Neuroinformatics最新文献

{"title":"Geometric Reliability of Super-Resolution Reconstructed Images from Clinical Fetal MRI in the Second Trimester.","authors":"Tommaso Ciceri,&nbsp;Letizia Squarcina,&nbsp;Alessandro Pigoni,&nbsp;Adele Ferro,&nbsp;Florian Montano,&nbsp;Alessandra Bertoldo,&nbsp;Nicola Persico,&nbsp;Simona Boito,&nbsp;Fabio Maria Triulzi,&nbsp;Giorgio Conte,&nbsp;Paolo Brambilla,&nbsp;Denis Peruzzo","doi":"10.1007/s12021-023-09635-5","DOIUrl":"10.1007/s12021-023-09635-5","url":null,"abstract":"<p><p>Fetal Magnetic Resonance Imaging (MRI) is an important noninvasive diagnostic tool to characterize the central nervous system (CNS) development, significantly contributing to pregnancy management. In clinical practice, fetal MRI of the brain includes the acquisition of fast anatomical sequences over different planes on which several biometric measurements are manually extracted. Recently, modern toolkits use the acquired two-dimensional (2D) images to reconstruct a Super-Resolution (SR) isotropic volume of the brain, enabling three-dimensional (3D) analysis of the fetal CNS.We analyzed 17 fetal MR exams performed in the second trimester, including orthogonal T2-weighted (T2w) Turbo Spin Echo (TSE) and balanced Fast Field Echo (b-FFE) sequences. For each subject and type of sequence, three distinct high-resolution volumes were reconstructed via NiftyMIC, MIALSRTK, and SVRTK toolkits. Fifteen biometric measurements were assessed both on the acquired 2D images and SR reconstructed volumes, and compared using Passing-Bablok regression, Bland-Altman plot analysis, and statistical tests.Results indicate that NiftyMIC and MIALSRTK provide reliable SR reconstructed volumes, suitable for biometric assessments. NiftyMIC also improves the operator intraclass correlation coefficient on the quantitative biometric measures with respect to the acquired 2D images. In addition, TSE sequences lead to more robust fetal brain reconstructions against intensity artifacts compared to b-FFE sequences, despite the latter exhibiting more defined anatomical details.Our findings strengthen the adoption of automatic toolkits for fetal brain reconstructions to perform biometry evaluations of fetal brain development over common clinical MR at an early pregnancy stage.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"549-563"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406722/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10298897","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 Method for In-Vivo Mapping of Axonal Diameter Distributions in the Human Brain Using Diffusion-Based Axonal Spectrum Imaging (AxSI).","authors":"Hila Gast,&nbsp;Assaf Horowitz,&nbsp;Ronnie Krupnik,&nbsp;Daniel Barazany,&nbsp;Shlomi Lifshits,&nbsp;Shani Ben-Amitay,&nbsp;Yaniv Assaf","doi":"10.1007/s12021-023-09630-w","DOIUrl":"https://doi.org/10.1007/s12021-023-09630-w","url":null,"abstract":"<p><p>In this paper we demonstrate a generalized and simplified pipeline called axonal spectrum imaging (AxSI) for in-vivo estimation of axonal characteristics in the human brain. Whole-brain estimation of the axon diameter, in-vivo and non-invasively, across all fiber systems will allow exploring uncharted aspects of brain structure and function relations with emphasis on connectivity and connectome analysis. While axon diameter mapping is important in and of itself, its correlation with conduction velocity will allow, for the first time, the explorations of information transfer mechanisms within the brain. We demonstrate various well-known aspects of axonal morphometry (e.g., the corpus callosum axon diameter variation) as well as other aspects that are less explored (e.g., axon diameter-based separation of the superior longitudinal fasciculus into segments). Moreover, we have created an MNI based mean axon diameter map over the entire brain for a large cohort of subjects providing the reference basis for future studies exploring relation between axon properties, its connectome representation, and other functional and behavioral aspects of the brain.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"469-482"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406702/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10392489","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":"Deep Learning Methods for Identification of White Matter Fiber Tracts: Review of State-of-the-Art and Future Prospective.","authors":"Nayereh Ghazi,&nbsp;Mohammad Hadi Aarabi,&nbsp;Hamid Soltanian-Zadeh","doi":"10.1007/s12021-023-09636-4","DOIUrl":"https://doi.org/10.1007/s12021-023-09636-4","url":null,"abstract":"<p><p>Quantitative analysis of white matter fiber tracts from diffusion Magnetic Resonance Imaging (dMRI) data is of great significance in health and disease. For example, analysis of fiber tracts related to anatomically meaningful fiber bundles is highly demanded in pre-surgical and treatment planning, and the surgery outcome depends on accurate segmentation of the desired tracts. Currently, this process is mainly done through time-consuming manual identification performed by neuro-anatomical experts. However, there is a broad interest in automating the pipeline such that it is fast, accurate, and easy to apply in clinical settings and also eliminates the intra-reader variabilities. Following the advancements in medical image analysis using deep learning techniques, there has been a growing interest in using these techniques for the task of tract identification as well. Recent reports on this application show that deep learning-based tract identification approaches outperform existing state-of-the-art methods. This paper presents a review of current tract identification approaches based on deep neural networks. First, we review the recent deep learning methods for tract identification. Next, we compare them with respect to their performance, training process, and network properties. Finally, we end with a critical discussion of open challenges and possible directions for future works.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"517-548"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10018299","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":"Big Brain Data Initiatives in Universiti Sains Malaysia: Data Stewardship to Data Repository and Data Sharing.","authors":"Nurfaten Hamzah,&nbsp;Nurul Hashimah Ahamed Hassain Malim,&nbsp;Jafri Malin Abdullah,&nbsp;Putra Sumari,&nbsp;Ariffin Marzuki Mokhtar,&nbsp;Siti Nur Syamila Rosli,&nbsp;Sharifah Aida Shekh Ibrahim,&nbsp;Zamzuri Idris","doi":"10.1007/s12021-023-09637-3","DOIUrl":"https://doi.org/10.1007/s12021-023-09637-3","url":null,"abstract":"<p><p>The sharing of open-access neuroimaging data has increased significantly during the last few years. Sharing neuroimaging data is crucial to accelerating scientific advancement, particularly in the field of neuroscience. A number of big initiatives that will increase the amount of available neuroimaging data are currently in development. The Big Brain Data Initiative project was started by Universiti Sains Malaysia as the first neuroimaging data repository platform in Malaysia for the purpose of data sharing. In order to ensure that the neuroimaging data in this project is accessible, usable, and secure, as well as to offer users high-quality data that can be consistently accessed, we first came up with good data stewardship practices. Then, we developed MyneuroDB, an online repository database system for data sharing purposes. Here, we describe the Big Brain Data Initiative and MyneuroDB, a data repository that provides the ability to openly share neuroimaging data, currently including magnetic resonance imaging (MRI), electroencephalography (EEG), and magnetoencephalography (MEG), following the FAIR principles for data sharing.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"589-600"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10371870","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":"Single Neuron Modeling Identifies Potassium Channel Modulation as Potential Target for Repetitive Head Impacts.","authors":"Daniel P Chapman, Stefano Vicini, Mark P Burns, Rebekah Evans","doi":"10.1007/s12021-023-09633-7","DOIUrl":"10.1007/s12021-023-09633-7","url":null,"abstract":"<p><p>Traumatic brain injury (TBI) and repetitive head impacts can result in a wide range of neurological symptoms. Despite being the most common neurological disorder in the world, repeat head impacts and TBI do not have any FDA-approved treatments. Single neuron modeling allows researchers to extrapolate cellular changes in individual neurons based on experimental data. We recently characterized a model of high frequency head impact (HFHI) with a phenotype of cognitive deficits associated with decreases in neuronal excitability of CA1 neurons and synaptic changes. While the synaptic changes have been interrogated in vivo, the cause and potential therapeutic targets of hypoexcitability following repetitive head impacts are unknown. Here, we generated in silico models of CA1 pyramidal neurons from current clamp data of control mice and mice that sustained HFHI. We use a directed evolution algorithm with a crowding penalty to generate a large and unbiased population of plausible models for each group that approximated the experimental features. The HFHI neuron model population showed decreased voltage gated sodium conductance and a general increase in potassium channel conductance. We used partial least squares regression analysis to identify combinations of channels that may account for CA1 hypoexcitability after HFHI. The hypoexcitability phenotype in models was linked to A- and M-type potassium channels in combination, but not by any single channel correlations. We provide an open access set of CA1 pyramidal neuron models for both control and HFHI conditions that can be used to predict the effects of pharmacological interventions in TBI models.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"501-516"},"PeriodicalIF":2.7,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10833395/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10281744","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":"Funcmasker-flex: An Automated BIDS-App for Brain Segmentation of Human Fetal Functional MRI data.","authors":"Emily S Nichols,&nbsp;Susana Correa,&nbsp;Peter Van Dyken,&nbsp;Jason Kai,&nbsp;Tristan Kuehn,&nbsp;Sandrine de Ribaupierre,&nbsp;Emma G Duerden,&nbsp;Ali R Khan","doi":"10.1007/s12021-023-09629-3","DOIUrl":"https://doi.org/10.1007/s12021-023-09629-3","url":null,"abstract":"<p><p>Fetal functional magnetic resonance imaging (fMRI) offers critical insight into the developing brain and could aid in predicting developmental outcomes. As the fetal brain is surrounded by heterogeneous tissue, it is not possible to use adult- or child-based segmentation toolboxes. Manually-segmented masks can be used to extract the fetal brain; however, this comes at significant time costs. Here, we present a new BIDS App for masking fetal fMRI, funcmasker-flex, that overcomes these issues with a robust 3D convolutional neural network (U-net) architecture implemented in an extensible and transparent Snakemake workflow. Open-access fetal fMRI data with manual brain masks from 159 fetuses (1103 total volumes) were used for training and testing the U-net model. We also tested generalizability of the model using 82 locally acquired functional scans from 19 fetuses, which included over 2300 manually segmented volumes. Dice metrics were used to compare performance of funcmasker-flex to the ground truth manually segmented volumes, and segmentations were consistently robust (all Dice metrics ≥ 0.74). The tool is freely available and can be applied to any BIDS dataset containing fetal bold sequences. Funcmasker-flex reduces the need for manual segmentation, even when applied to novel fetal functional datasets, resulting in significant time-cost savings for performing fetal fMRI analysis.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"565-573"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10016997","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":"De-Identification Technique with Facial Deformation in Head CT Images.","authors":"Tatsuya Uchida,&nbsp;Taichi Kin,&nbsp;Toki Saito,&nbsp;Naoyuki Shono,&nbsp;Satoshi Kiyofuji,&nbsp;Tsukasa Koike,&nbsp;Katsuya Sato,&nbsp;Ryoko Niwa,&nbsp;Ikumi Takashima,&nbsp;Hiroshi Oyama,&nbsp;Nobuhito Saito","doi":"10.1007/s12021-023-09631-9","DOIUrl":"https://doi.org/10.1007/s12021-023-09631-9","url":null,"abstract":"<p><p>Head CT, which includes the facial region, can visualize faces using 3D reconstruction, raising concern that individuals may be identified. We developed a new de-identification technique that distorts the faces of head CT images. Head CT images that were distorted were labeled as \"original images\" and the others as \"reference images.\" Reconstructed face models of both were created, with 400 control points on the facial surfaces. All voxel positions in the original image were moved and deformed according to the deformation vectors required to move to corresponding control points on the reference image. Three face detection and identification programs were used to determine face detection rates and match confidence scores. Intracranial volume equivalence tests were performed before and after deformation, and correlation coefficients between intracranial pixel value histograms were calculated. Output accuracy of the deep learning model for intracranial segmentation was determined using Dice Similarity Coefficient before and after deformation. The face detection rate was 100%, and match confidence scores were < 90. Equivalence testing of the intracranial volume revealed statistical equivalence before and after deformation. The median correlation coefficient between intracranial pixel value histograms before and after deformation was 0.9965, indicating high similarity. Dice Similarity Coefficient values of original and deformed images were statistically equivalent. We developed a technique to de-identify head CT images while maintaining the accuracy of deep-learning models. The technique involves deforming images to prevent face identification, with minimal changes to the original information.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"575-587"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10015017","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":"Localization and Registration of 2D Histological Mouse Brain Images in 3D Atlas Space.","authors":"Maryam Sadeghi, Arnau Ramos-Prats, Pedro Neto, Federico Castaldi, Devin Crowley, Pawel Matulewicz, Enrica Paradiso, Wolfgang Freysinger, Francesco Ferraguti, Georg Goebel","doi":"10.1007/s12021-023-09632-8","DOIUrl":"10.1007/s12021-023-09632-8","url":null,"abstract":"<p><p>To accurately explore the anatomical organization of neural circuits in the brain, it is crucial to map the experimental brain data onto a standardized system of coordinates. Studying 2D histological mouse brain slices remains the standard procedure in many laboratories. Mapping these 2D brain slices is challenging; due to deformations, artifacts, and tilted angles introduced during the standard preparation and slicing process. In addition, analysis of experimental mouse brain slices can be highly dependent on the level of expertise of the human operator. Here we propose a computational tool for Accurate Mouse Brain Image Analysis (AMBIA), to map 2D mouse brain slices on the 3D brain model with minimal human intervention. AMBIA has a modular design that comprises a localization module and a registration module. The localization module is a deep learning-based pipeline that localizes a single 2D slice in the 3D Allen Brain Atlas and generates a corresponding atlas plane. The registration module is built upon the Ardent python package that performs deformable 2D registration between the brain slice to its corresponding atlas. By comparing AMBIA's performance in localization and registration to human ratings, we demonstrate that it performs at a human expert level. AMBIA provides an intuitive and highly efficient way for accurate registration of experimental 2D mouse brain images to 3D digital mouse brain atlas. Our tool provides a graphical user interface and it is designed to be used by researchers with minimal programming knowledge.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"615-630"},"PeriodicalIF":2.7,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406728/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10020376","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":"CellRemorph: A Toolkit for Transforming, Selecting, and Slicing 3D Cell Structures on the Road to Morphologically Detailed Astrocyte Simulations.","authors":"Laura Keto,&nbsp;Tiina Manninen","doi":"10.1007/s12021-023-09627-5","DOIUrl":"https://doi.org/10.1007/s12021-023-09627-5","url":null,"abstract":"<p><p>Understanding functions of astrocytes can be greatly enhanced by building and simulating computational models that capture their morphological details. Novel computational tools enable utilization of existing morphological data of astrocytes and building models that have appropriate level of details for specific simulation purposes. In addition to analyzing existing computational tools for constructing, transforming, and assessing astrocyte morphologies, we present here the CellRemorph toolkit implemented as an add-on for Blender, a 3D modeling platform increasingly recognized for its utility for manipulating 3D biological data. To our knowledge, CellRemorph is the first toolkit for transforming astrocyte morphologies from polygonal surface meshes into adjustable surface point clouds and vice versa, precisely selecting nanoprocesses, and slicing morphologies into segments with equal surface areas or volumes. CellRemorph is an open-source toolkit under the GNU General Public License and easily accessible via an intuitive graphical user interface. CellRemorph will be a valuable addition to other Blender add-ons, providing novel functionality that facilitates the creation of realistic astrocyte morphologies for different types of morphologically detailed simulations elucidating the role of astrocytes both in health and disease.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 3","pages":"483-500"},"PeriodicalIF":3.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406679/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10392956","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":"NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data.","authors":"Jieqing Jiao,&nbsp;Fiona Heeman,&nbsp;Rachael Dixon,&nbsp;Catriona Wimberley,&nbsp;Isadora Lopes Alves,&nbsp;Juan Domingo Gispert,&nbsp;Adriaan A Lammertsma,&nbsp;Bart N M van Berckel,&nbsp;Casper da Costa-Luis,&nbsp;Pawel Markiewicz,&nbsp;David M Cash,&nbsp;M Jorge Cardoso,&nbsp;Sebastién Ourselin,&nbsp;Maqsood Yaqub,&nbsp;Frederik Barkhof","doi":"10.1007/s12021-022-09616-0","DOIUrl":"https://doi.org/10.1007/s12021-022-09616-0","url":null,"abstract":"<p><p>Current PET datasets are becoming larger, thereby increasing the demand for fast and reproducible processing pipelines. This paper presents a freely available, open source, Python-based software package called NiftyPAD, for versatile analyses of static, full or dual-time window dynamic brain PET data. The key novelties of NiftyPAD are the analyses of dual-time window scans with reference input processing, pharmacokinetic modelling with shortened PET acquisitions through the incorporation of arterial spin labelling (ASL)-derived relative perfusion measures, as well as optional PET data-based motion correction. Results obtained with NiftyPAD were compared with the well-established software packages PPET and QModeling for a range of kinetic models. Clinical data from eight subjects scanned with four different amyloid tracers were used to validate the computational performance. NiftyPAD achieved [Formula: see text] correlation with PPET, with absolute difference [Formula: see text] for linearised Logan and MRTM2 methods, and [Formula: see text] correlation with QModeling, with absolute difference [Formula: see text] for basis function based SRTM and SRTM2 models. For the recently published SRTM ASL method, which is unavailable in existing software packages, high correlations with negligible bias were observed with the full scan SRTM in terms of non-displaceable binding potential ([Formula: see text]), indicating reliable model implementation in NiftyPAD. Together, these findings illustrate that NiftyPAD is versatile, flexible, and produces comparable results with established software packages for quantification of dynamic PET data. It is freely available ( https://github.com/AMYPAD/NiftyPAD ), and allows for multi-platform usage. The modular setup makes adding new functionalities easy, and the package is lightweight with minimal dependencies, making it easy to use and integrate into existing processing pipelines.</p>","PeriodicalId":49761,"journal":{"name":"Neuroinformatics","volume":"21 2","pages":"457-468"},"PeriodicalIF":3.0,"publicationDate":"2023-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10085912/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9332639","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}