Brain Informatics最新文献

{"title":"Vision and convolutional transformers for Alzheimer's disease diagnosis: a systematic review of architectures, multimodal fusion and critical gaps.","authors":"Ibrahem Afifi, Mostafa Elgendy, Mohamed Abdelfatah, Shaker El-Sappagh","doi":"10.1186/s40708-025-00286-7","DOIUrl":"10.1186/s40708-025-00286-7","url":null,"abstract":"<p><p>Alzheimer's disease (AD), a significant public health challenge, requires accurate early diagnosis to improve patient outcomes. Vision Transformers (ViTs) and Convolutional Vision Transformers (CViTs) have emerged as powerful Deep Learning architectures for this task. Following PRISMA guidelines, this systematic review analyzes 68 studies selected from 564 publications (2021-2025) across five major databases: Scopus, Web of Science, ScienceDirect, IEEE Xplore, and PubMed. We introduce novel taxonomies to systematically categorize these works by model architecture, data modality, fusion strategy, and diagnostic objective. Our analysis reveals key trends, such as the rise of hybrid CViT frameworks, and critical gaps, including a limited focus on Mild Cognitive Impairment-to-AD progression. Critically, we also assess practical implementation details, revealing widespread challenges in algorithmic reproducibility. The discussion culminates in a forward-looking analysis of Large Vision Models and proposes future directions emphasizing the need for robust multimodal integration, lightweight transformer designs, and Explainable AI to advance AD research and bridge the critical gap between high-performance modeling and clinical applicability.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"1"},"PeriodicalIF":4.5,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12764722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145769333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the bidirectional relationships between MRI resting-state functional connectivity networks and cardiovascular diseases: a Mendelian randomization study.","authors":"Shiqiang Yang, Yuquan Wang, Ruiqin Han, Qi Zhang, Qing Gao, Hanjian Du, Xiaofei Hu","doi":"10.1186/s40708-025-00285-8","DOIUrl":"10.1186/s40708-025-00285-8","url":null,"abstract":"<p><strong>Objective: </strong>Brain functional connectivity alterations have been observed in cardiovascular diseases (CVDs), but the causality between brain resting-state functional connectivity networks and CVDs remains undetermined. We aimed to investigate the bidirectional causality between brain network connectivity and CVDs using Mendelian randomization (MR) analysis.</p><p><strong>Methods: </strong>Using genome-wide association study (GWAS) data from the UK Biobank (n = 34,691), we conducted bidirectional two-sample MR analyses between 191 resting-state functional MRI phenotypes and four major CVDs: hypertension, atrial fibrillation (AF), heart failure (HF), and coronary artery disease (CAD). Sensitivity analyses, including MR-Egger regression and weighted median methods, were conducted to ensure the robustness of causal estimates and to test for potential pleiotropy.</p><p><strong>Results: </strong>For hypertension, four networks showed negative causal associations (ORs 0.882-0.904), primarily involving motor, subcortical-cerebellar, default mode, and visual networks. In AF, we observed both increased connectivity in salience and default mode networks (ORs 1.157-1.288) and decreased connectivity in visual-motor networks (OR 0.790). For HF, three networks showed significant associations: decreased connectivity in visual and temporal networks (ORs 0.791-0.804) and increased connectivity in motor networks (OR 1.352). CAD was associated with increased connectivity in both default mode and central executive networks (ORs 1.145-1.147). These relationships remained robust after multiple sensitivity analyses.</p><p><strong>Conclusion: </strong>Our findings reveal novel bidirectional causal relationships between specific brain functional networks and CVDs, with distinct patterns of network involvement for different CVDs suggesting disease-specific mechanisms in the cardio-cerebral axis. These findings identify potential neuroimaging biomarkers for early detection and monitoring of cardiovascular diseases.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"36"},"PeriodicalIF":4.5,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12717317/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145763962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Learning image derived PDE-phenotypes from fMRI data.","authors":"Ion Bica, Ryan Trang, Rui Hu, Wanhua Su, Zhichun Zhai, Qingrun Zhang","doi":"10.1186/s40708-025-00283-w","DOIUrl":"10.1186/s40708-025-00283-w","url":null,"abstract":"<p><p>Partial differential equations (PDEs) model various physical phenomena, such as electromagnetic fields and fluid mechanics. Methods such as sparse identification of nonlinear dynamics (SINDy) and PDE-Net 2.0 have been developed to identify and model PDEs on the basis of data via sparse optimization and deep neural networks, respectively. While PDE models are less commonly applied to fMRI data, they have the potential to uncover hidden connections and essential components in brain activity. Using the ADHD200 dataset, we applied canonical independent component analysis (CanICA) and uniform manifold approximation (UMAP) for dimensionality reduction of fMRI data. We then used sparse ridge regression to identify PDEs from the reduced data, and applied significant PDE features for classification achieving high accuracy in distinguishing individuals with attention deficit hyperactivity disorder (ADHD). This study demonstrates a novel approach for extracting meaningful features from fMRI data for neurological disorder analysis to understand the role of oxygen transport (delivery & consumption) in the brain during neural activity, which is relevant for studying intracranial pathologies.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"35"},"PeriodicalIF":4.5,"publicationDate":"2025-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12686305/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145701710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Boosting brain tumor detection with an optimized ResNet and explainability via Grad-CAM and LIME.","authors":"K Afnaan, C G Arunbalaji, Tripty Singh, Rishab Kumar, Ganesh R Naik","doi":"10.1186/s40708-025-00279-6","DOIUrl":"10.1186/s40708-025-00279-6","url":null,"abstract":"<p><p>Detecting Brain Tumors is essential in medical imaging, as early and accurate diagnosis significantly improves treatment decisions and patient outcomes. Convolutional Neural Networks have demonstrated high efficiency in this domain, but their lack of interpretability remains a significant drawback for clinical adoption. This study explores the integration of Explainability techniques to enhance transparency in CNN-based classification and improve model performance through advanced optimization strategies. The primary research question addressed is how to improve the accuracy, generalization, and interpretability of CNNs for brain tumor Detection. While previous studies have demonstrated the effectiveness of deep learning for tumor detections, challenges such as class imbalance and overfitting of CNNs persist. To bridge this gap, we employ different dynamic learning rate modifiers, perform architectural enhancements, and apply XAI techniques, including Grad-CAM and LIME. Our experiments are conducted on three publicly available multiclass tumor datasets to ensure the generalizability of the proposed approach. Among the tested architectures, the enhanced ResNet model consistently outperformed others across all datasets, achieving the highest test accuracy, ranging from 99.36% to 99.65%. The techniques such as unfreezing layers, integrating various blocks, pooling, and dropout layers enhanced feature refinement and reduced overfitting. By incorporating XAI, we improve model interpretability, ensuring that clinically relevant regions in MRI scans are highlighted. These advancements contribute to highly reliable AI-assisted diagnostics, addressing significant challenges in medical image classification.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"33"},"PeriodicalIF":4.5,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12680819/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145678980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain-inspired signal processing for detecting stress during mental arithmetic tasks.","authors":"Kais Belwafi, Ahmed Alsuwaidi, Sami Mejri, Ridha Djemal","doi":"10.1186/s40708-025-00281-y","DOIUrl":"10.1186/s40708-025-00281-y","url":null,"abstract":"<p><p>Brain-Computer Interfaces provide promising alternatives for detecting stress and enhancing emotional resilience. This study introduces a lightweight, subject-independent method for detecting stress during arithmetic tasks, designed for low computational cost and real-time use. Stress detection is performed through ElectroEncephaloGraphy (EEG) signal analysis using a simplified processing pipeline. The method begins with preprocessing the EEG recordings to eliminate artifacts and focus on relevant frequency bands (α, β, and γ). Features are extracted by calculating band power and its deviation from a baseline. A statistical thresholding mechanism classifies stress and no-stress epochs without the need for subject-specific calibration. The approach was validated on a publicly available dataset of 36 subjects and achieved an average accuracy of 88.89%. The method effectively identifies stress-related brainwave patterns while maintaining efficiency, making it suitable for embedded and wearable devices. Unlike many existing systems, it does not require subject-specific training, enhancing its applicability in real-world environments.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"34"},"PeriodicalIF":4.5,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12686278/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145670138","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain-imager: a multimodal framework for image reconstruction and captioning from human brain activity.","authors":"Lu Meng, Zengyu Song, Jiayang Lu","doi":"10.1186/s40708-025-00282-x","DOIUrl":"https://doi.org/10.1186/s40708-025-00282-x","url":null,"abstract":"<p><strong>Objective: </strong>The reconstruction of visual stimuli and captions from brain activity offers a distinctive viewpoint on how perception reconstructs the external world within neural dynamics. Despite considerable advancements in deep generative models in recent years, simultaneously generating images and captions with both detailed accuracy and semantic consistency remains a significant challenge.</p><p><strong>Methods: </strong>We introduce panoptic segmentation and generative semantics for the first time, offering enhanced, multi-level data support and a novel perspective in the domain of brain decoding. Using multi-scale fusion techniques, we integrate pixel features from natural images with structural features from panoptic segmentation, creating a state-of-the-art \"initial guess.\" Building upon the neural paradigm that we discovered, we propose an innovative semantic connection strategy to guide image reconstruction. Additionally, by fine-tuning visual semantics within the encoded space compressed by a language model and further combining our advanced retrieval module with the comprehension capabilities of large language models (LLMs), we generate high-quality brain captions.</p><p><strong>Results: </strong>Experimental results demonstrate that we surpass current methods in visual decoding and brain captioning tasks. We offer a webpage to showcase the results: www.neuai4science.cn:5001/brain_visual_decode .</p><p><strong>Conclusion: </strong>Our proposed Brain-Imager framework, which incorporates multi-level data and semantic guidance, sets a new standard in the domain.</p><p><strong>Significance: </strong>This work provides a novel perspective on the relationship between text and image semantics and the visual pathways of the human brain, with potential applications in downstream tasks such as brain-computer interfaces. Additionally, our code is publicly available at https://github.com/songqianyi01/Brain-Imager .</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"32"},"PeriodicalIF":4.5,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12657701/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145640806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synergistic medical genetic evolutionary optimization and deep convolutional generative augmentation with SHAP-driven interpretability for precise Alzheimer's disease severity grading.","authors":"H C Bharath, N Pradeep, R Shashidhar, Yashwanth Nanjappa","doi":"10.1186/s40708-025-00280-z","DOIUrl":"10.1186/s40708-025-00280-z","url":null,"abstract":"<p><p>Alzheimer's disease (AD) diagnosis at an early yet accurate stage is critical to support effective treatment or intervention. Still it is not very feasible due to the presence of image data class imbalance, low interpretability of models, and a high computational cost. This research proposes a novel, end-to-end diagnostic framework that considers a Medical Genetic Algorithm (MedGA)-optimized Convolutional Neural Network (CNN) with a Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic MRIs and SHapley Additive Explanations (SHAP) to analyse and interpret the model. The given methodology is trained and tested on the Open Access Series of Imaging Studies (OASIS) dataset. The DCGAN component introduces 700 structurally coherent synthetic images (SSIM = 0.92) into the underrepresented Moderate Dementia class, improving the overall recall by 10% and balancing the dataset. MedGA succeeds in optimizing CNN hyperparameters and resulting in complexity reduction (20%) in networks without loss of testing accuracy (97%) at the four demonstrated stages of AD: Non-Demented, Very Mild Demented, Mild Demented, and Moderate Demented. SHAP analysis emphasises the role of key brain areas, the hippocampus and the amygdala in the results of classification accuracy, leading to 25% greater interpretability and clinician confidence. Comparative evaluation shows that the current framework is exceptionally better in terms of predictive performance and explainability than current state-of-the-art approaches. This combined method provides a powerful and adaptable device to categorize AD at an early age, with promising outcomes in precise diagnosis in health facilities.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"31"},"PeriodicalIF":4.5,"publicationDate":"2025-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12657675/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145606317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Biotuner: A python toolbox integrating music theory and signal processing for harmonic analysis of physiological and natural time series.","authors":"Antoine Bellemare-Pepin, Karim Jerbi","doi":"10.1186/s40708-025-00270-1","DOIUrl":"10.1186/s40708-025-00270-1","url":null,"abstract":"<p><strong>Background: </strong>The Biotuner Toolbox is an open-source Python toolbox for biosignals that integrates concepts from neuroscience, music theory, and signal processing. It introduces a harmonic perspective on physiological oscillations by applying musical constructs such as consonance, rhythm, and scale construction.</p><p><strong>Methods: </strong>The core biotuner_object processes neural, cardiac, and auditory time series, providing a unified interface for extracting spectral peaks, computing harmonicity metrics, and supporting downstream analyses. Companion modules extend harmonic analyses across temporal (time-resolved harmonicity), spatial (harmonic connectivity), and spectral (harmonic spectrum) dimensions.</p><p><strong>Results: </strong>Biotuner identifies harmonic structure across different biosignals, revealing significant variations in harmonicity between physiological states. Specifically, the toolbox extracts spectral peaks from complex signals using multiple algorithms, ensuring robust peak detection under varying signal-to-noise ratios. Moreover, we show how harmonicity metrics change across distinct sleep stages and capture variations in the slopes of the aperiodic (1/f) component of the power spectrum.</p><p><strong>Conclusion: </strong>Biotuner provides an extensible framework that unifies music-theoretic constructs with biosignal processing, enabling hypothesis-driven analyses for researchers and, in parallel, creative exploration of complex natural patterns for artists.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":"30"},"PeriodicalIF":4.5,"publicationDate":"2025-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12647408/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145565798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized rTMS treatment recommendation with retrieval-augmented LLM reasoning.","authors":"Lingling Xu, Haoran Xie, Xiaohui Tao, S Joe Qin, Fu Lee Wang, Raj Gururajan, Soman Elangovan, Yanmei Shen","doi":"10.1186/s40708-025-00275-w","DOIUrl":"10.1186/s40708-025-00275-w","url":null,"abstract":"<p><p>Repetitive transcranial magnetic stimulation (rTMS) is an effective, non-invasive neuromodulation therapy for major depressive disorder (MDD) and treatment-resistant depression (TRD). However, current clinical practice typically relies on standardized protocols that may not adequately account for individual patient variability. To address this gap, we propose a novel, interpretable framework for personalized rTMS treatment recommendations that combines a pretrained sentence embedding model with large language model (LLM)-based reasoning in a retrieval-augmented generation (RAG) setting. Specifically, our approach leverages a pretrained sentence embedding model to encode structured patient profiles into a dense semantic representation, enabling the retrieval of clinically similar cases. These retrieved examples serve as few-shot prompts for in-context learning (ICL), enabling the LLM to reason over these examples and synthesize customized rTMS treatment parameters. Unlike previous approaches that focus solely on individual aspects of personalization, our framework integrates all key parameters (frequency, intensity, and stimulation mode) into a comprehensive recommendation. We systematically evaluated various sentence embedding models and LLMs. Among them, Bge-large-en-v1.5 for few-shot retrieval and GPT-4o-mini for reasoning achieved the highest rTMS protocol matching accuracy of 78.18% using 15 few-shot examples. Our approach is fine-tuning-free, interpretable, and adaptable to real-world, resource-poor clinical settings, providing a promising step forward in data-driven personalized neurostimulation therapy.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"29"},"PeriodicalIF":4.5,"publicationDate":"2025-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12595160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145460095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing neural signal quality: a spike-denoising model with BiLSTM and attention mechanism.","authors":"Xinyu Xu, Nian Chen, Yuhao Deng, Tianhua Shen, Nannan Wei, Shicang Yu, Limin Zhang","doi":"10.1186/s40708-025-00278-7","DOIUrl":"10.1186/s40708-025-00278-7","url":null,"abstract":"<p><p>Neural signal spikes are recorded by microelectrode technology for specific applications. Nevertheless, spikes frequently encounter significant contamination from several noise sources, making efficient denoising quite challenging. Hence, this study presents a spike-denoising model with a bidirectional long short-term memory and attention mechanism combined with a shallow autoencoder to enhance the signal quality. To assess the effectiveness of the proposed method, various types of synthetic data, such as simulated white noise, correlated noise, colored noise and integrated noise, are used to show the performance. At very high noise levels, the proposed method maintains a high signal-to-noise ratio above 27 dB and the average Pearson 0.91. And the performance metrics of spike detection outperforms the traditional signal processing methods and the partial deep learning methods. Ultimately, the proposed method is used to process the real-world C57 fetal rat neural signals, which can recover a substantial amount of spikes from the obscured noise, showing the proposed method can effectively enhance the quality of neural signals damaged by noise.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"28"},"PeriodicalIF":4.5,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12515144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145276083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}