Neuroinformatics最新文献

{"title":"Targeting ROCK Isoforms by Natural Phytocompounds: An In-Silico Approach to Modulate Mitochondrial Fission and Mitophagy in Neurological Diseases.","authors":"Chesta Shandilya, Shalini Mani","doi":"10.1007/s12021-026-09822-0","DOIUrl":"10.1007/s12021-026-09822-0","url":null,"abstract":"<p><p>One of the major key organelles for neuronal survival is the mitochondria. To maintain synaptic functions and neurotransmission, these cells rely upon high ATP levels and the regulated process of mitochondrial dynamics (fission and fusion) and mitophagy. Dysregulation in any of the mitochondrial processes, such as excessive fission and/or impaired removal of damaged mitochondria, can lead to damage to nerve cells and cause neuroinflammation, a major cause of various neurodegenerative diseases. Rho-associated coiled-coil kinase isoforms (ROCK1 and ROCK2) have emerged as critical upstream regulators of mitochondrial dynamics, where ROCK1 promotes Drp1-mediated mitochondrial fission. On the other hand, ROCK2 suppresses PINK1-Parkin-dependent mitophagy. Overexpression of both ROCK isoforms leads to increased ROS production and reduced ATP generation, compromising neuronal health. Although synthetic ROCK inhibitors show neuroprotective potential, their long-term clinical use is limited due to their toxicity, poor pharmacokinetics, and suboptimal BBB penetration. In contrast, natural compounds may offer a safer, cheaper and more effective inhibitory effect. The current study follows an integrated in-silico approach to screen natural phytochemicals derived from Indian medicinal plants, known for their neuroprotective effects. Based on neuroprotective properties and less explored mechanistic potential,11 plants were selected. From these plants, a library of 1,223 natural product compounds was downloaded, and a further 428 unique phytocompounds were obtained after curation with the known inhibitor for reference. These compounds were screened against key proteins ROCK1 and ROCK2 using molecular docking, followed by pharmacokinetic and toxicity class, MM-GBSA free binding energy, and molecular dynamics simulations for 200 ns. Several natural phytocompounds showed comparable binding affinities with respect to known inhibitors. Based on all the in-silico observations, Scopolin and Lycorine emerged as promising ROCK1 inhibitors, while Cytisoside and N-Methyltyramine demonstrated strong ROCK2 inhibition. Notably, Coumarin exhibited stable and persistent interactions with both ROCK1 and ROCK2, with good binding energies, high blood-brain barrier permeability, and strong dynamic stability across simulations. Collectively, all these findings indicate the natural phytochemicals as viable modulators of mitochondrial quality control. Particularly, Coumarin emerges as a promising dual ROCK inhibitor; its potential to restore mitochondrial fission and mitophagy balance and mitigate neurodegenerative diseases may be further validated using in vitro assays.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149170282","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing the Transferability of Perturbational Similarity Maps Across Species, Cell Systems, and Assay Modalities.","authors":"Jing Wen","doi":"10.1007/s12021-026-09821-1","DOIUrl":"10.1007/s12021-026-09821-1","url":null,"abstract":"<p><p>We evaluated a mouse whole-brain perturbation map in human K562 and RPE1 Perturb-seq, HeLa morphology and nine L1000 cell-line matrices using three endpoints: global pair-rank concordance, source-defined top-k neighbour preservation, and within-validation CORUM retrieval. Strict orthology retained 71, 90, 695 and 273-311 shared targets, respectively. Under the primary zero-anchored RMS representation, Spearman rho was 0.026 and 0.035 in K562 and RPE1, 0.0022 and 0.0055 in the two HeLa conditions, and a median of 0.0055 across L1000 lines. RPE1 depended on representation: across-target standardisation gave rho = 0.102 (95% CI, 0.027-0.201), whereas omitting feature RMS scaling gave a descriptive rho = -0.034. No source-defined top-10 neighbour preservation result survived correction across 13 contexts (minimum q = 0.240). Source pair ranks also changed when response features were restricted. Within-validation CORUM retrieval identified complex structure in several external matrices but did not test preservation of source neighbours. Across 50 cell splits, K562/RPE1 median global rho was 0.600/0.710 and top-10 overlap was 0.497/0.517. Across 50 mouse-level splits of K562/RPE1-matched source profiles, median global rho was 0.731/0.703; a cross-species noise ceiling remains unestimated. The results are conditional on representation and feature selection and do not establish absence of transfer or transferability to neural systems.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149149254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Visual Support System for Stent-Assisted Treatment of Cerebral Aneurysms Using 3D Imaging and Augmented Reality.","authors":"Wojciech Chmiel, Piotr Szwed, Joanna Kwiecień, Michał Turek, Stanisław Jȩdrusik, Dariusz Pałka, Mikołaj Waśniewski, Piotr Kadłuczka, Damian Gumiela, Andrzej Zeja","doi":"10.1007/s12021-026-09812-2","DOIUrl":"10.1007/s12021-026-09812-2","url":null,"abstract":"<p><p>Precise visualization of vascular structures is crucial for planning and performing stent-assisted interventions in cerebral aneurysms. Traditional 2D imaging frequently lacks sufficient spatial information, which restricts clinicians' ability to evaluate vessel morphology, branching patterns, and how stents would interact with the vessel wall. This paper introduces an integrated visual support platform that unites workflow-driven medical image processing, numerical simulation of stent deployment, and augmented reality (AR) visualization via Microsoft HoloLens. The platform features a configurable workflow engine to handle medical data processing covering: vessel segmentation, medial-axis computation, mesh creation, and region-of-interest definition, followed by interactive simulation of stent expansion using computationally efficient approximation techniques. The resulting 3D models of the vasculature and stent are displayed in a dedicated AR environment that supports gesture-based interaction, real-time stent adjustment, and immersive navigation inside the vessels. This system provides intraoperative decision support by enabling clinicians to analyze stent placement, aneurysm coverage, and possible geometric limitations, and it also supports replay of simulations for training and educational purposes. The work shows that integrating 3D imaging, numerical modeling of stent-vessel interactions, and augmented reality can markedly improve precision, safety, and situational awareness in neurointerventional procedures.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13642533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149117675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Beyond the General Linear Model: Linear Mixed-Effects Modeling of fMRI Data from the Human Connectome Project.","authors":"Daniele Orzechowski, Ronaldo Martins da Costa","doi":"10.1007/s12021-026-09820-2","DOIUrl":"10.1007/s12021-026-09820-2","url":null,"abstract":"<p><p>We present a validated, containerised pipeline for Linear Mixed-Effects (LME) analysis of task fMRI, built for reproducibility: the fitting backend must be declared explicitly, outputs are versioned, and every step records a manifest of software, hardware, and parameters. The implementation is cross-validated against the R reference standard lme4 (mean [Formula: see text] across 21 regions; significance concordance 21/21) and benchmarked at [Formula: see text] the per-voxel cost of the GLM, completing a whole-brain analysis in under six hours with 100% convergence. In a case study on emotion task data from 142 HCP subjects (283 runs), voxelwise agreement with the conventional General Linear Model (GLM) is high ([Formula: see text]), with LME more conservative under FDR correction (74,004 vs. 108,627 significant voxels at [Formula: see text]; Dice [Formula: see text]). An intermediate LME omitting the run fixed effect is almost indistinguishable from the GLM (Dice [Formula: see text]), locating the sensitivity difference in the run covariate rather than in hierarchical variance decomposition. Whole-brain ICC mapping shows heterogeneous within-subject variability (mean ICC [Formula: see text]), and run effects are significant in 13/21 ROIs, surviving adjustment for head motion. This decomposition strategy offers a reusable diagnostic for sensitivity differences between statistical approaches.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149082605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal Dynamics of Dorsal-Striatal Protein Networks During Radial-Arm Maze Training.","authors":"S Gutman, N Borovok, M Kirby, Y Levin, I Michaelevski","doi":"10.1007/s12021-026-09819-9","DOIUrl":"10.1007/s12021-026-09819-9","url":null,"abstract":"<p><p>Repeated radial-arm maze (RAM) exposure engages spatial, motivational, motor, and reward-related processes, but how the dorsal-striatal proteome is organized across repeated training remains unclear. We profiled bulk dorsal striatum from male mice collected after RAM trials on Days 0, 1, 3, and 5, together with a habituated, food-restricted NoRAM control collected before the first trial, using ion-mobility-enhanced data-independent LC-MS/MS. Complementary exploratory factor analysis and weighted gene co-expression network analysis resolved a dominant RAM-versus-NoRAM difference and superimposed, sampling-dependent changes within the trained series. The broad RAM-associated pattern was enriched in ribosomal and translation-initiation proteins, proteasome components, and synaptic-vesicle trafficking. The Day-1-to-Day-3 contrast shifted emphasis toward actin/cytoskeletal regulation, membrane trafficking, mitochondrial respiration, and ATP-generating processes, whereas later Day-5 contrasts emphasized postsynaptic-density organization, vesicle recycling, protein-quality control, and metabolic support. Integration of the two analytical frameworks identified 65 shared central proteins linking synaptic signaling and receptor organization with proteostasis, energy production, and structural remodeling. Candidate transcription-factor and microRNA target enrichments nominated possible regulatory associations but were not directly measured. Because NoRAM controls were collected before task exposure and no activity-, reward-, or repeated-testing-matched control was included, the observed changes cannot be assigned specifically to spatial learning or a navigational strategy. Moreover, pooled bulk tissue precludes cell-type, subregional, and synapse-specific inference. The study therefore provides a biology-centered, hypothesis-generating map of overlapping dorsal-striatal protein networks associated with repeated RAM exposure and prioritizes molecular systems for direct temporal and functional validation.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149031713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Mutual Information and Pearson Correlation on Neural Evoked Responses.","authors":"Anni Hukari, Silvia Federica Cotroneo, Riitta Salmelin","doi":"10.1007/s12021-026-09784-3","DOIUrl":"10.1007/s12021-026-09784-3","url":null,"abstract":"<p><p>In neural evoked responses, small variations in the timing or duration of responses can be observed when the same functional response is recorded in different trials, different experimental conditions or by different sensors. These variations limit the ability of correlation-based methods to detect similarities between signals. Mutual information (MI) provides an alternative similarity measure, capable of capturing both linear and non-linear dependencies, yet its practical use is hindered by lack of consensus on estimators for continuous data and the limited understanding of the behavior of the estimators on realistic signals. In this work, we investigate how to estimate the similarity of neural evoked responses by systematically comparing sample Pearson correlation with three of the most common MI estimators. We describe their behavior using both simulated signals and real magnetoencephalographic data. In the simulations, the estimators are tested against a set of transformations that depict realistic changes in neural evoked responses. Subsequently, we propose guidelines for defining adaptive lower bounds on the similarity estimates and analyzing the similarity rankings induced by the different estimators. Our findings reveal trade-offs between measures sensitivity and different signal properties. We confirm that Pearson correlation is reliable in describing linear relationships for low-noise signals, and we identify parameter settings that stabilize MI estimators, enabling them to capture complex signal dependencies. Together, these results introduce practical parameter choices and thresholding strategies for mutual information and provide guidance for selecting and interpreting similarity measures in the analysis of neural evoked time series.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582061/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148984065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circle of Willis-Guided Localization for Simultaneous Detection and Classification of Large Vessel Occlusions in Brain CTA.","authors":"Valeriia Abramova, Arnau Oliver, Uma M Lal-Trehan Estrada, Rachika E Hamadache, Paola Martínez Arias, Jordi Freixenet, Mikel Terceño, Yolanda Silva, Xavier Lladó","doi":"10.1007/s12021-026-09817-x","DOIUrl":"10.1007/s12021-026-09817-x","url":null,"abstract":"<p><p>Large vessel occlusions (LVOs) are blockages in the brain's major arteries that can cause severe neurological damage. Rapid and accurate detection using computed tomography angiography (CTA) is critical for timely stroke treatment. Here, we present a fully automated approach that detects LVOs and classifies the affected vessel simultaneously. Our method incorporates a spatial prior by using Circle of Willis (CoW) segmentation as additional input, guiding the model to anatomically relevant regions. We evaluated the two strategies, the global approach using the full CTA volume, and the local one focused on CoW regions. Both achieved high performance. Detection sensitivity was 0.97 at 0.20 false positives per image for the global approach, and 0.97 at 0.13 false positives for the local approach. Classification accuracy reached 94% and 91% for global and local strategies, respectively. Importantly, the local approach was 3.3× faster, offering a computationally efficient solution, a critical advantage in acute stroke care, where every minute impacts patient outcomes.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 4","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13582341/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148984040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Neuroinformatics Framework for Evaluating Functional Connectivity Metrics in Small-Sample Resting-State fMRI: An Age-Stratified Autism Study.","authors":"Hossein Haghighat","doi":"10.1007/s12021-026-09816-y","DOIUrl":"10.1007/s12021-026-09816-y","url":null,"abstract":"<p><p>Functional connectivity (FC) analysis using resting-state fMRI is widely applied to study brain organization in autism spectrum disorder (ASD). However, variability in FC estimation, limited sample sizes, and high-dimensional feature spaces often produce unstable and over-optimistic machine-learning results, highlighting the need for rigorous methodological benchmarking. We propose a neuroinformatics framework for systematic evaluation of multiple FC measures under small-sample rs-fMRI conditions. Data were analyzed across children, adolescents, and adults. Group independent component analysis followed by dual regression extracted subject-specific network time series. Five FC measures-full correlation, partial correlation, bivariate Granger causality, coherence, and mutual information-captured linear, nonlinear, time-, and frequency-domain interactions. To ensure leakage-aware evaluation, feature selection was performed strictly within training folds using leave-one-out cross-validation, and multiple machine-learning classifiers were used as standardized evaluation tools. Performance of FC measures varied across developmental stages. Linear connectivity measures showed more stable behavior in childhood, nonlinear information-theoretic measures were most informative in adolescence, and frequency-domain measures demonstrated stronger performance in adulthood. Unlike studies focused primarily on diagnostic accuracy, this work emphasizes comparative methodological evaluation and explicitly addresses data leakage and overfitting through strict cross-validation and training-only feature selection. FC-based machine-learning outcomes are strongly influenced by developmental stage and methodological choice. The proposed leakage-aware framework provides a practical benchmark for evaluating FC measures in small-sample rs-fMRI Studies and supports more reliable neuroimaging machine-learning research.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148978582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BasNet: Attention U-Net-Based Automated Axon Segmentation in Bielschowsky Silver-Stained Histology.","authors":"Lukas Schönenberger, Laurin Egli, Dimitrios Gkotsoulias, Christine Stadelmann, Cristina Granziera","doi":"10.1007/s12021-026-09815-z","DOIUrl":"10.1007/s12021-026-09815-z","url":null,"abstract":"<p><p>Reliable automatic quantification of axon density in histologic sections is important for both research and clinical neuropathology applications - however, it remains a challenge for silver-impregnation stains, such as Bielschowsky. Classical color-deconvolution approaches perform poorly on silver stains, and manual annotation of densely packed axons is time-consuming and subject to inter-rater variability. We developed an automated axon segmentation pipeline in Bielschowsky silver-stained histological brain sections based on an attention U-Net architecture with attention-gated skip connections, trained using Focal Tversky Loss to handle class imbalance and thin structure recovery. Ground truth was manually annotated on 33 image tiles (covering over 25 million pixels) derived from 26 whole-slide images from varying brain regions of four multiple sclerosis patients. Slides were prepared by different laboratory technicians at different time points to capture realistic staining variability. Model performance was evaluated on a held-out test set of unseen tiles. On the test set (eight held-out tiles) the model achieved a mean pixel-wise F1/Dice of 0.717 and an intersection over union (IoU) of 0.584. A dedicated inter-rater experiment on two representative tiles yielded rater-to-rater F1 scores of 0.637 and 0.671, while model-to-rater agreement (F1: 0.681-0.736) met or exceeded that human ceiling. Generated whole-slide density and orientation visualizations accurately reflected regional axon distributions and provided quantitative readouts suitable for downstream analysis. Our attention U-Net provides an open-source solution for axon segmentation from Bielschowsky-stained sections, reducing manual effort and enabling reproducible, slide-level quantitative metrics. This tool can facilitate studies of axonal pathology across research and clinical settings.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13553709/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Towards Multi-Brain Decoding in Autism: A Self-Supervised Learning Approach.","authors":"Ghazaleh Ranjbaran, Quentin Moreau, Adrien Dubois, Guillaume Dumas","doi":"10.1007/s12021-026-09805-1","DOIUrl":"10.1007/s12021-026-09805-1","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 3","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13545141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}