Frontiers in Neuroinformatics最新文献

{"title":"A 3D-printed phantom to validate subject orientation in 3D imaging and recordings.","authors":"Guillaume Jean-Paul Claude Becq, Olivier Montigon, Sarvenaz Keshmiri, Aurélien Delphin, Paul Galloux, Hervé Mathieu, Benjamin Lemasson, Emmanuel L Barbier","doi":"10.3389/fninf.2026.1613151","DOIUrl":"10.3389/fninf.2026.1613151","url":null,"abstract":"<p><p>A phantom defined by a 3D-printed system of markers representing an anatomical coordinate system is proposed. This phantom is designed for use in magnetic resonance and X-ray imaging devices to validate acquisition parameters and prevent laterality errors during image acquisition and processing. The phantom allows visualization of the coordinate system axes - left-right, posterior-anterior, and inferior-superior - in all orthogonal slices of a volume. A computational method, using the phantom as a reference, is also proposed to automatically detect and correct flips and permutations in RAS coordinate system representations. Testing the phantom across four modalities available in our platforms, followed by conversions of the recordings to the NIfTI format, enables the detection, correction, and adjustment of protocols in 4 out of 5 configurations. The 3D printing models and orientation detection/correction code are shared with the community as open-source and open-access resources to enable quick, cost-effective, and accessible production.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1613151"},"PeriodicalIF":2.9,"publicationDate":"2026-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13254079/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148249997","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":"IntegriLAB: a blockchain-enabled electronic lab notebook for reproducible neuroimaging research.","authors":"Rubaida Easmin, Mattia Veronese, Paul Allen, Giovanna Nordio, Federico Turkheimer","doi":"10.3389/fninf.2026.1753937","DOIUrl":"10.3389/fninf.2026.1753937","url":null,"abstract":"<p><p>In the era of data-intensive science, managing and verifying research processes that include raw data, analysis scripts, workflows, and documentation, remains a major challenge, particularly in complex fields like neuroimaging were multiple processing and analyses stages can take place. Fragmented tools, inconsistent documentation, and poor platform integration continue to undermine reproducibility. While current Electronic Lab Notebooks (ELNs) aid organization, they often lack cross-platform interoperability, authorship certification, and immutable auditability. To address these gaps, this study proposes a novel ELN framework IntegriLAB, that consolidates traditional data, document and code repositories into a centralized, web-based system for end-to-end project tracking. As a proof of concept, our case study integrates DataLad, Overleaf and GitHub. It allows real-time monitoring across the research cycle while preserving familiar workflows. A key innovation is the use of LabTrace built on green blockchain technology to certify research activities through cryptographically signed, immutable records, ensuring data integrity and verifiable authorship with minimal environmental impact. By unifying project management, secure data handling, and blockchain-based verification, the proposed ELN advances reproducibility, fosters trust, and strengthens collaborative research practices.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1753937"},"PeriodicalIF":2.9,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13233703/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148198100","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":"Long-range correlations in alpha-band of electroencephalogram: a nonlinear embedding and detrended fluctuation analysis.","authors":"Dasari Shivakumar, Cota Navin Gupta, Budhaditya Hazra","doi":"10.3389/fninf.2026.1823408","DOIUrl":"10.3389/fninf.2026.1823408","url":null,"abstract":"<p><p>Understanding the temporal organization of brain activity requires methods that capture scale-free dynamics while accounting for the high-dimensional, spatially correlated nature of the electroencephalogram (EEG) data. We propose a novel framework that integrates nonlinear manifold learning (Isometric mapping) with detrended fluctuation analysis (DFA) to quantify long-range temporal correlations (LRTC) in the alpha-band of EEG signals. We applied this framework to two music related EEG datasets, as music is known to evoke different emotions and synchronize brain activity. The first dataset was obtained during live Indian classical music (ICM) listening that included two ragas, Yaman and Puriya Dhanashree. EEG was recorded from 13 healthy volunteers (24 channels, sampled at 500 Hz). The second dataset is the Music BCI dataset (006-2015), which includes Jazz and Synth-pop musical clips, with EEG collected from 11 subjects (64 channels, downsampled to 200 Hz). The EEG data from both datasets were preprocessed, band-limited to 8-13 Hz, and segmented into non-overlapping 2-s windows. Alpha-band power was extracted from each channel to form the feature matrix used for embedding. For the ICM dataset, Isometric mapping (Isomap) produced a low-dimensional representation (<i>d</i> = 3), which we analyzed using two approaches: (i) a norm-based approach and (ii) a mean-based approach. For comparison, an equivalent PCA-based pipeline (<i>d</i> = 5) was implemented. The Isomap mean-based DFA yielded consistent scaling exponents (α) in the range of 0.66-0.70, with higher goodness-of-fit (<i>R</i> ²) and narrower bootstrap confidence intervals than the norm-based approach. PCA produced similar trends but required more dimensions. Paired <i>t</i>-tests showed that the Isomap mean-based approach detected music-related changes more sensitively than PCA (Yaman <i>p</i> = 0.02; Puriya Dhanashree <i>p</i> = 0.008). Comparable results were also observed for the second Music BCI dataset, where Isomap achieved a compact representation with <i>d</i> = 5, compared to <i>d</i> = 8 for PCA. In this dataset as well, the mean-based DFA yielded α values in the range of 0.62-0.65 and higher goodness-of-fit. Overall, the results suggest that combining nonlinear manifold embeddings with mean-based DFA provides a compact and robust framework for characterizing scale-free temporal structure in EEG data.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1823408"},"PeriodicalIF":2.9,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13229972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148162425","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 high-resolution dataset of mouse brain vasculature for deep learning-based reconstruction.","authors":"Xinwei Du, Shijun Li, Xiaojun Wang, Yuan Shen, Tingwei Quan","doi":"10.3389/fninf.2026.1809341","DOIUrl":"10.3389/fninf.2026.1809341","url":null,"abstract":"<p><p>Vascular network reconstruction is a crucial step in extracting vessel morphology and establishing its topological relationships from biological imaging data, holding significant scientific importance for studying brain structure and function, metabolism, and disease mechanisms. Current methods for vascular network reconstruction typically follow a \"segment-first, then reconstruct\" pipeline: first generating a binary segmentation from vascular images, followed by topological modeling. However, due to significant variations in vessel diameters, frequent presence of luminal voids in large vessels, and the complex, densely distributed nature of capillaries, existing approaches still face notable limitations in reconstruction accuracy. To address this, this study introduces and releases an annotated dataset of mouse brain vasculature. The dataset comprises 60 3D image blocks with the size of 512 × 512 × 512 acquired from four mouse brain samples using fluorescence micro-optical sectioning tomography (fMOST) imaging. It encompasses diverse structural morphologies ranging from large vessels to capillaries, with detailed annotations specifically targeting challenging vascular regions. Additionally, we provide a standardized vascular annotation pipeline and associated tools. This dataset aims to serve as a benchmark to support the development, evaluation, and comparison of algorithms for vascular network segmentation, reconstruction, and related tasks.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1809341"},"PeriodicalIF":2.9,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13226487/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148155895","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":"Reliability and diagnostic performance of an automated MRI-based classifier compared with radiologists in Alzheimer's disease.","authors":"Nurmakhan Zholshybek, Elnora Abdurakhmanova, Almas Bimakhan, Dinara Jumadilova, Zhanibek Baiturlin, Joseph Almazan, Srinivasa Rao Bolla","doi":"10.3389/fninf.2026.1821249","DOIUrl":"10.3389/fninf.2026.1821249","url":null,"abstract":"<p><p>Reliable imaging biomarkers are essential for improving early detection of Alzheimer's disease (AD). We evaluated whether an automated MRI-based classifier provides diagnostic performance comparable to expert radiologists in differentiating cognitively normal (CN) individuals from patients with AD using standardized ADNI data. Thirty-eight structural MRI datasets (20 CN, 18 AD) were analyzed. An automated multi-class volumetric classifier and two board-certified radiologists independently assigned probability scores across seven diagnostic categories. Performance was evaluated using a partial-credit scoring rule to account for probabilistic ties. Diagnostic performance for CN-AD discrimination was assessed using accuracy, sensitivity, specificity, receiver operating characteristic (ROC) analysis, inter-observer agreement metrics, Brier scores for calibration, and decision curve analysis (DCA) for clinical utility. The automated classifier achieved an accuracy of 0.66, sensitivity of 0.56, and specificity of 0.75. Radiologists demonstrated comparable performance with inherent inter-observer variability. Agreement between automated and human assessments was fair at the categorical level, with low concordance for continuous probability estimates. ROC analysis based on continuous AD probabilities demonstrated high discrimination performance for the automated model (AUC = 0.90), exceeding that of radiologists (AUC = 0.71 and 0.62). DCA indicated that the automated pipeline provides a positive net benefit as a second-opinion tool. This exploratory study emphasizes the impact of evaluation frameworks on performance metrics and supports further validation using multi-modal data in larger cohorts.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1821249"},"PeriodicalIF":2.9,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13213848/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148053523","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":"Editorial: Multimodal brain data integration and computational modeling.","authors":"Jony Sheynin, Maryam Naseri, Alejandro Luis Callara","doi":"10.3389/fninf.2026.1838147","DOIUrl":"10.3389/fninf.2026.1838147","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1838147"},"PeriodicalIF":2.9,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13200853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148028904","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 deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI.","authors":"Omara Mustafa, Salem Alhatamleh, Hamad Yahia Abu Mhanna, Abdallah Almahmoud, Rami Malkawi, Majd Malkawi, Abdel-Baset Bani Yaseen, Hanan Fawaz Akhdar, Hatem Malkawi, Fatimah Maashey, Latifah Alghulayqah, Mohammad Amin","doi":"10.3389/fninf.2026.1795354","DOIUrl":"10.3389/fninf.2026.1795354","url":null,"abstract":"<p><strong>Background: </strong>Brain tumor diagnosis from magnetic resonance imaging (MRI) remains a challenging task due to the high variability in tumor appearance and the limitations of manual interpretation.</p><p><strong>Methods: </strong>To address these challenges, this paper proposes NeuroFusionNet, a deep learning framework for automated brain tumor classification from MRI. The framework integrates GAN-based synthetic image generation with transfer learning using a fine-tuned VGG16 backbone. Real and GAN-generated MRI images are passed through VGG16 to extract discriminative feature representations, which are then used for final classification. To adapt the model to domain-specific MRI characteristics while preserving pretrained knowledge, the last ten layers of VGG16 are fine-tuned and the remaining layers are kept frozen.</p><p><strong>Results: </strong>The effectiveness of NeuroFusionNet is validated on two publicly available brain MRI datasets. Experimental results demonstrate that the proposed learning framework achieves classification accuracies of 99.05 and 98.75% on the Brain Tumor MRI Dataset and the MRI with Bounding Boxes Dataset, respectively, consistently outperforming several state-of-the-art neural architectures, including VGG16, VGG19, MobileNetV2, DenseNet121, and NASNetLarge.</p><p><strong>Conclusion: </strong>The results suggest that NeuroFusionNet is effective for the evaluated public MRI datasets; additional external validation is required.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1795354"},"PeriodicalIF":2.9,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13131096/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147813149","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":"BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning.","authors":"Mageshwar Selvakumar, Andrea Mendez Torrijos, Laura Cristina Konerth, Stefanie Horndasch, Raja Atreya, Arnd Doerfler, Georg Schett, Juergen Rech, Andreas Hess","doi":"10.3389/fninf.2026.1760583","DOIUrl":"10.3389/fninf.2026.1760583","url":null,"abstract":"<p><p>Neuroimaging presents us with an in-depth understanding about brain structure and function, yet the data complexity poses significant analytical challenges. Current frameworks suffer from issues such as scalability, poor integration with traditional statistics and a need for a programing background, which hinder researchers from focusing on neuroscience questions. To address these limitations, we present BrainInsights, an integrated and automated GUI-based pipeline ecosystem designed to facilitate the analysis of multi-modal or multi-parametric neuroimaging data in a flexible way. The framework comprises three core tools: MARIA (MAgnetic Resonance Imaging data Analysis and inspection tool) for data inspection and hypotheses testing, ML Pipeline for automated feature selection and model construction, and ML DaViz for model evaluation and bio-signature generation. Deployed as a singularity container, the system ensures reproducibility and scalability across computing environments. We validated BrainInsights using diverse datasets, including multi-parametric MRI studies of Anorexia Nervosa, Crohn's disease, and Rheumatoid Arthritis. Specifically, the framework distinguished young Anorexia Nervosa patients from controls with a balanced accuracy of 65%, while in the PreCePRA trial, it predicted Rheumatoid Arthritis treatment response with a balanced accuracy of up to 95.4% using functional pain markers. The results demonstrate the ability of the framework to achieve high separation of subgroups and treatment success and additionally bridge hypotheses-driven statistical analysis with data-driven machine learning analysis. By enabling interpretability tools like SHAP, BrainInsights empowers researchers to move beyond \"black-box\" modeling to uncover stable, biologically plausible bio-signatures. Ultimately, this framework aids in accelerating the translation of complex neuroimaging data into meaningful clinical insights.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1760583"},"PeriodicalIF":2.9,"publicationDate":"2026-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13126547/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147813273","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":"Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals.","authors":"Tabassum Gull Jan, Sajad Mohammad Khan, Sajid Yousuf Bhat, Zaid Ahmad Wani, Syed Immamul Ansarullah, Sami Alshmrany, Shafat Khan","doi":"10.3389/fninf.2026.1765088","DOIUrl":"10.3389/fninf.2026.1765088","url":null,"abstract":"<p><strong>Purpose: </strong>Dyslexia is a prevalent neurodevelopmental disorder that impairs a children's ability to reading, writing, and language processing despite normal cognitive skills. Early identification is vital for timely support and interventions in children with dyslexia. This study aimed to develop an efficient EEG-based pipeline for dyslexia detection using deep learning techniques, while providing a consistent evaluation protocol for fair comparison across models and prior approaches.</p><p><strong>Methods: </strong>EEG recordings were acquired from 51 participants (26: dyslexic and 25: non-dyslexic), aged 5-10 years, during cognitive task performance. These signals were processed, segmented, and decomposed into standard frequency bands (alpha, beta, delta, and theta) using the discrete wavelet transform to capture discriminative neural patterns. Filter-based feature selection techniques were applied before classification to optimize performance and reduce redundancy to identify the most informative features. These ranked and individual band-wise features were systematically evaluated with classical machine learning baselines (Decision Trees, SVM, k-NN, and ensemble learners) alongside the proposed deep neural networks. In addition, we benchmarked end-to-end raw-EEG deep learning baselines (1D-CNN, LSTM, and EEGNet) and re-implemented representative existing pipelines, all evaluated on our dataset using the same evaluation protocol.</p><p><strong>Results: </strong>The proposed compact deep neural network with four hidden layers achieved the best performance, reaching classification accuracy of 98.85%, outperforming all baseline models, raw-EEG deep learning baselines, and re-implemented approaches.</p><p><strong>Conclusion: </strong>These findings support the feasibility of DWT-driven EEG analysis combined with deep learning for more accurate and early dyslexia detection. The proposed approach holds promise as a non-invasive screening tool to support improved educational outcomes through early diagnosis and targeted intervention.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1765088"},"PeriodicalIF":2.9,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13057293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147644122","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: A Physics Informed Neural Network (PINN) framework for fractional order modeling of Alzheimer's disease.","authors":"Adnan Mehmood, Muhammad Farman, Farkhanda Afzal, Kottakkaran Sooppy Nisar, Mohammed Altaf Ahmed, Mohamed Hafez","doi":"10.3389/fninf.2026.1821637","DOIUrl":"10.3389/fninf.2026.1821637","url":null,"abstract":"<p><p>[This corrects the article DOI: 10.3389/fninf.2026.1748481.].</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1821637"},"PeriodicalIF":2.5,"publicationDate":"2026-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13044588/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147622310","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}