Brain Informatics最新文献

{"title":"Breakthrough percepts of familiar faces.","authors":"Omid Hajilou, Howard Bowman","doi":"10.1186/s40708-026-00304-2","DOIUrl":"10.1186/s40708-026-00304-2","url":null,"abstract":"<p><p>In Rapid Serial Visual Presentation (RSVP), the vast majority of stimuli are not consciously perceived, but the salient ones breakthrough into awareness and can be reported. In addition, these breakthrough events are observable with EEG, since they generate a P3 or other distinguishing components. The Fringe-P3 method is based upon these characteristics. Concealed knowledge studies have successfully employed this Fringe-P3 method using own-name and own email-address, with the method being shown to be less vulnerable to counter-measures than other approaches. It has also been shown that famous faces presented in RSVP differentially break into awareness and generate a distinct evoked response component. In this paper, we further enhance the applicability of the Fringe-P3 method by demonstrating the effectiveness of the method on personally-familiar faces. While salient, such stimuli do not have the exquisite salience of famous faces, being a better match to the level of salience that might be found in forensic applications. Our findings suggest that the Fringe-P3 method could be used to detect intrinsic salience of familiar faces, even when there was no task associated with these faces. We investigated the sensitivity of the ERP-based RSVP paradigm to infer recognition of familiar faces, and performed statistical inference in the Time and Frequency domains, to differentiate between known and unknown faces, at group and participant levels.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13187101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147943062","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 deep hybrid CNN-BiLSTM-BiGRU architecture with explainability for mild cognitive impairment detection using EEG.","authors":"Aishik Tokdar, Lakshya Agarwal, Shataghnee Chatterjee, Sukriti","doi":"10.1186/s40708-026-00302-4","DOIUrl":"10.1186/s40708-026-00302-4","url":null,"abstract":"<p><p>Accurate detection of Mild Cognitive Impairment (MCI) is critical for timely intervention and for slowing progression to Alzheimer's disease. Electroencephalography (EEG) offers a non-invasive and cost-effective measure of brain activity; however, its complex, non-linear dynamics limit conventional analysis. We propose a CNN-Res-SE-BiLSTM-BiGRU framework for the automated detection of MCI directly from raw EEG. Convolutional and residual blocks capture local temporal structure, bidirectional recurrent layers model long-range dependencies, and Squeeze-and-Excitation (SE) modules provide channel-wise attention. Predicted probabilities are calibrated using temperature scaling, and operating thresholds are selected on the validation set using Youden's J statistic. The model is evaluated using five-fold cross-validation under both subject-dependent and strict subject-independent protocols on a primary resting-state dataset, with additional subject-independent validation on an odor EEG dataset. Under subject-independent evaluation on the odor dataset, the proposed model achieved an accuracy of 0.956 ± 0.051, with ROC-AUC of 0.971 ± 0.051 and PR-AUC of 0.934 ± 0.132. UMAP-based visualization and explainable AI analyses (SHAP and LIME) provide interpretable insight into the learned spatiotemporal patterns and sample-specific decisions. These results demonstrate robust, interpretable EEG-based MCI detection with potential clinical utility.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13194862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147869800","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":"Benchmarking resting state fMRI connectivity pipelines for classification: robust accuracy despite processing variability in cross-site eye state prediction.","authors":"Tatiana Medvedeva, Irina Knyazeva, Ruslan Masharipov, Alexander Korotkov, Denis Cherednichenko, Maxim Kireev","doi":"10.1186/s40708-026-00305-1","DOIUrl":"10.1186/s40708-026-00305-1","url":null,"abstract":"<p><p>The rapid evolution of machine learning (ML) methods has yielded promising results in human brain neuroscience. However, the reproducibility of ML applications in neuroimaging remains limited, challenging the generalizability of inferences to broader populations. In addition to the inherent variability of the brain activity (both in healthy and pathological states), poor reproducibility is further enhanced by inconsistencies in data preprocessing techniques and methods for calculating functional connectivity (FC), which are used as parameters for brain state classification. To systematically assess the impact of abovementioned factors on ML applications to fMRI data, we benchmarked a comprehensive set of FC analysis pipelines for the classification task between fMRI data recorded in two fundamentally different states: eyes open and eyes closed. In contrast to studies involving heterogeneous clinical populations or using complex cognitive tasks, our controlled experimental design - based on two independent datasets of healthy participants collected in different laboratories - minimizes variability related to a task design or pathological brain states. Classification accuracy and reproducibility were compared for 256 distinct FC analysis pipelines, covering common preprocessing approaches, brain parcellation schemes, and connectivity metrics. Notably, we employed two ways of validation: a direct cross-site validation strategy - when a model was trained on one site and tested on another, and few-shot domain adaptation - when a few samples of testing site were added to the train set. Despite the substantial variability in pipeline configurations, we observed consistently high classification accuracy (~ 90%), confirming that FC-based models can robustly discriminate between well-defined brain states (eye conditions) across different acquisition sites. Best results both in terms of classification accuracy and stability were observed using Pearson correlation and tangent space parametrization as FC, Brainnetome as atlas, and confound regression strategies based on the CompCor method. These findings highlight the resilience of rs-fMRI FC-derived characteristics to methodological variation and support their utility in the discovery of biomarkers, particularly in settings that involve stable and reproducible brain states.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13197499/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147844006","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 deep-learning framework for brain tumor segmentation via three-dimensional mass-preserving geometric transformation.","authors":"Tsung-Ming Huang, Kai-Qian Zheng, Wen-Wei Lin, Tiexiang Li, Shing-Tung Yau","doi":"10.1186/s40708-026-00307-z","DOIUrl":"10.1186/s40708-026-00307-z","url":null,"abstract":"<p><p>This article presents a robust and efficient framework for brain tumor segmentation based on deep learning. We introduce a novel three-dimensional (3D) mass-preserving geometric transformation (MPGT) that employs a homotopy method to transform irregular brain magnetic resonance (MR) images into standardized solid cubes. This transformation preserves local mass ratios while maintaining global structural integrity, providing a structured input for deep learning models. Furthermore, we propose a modified two-phase segmentation strategy to minimize inference time and a postprocessing technique to enhance lesion-wise performance. Extensive validation on the Brain Tumor Segmentation (BraTS) Challenge 2023 dataset demonstrates that our method, when integrated with nnU-Net, achieves competitive Dice scores of 0.9282 (Whole Tumor), 0.8812 (Tumor Core), and 0.8527 (Enhanced Tumor). These results are superior to or comparable with top-ranking competition entries.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13158315/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147843986","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 network classification considering directed propagation mechanisms of dynamic graphs.","authors":"Xinlei Wang, Zhongyang Wang, Keyan Cao","doi":"10.1186/s40708-026-00306-0","DOIUrl":"10.1186/s40708-026-00306-0","url":null,"abstract":"<p><p>The classification of functional brain networks plays an important role in the diagnosis of neurodegenerative diseases, brain decoding and other fields. Functional brain networks can effectively reflect the functional connection relationships between brain regions or neurons and accurately represent brain activities. Therefore, a large number of problems related to the classification of functional brain networks have been studied. However, the traditional functional brain network merely measures the static correlation between brain regions or neurons in a simple way, and does not reflect the causal transmission effect between brain regions. This directionality is crucial for the regulatory relationship between brain regions. Furthermore, since the brain is constantly in a state of dynamic change, the dynamics of functional connectivity also plays a very important role in the classification of functional brain networks. Therefore, we propose a classification framework named Dynamic Directed Propagation Networks (DDPN) for functional brain networks considering the dynamic directed propagation mechanism. This method effectively captures the dynamics and directionality of the dynamic directed brain network and further improves the classification accuracy of the functional brain network. To verify the effectiveness of the proposed method, we conduct experiments on real datasets. The experiments show that the proposed method improved by 3.1-4.1% compared with state-of-the art methods in two datasets.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13158336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821770","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":"BrainFusionNet: a deep learning and XAI model to understand local, global, and sequential features of MRI images for improved brain tumour detection.","authors":"Md Taimur Ahad, Bo Song, Yan Li","doi":"10.1186/s40708-026-00303-3","DOIUrl":"10.1186/s40708-026-00303-3","url":null,"abstract":"<p><p>The noise of Magnetic Resonance Imaging (MRI) poses challenges for Deep Learning (DL) when tumor boundaries are obscured, tumor location and appearance are complex due to overlap between tumor and non-tumor cells, and modality identification is difficult because tumor features vanish in the later layers of the DL. Effective feature extraction from given MRI is a possible solution to overcome this challenge. Therefore, we develop BrainFusionNet that combines Convolutional Neural Networks (CNNs), Vision Transformers (ViT), and Gated Recurrent Units (GRUs) to extract spatial, contextual, and sequential features from MRI images for improved brain tumor classification. Furthermore, explainable AI such as SHAP, LIME, and Grad-CAM are integrated to visualise and highlight image regions that contribute to BrainFusionNet's decision-making process. The proposed BrainFusionNet model is evaluated on two publicly available MRI datasets. K-fold validation suggests 98% accuracy on both datasets. The model was compared with the six state-of-the-art (SOTA) CNNs and transfer learning. Among the SOTA CNNs, DenseNet121 and VGG16 achieved the highest accuracy of 96%. The novelty of BrainFusionNet is that the hybrid model effectively extracts local and global features from MRI images, even in small-scale tumor regions and small tumor sizes. The model has a balanced sequential CNN architecture to capture low-level and deeper-layer features; a customized ViT that captures local features, stabilizes gradient flow, and reduces the risk of vanishing gradients during MRI image training. The CNN and ViT outputs are fed into a GRU for final classification. Furthermore, we analyze pixel intensities to determine whether MRI image quality affects image classification. Our findings are very novel in image interpretation, as we found that the distribution of pixel intensities in MRI images affects DL performance.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13237402/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784077","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":"Anatomical-connectivity-guided functional connectivity reveals task-relevant pathways during proactive task-switching via recurrent graph neural networks.","authors":"Siyu Wang, Atsushi Miyata, Teruhisa Okuya, Hiroto Yanagawa, Ayaka Sakaki, Natsuhiro Ichinose, Takatsune Kumada","doi":"10.1186/s40708-026-00300-6","DOIUrl":"10.1186/s40708-026-00300-6","url":null,"abstract":"<p><p>In an increasingly complex society, cognitive flexibility is essential for effective multitasking. We developed a novel approach—Anatomical-Connectivity-Guided Functional Connectivity (ACG-FC)—and applied it to the preparatory phase of a task-cueing paradigm to examine functional coordination mechanisms among brain regions critical for cognitive flexibility during proactive control and temporal expectation. This approach emphasizes the importance of inter-regional collaboration by integrating the cortical structural connectome, which reflects transmission pathways with electroencephalography (EEG) data within recurrent graph neural networks (RGNNs) to evaluate the functional relevance of brain regions and their associated neural connections. In generalizing signal pattern differences across proactive control processes, RGNNs outperformed other symbolic machine learning and deep neural network (DNN) models commonly applied to neurophysiological data, indicating that spatial information from the structural connectome enhances EEG pattern recognition during executive control. Meanwhile, single-population attribution analysis revealed that general proactive task preparation involves functional coordination within three synchronous ACG-FC subnetworks clustered around the frontoparietal \"multiple demand\" system. This supports multiple late-latency neural priming processes during execution preparation, associated with functionally distinct yet highly complementary subnetworks in the prefrontal, cingulate, and temporal cortices. Furthermore, dual-population attribution analysis indicated that scattered ACG-FC differences specifically underlie distinct modes of proactive control or target anticipation. ACG-FC extends conventional functional connectivity (FC) from signal-dependent pairwise analyses to signal-structure-dependent network analyses by using a group-level anatomical connectome as a structural prior to guide the extraction of functional interactions, offering a new perspective on task relevance between cognitive activities and their neural bases.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13237348/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147784106","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":"SegAnyNeuron: a neural image segmentation network with strong generalization performance by modeling image intensity variation.","authors":"Lin Cai, Ying Zhang, Quanwei Ding, Xiaojun Wang, Pei Sun, Shaoqun Zeng, Tingwei Quan","doi":"10.1186/s40708-026-00298-x","DOIUrl":"10.1186/s40708-026-00298-x","url":null,"abstract":"<p><p>Neuron reconstruction is a critical step in obtaining quantitative parameters of fine neuronal morphology from microscopic imaging data. Deep neural networks have been extensively applied in this field, with the predominant approach focusing on accurately identifying the voxels occupied by neurites and enhancing the image intensity of these identified voxels to improve the accuracy of three-dimensional reconstruction. However, significant domain shifts in image intensity between foreground and background constrains the generalization performance of existing segmentation models across multi-modal datasets. Consequently, a universal representation framework with cross-domain robustness has yet to be established. In this study, we leverage the directional properties of neurites within neuronal images to extract spatial orientation features at the voxel scale. These features are integrated through multi-channel representation, which constrains the network's spatial perception of neuronal morphology, thereby significantly reducing the impact of image domain discrepancies on model adaptability. Experimental results demonstrate that our proposed method improves F1-Scores by 7.9%-22.8% compared to the baseline UNet-3D model and outperforms existing state-of-the-art approaches by 6.0%-9.1% in F1-Scores when evaluated on cross-modal neuronal image datasets. This work provides a novel biologically informed solution to address domain generalization challenges in neuronal image segmentation. </p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13103207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147718403","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":"Pipeline evaluation of a state-of-the-art AI algorithm for detection of focal cortical dysplasia: insights into potential failure sources.","authors":"Mateus A Esmeraldo, Stefanie Chambers, Yanniklas Kravutske, Eduardo P Reis, Gregor Kasprian, Ana Filipa Geraldo, Sergios Gatidis, Bruno P Soares","doi":"10.1186/s40708-026-00299-w","DOIUrl":"10.1186/s40708-026-00299-w","url":null,"abstract":"<p><strong>Purpose: </strong>MELD Graph is a state-of-the-art artificial intelligence (AI) model for automated detection of focal cortical dysplasia (FCD), but its performance remains limited, highlighting the need to investigate which aspects of the pipeline affect its accuracy.</p><p><strong>Methods: </strong>A retrospective failure-mode analysis of the MELD Graph pipeline was performed in 242 subjects, with model predictions and FreeSurfer segmentations reviewed to classify errors as segmentation-associated or algorithm-related. FCD imaging features salient to humans were quantified, with statistical associations examined for both MELD Graph detection and focal FreeSurfer segmentation failure.</p><p><strong>Results: </strong>MELD Graph demonstrated overall performance similar to previously published non-harmonized results, achieving a sensitivity of 69%, specificity of 44%, and positive predictive value (PPV) of 75%. Focal FreeSurfer segmentation failures were associated with 21% of false negative patients, 25% of false positive clusters in patients, and 16% of false positive clusters in controls. Following manual cortical segmentation correction and rerunning of MELD Graph, 67% of the segmentation-associated missed lesions were detected, and segmentation-associated false positive clusters were reduced or eliminated in 75% of controls with such clusters. Higher conspicuity on T1-weighted images was associated with MELD Graph detection, whereas greater conspicuity on T2-FLAIR images relative to T1 was associated with detection failure. Non-bottom-of-sulcus lesion location, higher human conspicuity measures, and low T1 image quality were positively associated with focal FreeSurfer segmentation failures.</p><p><strong>Conclusion: </strong>FreeSurfer segmentation failures are a significant potential source of error in the MELD Graph pipeline. FCD imaging features salient to humans and image quality were also associated with variability in algorithm performance. Robust cortical segmentation and stronger integration of T2-FLAIR imaging features may be beneficial for automated FCD detection tools.</p><p><strong>Clinical trial registration: </strong>Not applicable. This study is a retrospective analysis of previously acquired open-source imaging datasets and does not constitute a clinical trial.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13133319/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147610148","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":"Filter bank CSP with Riemannian weighting for disability-centric motor imagery brain computer interface.","authors":"Souissi Jihen, Sourour Karmani, Kais Belwafi, Mahdi Jemmali, Ridha Djemal","doi":"10.1186/s40708-026-00295-0","DOIUrl":"10.1186/s40708-026-00295-0","url":null,"abstract":"<p><p>Brain-computer interfaces (BCIs) were initially created to help individuals with disabilities control devices and communicate without muscle movement. Today, BCIs are used for prosthetic control, cognitive enhancement, and neurological rehabilitation. The BCI system depends on analyzing electroencephalogram (EEG) signals captured from the brain. Decoding these EEG signals is a complex process that combines multiple algorithms to extract meaningful information from these intricate and noisy signals. One of the most popular techniques is the Common Spatial Patterns (CSP), which helps preserve useful and sensitive information. This paper presents an optimized extension of the CSP model for extracting EEG data features in a multiclass setting using Riemannian geometry-based weighting. The use of weighting based on Riemannian geometry enhances the robustness of covariance matrix computation, thereby decreasing the influence of noise that can significantly distort the mean of covariance matrices in the traditional CSP method. The proposed approach is also extended by the integration of a multi-band filter bank, providing a more detailed examination of EEG signals. Three classifiers, Linear Discriminant Analysis (LDA), Random Forest Classifier (RFC), and Multi-Layer Perceptron (MLP), are employed to differentiate features across four motor imagery tasks. LDA achieves an accuracy of 80.40%, while MLP and RFC reach 80.02% and 80.90%, respectively. The results obtained using a majority vote combining the decisions of the three classifiers are 81.83% for accuracy and Recall, 82.74% for precision, and 81.87% for F1-score. The proposed architecture is evaluated using the BCI Competition IV set 2a dataset, proving its effectiveness in EEG signal classification for BCI applications.</p>","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":" ","pages":""},"PeriodicalIF":8.3,"publicationDate":"2026-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13133326/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147522016","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}