Neuroinformatics最新文献

{"title":"Full Model Optimisation of the Processing Pipeline in Functional Near-Infrared Spectroscopy.","authors":"Robert J Ward, Gustavo Rodriguez-Gomez, Felipe Orihuela-Espina","doi":"10.1007/s12021-026-09772-7","DOIUrl":"https://doi.org/10.1007/s12021-026-09772-7","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678382","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":"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":"https://doi.org/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":3.1,"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":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"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":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"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":"https://doi.org/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":3.1,"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":"https://doi.org/10.1007/s12021-026-09774-5","url":null,"abstract":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 2","pages":""},"PeriodicalIF":3.1,"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":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":""},"PeriodicalIF":3.1,"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":3.1,"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":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":"12"},"PeriodicalIF":3.1,"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":"","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"24 1","pages":"11"},"PeriodicalIF":3.1,"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}