Frontiers in Neuroinformatics最新文献

{"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":"https://doi.org/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":2.5,"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":"https://doi.org/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":2.5,"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":"https://doi.org/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":2.5,"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":"https://doi.org/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}

{"title":"CycleGAN models show consistent brain MRI synthesis across datasets supporting downstream tissue characterization in multiple sclerosis.","authors":"Shayan Shahrokhi, Olayinka Oladosu, Rehman Tariq, Yunyan Zhang","doi":"10.3389/fninf.2026.1762794","DOIUrl":"10.3389/fninf.2026.1762794","url":null,"abstract":"<p><strong>Background: </strong>Secondary quantitative analysis of brain magnetic resonance imaging (MRI) can provide valuable information for many neurological diseases, including multiple sclerosis (MS), but it demands complete datasets that are often unavailable clinically. We investigated how image synthesis via deep learning using cycle-consistent generative adversarial networks (CycleGANs) compared with Pix2Pix as a related method, based on T1-weighted and T2-weighted brain MRI in MS, following verification on two streamlined datasets. The synthesized images were also evaluated against the source data.</p><p><strong>Methods: </strong>The streamlined datasets involved 1,113 healthy participants from the Human Connectome Project (HCP) and 318 participants from the Parkinson's Progression Markers Initiative (PPMI). The MS cohort in this study included 105 participants scanned with different protocols. Image synthesis was bidirectional between T1- and T2-weighted MRI using CycleGAN with and without spectral normalization, as well as Pix2Pix. Utility testing focused on T1-weighted MRI that was most often unavailable in MS, and that involved lesion detection, brain volumetry, and lesion texture analysis.</p><p><strong>Results: </strong>All CycleGAN models performed competitively, while Pix2Pix performed better, mostly with streamlined datasets (<i>p</i> < 0.001). The average peak signal-to-noise ratio ranged from 24.860-28.570 versus 28.520-31.100, and the structural similarity index ranged from 0.838-0.901 versus 0.924-0.943. With spectral normalization, CycleGAN improved in PPMI but not in HCP and generally not in MS (<i>p</i> < 0.001). Furthermore, the synthesized images showed high similarity to the source data in utility tests, although Pix2Pix T1 images appeared more heterogeneous in lesion texture than source T1 images.</p><p><strong>Conclusion: </strong>CycleGAN without spectral normalization appeared feasible for synthesizing common clinical brain MRI, including T1-weighted images usable for subsequent quantitative analysis in MS.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1762794"},"PeriodicalIF":2.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13018141/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147573227","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 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.2026.1748481","DOIUrl":"10.3389/fninf.2026.1748481","url":null,"abstract":"<p><p>This study presents a novel fractional order model of Alzheimer's disease (mental disorder) using the Caputo derivative to accurately capture long term memory and hereditary effects in neurodegeneration. The mathematical model incorporates key pathological constituents including neurons, amyloid beta (<i>A</i> _β), tau proteins and microglial responses, allowing detailed simulation of their dynamic interactions. Fundamental properties of the model, including positivity, boundedness, invariant regions and equilibrium points, are rigorously analyzed to ensure biological feasibility. Sensitivity analysis identifies amyloid toxicity as the most influential driver of neuronal loss underscoring its central role in AD progression. Furthermore, a Physics Informed Neural Network (PINN) is developed to approximate system dynamics from noisy observations while ensuring compliance with biological and physical constraints. Compared to standard neural networks the PINN exhibits superior accuracy and robustness especially under data scarcity. By integrating fractional calculus, optimal control and machine learning, this work advances computational modeling of Alzheimer's disease and offers insights into therapeutic optimization.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1748481"},"PeriodicalIF":2.5,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12957160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147364682","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: Machine learning algorithms for brain imaging: new frontiers in neurodiagnostics and treatment.","authors":"Avinash Tandle, Shailesh Appukuttan, Hamed Honari","doi":"10.3389/fninf.2026.1794013","DOIUrl":"10.3389/fninf.2026.1794013","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1794013"},"PeriodicalIF":2.5,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12933264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147304316","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":"Macular: a multi-scale simulation platform for the retina and the primary visual system.","authors":"Bruno Cessac, Erwan Demairy, Jérôme Emonet, Evgenia Kartsaki, Thibaud Kloczko, Côme Le Breton, Nicolas Niclausse, Selma Souihel, Jean-Luc Szpyrka, Julien Wintz","doi":"10.3389/fninf.2025.1726374","DOIUrl":"https://doi.org/10.3389/fninf.2025.1726374","url":null,"abstract":"<p><p>We developed Macular, a simulation platform with a graphical interface, designed to produce <i>in silico</i> experiment scenarios for the retina and the primary visual system. A scenario involves generating a three-dimensional structure with interconnected layers, each layer corresponding to a type of \"cell\" in the retina or visual cortex. The cells can correspond to neurons or more complex structures (such as cortical columns). Inputs are arbitrary videos. The user can use the cells and synapses provided with the software or create their own using a graphical interface where they enter the constituent equations in text format (e.g., LaTeX). They also create the three-dimensional structure via the graphical interface. Macular then <i>automatically</i> generates and compiles the C++ code and generates the simulation interface. This allows the user to view the input video and the three-dimensional structure in layers. It also allows the user to select cells and synapses in each layer and view the activity of their state variables. Finally, the user can adjust the phenomenological parameters of the cells or synapses via the interface. We provide several example scenarios, corresponding to published articles, including an example of a retino-cortical model. Macular was designed for neurobiologists and modelers, specialists in the primary visual system, who want to test hypotheses <i>in silico</i> without the need for programming. By design, this tool allows simulation of natural or altered conditions (e.g., pharmacology, pathology, and development).</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1726374"},"PeriodicalIF":2.5,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12907302/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146212819","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":"On the need for abstract, deep reinforcement learning models in neuroscience.","authors":"Santina Duarte, Xena Al-Hejji, Edgar Bermudez Contreras, Eric Chalmers","doi":"10.3389/fninf.2026.1729805","DOIUrl":"10.3389/fninf.2026.1729805","url":null,"abstract":"","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"20 ","pages":"1729805"},"PeriodicalIF":2.5,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12886432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146164950","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":"Computational reconstruction of evolutionary selection in human brain networks.","authors":"Lukasz Piszczek, Clara Fazzari, Sophia Ulonska, Katja Bühler, Wulf Haubensak","doi":"10.3389/fninf.2025.1623174","DOIUrl":"10.3389/fninf.2025.1623174","url":null,"abstract":"<p><strong>Introduction: </strong>The accumulation of genomic and brain data opens new opportunities for resource friendly, data driven brain exploration. A key challenge is to develop versatile and accessible strategies that integrate and mine multimodal datasets for novel neuroscientific insights. Here, we optimized an integrated workflow for mapping multigenic evolutionary traits in the human brain across cognitive, cellular, and molecular levels.</p><p><strong>Methods: </strong>At the input stage, the workflow fuses an evolutionary genetic dataset with searchable synthetic functional magnetic resonance imaging (fMRI) databases that are pre clustered into concise psychological domains for improved interpretability. At its core, a Genetic Algorithm for Generalized Biclustering (GABi) mines gene sets under evolutionary selection that also show high expression correlation with fMRI networks.</p><p><strong>Results: </strong>Applying this workflow, we identified evolutionary patterns spanning cognitive traits, brain cell types, and molecular mechanisms. Focusing on socio affective traits, the algorithm highlighted peaks in adaptive selection in networks for social interaction (language) and social concepts (theory of mind) across hominid, early hominin, and anatomically modern human (AMH) ancestry. These traits emerge from a broad spectrum of excitatory (glutamatergic) and inhibitory (GABAergic) neuronal, as well as non neuronal, cell types. The associated Gene Ontology (GO) terms were enriched for cell signaling, synaptic organization, and neuronal morphology.</p><p><strong>Discussion: </strong>Together, these findings demonstrate an integrated workflow for molecular to systems level exploration of the brain and provide new perspectives on the evolutionary history of human socio affective functions. This approach can be adapted to screen for functional traits in the context of mental disorders or applied to the brains of other phylogenies in a similar manner.</p>","PeriodicalId":12462,"journal":{"name":"Frontiers in Neuroinformatics","volume":"19 ","pages":"1623174"},"PeriodicalIF":2.5,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12883834/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146156375","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}