Neuroinformatics最新文献

{"title":"Integrated Single-Cell and System Network Analysis: Exploring Cellular Communication Network Complexity and Signal Transmission Dysregulation in Down Syndrome Brain.","authors":"Xuehai Ma, Mengdan Wang, Jing Yang, Gang Li, Yan Wang, Hongkun Fang, Shuo Zhang","doi":"10.1007/s12021-025-09749-y","DOIUrl":"10.1007/s12021-025-09749-y","url":null,"abstract":"<p><p>Down syndrome (DS) is a widespread chromosomal disorder primarily associated with cognitive impairment and progressive neurodegenerative changes. Clinically, age 50 years is considered a pivotal turning point in the health trajectory of individuals with DS. Before this age, they primarily face developmental challenges including significant cognitive deficits and difficulties in social interaction. However, as they age, they increasingly exhibit more severe neurodegenerative changes, including Alzheimer's disease (AD)-like cognitive decline and dementia symptoms. This study aimed to dissect intricate gene expression patterns in key neuronal cell types within the DS cerebral cortex and to examine how these patterns evolve with age. We conducted a detailed gene expression analysis of key neuronal cells, including inhibitory neurons, excitatory neurons, microglia, and oligodendrocyte progenitor cells, in individuals with DS. Additionally, the bioinformatics tool NeuronChat was employed to investigate the intercellular communication networks in the DS brain. Individuals with DS were divided into younger and older groups, with age 50 years as the boundary. Through comparative analysis, our findings indicated that aging in DS is associated with exacerbated neuronal dysfunction, decreased energy metabolism in microglia, and increased neurodegenerative traits in oligodendrocyte progenitor cells. Notably, compared to the control group, the DS brain showed increased complexity in cellular communication networks, reflecting an effort to maintain adaptability during syndrome progression. However, this increased complexity does not translate into effective signal transmission, suggesting significant disruptions in the function and structure of the neural network. This study provides a deeper understanding of cell function abnormalities and signal transmission irregularities in DS. By integrating single-cell and systemic network analyses, we revealed complex pathophysiological mechanisms, laying a foundational framework for developing new treatment methods. Our comprehensive analysis emphasizes the necessity for targeted strategies to address the multifaceted nature of DS pathogenesis and improve treatment outcomes.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147639860","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":"Classifying Sleep Slow Oscillations in Low Density EEG.","authors":"Jeffrey Gaither, Peter White, Sara C Mednick, Paola Malerba","doi":"10.1007/s12021-026-09776-3","DOIUrl":"10.1007/s12021-026-09776-3","url":null,"abstract":"<p><p>Slow oscillations (SOs, 0.5–1.5 Hz) are electrographic signatures of highly synchronous cortical events contributing to slow wave activity (SWA, 0.5–4 Hz) during sleep. An objective, EEG-manifold-based characterization of SOs could enable clinically actionable investigations on their functional role in cognition and health. In an earlier study, our group presented a data-driven approach showing that SOs group into Global, Frontal, and Local patterns, based on near-simultaneous detection across multiple scalp electrodes. However, this method requires sleep acquisitions with at least 24 head electrodes, limiting its applicability in standard diagnostic polysomnography and at-home studies. Here, we introduce a deterministic classification method that leverages machine learning to identify space-time profiles of SO types in low-density EEG (2–8 electrodes). Using an xgboost architecture trained on data from eight or fewer scalp EEG channels, our gradient-boosted tree model drew from more than 300 features to predict SO class (i.e., space-time profile) with high fidelity. Leveraging the feature-based nature of our classifier, we also used the SHAP feature-importance package to isolate the biophysical properties most characteristic of each SO type. Notably, we found that large amplitudes at central electrodes distinguish Global from Frontal SOs, while large amplitudes at frontal electrodes distinguish both types from Local SOs. Finally, we obtained strong predictions using only a minimal setup – 20 features gathered from only F3-F4-C3-C4. Using as few as four leads, this interpretable, low-density EEG approach enables routine spatiotemporal classification of SOs in clinical and research settings and clarifies the biophysical signatures that distinguish them.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13056764/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147629042","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":"Limitations of Variational Laplace-Based Dynamic Causal Modelling for Multistable Cortical Circuits.","authors":"Abdoreza Asadpour, Amin Azimi, KongFatt Wong-Lin","doi":"10.1007/s12021-025-09759-w","DOIUrl":"10.1007/s12021-025-09759-w","url":null,"abstract":"<p><p>Dynamic causal modelling (DCM) is widely used to infer effective connectivity from neuroimaging data. However, its applicability to neural systems with complex, multistable dynamics remains uncertain—particularly when using the standard estimation approach based on variational Bayesian inference under the Laplace approximation. To investigate this limitation, we constructed biologically grounded cortical columnar neural mass models exhibiting three distinct multistable regimes: bistable fixed points associated with decision-making, coexisting oscillatory states through period-doubling bifurcations, and deterministic chaotic dynamics. These models were used to simulate local field potentials, which served as inputs to DCM. Bayesian model selection successfully identified the correct model architecture in all cases. However, Bayesian model averaging of the winning models failed to accurately estimate extrinsic connectivity parameters, leading to substantial discrepancies between the dynamics of the reconstructed and ground-truth systems. These results suggest that even when model selection is accurate, parameter estimation can break down under complex dynamics. Compared to previous applications of DCM to simpler neural systems, our study highlights significant limitations in its ability to capture the structure of multistable and globally nonlinear dynamics. We conclude that caution is warranted when applying variational Laplace-based DCM procedures to experimental paradigms involving bifurcations, chaotic trajectories, or other forms of dynamical complexity.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13043521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595830","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":"The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).","authors":"Jyotismita Chaki, Gopikrishna Deshpande","doi":"10.1007/s12021-026-09775-4","DOIUrl":"10.1007/s12021-026-09775-4","url":null,"abstract":"<p><p>This paper presents a bibliometric analysis of the fast-growing area of deep learning in neuroimaging. Using data from the Scopus database, we analyzed 12564 peer-reviewed publications originating from 102 countries, published in 2259 sources over the period from 2014 to 2024. The field demonstrated a compound average annual growth rate of 51.7%. We found that China emerged as the most productive contributor, accounting for 22.9% of the total publications and 18% of total citations. The Chinese Academy of Sciences was identified as the most productive research institution with 149 publications and 1557 citations, while Lecture Notes in Computer Science was noted as the most highly cited source in this domain. High usage of deep learning, brain, and magnetic resonance imaging identified the most prominent research themes. Also, our analysis noted strong research emphasis on the application of various deep learning architectures for the diagnosis and study of important neurological disorders like Parkinson's Disease, Alzheimer's Disease, and Mild Cognitive Impairment. The article would be useful in understanding the current state-of-the-art deep learning for neuroimaging by identifying key research trends, influential institutions, and prominent research themes. In this way, it will contribute to helping future researchers go further in this fast-growing field.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147534130","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":"Global Research Trends, Hotspots and Collaborative Networks in Brain-Derived Extracellular Vesicles: A Multi-Database Bibliometric Analysis.","authors":"Adhish Mazumder, Shubhankhi Dey, Prasenjit Mitra","doi":"10.1007/s12021-026-09774-5","DOIUrl":"10.1007/s12021-026-09774-5","url":null,"abstract":"<p><p>Brain-derived extracellular vesicles (bdEVs) are emerging mediators of intercellular communication with increasing significance in neuroscience and translational medicine. We conducted a cross-database bibliometric analysis of PubMed, Scopus, and Web of Science (2000–June 2025) to map global research trends, hotspots, and collaboration networks in bdEV research. After multi-stage deduplication and screening, the remaining original research articles were included for quantitative and network analyses. Publication output rose sharply after 2018 and peaked in 2024, reflecting rapid field expansion. The United States and China were the most productive contributors, with recent leadership shifting toward China with international collaboration intensifying, particularly through US–China partnerships. A compact author core and leading institutions such as Johns Hopkins University and the University of California drive the field, while the Journal of Extracellular Vesicles and several multidisciplinary and clinical journals serve as primary outlets. Keyword and cluster analyses revealed dominant themes such as extracellular vesicles/exosomes, astrocytes, neuroinflammation, biomarkers, and neurodegenerative disease alongside emerging foci in cell-type–specific bdEV biology, multi-omics profiling, and translational biomarker development. Funding is led by the NIH, European Union, and National Natural Science Foundation of China. Overall, bdEV research has matured into a globally networked, interdisciplinary domain with increasing translational potential. Future priorities include methodological harmonization, expanded international collaboration, and targeted efforts to validate bdEV biomarkers and therapeutic applications.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147481549","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 Comprehensive Analysis of Inflammation Regulatory Biomarkers among three Neuropsychiatric Disorders using Transcriptomic Approach.","authors":"Indranil Chakraborty, Rahul Shaw, Kuntal Pal","doi":"10.1007/s12021-025-09765-y","DOIUrl":"10.1007/s12021-025-09765-y","url":null,"abstract":"<p><p>Neuropsychiatric disorders (NPDs) affect more than 125 million individuals globally. Major depressive disorder (MDD), bipolar disorder (BPD) and schizophrenia remain as the most addressed NPDs. Several inflammatory molecules such as IL-1β, IL-6, TNF-α, etc. are differentially expressed in different diseases, yet a common distinguishing biomarker is lacking. Therefore, it is potentially important to ascertain a common marker among three diseases. RNA sequencing (RNA-seq) data of three diseases were downloaded from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were obtained using edgeR package, unveiling that majority of the genes which were downregulated in MDD are being upregulated in the other two disorders. Gene ontology (GO) enrichment of 332 common genes among DEG list from three disorders were performed. Inflammation processes and immune-related genes were discovered using biological process enrichment, out of which five genes were shown to have high diagnostic efficiency value, indicating the capability of becoming a biomarker for the said disease. The genes were collectively termed as gene of interest (GOI). Immune cell deconvolution was performed to view the abundance of immune cell types in the concerned tissues. Eventually, protein-protein interaction (PPI) network, transcription factor (TF) network and miRNA network were prepared to understand the interactome within the genes. This study opens an insight into the field of psychiatric disorder treatment by providing five newer inflammatory biomarkers in addition to previously established markers, thereby, paving the way for advanced structural linking of the candidate markers to the concerned disease models using patient-derived cells.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":""},"PeriodicalIF":2.9,"publicationDate":"2026-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147311911","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":"Dual-Modal Deep Learning with In-Domain Training and Attention for Infant Brain Myelination Prediction.","authors":"Mamilla Sri Harshitha, Mythri G, Anju Thomas, Bibin Francis, Varun P Gopi, Abhishek Sehrawat","doi":"10.1007/s12021-025-09750-5","DOIUrl":"10.1007/s12021-025-09750-5","url":null,"abstract":"<p><p>Myelin plays a critical role in the central nervous system, and its maturation is essential for understanding brain development. However, assessing myelin progression remains challenging due to variability across age groups. Radiologists typically rely on developmental atlases and age-based milestones, but manual evaluation is time-consuming and prone to inter-observer variability. This paper presents a novel dual-input deep learning framework that leverages both [Formula: see text] and [Formula: see text]-weighted MRI modalities for automated myelin maturation assessment. Each modality is processed through an in-domain trained DenseNet121 feature extractor, followed by Channel and Multi-Head Attention Blocks to enhance feature prioritization and spatial contextualization. Cross-Attention enables effective inter-modality information exchange, while early fusion via concatenation integrates structural insights from both contrasts. The fused features are refined using Global Average Pooling and passed to a regression-optimized dense layer. Trained on 710 samples and tested on 123 from a publicly available dataset (833 total), the model achieved a Mean Absolute Error (MAE) of 1.18 months, a Pearson Correlation Coefficient (PCC) of 0.98, a Coefficient of Determination ([Formula: see text]) of 0.96, and a Concordance Correlation Coefficient (CCC) of 0.98. Visual interpretability through Grad-CAM revealed the model's focus on clinically meaningful brain regions, with abnormal cases showing heightened activation in peripheral and ventral areas. These findings confirm the model's ability to deliver accurate and interpretable predictions, supporting its potential for real-world diagnostic integration in pediatric neuroimaging.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":"13"},"PeriodicalIF":2.9,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146221085","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":"WaveNet's Precision in iEEG Classification.","authors":"Casper David van Laar, Khubaib Ahmed","doi":"10.1007/s12021-026-09771-8","DOIUrl":"10.1007/s12021-026-09771-8","url":null,"abstract":"<p><p>This study introduces a WaveNet-based deep learning model designed to automate the classification of Intracranial Electroencephalography (iEEG) signals into physiological activity, pathological/epileptic activity, power-line noise, and other non-cerebral artifacts categories. Traditional methods for iEEG signal classification, which rely on expert visual review, are becoming increasingly impractical due to the growing complexity and volume of iEEG recordings. Leveraging a publicly available annotated dataset from Mayo Clinic and St. Anne’s University Hospital, the WaveNet model was trained, validated, and tested on 209,231 samples with a 70/20/10 % split. The model achieved a classification accuracy exceeding previous non specialized CNN and LSTM-based approaches, and was benchmarked against a Temporal Convolutional Network (TCN) baseline. Notably, the model achieves high discrimination of noise and artifact classes (precision = 0.98 and [Formula: see text], respectively). Although, classification between physiological and pathological signals exhibits a modest but clinically interpretable overlap, with F1-scores of 0.96 and 0.90 and 175 and 272 cross-class false positives, respectively. Reflecting inherent clinical overlap. WaveNet’s architecture, originally developed for raw audio synthesis, is well-suited for iEEG data due to its use of dilated causal convolutions and residual connections, enabling it to capture both fine-grained and long-range temporal dependencies. The research also details the preprocessing pipeline, including dynamic dataset partitioning, the use of focal loss to combat class imbalances and normalization steps that support model high performance. While results demonstrate strong in-distribution performance, generalizability across datasets and clinical settings has yet to be established.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":"12"},"PeriodicalIF":2.9,"publicationDate":"2026-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146203502","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 Complex Network-Based Approach for Detecting and Characterizing Power Neurons in Drosophila.","authors":"Enrico Corradini, Federica Parlapiano, Giorgio Terracina, Domenico Ursino","doi":"10.1007/s12021-026-09773-6","DOIUrl":"10.1007/s12021-026-09773-6","url":null,"abstract":"<p><p>Connectome analysis investigates the connections in the brain to understand how brain regions communicate with each other and how brain structure relates to its function. In recent years, researchers have reconstructed the structural connectome of several organisms, the most complex being Drosophila melanogaster. Two research groups have reconstructed the larval and adult connectomes of this organism and have applied network analysis to learn more about the Drosophila brain and its behavior. In this paper, we aim to continue the work of these two research groups at the larval and adult stages. Specifically, we construct several derived network representations and define a set of techniques that use the main concepts and measures of complex network analysis to extract new knowledge about Drosophila connectomes at the larval and adult stages. First, we conduct an Exploratory Data Analysis on the larval and adult connectomes to detect similarities and differences between them. Then, we define the concept of power neurons and illustrate an approach to detect them. Next, we demonstrate that power neurons represent a limited set of highly interconnected neurons that form a backbone and that, given their peculiar connectivity properties, may play a strategic role in brain functions. Finally, we extract a set of connectome motifs that allow us to learn about various features characterizing power neurons. We demonstrate that complex network analysis can allow the extraction of relevant knowledge about connectomes. Furthermore, we show that a very small number of power neurons can strongly influence all other neurons in the Drosophila brain.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":"11"},"PeriodicalIF":2.9,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12904900/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146182933","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":"Robust Containerization of the High Angular Resolution Functional Imaging (HARFI) Pipeline.","authors":"Zhiyuan Li, Kurt G Schilling, Bennett A Landman","doi":"10.1007/s12021-026-09769-2","DOIUrl":"10.1007/s12021-026-09769-2","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":"10"},"PeriodicalIF":2.9,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146144639","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}