were found to be more reliable. ConclusionsResponders were found to show a reduction in cross-hemispheric connectivity and an increase in lateral asymmetry, both in the {beta}1 band while no such change was observed for the non-responders. SignificanceDecrease in cross-hemispheric connectivity and increase in lateral asymmetry in the {beta}1 band may represent candidate neurophysiological indicators of early treatment response, but they require independent replication before any clinical application can be considered.

Background: Developing generalizable neuroimaging models is often hindered by limited labeled data which has led to an increased interest in unsupervised inverse learning. Existing approaches often neglect geometric principles and struggle with diverse pathologies. We propose a symmetry-informed inverse learning foundation model to address these shortcomings for robust and efficient anomaly detection in brain MRI. Methods: Our framework employs a reconstruction-to-embedding pipeline, trained exclusively on healthy brain MRI slices. A 2D U-Net uses a novel, symmetry-aware masking strategy to reconstruct a disorder-free slice. Difference maps are embedded into a 1024-dimensional latent space via a Beta-VAE. Anomaly scoring is performed using Mahalanobis distance. We evaluated generalization by fine-tuning on external lesion datasets, BraTS Africa (SSA), and the ADNI-derived Alzheimer disease cohort (Alz). Results: On the source metastasis (Mets) dataset, the framework achieved high performance (AB1+MSE: 99.28% accuracy, 99.79% sensitivity). Generalization to the external lesion dataset (SSA) was robust, with the Symmetry ROC configuration achieving 91.93% accuracy. Transfer to the Alzheimer dataset (Alz) was more challenging, achieving a peak accuracy of 70.54% with a high false-positive rate, suggesting difficulty in separating subtle, diffuse changes. Conclusion: The symmetry-informed inverse learning framework establishes a robust foundation model for neuroimaging, showing strong performance for focal lesions and successful generalization under domain shift. Limitations in diffuse neurodegeneration underscore the necessity for richer representations and multimodal integration to improve future foundation models.

The brains remarkable complexity and cellular heterogeneity necessitate precise anatomical annotation to ensure that imaging-based analyses accurately resolve region-specific features. Few computational tools currently exist that allow for the accurate and rapid registration of single brain images to standard brain atlases. To address this limitation, we developed ROIMAPer, a novel FIJI plugin for rapid registration of individual brain slices. ROIMAPer includes eight atlases spanning mouse, rat, and human brain anatomy across multiple developmental stages, making it broadly applicable across diverse experimental contexts. It allows for linear and affine scaling of the reference atlas to the experimental image and is optimized for serial processing of large quantities of images. We demonstrated the accuracy of ROIMAPer through quantification of in situ hybridization data from the Allen Gene Expression Atlas of seven marker genes across major brain regions and of four marker genes across hippocampal subfields. Quantification of marker genes within their assigned brain regions closely matched the ground truth across all major regions. At a finer resolution, marker-gene quantification within hippocampal subregions aligned with the experimental data, although discrepancies with the ground truth were observed for Mcu. Overall, ROIMAPer provides broad utility for open-source brain image analysis from multiple species. Significance StatementWe present an open-source, user-friendly, and accessible tool for registration of individual brain slices to anatomical reference atlases, compatible with the image analysis platform FIJI. The field lacks tools that offer a span of cross-species atlases, FIJI-compatibility, intuitive linear scaling methods, and low user-input without requiring high computational skill. Our tool minimizes user-involvement, allows for processing of larger datasets through more effective resource management, and speeds-up previously tedious processing steps.

Octopus vulgaris and other cephalopods are of increasing interest as neurobiological model organisms. This protocol describes a method to record calcium activity from individual cells in acute brain slices from Octopus vulgaris hatchlings during exogenous application of neurotransmitters. Using this protocol, we characterized single-cell responses to specific neurotransmitters in the optic lobes, which process visual information. The approach is readily adaptable to other cephalopods and small invertebrate species. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=146 HEIGHT=200 SRC="FIGDIR/small/711860v1_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@1564eaeorg.highwire.dtl.DTLVardef@147b682org.highwire.dtl.DTLVardef@11f3b85org.highwire.dtl.DTLVardef@17c9d70_HPS_FORMAT_FIGEXP M_FIG C_FIG

Scientists have for decades attempted to automate the manual sleep staging problem not only for human polysomnography data but also for rodent data. No model has, however, succeeded in fully replacing the manual procedure across clinics and laboratories. We hypothesize that this is due to the models limited ability to generalize to data from unseen laboratories. Our findings show that despite the high performance of four state-of-the-art models reported in initial publications, the published models struggle to generalize to other laboratories. We further show a significant improvement in model performance across labs by re-training them on a diverse dataset from five different sites. To assess the contribution of variability in manual scoring, ten experts from five laboratories all labelled the same nine mouse sleep recordings. The result revealed substantial scoring variability, particularly for rapid eye movement (REM) sleep, both within and between labs. In conclusion our study demonstrates that key challenges in the generalizability of state-of-the-art sleep scoring models are signal variability and label noise. Our study highlights the need for a standardized set of mouse sleep scoring guidelines to enable consistency and collaboration across the field. Until such a consensus is reached, we present four sufficiently robust models trained on diverse datasets that can serve as standardized tools across labs.

BackgroundFlexibility and robustness of neuronal function are closely linked to degeneracy, the ability of distinct structural or parametric configurations to produce similar functional outcomes. At the cellular level, this often manifests as ion-channel degeneracy, in which multiple combinations of intrinsic conductances yield comparable electrophysiological phenotypes. MethodologyWe used a population-based, data-driven modelling framework to generate large ensembles of biophysically detailed CA1 pyramidal neuron models constrained by somatic electrophysiological features extracted from patch-clamp recordings in acute slices from early-birth rats. 10 reconstructed morphologies were incorporated, and model populations were analyzed using parameter correlation analysis, principal component analysis, and generalization tests to assess robustness, degeneracy, and morphology dependence of intrinsic properties. ConclusionsAcross the model population, similar somatic firing behaviours emerged from widely different combinations of intrinsic parameters, demonstrating robust two-level ion channel degeneracy both within and across morphologies. Each morphology occupied a distinct region of parameter space, indicating morphology-specific compensatory effects, while weak pairwise parameter correlations suggested distributed compensation rather than tight parameter dependencies. Even with a fixed morphology, multiple parameter subspaces supported comparable electrophysiological phenotypes. Generalization across morphologies was structure-dependent and non-reciprocal, with successful parameter similarity occurring preferentially between structurally similar neurons. Interestingly, to accurately simulate spike-frequency adaptation, it was important to retain some kinetic properties of the ion channel models as free parameters during optimization. Together, these findings show that dendrite morphology shapes the valid parameter space, and similar electrophysiology of CA1 pyramidal neurons arises from the interplay between structural variability and ion-channel diversity. This work highlights the importance of population-based modelling for capturing biological variability and provides insights into how neuronal robustness might be maintained despite substantial heterogeneity, and offers a scalable pipeline for generating biophysically realistic CA1 neuron populations for use in network simulations. Author summaryNeurons must reliably process information even though their internal components, such as ion channels and cellular shape, can vary widely from cell to cell. How stable behaviour emerges from such variability is a fundamental question in neuroscience. In this study, we explored this problem using detailed computer models of early-birth rat hippocampal CA1 pyramidal neurons, a cell type that plays a central role in learning and memory. Instead of building a single "average" neuron model, we created large populations of models that all reproduced key experimental recordings but differed in their internal parameters. We found that neurons with different shapes and different combinations of ion channels could nevertheless generate similar electrical activity. This phenomenon, known as ion channel degeneracy, allows neurons to remain functional despite biological variability or perturbations. Our results show that neuronal shape strongly influences which parameter combinations are viable, but that multiple solutions exist even for the same morphology. The population of models we provide offers a resource for future studies of early-birth CA1 pyramidal cell function and dysfunction.

Background: Prospective stroke registries have advanced our understanding of cerebrovascular disease, yet most reduce neuroimaging to categorical variables, forfeiting the multidimensional information inherent in clinical imaging. We describe the CRCS-K Imaging Repository, a prospective multicenter platform that systematically collects all stroke neuroimaging and integrates artificial intelligence (AI)-based automated quantification with clinical and outcome data through a dedicated research platform, AISCAN. Methods: Building upon the Clinical Research Collaboration for Stroke in Korea (CRCS-K), a nationwide prospective registry, all neuroimaging (computed tomography [CT], magnetic resonance [MR], and angiography) performed during index hospitalization of consecutive acute ischemic stroke patients was collected from 18 comprehensive stroke centers. Imaging underwent centralized quality verification, sequence classification, and AI-based quantification. As a proof-of-concept application, we examined the association between pre-treatment imaging modality, treatment workflow efficiency, and functional outcomes in patients receiving intravenous thrombolysis (IVT) or endovascular treatment (EVT). Results: From June 2022 through May 2025, 225,159 imaging sequences were collected from 20,792 patients. AI-based quantification modules converted these into standardized numeric features encompassing ischemic lesion volumes, perfusion parameters, white matter hyperintensity burden, and cerebral microbleed counts. Substantial inter-hospital variation in imaging modality selection was observed, with MR-first workflows ranging from 1.0% to 56.7% across centers. In the proof-of-concept analysis, each additional imaging sequence was associated with prolonged door-to-treatment times for both IVT and EVT. Propensity score overlap-weighted analyses suggested numerically more favorable functional outcomes with CT-based imaging among EVT-treated patients, whereas differences among IVT-treated patients were smaller and less consistent. Conclusions: The CRCS-K Imaging Repository demonstrates the feasibility of large-scale, prospective neuroimaging collection integrated with AI-based quantification and clinical data. The infrastructure enables clinically consequential questions that conventional registries cannot address.

A mean field model (MFM) is a mesoscopic description of neuronal population dynamics that can reduce the complexity of neural microcircuits into equations preserving key functional properties. The generation of a MFM is a complex mathematical process that starts with the incorporation of single neuron input/output relationships and local connectivity. Once neuron electroresponsiveness and synaptic properties are defined, in principle, the process can be automatized. Here we develop a tool for automatic MFM derivation from biophysically grounded spiking networks (Auto-MFM) by performing micro-to-mesoscale parameter remapping, estimating input/output relationships specific for different neuronal populations (i.e., transfer functions), and optimizing transfer function parameters. Auto-MFM was tested using a spiking cerebellar circuit as a generative model. The cerebellar MFM derived with Auto-MFM accurately reproduced cerebellar population dynamics of the corresponding spiking network, matching mean and time-varying firing rates across a wide range of stimulation patterns. Auto-MFM allowed us to model and explore physiological and pathological circuit variants; indeed, it was used to map ataxia-related structural connectivity alterations of the cerebellar network, in which Purkinje cells with simplified dendritic structure altered the cerebellar connectivity. Furthermore, Auto-MFM was used to create a library of cerebellar MFMs by sweeping the level of the excitatory conductance at mossy fiber - granule cell synapse, which is altered in several neuropathologies. Auto-MFM is thus proving a flexible and powerful tool to generate region-specific MFMs of healthy and pathological brain networks to be embedded in brain digital models.

IntroductionPreterm birth is a major risk factor for disrupted brain development and subsequent neurodevelopmental disorders, yet the underlying mechanisms remain poorly understood. Further, typical neuroimaging analyses are particularly challenging in the neonatal brain: data is frequently low quality and a lack of cellular development violates the assumptions relied on by many commonly-used techniques. In this study, we develop and present an advanced diffusion magnetic resonance imaging method to examine the microstructural organization of white matter in a clinically-acquired cohort of premature neonates. MethodsUsing a novel approach that resolves multiple tissue compartments within the brain, we provide highly detailed orientation and quantification of white matter fibers and tissue signal fraction. We also utilize a series of automated segmentation algorithms to identify and measure these metrics across key tracts and subcortical regions. We investigate how these measures relate to postmenstrual age, as well as to clinical factors reflecting neonatal illness severity. ResultsWe report successful segmentation and reconstruction of numerous white matter tracts throughout the neonatal brain. We further demonstrate the utility and functionality of microstructural analysis in a variety of pathologies commonly encountered in the neonatal clinical environment. Our results demonstrate tract-specific developmental trajectories, with early-maturing pathways showing higher microstructural organization. Exploratory analyses suggest that neonatal illness severity has modest, tissue-specific associations with microstructural properties. DiscussionThis work demonstrates that advanced microstructural imaging methods can extract meaningful white matter measurements from clinically-acquired scans, providing a practical framework for studying neonatal brain development in real-world hospital settings. These metrics are able to be calculated at extremely young ages, potentially allowing non-invasive study of vulnerable populations before detailed behavioral or neurological assessments are feasible.

Building a simple model that precisely and functionally characterizes a neuron is a challenging and important task to select the best concise and computationally efficient model. However, this type of work has only been done for subthreshold properties of neurons. Here, we take a different perspective and suggest a method to obtain point-neuron models from morphologically-detailed models with dendrites. To do this, we focus on the functional characterization of the neuron response under in vivo conditions, and compute the transfer function of the detailed model. The parameters of this transfer function, in terms of mean voltage, voltage standard deviation and correlation time, can be used to compute the "best" point-neuron model that generates a transfer function very close to that of the morphologically-detailed model. We illustrate this approach for two very different neuronal morphologies, one from Drosophila larvae and one from mammals. In conclusion, this approach provides a tool to generate point-neuron models from detailed models, based on a functional characterization of the neuron response. Significance StatementThis study provides a new computational method to reduce morphological models into point-neuron models. To do so, we calculate the transfer function parameters, ie the voltage standard deviation, the mean voltage and the correlation time, of the morphological model and fit a point neuron-model onto this data. Here, we successfully apply this approach for two very different neuron morphologies, a drosophila neuron and a rat motoneuron.

Optimization of deep brain stimulation (DBS) therapy for neurological and neuropsychiatric disorders depends on objective quantitative biomarkers that can guide stimulation parameter adjustments. With the recent introduction of new-generation DBS systems capable of simultaneously stimulating brain activity and recording local field potentials (LFP), there is increasing demand for platforms that enable efficient visualization and analysis of these signals for electrophysiological biomarkers identification. To address the limitations of currently available toolboxes that require advanced signal processing skills and rely on proprietary software, we present NeoDBS, an open-source Python platform designed for ingestion and advance signal visualization and processing of LFP signals from DBS systems through an easy-to-use graphical interface. NeoDBS is a user-centered platform that offers predefined analysis pipelines with the aim of facilitating electrophysiological biomarker investigation for DBS across different brain disorders. Custom analysis pipelines are also available for users to leverage the signal analysis tools to their research needs. Critical functionalities for longitudinal biomarker research are featured in NeoDBS, such as batch file processing and event-locked analysis for in-clinic and at-home recordings. This combination of accessibility, user-experience and advanced signal processing tools makes NeoDBS an environment that propels easy and fast electrophysiological biomarker research for DBS, across patients, sessions, and stimulation parameters.

Previous studies exploring the connection between early language development and brain anatomy have shown that cortical areas relating to individual differences in language skills are diverse and vary depending on the age of child. However, due to lack of large longitudinal samples, current literature is limited in answering the extent to which individual differences in language development prior to school age are reflected in areas of the cortex. To fill this gap, we compared gray matter density between participants that belonged to different longitudinally defined language profiles from 14 months to five years of age in a large population-based sample. Participants were 166 children from the FinnBrain Birth Cohort Study who had longitudinal language data from 14 months to five years of age and magnetic resonance imaging data at five years of age. Three groups of language development were used as per our prior study: persistent low, stable average, and stable high. Voxel-based morphometry metrics were calculated using SPM12 and the three language profile groups were compared to one another. Covariates included sex and age at brain scan. The statistics were thresholded at p < 0.01 and false discovery rate corrected at the cluster level. Of the three longitudinal language profiles, the stable high group had higher gray matter density than the persistent low group in the right superior frontal gyrus. No differences were found between the stable average and stable high groups, nor persistent low and stable average groups. The identified superior frontal cortical area belongs to executive functions neural network. This finding adds to the cumulating evidence that individual differences in language development are reflected in growth of gray matter supporting general processing ability rather than specialized language regions. The results suggest that cognitive development and early language development are linked through shared principles of neural growth, identifiable already at age five. Key pointsO_LIAn association between early language development from 14 months to five years of age and gray matter density differences of the right superior frontal gyrus was found at the age of five years. Children following the strongest language trajectory were more likely to exhibit higher gray matter density of the right superior frontal gyrus than children following the weakest trajectory. C_LIO_LIAs the superior frontal gyrus is part of executive functions network, we propose that individual differences in early language development are more defined by general learning mechanisms supported by those networks, rather than language specific pathways. C_LI

Linking structural connectivity (SC) to functional connectivity (FC) through mechanistic models remains challenging in network neuroscience. In this study, empirical data of diffusion magnetic resonance imaging (dMRI) and resting-state functional MRI (rs-fMRI) were used to reconstruct SC and FC of a swine connectome. We evaluated a structurally constrained Kuramoto phase-oscillator framework to reproduce resting-state FC and then assessed the models sensitivity to traumatic brain injury (TBI) and its longitudinal progression post-TBI. A joint tuning procedure was implemented to calibrate data-informed natural frequencies and global coupling strength. The tuned Kuramoto model was then used to evolve oscillator phases constrained by the SC, followed by a Balloon-Windkessel hemodynamic model. The optimized model produced significant edge-wise correspondence between averaged simulated FC and the empirical FC (r = 0.61, p < 0.001). Graph-theoretical analysis across network densities (30-50%) showed strong agreement for global efficiency, characteristic path length, and clustering coefficient, while modularity and small-worldness exhibited deviations. Longitudinal analysis of the swine TBI dataset revealed modest reductions in structure-function coupling over time but no significant differences across injury severities. These results demonstrate that optimized Kuramoto models can reproduce key functional network features while preserving inter-subject variability.