Neuroinformatics最新文献

{"title":"International Collaborations at the Intersection of Brain Sciences and Artificial Intelligence.","authors":"John Darrell Van Horn, Emiliano Ricciardi","doi":"10.1007/s12021-025-09736-3","DOIUrl":"10.1007/s12021-025-09736-3","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"36"},"PeriodicalIF":2.7,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144340572","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":"Cerebellar Micro Complex Model Using Histologic Boolean Mapping Simulates Adaptive Motor Control.","authors":"Gregoris A Orphanides, Christoforos Demosthenous, Ariadni Georgianakis, Vasilis Stylianides, Konstantinos Antoniou, Petros Kyriacou, Andreas A Ioannides, Alberto Capurro","doi":"10.1007/s12021-025-09730-9","DOIUrl":"10.1007/s12021-025-09730-9","url":null,"abstract":"<p><p>Despite extensive cerebellar research, the functional role of individual cerebellar micro complexes (CmCs) in motor coordination remains debated. This study aimed to utilise a reductionist approach to model the CmC function in motor control using the Histologic Boolean Mapping (HBM-VNR) framework and validate it through replication of features observed in the literature. HBM-VNR modelled each neuron within the CmC as a Boolean expression derived from its architectural connectivity. The model incorporates the Variable Neuronal Response (VNR) synaptic model, introducing probabilistic post-synaptic firing to reflect physiological variability. Motor control dynamics follow the cerebellar brain inhibition phenomenon, where Deep Cerebellar Nucleus (DCN) firing activates the antagonist muscles. The model performed the task of feedback-control in an idealised joint following a desired sinusoidal position. HBM-VNR produced a minimalistic model that reproduced adaptive compensation to external forces and predicted intention tremor when CmC population was reduced, and the expected ethanol induced motor impairments. Simulated firing patterns of the DCN and Purkinje cell showed patterns resembling real recordings both in physiological and pathological situations. The Shifting Central Frequency Hypothesis (SCFH) was suggested to explain the CmC comparator functionality. This study presents HBM-VNR as a histologically grounded modelling approach for neural circuits. HBM-VNR simulated adaptive motor control and predicted neocerebellar syndrome symptomatology and alcohol intoxication effects. SCFH offers a computational mechanism consistent with the cerebellar internal model theories and places CmC as the basis for motor learning in line with the literature, positioning HBM-VNR as a scalable framework for neuroanatomical modelling.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 3","pages":"35"},"PeriodicalIF":2.7,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12174260/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318526","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":"Classification of Major Depressive Disorder Using Graph Attention Mechanism with Multi-Site rs-fMRI Data.","authors":"Shiyue Su, Yicai Ning, Zijian Guo, Weifeng Yang, Manyun Zhu, Qilin Zhou, Xuan He","doi":"10.1007/s12021-025-09731-8","DOIUrl":"https://doi.org/10.1007/s12021-025-09731-8","url":null,"abstract":"<p><p>Major Depressive Disorder (MDD) significantly impacts global health, impairing individual functioning and increasing socioeconomic burden. Developing innovative, interpretable approaches for its identification is essential for improving diagnosis and guiding treatment. This study introduces a novel framework designed to classify MDD using resting-state functional MRI (rs-fMRI) data. Our framework follows three stages: First, Node2Vec extracts rich, low-dimensional brain region embeddings from functional connectivity (FC) networks, capturing their complex topological information. Second, these informative embeddings feed a Graph Attention Network (GAT) which, via multi-head attention, identifies and weighs discriminative inter-regional functional connections, refining them into a potent graph representation. Third, these GAT-derived representations are classified by an ensemble classifier (Random Forest, SVM, MLP) for robust MDD identification. The model achieved classification accuracies of 78.73% and 92.94% on the REST-meta-MDD and SRPBS-MDD datasets, respectively. Moreover, the attention mechanism revealed that resting-state functional connectivity of regions within the Default Mode Network (DMN) and Frontoparietal Network (FPN) were among the most discriminative features for distinguishing MDD from healthy controls. The attention mechanism enhanced interpretability by highlighting significant brain regions linked to MDD. Compared to traditional methods, this GNN-based approach effectively captures complex brain connectivity patterns and offers improved interpretability, ultimately aiding healthcare professionals in diagnosing MDD more accurately.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"34"},"PeriodicalIF":2.7,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144286945","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":"Estimation of Task-Related Dynamic Brain Connectivity via Data Inflation and Classification Model Explainability.","authors":"Peter Rogelj","doi":"10.1007/s12021-025-09733-6","DOIUrl":"10.1007/s12021-025-09733-6","url":null,"abstract":"<p><p>Study of brain function often involves analyzing task-related switching between intrinsic brain networks, which connect various brain regions. Functional brain connectivity analysis methods aim to estimate these networks but are limited by the statistical constraints of windowing functions, which reduce temporal resolution and hinder explainability of highly dynamic processes. In this work, we propose a novel approach to functional connectivity analysis through the explainability of EEG classification. Unlike conventional methods that condense raw data into extracted features, our approach inflates raw EEG data by decomposition into meaningful components that explain processes in the application domain. To uncover the brain connectivity that affects classification decisions, we introduce a new method of dynamic influence data inflation (DIDI), which extracts signals representing interactions between electrode regions. These inflated data are then classified using an end-to-end neural network classifier architecture designed for raw EEG signals. Saliency map estimation from trained classifiers reveals the connectivity dynamics affecting classification decisions, which can be visualized as dynamic connectivity support maps for improved interpretability. The methodology is demonstrated on two publicly available datasets: one for imagined motor movement classification and the other for emotion classification. The results highlight the dual benefits of our approach: in addition to providing interpretable insights into connectivity dynamics it increases classification accuracy.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"33"},"PeriodicalIF":2.7,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12133929/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144210067","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":"Predicting Placebo Responses Using EEG and Deep Convolutional Neural Networks: Correlations with Clinical Data Across Three Independent Datasets.","authors":"Mariam Khayretdinova, Polina Pshonkovskaya, Ilya Zakharov, Timothy Adamovich, Andrey Kiryasov, Andrey Zhdanov, Alexey Shovkun","doi":"10.1007/s12021-025-09725-6","DOIUrl":"10.1007/s12021-025-09725-6","url":null,"abstract":"<p><p>Identifying likely placebo responders can help design more efficient clinical trials by stratifying participants, reducing sample size requirements, and enhancing the detection of true drug effects. In response to this need, we developed a deep convolutional neural network (DCNN) model using resting-state EEG data from the EMBARC study, achieving a balanced accuracy of 69% in predicting placebo responses in patients with major depressive disorder (MDD). We then applied this model to two additional datasets, LEMON and CAN-BIND-which did not include placebo groups-to investigate potential relationships between the model's predictions and various clinical features in independent samples. Notably, the model's predictions correlated with factors previously linked to placebo response in MDD, including age, extraversion, and cognitive processing speed. These findings highlight several factors associated with placebo susceptibility, offering insights that could guide more efficient clinical trial designs. Future research should explore the broader applicability of such predictive models across different medical conditions, and replicate the current EEG-based model of placebo response in independent samples.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"32"},"PeriodicalIF":2.7,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12089153/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103127","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":"Alterations in Gray Matter Structure Linked to Frequency-Specific Cortico-Subcortical Connectivity in Schizophrenia via Multimodal Data Fusion.","authors":"Marlena Duda, Ashkan Faghiri, Aysenil Belger, Juan R Bustillo, Judith M Ford, Daniel H Mathalon, Bryon A Mueller, Godfrey D Pearlson, Steven G Potkin, Adrian Preda, Jing Sui, Theo G M Van Erp, Vince D Calhoun","doi":"10.1007/s12021-025-09728-3","DOIUrl":"https://doi.org/10.1007/s12021-025-09728-3","url":null,"abstract":"<p><p>Schizophrenia (SZ) is a complex psychiatric disorder that is currently defined by symptomatic and behavioral, rather than biological, criteria. Neuroimaging is an appealing avenue for SZ biomarker development, as several neuroimaging-based studies have shown measurable group differences in brain structure, as well as functional brain alterations in both static and dynamic functional network connectivity (sFNC and dFNC, respectively), between SZ and controls. The recently proposed filter-banked connectivity (FBC) method extends the standard dFNC sliding-window approach to estimate FNC within an arbitrary number of distinct frequency bands. Initial FBC results found that individuals with SZ spend more time in a less structured, more disconnected low-frequency (i.e., static) FNC state than HC, as well as preferential SZ occupancy in high-frequency connectivity states, suggesting a frequency-specific component underpinning the functional dysconnectivity observed in SZ. Building on these findings, we sought to link such frequency-specific patterns of FNC to covarying data-driven structural brain networks in the context of SZ. Specifically, we employ a multi-set canonical correlation analysis + joint independent components analysis (mCCA + jICA) data fusion framework to study the connection between gray matter volume (GMV) maps and FBC states across the full connectivity frequency spectrum. Our multimodal analysis identified two joint sources that captured co-varying patterns of frequency-specific functional connectivity and alterations in GMV with significant group differences in loading parameters between the SZ group and HC. The first joint source linked frequency-modulated connections between the subcortical and sensorimotor networks and GMV alterations in the frontal and temporal lobes, while the second joint source identified a relationship between low-frequency cerebellar-sensorimotor connectivity and structural changes in both the cerebellum and motor cortex. Together, these results show a strong connection between cortico-subcortical functional connectivity at both high and low frequencies and alterations in cortical GMV that may be relevant to the pathogenesis and pathophysiology of SZ.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"31"},"PeriodicalIF":2.7,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144005839","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":"Semi-automated Analysis of Beading in Degenerating Axons.","authors":"Pretheesh Kumar V C, Pramod Pullarkat","doi":"10.1007/s12021-025-09726-5","DOIUrl":"https://doi.org/10.1007/s12021-025-09726-5","url":null,"abstract":"<p><p>Axonal beading is a key morphological indicator of axonal degeneration, which plays a significant role in various neurodegenerative diseases and drug-induced neuropathies. Quantification of axonal susceptibility to beading using neuronal cell culture can be used as a facile assay to evaluate induced degenerative conditions, and thus aid in understanding mechanisms of beading and in drug development. Manual analysis of axonal beading for large datasets is labor-intensive and prone to subjectivity, limiting the reproducibility of results. To address these challenges, we developed a semi-automated Python-based tool to track axonal beading in time-lapse microscopy images. The software significantly reduces human effort by detecting the onset of axonal swelling. Our method is based on classical image processing techniques rather than an AI approach. This provides interpretable results while allowing the extraction of additional quantitative data, such as bead density, coarsening dynamics, and morphological changes over time. Comparison of results obtained through human analysis and the software shows strong agreement. The code can be easily extended to analyze diameter information of ridge-like structures in branched networks of rivers, road networks, blood vessels, etc.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"30"},"PeriodicalIF":2.7,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023116","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":"Mathematical and Dynamic Modeling of the Anatomical Localization of the Insula in the Brain.","authors":"Eren Ogut","doi":"10.1007/s12021-025-09727-4","DOIUrl":"https://doi.org/10.1007/s12021-025-09727-4","url":null,"abstract":"<p><p>The insula, a deeply situated cortical structure beneath the Sylvian sulcus, plays a critical role in sensory integration, emotion regulation, and cognitive control in the brain. Although several studies have described its anatomical and functional characteristics, mathematical models that quantitatively represent the insula's complex structure and connectivity are lacking. This study aimed to develop a mathematical model to represent the anatomical localization and functional organization of the insula, drawing on current neuroimaging findings and established anatomical data. A three-dimensional (3D) ellipsoid model was constructed to mathematically represent the anatomical boundaries of the insula using Montreal Neurological Institute (MNI) coordinate data. This geometric model adapts the ellipsoid equation to reflect the spatial configuration of the insula and is primarily based on cytoarchitectonic mapping and anatomical literature. Relevant findings from prior imaging research, particularly those reporting microstructural variations across insular subdivisions, were reviewed and conceptually integrated to guide the model's structural assumptions and interpretation of potential applications. The ellipsoid-based 3D model accurately represented the anatomical dimensions and spatial localization of the right insula, centered at the MNI coordinates (40, 5, 5 mm), and matched well with the known volumetric data. Functional regions (face, hand, and foot) were successfully plotted within the model, and statistical analysis confirmed significant differences along the anteroposterior and superoinferior axes (p < 0.01 and p < 0.05, respectively). Dynamic simulations revealed oscillatory patterns of excitatory and inhibitory neural activity, consistent with established insular neurophysiology. Additionally, connectivity modeling demonstrated strong bidirectional interactions between the insula and key regions, such as the prefrontal cortex and anterior cingulate cortex (ACC), reflecting its integrative role in brain networks. This study presents a scientifically validated mathematical model that captures the anatomical structure, functional subdivisions, and dynamic connectivity patterns of the insula. By integrating anatomical data with computational simulations, this model provides a foundation for future research in neuroimaging, functional mapping, and clinical applications involving insula-related disorders.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"29"},"PeriodicalIF":2.7,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12018515/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144024879","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":"SlicesMapi: An Interactive Three-Dimensional Registration Method for Serial Histological Brain Slices.","authors":"Zoutao Zhang, Lingyi Cai, Wenwei Li, Hui Gong, Anan Li, Zhao Feng","doi":"10.1007/s12021-025-09724-7","DOIUrl":"https://doi.org/10.1007/s12021-025-09724-7","url":null,"abstract":"<p><p>Brain slicing is a commonly used technique in brain science research. In order to study the spatial distribution of labeled information, such as specific types of neurons and neuronal circuits, it is necessary to register the brain slice images to the 3D standard brain space defined by the reference atlas. However, the registration of 2D brain slice images to a 3D reference brain atlas still faces challenges in terms of accuracy, computational throughput, and applicability. In this paper, we propose the SlicesMapi, an interactive 3D registration method for brain slice sequence. This method corrects linear and non-linear deformations in both 3D and 2D spaces by employing dual constraints from neighboring slices and corresponding reference atlas slices and guarantees precision by registering images with full resolution, which avoids the information loss of image down-sampling implemented in the deep learning based registration methods. This method was applied to deal the challenges of unknown slice angle registration and non-linear deformations between the 3D Allen Reference Atlas and slices with cytoarchitectonic or autofluorescence channels. Experimental results demonstrate Dice scores of 0.9 in major brain regions, highlighting significant advantages over existing methods. Compared with existing methods, our proposed method is expected to provide a more accurate, robust, and efficient spatial localization scheme for brain slices. Therefore, the proposed method is capable of achieving enhanced accuracy in slice image spatial positioning.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"28"},"PeriodicalIF":2.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054483","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":"Genetic Insights into Brain Morphology: a Genome-Wide Association Study of Cortical Thickness and T₁-Weighted MRI Gray Matter-White Matter Intensity Contrast.","authors":"Nicholas J Kim, Nahian F Chowdhury, Kenneth H Buetow, Paul M Thompson, Andrei Irimia","doi":"10.1007/s12021-025-09722-9","DOIUrl":"10.1007/s12021-025-09722-9","url":null,"abstract":"<p><p>In T₁-weighted magnetic resonance imaging (MRI), cortical thickness (CT) and gray-white matter contrast (GWC) capture brain morphological traits and vary with age-related disease. To gain insight into genetic factors underlying brain structure and dynamics observed during neurodegeneration, this genome-wide association study (GWAS) quantifies the relationship between single nucleotide polymorphisms (SNPs) and both CT and GWC in UK Biobank participants (N = 43,002). To our knowledge, this is the first GWAS to investigate the genetic determinants of cortical T₁-MRI GWC in humans. We found 251 SNPs associated with CT or GWC for at least 1% of cortical locations, including 42 for both CT and GWC; 127 for only CT; and 82 for only GWC. Identified SNPs include rs1080066 (THSB1, featuring the strongest association with both CT and GWC), rs13107325 (SLC39A8, linked to CT at the largest number of cortical locations), and rs864736 (KCNK2, associated with GWC at the largest number of cortical locations). Dimensionality reduction reveals three major gene ontologies constraining CT (neural signaling, ion transport, cell migration) and four constraining GWC (neural cell development, cellular homeostasis, tissue repair, ion transport). Our findings provide insight into genetic determinants of GWC and CT, highlighting pathways associated with brain anatomy and dynamics of neurodegeneration. These insights can assist the development of gene therapies and treatments targeting brain diseases.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 2","pages":"26"},"PeriodicalIF":2.7,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11961481/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143755548","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}