Brain Informatics最新文献

{"title":"A gender-aware saliency prediction system for web interfaces using deep learning and eye-tracking data.","authors":"Pablo Villanueva González, Cristobal Subiabre Cuevas, Lino Jeldez, Benjamin Torrealba Troncoso, María Flavia Guiñazú, Juan D Velásquez","doi":"10.1186/s40708-025-00274-x","DOIUrl":"10.1186/s40708-025-00274-x","url":null,"abstract":"<p><p>Understanding how demographic factors influence visual attention is crucial for the development of adaptive and user-centered web interfaces. This paper presents a gender-aware saliency prediction system based on fine-tuned deep learning models and demographic-specific gaze behavior. We introduce the WIC640 dataset, which includes 640 web page screenshots categorized by content type and country of origin, along with eye-tracking data from 85 participants across four age groups and both genders. To investigate gender-related differences in visual saliency, we fine-tuned TranSalNet, a Transformer-based saliency prediction model, on the WIC640 dataset. Our experiments reveal distinct gaze behavior patterns between male and female users. The female-trained model achieved a correlation coefficient (CC) of 0.7786, normalized scanpath saliency (NSS) of 2.4224, and Kullback-Leibler divergence (KLD) of 0.5447; the male-trained model showed slightly lower performance (CC = 0.7582, NSS = 2.3508, KLD = 0.5986). Interestingly, the general model trained on the complete dataset outperformed both gender-specific models, highlighting the importance of inclusive training data. Statistical analysis revealed significant gender-related differences in 9 out of 12 saliency features and a trend of reduced fixation dispersion with increasing age. While this study does not yet incorporate temporal gaze modeling, the results suggest practical benefits for intelligent systems aiming to personalize user experiences based on demographic features. The WIC640 dataset is publicly available and offers a valuable resource for future research on adaptive AI systems, visual attention modeling, and demographic-aware interface design.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"25"},"PeriodicalIF":4.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491136/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207957","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":"Regulation of tau protein by circCwc27: shared pathogenic mechanisms in type 2 diabetes mellitus and Alzheimer's disease.","authors":"Keying Fang, Bin Jiao, Lu Shen, Shilin Luo","doi":"10.1186/s40708-025-00277-8","DOIUrl":"10.1186/s40708-025-00277-8","url":null,"abstract":"<p><strong>Background: </strong>Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are distinct yet interconnected disorders that frequently co-occur. While insulin resistance and impaired glucose metabolism have been implicated in their shared pathogenesis, the molecular mechanisms underlying this comorbidity remain incompletely understood. Emerging evidence suggests that circular RNAs (circRNAs), particularly those enriched in neural and metabolic tissues, may play regulatory roles in both diseases.</p><p><strong>Methods: </strong>We conducted integrated transcriptomic analyses using Gene Expression Omnibus (GEO) datasets to identify differentially expressed genes in AD and T2DM. Protein-protein interaction (PPI) network construction and enrichment analyses identified common hub genes and dysregulated pathways. Functional studies were performed in SH-SY5Y and HEK293 cell models to explore the biological impact of Circular RNA Cwc27 (circCwc27), a circRNA derived from the Cwc27 gene.</p><p><strong>Results: </strong>Among 86 commonly upregulated genes, Cwc27 emerged as a central hub with significant connectivity in the AD-T2DM interaction network. Functional enrichment analysis revealed circCwc27's association with RNA splicing, mRNA surveillance, and PI3K-Akt signaling. Overexpression of circCwc27 increased total and phosphorylated Tau protein levels, enhanced Tau seeding activity, and reduced intracellular glycogen storage-hallmarks of AD neuropathology and metabolic dysregulation in T2DM. Notably, these effects occurred independently of Akt-GSK3β activation or APP expression, suggesting a unique regulatory axis involving Tau protein.</p><p><strong>Conclusion: </strong>Our findings identify circCwc27 as a novel molecular bridge linking AD and T2DM via Tau upregulation and metabolic impairment. This dual role highlights its potential as both a biomarker and therapeutic target for addressing the shared pathophysiological mechanisms of neurodegeneration and metabolic disease.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"26"},"PeriodicalIF":4.5,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12491135/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145207971","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":"Post-stroke brain functional reorganization is associated with glymphatic dysfunction during stroke recovery.","authors":"Yueyan Bian, Jiajia Zhang, Yifei Zhang, Hongxia Zhang, Xuejia Jia, Hongkai Wang, Xiuqin Jia, Qi Yang","doi":"10.1186/s40708-025-00273-y","DOIUrl":"10.1186/s40708-025-00273-y","url":null,"abstract":"","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"24"},"PeriodicalIF":4.5,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12484500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145201236","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":"A hybrid neuromarketing approach exploiting EEG graph signal processing and gaze dynamic patterning.","authors":"Fotis P Kalaganis, Kostas Georgiadis, Vangelis P Oikonomou, Nikos A Laskaris, Spiros Nikolopoulos, Ioannis Kompatsiaris","doi":"10.1186/s40708-025-00272-z","DOIUrl":"10.1186/s40708-025-00272-z","url":null,"abstract":"<p><p>In this study, we propose a hybrid decoding scheme for classifying consumer intent in a binary decision-making scenario (\"Buy\" vs. \"NoBuy\"), using simultaneous electroencephalography (EEG) and eye-tracking data. The proposed framework integrates graph signal processing-based features derived from EEG functional connectivity with descriptive statistics from eye movement patterns. Given the imbalanced nature of the targeted classification task, the performance of the proposed hybrid scheme is being assessed at the individual subject level via the employment of Cohen's kappa and F1-score metrics, both of which are well-suited for handling class imbalance by accounting for agreement beyond chance and balancing precision and recall, respectively. The reported results showcase the superiority of the proposed hybrid decoding scheme, as the averaged scores for both Cohen's kappa and F1-score are exceeding (with statistical significance at 0.05) the presented competing approaches by 0.08-0.30 and 0.06-0.23 respectively. Additionally, our connectivity analysis confirmed two key findings: (i) strong couplings were consistently observed between electrodes spanning distinct brain regions, such as the prefrontal and occipital cortices, in addition to the commonly reported frontal dipoles; and (ii) the most salient functional connections varied across individuals, with only a limited subset shared among subjects. These results highlight the potential of multimodal decoding approaches and subject-specific connectivity patterns in advancing the classification of consumer decision behavior.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"23"},"PeriodicalIF":4.5,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12457268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145132154","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":"Revolutionizing cross professional collaboration outcomes in TBI: emerging trends in diagnostics, personalized medicine, technological innovations and neurorehabilitation.","authors":"Mubin Mustafa Kiyani, Shahid Bashir, Benish Shahzadi, Hamid Khan, Maisra Azhar Butt, Syed Ali Hussain, Turki Abualait","doi":"10.1186/s40708-025-00271-0","DOIUrl":"10.1186/s40708-025-00271-0","url":null,"abstract":"<p><p>Traumatic brain injury (TBI) is still considered a major cause of morbidity and mortality worldwide, and its prevalence is increasing daily. TBI patients are facing difficult diagnostic, management, and rehabilitative challenges. This review article represents the most relevant recent findings in all areas of TBI concerning pathophysiology, diagnostics, therapeutics, rehabilitation approaches and neuroplasticity. In recent years, the diagnosis of TBI has improved more often due to advancements in neuroimaging, biomarkers, and artificial intelligence. Pharmacological treatments, stem cell therapy, and neuroprotective strategies are also associated with a wide range of therapeutic innovations that could open new fields of acute management. Consequently, practice has also changed, and cross professional treatments have been adopted with the aid of the latest technology for acute TBI recovery. TBI management faces multiple challenges in special populations, such as pediatric patients, elderly people, and soldiers. Personalized medicine, big data analytics, and global collaboration are emphasized as future research directions. This detailed study should serve to remind researchers and clinicians alike of the ongoing need for innovation to deliver better care pathways appropriate for TBI patients.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"22"},"PeriodicalIF":4.5,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12405072/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972732","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 rehabilitation insights: on the path of bridging artificial and biological neural networks.","authors":"Abdullatif Baba","doi":"10.1186/s40708-025-00266-x","DOIUrl":"10.1186/s40708-025-00266-x","url":null,"abstract":"<p><p>This paper introduces the conceptual parallel between the ANN training process and the learning mechanisms of the human brain. Then, we briefly discuss a set of recently achieved experimental findings from a prior study that delves into various scenarios, aiding in comprehending the functionality of impaired or damaged neurons within a neural system. The key contribution of this paper is to present a novel variant of the Adam optimizer that incorporates a dynamic momentum adjustment factor, adaptive learning rate, and elastic weight consolidation technique. This enhanced version draws inspiration from biological processes to improve learning stability in artificial neural networks, with conceivable relevance to neural adaptation and rehabilitation research.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"21"},"PeriodicalIF":4.5,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12397469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144972746","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":"Motor imagery decoding network with multisubject dynamic transfer.","authors":"Zhi Li, Mingai Li, Yufei Yang","doi":"10.1186/s40708-025-00267-w","DOIUrl":"10.1186/s40708-025-00267-w","url":null,"abstract":"<p><p>Brain computer interface (BCI) provides a promising and intelligent rehabilitation method for motor function, and it is crucial to acquire the patient's movement intentions accurately through decoding motor imagery EEG (MI-EEG) . Because of the inter-individual heterogeneity, the decoding model should demonstrate dynamic adaptation abilities.Domain adaptation (DA) is effective to enhance model generalization by reducing the inherent distribution difference among subjects. However, the existing DA methods usually mix the multiple source domains into a new domain, the resulting multi-source domain conflict may cause negative transfer. In this paper, we propose a multi-source dynamic conditional domain adaptation network (MSDCDA). First, a multi-channel attention block is employed in the feature extractor to focus on the channels relevant to the corresponding MI task. Subsequently, the shallow spatial-temporal features are extracted using a spatial-temporal convolution block. And a dynamic residual block is applied in the feature extractor to dynamically adapt specific features of each subject to alleviate conflicts among multiple source domains since each domain is viewed as a distribution of electroencephalogram (EEG) signals. Furthermore, we employ the Margin Disparity Discrepancy (MDD) as the metric to achieve conditional distribution domain adaptation between the source and target domains through adversarial learning with an auxiliary classifier. MSDCDA achieved accuracies of 78.55 <math><mo>%</mo></math> and 85.08 <math><mo>%</mo></math> on Datasets IIa and IIb of BCI Competition IV, respectively. Our experimental results demonstrate that MSDCDA can effectively address multi-source domain conflicts and significantly enhance the decoding performance of target subjects. This study positively facilitates the application of BCI based on motor function rehabilitation.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"20"},"PeriodicalIF":4.5,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356803/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856704","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":"An automated extraction of spectral-temporal and spatial-temporal features of EEG for emotion detection.","authors":"Monira Islam, Tan Lee","doi":"10.1186/s40708-025-00265-y","DOIUrl":"10.1186/s40708-025-00265-y","url":null,"abstract":"<p><p>Emotion is an integral part of human cognitive processes and behaviors. Automatic detection and classification of human emotion has been a goal of applied research. This study presents an approach to detecting emotion from multivariate electroencephalogram (EEG) with signal processing methods applied in the temporal, spectral, and spatial domains. In this work, the noise-assisted multivariate empirical mode decomposition (NA-MEMD) is applied to EEG to extract a set of narrow-band intrinsic mode functions (IMF), upon which spectral analysis and spatial connectivity analysis are performed. Applying Hilbert spectral analysis to those IMFs results in the marginal Hilbert spectrum (MHS). MHS is computed for each EEG channel to obtain the spectral energy of each segment. The spectral energy across multiple EEG channels within the same segment is aggregated while the consecutive frames are stacked to give spectral-temporal feature representation. Again, connectivity analysis is performed at each instant with a non-linear measure named phase locking value (PLV) to construct the connectivity map containing spatial-temporal features. A 2D CNN-BiLSTM is adopted to perform emotion detection with the MHS and the PLV features. On classifying high versus low states in valence, arousal, dominance, and liking, PLV showed better performance than MHS with 97.61%, 96.09%, 96.75%, and 97.23% accuracy, respectively, for DEAP dataset. Meanwhile, the highest accuracy of 94.71% is attained on 4-class task. PLV of high oscillatory IMFs outperforms the reported systems with conventional EEG features.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"19"},"PeriodicalIF":4.5,"publicationDate":"2025-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144769170","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":"A single-snapshot inverse solver for two-species graph model of tau pathology spreading in human Alzheimer's disease.","authors":"Zheyu Wen, Ali Ghafouri, George Biros","doi":"10.1186/s40708-025-00264-z","DOIUrl":"10.1186/s40708-025-00264-z","url":null,"abstract":"<p><p>We propose a method that uses a two-species ordinary differential equation (ODE) model for subject-specific misfolded tau protein spreading in Alzheimer's disease (AD) and calibrates it from magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. The ODE model is a variant of the heterodimer Fisher-Kolmogorov (HFK) model. The unknown model parameters are the initial condition (IC) for tau and three scalar parameters representing the migration, proliferation, and clearance of tau proteins. Driven by imaging data, these parameters are estimated by formulating a constrained optimization problem with a sparsity regularization for the IC. This optimization problem is solved with a projection-based quasi-Newton algorithm. We evaluate the performance of our method on both synthetic and clinical data. Subjects are from the AD Neuroimaging Initiative (ADNI) datasets: 455 cognitively normal (CN), 212 mild cognitive impairment (MCI), and 45 AD subjects. We compare the performance of our approach to the commonly used Fisher-Kolmogorov (FK) model with a fixed IC at the entorhinal cortex (EC). Our method demonstrates an average improvement of 19.6% relative error compared to the FK model on the AD dataset. HFK also achieves an R-squared score of 0.591 for fitting AD data compared with 0.256 from FK model results with IC fixing at EC. The inverted IC from our scheme indicates that the EC is the most likely initial seeding region if subcortical regions are excluded from the analysis. However, other regions also have probability to be the IC seeding regions. Furthermore, for cases that have longitudinal data, we estimate a subject-specific AD onset time.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12240917/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144601798","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":"Domain Adaptation-enhanced searchlight: enabling classification of brain states from visual perception to mental imagery.","authors":"Alexander Olza, David Soto, Roberto Santana","doi":"10.1186/s40708-025-00263-0","DOIUrl":"10.1186/s40708-025-00263-0","url":null,"abstract":"<p><p>In cognitive neuroscience and brain-computer interface research, accurately predicting imagined stimuli is crucial. This study investigates the effectiveness of Domain Adaptation (DA) in enhancing imagery prediction using primarily visual data from fMRI scans of 18 subjects. Initially, we train a baseline model on visual stimuli to predict imagined stimuli, utilizing data from 14 brain regions. We then develop several models to improve imagery prediction, comparing different DA methods. Our results demonstrate that DA significantly enhances imagery prediction in binary classification on our dataset, as well as in multiclass classification on a publicly available dataset. We then conduct a DA-enhanced searchlight analysis, followed by permutation-based statistical tests to identify brain regions where imagery decoding is consistently above chance across subjects. Our DA-enhanced searchlight predicts imagery contents in a highly distributed set of brain regions, including the visual cortex and the frontoparietal cortex, thereby outperforming standard cross-domain classification methods. The complete code and data for this paper have been made openly available for the use of the scientific community.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"12 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12206218/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144530143","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}