Neuroinformatics最新文献

{"title":"Performance of Convolutional Neural Network Models in Meningioma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.","authors":"Ting-Wei Wang, Jia-Sheng Hong, Wei-Kai Lee, Yi-Hui Lin, Huai-Che Yang, Cheng-Chia Lee, Hung-Chieh Chen, Hsiu-Mei Wu, Weir Chiang You, Yu-Te Wu","doi":"10.1007/s12021-024-09704-3","DOIUrl":"10.1007/s12021-024-09704-3","url":null,"abstract":"<p><strong>Background: </strong>Meningioma, the most common primary brain tumor, presents significant challenges in MRI-based diagnosis and treatment planning due to its diverse manifestations. Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and efficiency of meningioma segmentation from MRI scans. This systematic review and meta-analysis assess the effectiveness of CNN models in segmenting meningioma using MRI.</p><p><strong>Methods: </strong>Following the PRISMA guidelines, we searched PubMed, Embase, and Web of Science from their inception to December 20, 2023, to identify studies that used CNN models for meningioma segmentation in MRI. Methodological quality of the included studies was assessed using the CLAIM and QUADAS-2 tools. The primary variable was segmentation accuracy, which was evaluated using the Sørensen-Dice coefficient. Meta-analysis, subgroup analysis, and meta-regression were performed to investigate the effects of MRI sequence, CNN architecture, and training dataset size on model performance.</p><p><strong>Results: </strong>Nine studies, comprising 4,828 patients, were included in the analysis. The pooled Dice score across all studies was 89% (95% CI: 87-90%). Internal validation studies yielded a pooled Dice score of 88% (95% CI: 85-91%), while external validation studies reported a pooled Dice score of 89% (95% CI: 88-90%). Models trained on multiple MRI sequences consistently outperformed those trained on single sequences. Meta-regression indicated that training dataset size did not significantly influence segmentation accuracy.</p><p><strong>Conclusion: </strong>CNN models are highly effective for meningioma segmentation in MRI, particularly during the use of diverse datasets from multiple MRI sequences. This finding highlights the importance of data quality and imaging sequence selection in the development of CNN models. Standardization of MRI data acquisition and preprocessing may improve the performance of CNN models, thereby facilitating their clinical adoption for the optimal diagnosis and treatment of meningioma.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"14"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11706894/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957925","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 Self-supervised Deep Learning Model for Diagonal Sulcus Detection with Limited Labeled Data.","authors":"Delfina Braggio, Hernán C Külsgaard, Mariana Vallejo-Azar, Mariana Bendersky, Paula González, Lucía Alba-Ferrara, José Ignacio Orlando, Ignacio Larrabide","doi":"10.1007/s12021-024-09700-7","DOIUrl":"10.1007/s12021-024-09700-7","url":null,"abstract":"<p><p>Sulci are a fundamental part of brain morphology, closely linked to brain function, cognition, and behavior. Tertiary sulci, characterized as the shallowest and smallest subtype, pose a challenging task for detection. The diagonal sulcus (ds), located in a crucial area in language processing, has a prevalence between 50% and 60%. Automatic detection of the ds is an unexplored field: while some sulci segmenters include the ds, their accuracy is usually low. In this work, we present a deep learning based model for ds detection using a fine-tuning approach with limited training labeled data. A convolutional autoencoder was employed to learn specific features related to brain morphology with unlabeled data through self-supervised learning. Subsequently, the pre-trained network was fine-tuned to detect the ds using a less extensive labeled dataset. We achieved a mean F1-score of 0.7176 (SD=0.0736) for the test set and a F1-score of 0.72 for a second held-out set, surpassing the results of a standard software and other alternative deep learning models. We conducted an interpretability analysis of the results using occlusion maps and observed that the models focused on adjacent sulci to the ds for prediction, consistent with the approach taken by experts in manual annotation. We also analyzed the challenges of manual labeling by conducting a thorough examination of interrater agreement on a small dataset and its relationship with our model's performance. Finally, we applied our method on a population analysis and reported the prevalence of ds in a case study.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"13"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation Study of Envelope Wave Electrical Nerve Stimulation Based on a Real Head Model.","authors":"Yuhao Liu, Renling Zou, Liang Zhao, Linpeng Jin, Xiufang Hu, Xuezhi Yin","doi":"10.1007/s12021-024-09711-4","DOIUrl":"10.1007/s12021-024-09711-4","url":null,"abstract":"<p><p>In recent years, the modulation of brain neural activity by applied electromagnetic fields has become a hot spot in neuroscience research. Transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) are two common non-invasive neuromodulation techniques. However, conventional tACS has limited stimulation effects in the deeper parts of the brain. In this study, a method of low and medium frequency envelope wave neurostimulation is proposed, and its effectiveness and safety are evaluated by simulation and human experiment. First, we built a real head model from head MRI image data and used the finite element method to calculate the current distribution of the envelope wave in the brain. Then, a single-compartment neuron model was constructed in NEURON software to simulate the action potential generation of neurons under different frequencies of electrical stimulation. Finally, a human experiment was conducted to investigate the threshold of human perception of envelope wave electrical stimulation. The results show that envelope wave can both increase the depth of stimulation and induce neurons to generate effective action potentials. In envelope wave electrical stimulation, the optimal modulating wave frequency was 50 Hz, and the carrier frequency was 2 kHz-3 kHz. This method is expected to play an important role in the non-invasive treatment of neurological and psychiatric disorders.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"23 1","pages":"15"},"PeriodicalIF":2.7,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interdisciplinary and Collaborative Training in Neuroscience: Insights from the Human Brain Project Education Programme.","authors":"Alice Geminiani, Judith Kathrein, Alper Yegenoglu, Franziska Vogel, Marcelo Armendariz, Ziv Ben-Zion, Petrut Antoniu Bogdan, Joana Covelo, Marissa Diaz Pier, Karin Grasenick, Vitali Karasenko, Wouter Klijn, Tina Kokan, Carmen Alina Lupascu, Anna Lührs, Tara Mahfoud, Taylan Özden, Jens Egholm Pedersen, Luca Peres, Ingrid Reiten, Nikola Simidjievski, Inga Ulnicane, Michiel van der Vlag, Lyuba Zehl, Alois Saria, Sandra Diaz-Pier, Johannes Passecker","doi":"10.1007/s12021-024-09682-6","DOIUrl":"10.1007/s12021-024-09682-6","url":null,"abstract":"<p><p>Neuroscience education is challenged by rapidly evolving technology and the development of interdisciplinary approaches for brain research. The Human Brain Project (HBP) Education Programme aimed to address the need for interdisciplinary expertise in brain research by equipping a new generation of researchers with skills across neuroscience, medicine, and information technology. Over its ten year duration, the programme engaged over 1,300 experts and attracted more than 5,500 participants from various scientific disciplines in its blended learning curriculum, specialised schools and workshops, and events fostering dialogue among early-career researchers. Key principles of the programme's approach included fostering interdisciplinarity, adaptability to the evolving research landscape and infrastructure, and a collaborative environment with a focus on empowering early-career researchers. Following the programme's conclusion, we provide here an analysis and in-depth view across a diverse range of educational formats and events. Our results show that the Education Programme achieved success in its wide geographic reach, the diversity of participants, and the establishment of transversal collaborations. Building on these experiences and achievements, we describe how leveraging digital tools and platforms provides accessible and highly specialised training, which can enhance existing education programmes for the next generation of brain researchers working in decentralised European collaborative spaces. Finally, we present the lessons learnt so that similar initiatives may improve upon our experience and incorporate our suggestions into their own programme.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"657-678"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579076/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584770","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 Bayesian Multiplex Graph Classifier of Functional Brain Connectivity Across Diverse Tasks of Cognitive Control.","authors":"Sharmistha Guha, Jose Rodriguez-Acosta, Ivo D Dinov","doi":"10.1007/s12021-024-09670-w","DOIUrl":"10.1007/s12021-024-09670-w","url":null,"abstract":"<p><p>This article seeks to investigate the impact of aging on functional connectivity across different cognitive control scenarios, particularly emphasizing the identification of brain regions significantly associated with early aging. By conceptualizing functional connectivity within each cognitive control scenario as a graph, with brain regions as nodes, the statistical challenge revolves around devising a regression framework to predict a binary scalar outcome (aging or normal) using multiple graph predictors. Popular regression methods utilizing multiplex graph predictors often face limitations in effectively harnessing information within and across graph layers, leading to potentially less accurate inference and predictive accuracy, especially for smaller sample sizes. To address this challenge, we propose the Bayesian Multiplex Graph Classifier (BMGC). Accounting for multiplex graph topology, our method models edge coefficients at each graph layer using bilinear interactions between the latent effects associated with the two nodes connected by the edge. This approach also employs a variable selection framework on node-specific latent effects from all graph layers to identify influential nodes linked to observed outcomes. Crucially, the proposed framework is computationally efficient and quantifies the uncertainty in node identification, coefficient estimation, and binary outcome prediction. BMGC outperforms alternative methods in terms of the aforementioned metrics in simulation studies. An additional BMGC validation was completed using an fMRI study of brain networks in adults. The proposed BMGC technique identified that sensory motor brain network obeys certain lateral symmetries, whereas the default mode network exhibits significant brain asymmetries associated with early aging.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"457-472"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11578796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141301973","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":"Classifying Neuronal Cell Types Based on Shared Electrophysiological Information from Humans and Mice.","authors":"Ofek Ophir, Orit Shefi, Ofir Lindenbaum","doi":"10.1007/s12021-024-09675-5","DOIUrl":"10.1007/s12021-024-09675-5","url":null,"abstract":"<p><p>The brain is an intricate system that controls a variety of functions. It consists of a vast number of cells that exhibit diverse characteristics. To understand brain function in health and disease, it is crucial to classify neurons accurately. Recent advancements in machine learning have provided a way to classify neurons based on their electrophysiological activity. This paper presents a deep-learning framework that classifies neurons solely on this basis. The framework uses data from the Allen Cell Types database, which contains a survey of biological features derived from single-cell recordings from mice and humans. The shared information from both sources is used to classify neurons into their broad types with the help of a joint model. An accurate domain-adaptive model, integrating electrophysiological data from both mice and humans, is implemented. Furthermore, data from mouse neurons, which also includes labels of transgenic mouse lines, is further classified into subtypes using an interpretable neural network model. The framework provides state-of-the-art results in terms of accuracy and precision while also providing explanations for the predictions.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"473-486"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141555808","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":"Bridging the Gap: How Neuroinformatics is Preparing the Next Generation of Neuroscience Researchers.","authors":"Mathew Abrams, John Darrell Van Horn","doi":"10.1007/s12021-024-09693-3","DOIUrl":"10.1007/s12021-024-09693-3","url":null,"abstract":"<p><p>Neurotechnology and big data are two rapidly advancing fields that have the potential to transform our understanding of the brain and its functions. Advancements in neurotechnology have enabled researchers to investigate the function of the brain at unprecedented levels of granularity at the functional, molecular, and anatomical levels. Thus, resulting in the collection of not only more data, but also larger datasets. To fully harness the potential of big data and advancements in neurotechnology to improve our understanding of the nervous system, there is a need to train a new generation of neuroscientists capable of not only domain expertise, but also the computational and data science skills required to interrogate and integrate big data. Importantly, neuroinformatics is the subdiscipline of neuroscience devoted to the development of neuroscience data and knowledge bases together with computational models and analytical tools for sharing, integration and analysis of experimental data, and advancement of theories about the nervous system function. While there are only a few formal training programs in neuroinformatics, and since neuroinformatics is rarely incorporated into traditional neuroscience training programs, the neuroinformatics community has attempted to bridge the gap between the traditional neuroscience education programs and the needs of the next generation of neuroscience researchers through community initiatives and workshops. Thus, the purpose of this special collection is to highlight several such community efforts which span from in-person workshops to large-scale, global virtual training consortiums and from training students to training-the-trainers.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"619-622"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.","authors":"Michał Szczepanik, Adina S Wagner, Stephan Heunis, Laura K Waite, Simon B Eickhoff, Michael Hanke","doi":"10.1007/s12021-024-09665-7","DOIUrl":"10.1007/s12021-024-09665-7","url":null,"abstract":"<p><p>Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"635-645"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140854285","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":"Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging - A Symposium Review.","authors":"Joshua K Marchant, Natalie G Ferris, Diana Grass, Magdelena S Allen, Vivek Gopalakrishnan, Mark Olchanyi, Devang Sehgal, Maxina Sheft, Amelia Strom, Berkin Bilgic, Brian Edlow, Elizabeth M C Hillman, Meher R Juttukonda, Laura Lewis, Shahin Nasr, Aapo Nummenmaa, Jonathan R Polimeni, Roger B H Tootell, Lawrence L Wald, Hui Wang, Anastasia Yendiki, Susie Y Huang, Bruce R Rosen, Randy L Gollub","doi":"10.1007/s12021-024-09686-2","DOIUrl":"10.1007/s12021-024-09686-2","url":null,"abstract":"<p><p>Advances in the spatiotemporal resolution and field-of-view of neuroimaging tools are driving mesoscale studies for translational neuroscience. On October 10, 2023, the Center for Mesoscale Mapping (CMM) at the Massachusetts General Hospital (MGH) Athinoula A. Martinos Center for Biomedical Imaging and the Massachusetts Institute of Technology (MIT) Health Sciences Technology based Neuroimaging Training Program (NTP) hosted a symposium exploring the state-of-the-art in this rapidly growing area of research. \"Mesoscale Brain Mapping: Bridging Scales and Modalities in Neuroimaging\" brought together researchers who use a broad range of imaging techniques to study brain structure and function at the convergence of the microscopic and macroscopic scales. The day-long event centered on areas in which the CMM has established expertise, including the development of emerging technologies and their application to clinical translational needs and basic neuroscience questions. The in-person symposium welcomed more than 150 attendees, including 57 faculty members, 61 postdoctoral fellows, 35 students, and four industry professionals, who represented institutions at the local, regional, and international levels. The symposium also served the training goals of both the CMM and the NTP. The event content, organization, and format were planned collaboratively by the faculty and trainees. Many CMM faculty presented or participated in a panel discussion, thus contributing to the dissemination of both the technologies they have developed under the auspices of the CMM and the findings they have obtained using those technologies. NTP trainees who benefited from the symposium included those who helped to organize the symposium and/or presented posters and gave \"flash\" oral presentations. In addition to gaining experience from presenting their work, they had opportunities throughout the day to engage in one-on-one discussions with visiting scientists and other faculty, potentially opening the door to future collaborations. The symposium presentations provided a deep exploration of the many technological advances enabling progress in structural and functional mesoscale brain imaging. Finally, students worked closely with the presenting faculty to develop this report summarizing the content of the symposium and putting it in the broader context of the current state of the field to share with the scientific community. We note that the references cited here include conference abstracts corresponding to the symposium poster presentations.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"679-706"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142299585","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":"Anatomic Interpretability in Neuroimage Deep Learning: Saliency Approaches for Typical Aging and Traumatic Brain Injury.","authors":"Kevin H Guo, Nikhil N Chaudhari, Tamara Jafar, Nahian F Chowdhury, Paul Bogdan, Andrei Irimia","doi":"10.1007/s12021-024-09694-2","DOIUrl":"10.1007/s12021-024-09694-2","url":null,"abstract":"<p><p>The black box nature of deep neural networks (DNNs) makes researchers and clinicians hesitant to rely on their findings. Saliency maps can enhance DNN explainability by suggesting the anatomic localization of relevant brain features. This study compares seven popular attribution-based saliency approaches to assign neuroanatomic interpretability to DNNs that estimate biological brain age (BA) from magnetic resonance imaging (MRI). Cognitively normal (CN) adults (N = 13,394, 5,900 males; mean age: 65.82 ± 8.89 years) are included for DNN training, testing, validation, and saliency map generation to estimate BA. To study saliency robustness to the presence of anatomic deviations from normality, saliency maps are also generated for adults with mild traumatic brain injury (mTBI, <math><mi>N</mi></math> = 214, 135 males; mean age: 55.3 ± 9.9 years). We assess saliency methods' capacities to capture known anatomic features of brain aging and compare them to a surrogate ground truth whose anatomic saliency is known a priori. Anatomic aging features are identified most reliably by the integrated gradients method, which outperforms all others through its ability to localize relevant anatomic features. Gradient Shapley additive explanations, input × gradient, and masked gradient perform less consistently but still highlight ubiquitous neuroanatomic features of aging (ventricle dilation, hippocampal atrophy, sulcal widening). Saliency methods involving gradient saliency, guided backpropagation, and guided gradient-weight class attribution mapping localize saliency outside the brain, which is undesirable. Our research suggests the relative tradeoffs of saliency methods to interpret DNN findings during BA estimation in typical aging and after mTBI.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":" ","pages":"591-606"},"PeriodicalIF":2.7,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11579113/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142584767","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}