Frontiers in neuroimaging最新文献

{"title":"Label-efficient deep semantic segmentation of intracranial hemorrhages in CT-scans.","authors":"Antoine Spahr,&nbsp;Jennifer Ståhle,&nbsp;Chunliang Wang,&nbsp;Magnus Kaijser","doi":"10.3389/fnimg.2023.1157565","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1157565","url":null,"abstract":"<p><p>Intracranial hemorrhage (ICH) is a common finding in traumatic brain injury (TBI) and computed tomography (CT) is considered the gold standard for diagnosis. Automated detection of ICH provides clinical value in diagnostics and in the ability to feed robust quantification measures into future prediction models. Several studies have explored ICH detection and segmentation but the research process is somewhat hindered due to a lack of open large and labeled datasets, making validation and comparison almost impossible. The complexity of the task is further challenged by the heterogeneity of ICH patterns, requiring a large number of labeled data to train robust and reliable models. Consequently, due to the labeling cost, there is a need for label-efficient algorithms that can exploit easily available unlabeled or weakly-labeled data. Our aims for this study were to evaluate whether transfer learning can improve ICH segmentation performance and to compare a variety of transfer learning approaches that harness unlabeled and weakly-labeled data. Three self-supervised and three weakly-supervised transfer learning approaches were explored. To be used in our comparisons, we also manually labeled a dataset of 51 CT scans. We demonstrate that transfer learning improves ICH segmentation performance on both datasets. Unlike most studies on ICH segmentation our work relies exclusively on publicly available datasets, allowing for easy comparison of performances in future studies. To further promote comparison between studies, we also present a new public dataset of ICH-labeled CT scans, Seq-CQ500.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1157565"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406224/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9956739","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":"Optimizing cognitive neuroscience experiments for separating event- related fMRI BOLD responses in non-randomized alternating designs.","authors":"Soukhin Das,&nbsp;Weigang Yi,&nbsp;Mingzhou Ding,&nbsp;George R Mangun","doi":"10.3389/fnimg.2023.1068616","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1068616","url":null,"abstract":"<p><p>Functional magnetic resonance imaging (fMRI) has revolutionized human brain research. But there exists a fundamental mismatch between the rapid time course of neural events and the sluggish nature of the fMRI blood oxygen level-dependent (BOLD) signal, which presents special challenges for cognitive neuroscience research. This limitation in the temporal resolution of fMRI puts constraints on the information about brain function that can be obtained with fMRI and also presents methodological challenges. Most notably, when using fMRI to measure neural events occurring closely in time, the BOLD signals may temporally overlap one another. This overlap problem may be exacerbated in complex experimental paradigms (stimuli and tasks) that are designed to manipulate and isolate specific cognitive-neural processes involved in perception, cognition, and action. Optimization strategies to deconvolve overlapping BOLD signals have proven effective in providing separate estimates of BOLD signals from temporally overlapping brain activity, but there remains reduced efficacy of such approaches in many cases. For example, when stimulus events necessarily follow a non-random order, like in trial-by-trial cued attention or working memory paradigms. Our goal is to provide guidance to improve the efficiency with which the underlying responses evoked by one event type can be detected, estimated, and distinguished from other events in designs common in cognitive neuroscience research. We pursue this goal using simulations that model the nonlinear and transient properties of fMRI signals, and which use more realistic models of noise. Our simulations manipulated: (i) Inter-Stimulus-Interval (ISI), (ii) proportion of so-called null events, and (iii) nonlinearities in the BOLD signal due to both cognitive and design parameters. We offer a theoretical framework along with a python toolbox called deconvolve to provide guidance on the optimal design parameters that will be of particular utility when using non-random, alternating event sequences in experimental designs. In addition, though, we also highlight the challenges and limitations in simultaneously optimizing both detection and estimation efficiency of BOLD signals in these common, but complex, cognitive neuroscience designs.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1068616"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406298/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10337580","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":"Evaluating drug distribution in children and young adults with diffuse midline glioma of the pons (DIPG) treated with convection-enhanced drug delivery.","authors":"Elwira Szychot,&nbsp;Dolin Bhagawati,&nbsp;Magdalena Joanna Sokolska,&nbsp;David Walker,&nbsp;Steven Gill,&nbsp;Harpreet Hyare","doi":"10.3389/fnimg.2023.1062493","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1062493","url":null,"abstract":"<p><strong>Aims: </strong>To determine an imaging protocol that can be used to assess the distribution of infusate in children with DIPG treated with CED.</p><p><strong>Methods: </strong>13 children diagnosed with DIPG received between 3.8 and 5.7 ml of infusate, through two pairs of catheters to encompass tumor volume on day 1 of cycle one of treatment. Volumetric T2-weighted (T2W) and diffusion-weighted MRI imaging (DWI) were performed before and after day 1 of CED. Apparent diffusion coefficient (ADC) maps were calculated. The tumor volume pre and post CED was automatically segmented on T2W and ADC on the basis of signal intensity. The ADC maps pre and post infusion were aligned and subtracted to visualize the infusate distribution.</p><p><strong>Results: </strong>There was a significant increase (<i>p</i> < 0.001) in mean ADC and T2W signal intensity (SI) ratio and a significant (<i>p</i> < 0.001) increase in mean tumor volume defined by ADC and T2W SI post infusion (mean ADC volume pre: 19.8 ml, post: 24.4 ml; mean T2W volume pre: 19.4 ml, post: 23.4 ml). A significant correlation (<i>p</i> < 0.001) between infusate volume and difference in ADC/T2W SI defined tumor volume was observed (ADC, r = 0.76; T2W, r = 0.70). Finally, pixel-by-pixel subtraction of the ADC maps pre and post infusion demonstrated a volume of high signal intensity, presumed infusate distribution.</p><p><strong>Conclusions: </strong>ADC and T2W MRI are proposed as a combined parameter method for evaluation of CED infusate distribution in brainstem tumors in future clinical trials.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1062493"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406269/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9956734","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":"Efficient evaluation of the Open QC task fMRI dataset.","authors":"Joset A Etzel","doi":"10.3389/fnimg.2023.1070274","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1070274","url":null,"abstract":"<p><p>This article is an evaluation of the task dataset as part of the Demonstrating Quality Control (QC) Procedures in fMRI (FMRI Open QC Project) methodological research topic. The quality of both the task and fMRI aspects of the dataset are summarized in concise reports created with R, AFNI, and knitr. The reports and underlying tests are designed to highlight potential issues, are pdf files for easy archiving, and require relatively little experience to use and adapt. This article is accompanied by both the compiled reports and the source code and explanation necessary to use them.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1070274"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9956736","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":"DSC MRI in the human brain using deoxyhemoglobin and gadolinium-Simulations and validations at 3T.","authors":"Jacob Benjamin Schulman,&nbsp;Ece Su Sayin,&nbsp;Angelica Manalac,&nbsp;Julien Poublanc,&nbsp;Olivia Sobczyk,&nbsp;James Duffin,&nbsp;Joseph A Fisher,&nbsp;David Mikulis,&nbsp;Kâmil Uludağ","doi":"10.3389/fnimg.2023.1048652","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1048652","url":null,"abstract":"<p><strong>Introduction: </strong>Dynamic susceptibility contrast (DSC) MRI allows clinicians to determine perfusion parameters in the brain, such as cerebral blood flow, cerebral blood volume, and mean transit time. To enable quantification, susceptibility changes can be induced using gadolinium (Gd) or deoxyhemoglobin (dOHb), the latter just recently introduced as a contrast agent in DSC. Previous investigations found that experimental parameters and analysis choices, such as the susceptibility amplitude and partial volume, affect perfusion quantification. However, the accuracy and precision of DSC MRI has not been systematically investigated, particularly in the lower susceptibility range.</p><p><strong>Methods: </strong>In this study, we compared perfusion values determined using Gd with values determined using a contrast agent with a lower susceptibility-dOHb-under different physiological conditions, such as varying the baseline blood oxygenation and/or magnitude of hypoxic bolus, by utilizing numerical simulations and conducting experiments on healthy subjects at 3T. The simulation framework we developed for DSC incorporates MRI signal contributions from intravascular and extravascular proton spins in arterial, venous, and cerebral tissue voxels. This framework allowed us to model the MRI signal in response to both Gd and dOHb.</p><p><strong>Results and discussion: </strong>We found, both in the experimental results and simulations, that a reduced intravascular volume of the selected arterial voxel, reduced baseline oxygen saturation, greater susceptibility of applied contrast agent (Gd vs. dOHb), and/or larger magnitude of applied hypoxic bolus reduces the overestimation and increases precision of cerebral blood volume and flow. As well, we found that normalizing tissue to venous rather than arterial signal increases the accuracy of perfusion quantification across experimental paradigms. Furthermore, we found that shortening the bolus duration increases the accuracy and reduces the calculated values of mean transit time. In summary, we experimentally uncovered an array of perfusion quantification dependencies, which agreed with the simulation framework predictions, using a wider range of susceptibility values than previously investigated. We argue for caution when comparing absolute and relative perfusion values within and across subjects obtained from a standard DSC MRI analysis, particularly when employing different experimental paradigms and contrast agents.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1048652"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406263/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10337587","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":"Development of a Bayesian multimodal model to detect biomarkers in neuroimaging studies.","authors":"Dulal K Bhaumik,&nbsp;Yue Wang,&nbsp;Pei-Shan Yen,&nbsp;Olusola A Ajilore","doi":"10.3389/fnimg.2023.1147508","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1147508","url":null,"abstract":"<p><p>In this article, we developed a Bayesian multimodal model to detect biomarkers (or neuromarkers) using resting-state functional and structural data while comparing a late-life depression group with a healthy control group. Biomarker detection helps determine a target for treatment intervention to get the optimal therapeutic benefit for treatment-resistant patients. The borrowing strength of the structural connectivity has been quantified for functional activity while detecting the biomarker. In the biomarker searching process, thousands of hypotheses are generated and tested simultaneously using our novel method to control the false discovery rate for small samples. Several existing statistical approaches, frequently used in analyzing neuroimaging data have been investigated and compared via simulation with the proposed approach to show its excellent performance. Results are illustrated with a live data set generated in a late-life depression study. The role of detected biomarkers in terms of cognitive function has been explored.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1147508"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406277/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9963087","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":"Measuring white matter microstructure in 1,457 cannabis users and 1,441 controls: A systematic review of diffusion-weighted MRI studies.","authors":"Emily Anne Robinson,&nbsp;John Gleeson,&nbsp;Arush Honnedevasthana Arun,&nbsp;Adam Clemente,&nbsp;Alexandra Gaillard,&nbsp;Maria Gloria Rossetti,&nbsp;Paolo Brambilla,&nbsp;Marcella Bellani,&nbsp;Camilla Crisanti,&nbsp;H Valerie Curran,&nbsp;Valentina Lorenzetti","doi":"10.3389/fnimg.2023.1129587","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1129587","url":null,"abstract":"<p><strong>Introduction: </strong>Cannabis is the most widely used regulated substance by youth and adults. Cannabis use has been associated with psychosocial problems, which have been partly ascribed to neurobiological changes. Emerging evidence to date from diffusion-MRI studies shows that cannabis users compared to controls show poorer integrity of white matter fibre tracts, which structurally connect distinct brain regions to facilitate neural communication. However, the most recent evidence from diffusion-MRI studies thus far has yet to be integrated. Therefore, it is unclear if white matter differences in cannabis users are evident consistently in selected locations, in specific diffusion-MRI metrics, and whether these differences in metrics are associated with cannabis exposure levels.</p><p><strong>Methods: </strong>We systematically reviewed the results from diffusion-MRI imaging studies that compared white matter differences between cannabis users and controls. We also examined the associations between cannabis exposure and other behavioral variables due to changes in white matter. Our review was pre-registered in PROSPERO (ID: 258250; https://www.crd.york.ac.uk/prospero/).</p><p><strong>Results: </strong>We identified 30 diffusion-MRI studies including 1,457 cannabis users and 1,441 controls aged 16-to-45 years. All but 6 studies reported group differences in white matter integrity. The most consistent differences between cannabis users and controls were lower fractional anisotropy within the arcuate/superior longitudinal fasciculus (7 studies), and lower fractional anisotropy of the corpus callosum (6 studies) as well as higher mean diffusivity and trace (4 studies). Differences in fractional anisotropy were associated with cannabis use onset (4 studies), especially in the corpus callosum (3 studies).</p><p><strong>Discussion: </strong>The mechanisms underscoring white matter differences are unclear, and they may include effects of cannabis use onset during youth, neurotoxic effects or neuro adaptations from regular exposure to tetrahydrocannabinol (THC), which exerts its effects by binding to brain receptors, or a neurobiological vulnerability predating the onset of cannabis use. Future multimodal neuroimaging studies, including recently developed advanced diffusion-MRI metrics, can be used to track cannabis users over time and to define with precision when and which region of the brain the white matter changes commence in youth cannabis users, and whether cessation of use recovers white matter differences.</p><p><strong>Systematic review registration: </strong>www.crd.york.ac.uk/prospero/, identifier: 258250.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1129587"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406316/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10319559","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":"Editorial: Deep learning in neuroimaging-based neurological disease analysis.","authors":"Xiaoxiao Li,&nbsp;Yu Zhang,&nbsp;Qingyu Zhao","doi":"10.3389/fnimg.2023.1127719","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1127719","url":null,"abstract":"COPYRIGHT © 2023 Li, Zhang and Zhao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Deep learning in neuroimaging-based neurological disease analysis","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1127719"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406280/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10332275","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":"Generating dynamic carbon-dioxide traces from respiration-belt recordings: Feasibility using neural networks and application in functional magnetic resonance imaging.","authors":"Vismay Agrawal,&nbsp;Xiaole Z Zhong,&nbsp;J Jean Chen","doi":"10.3389/fnimg.2023.1119539","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1119539","url":null,"abstract":"<p><strong>Introduction: </strong>In the context of functional magnetic resonance imaging (fMRI), carbon dioxide (CO₂) is a well-known vasodilator that has been widely used to monitor and interrogate vascular physiology. Moreover, spontaneous fluctuations in end-tidal carbon dioxide (PETCO₂) reflects changes in arterial CO₂ and has been demonstrated as the largest physiological noise source for denoising the low-frequency range of the resting-state fMRI (rs-fMRI) signal. However, the majority of rs-fMRI studies do not involve CO₂ recordings, and most often only heart rate and respiration are recorded. While the intrinsic link between these latter metrics and CO₂ led to suggested possible analytical models, they have not been widely applied.</p><p><strong>Methods: </strong>In this proof-of-concept study, we propose a deep-learning (DL) approach to reconstruct CO2 and PETCO2 data from respiration waveforms in the resting state.</p><p><strong>Results: </strong>We demonstrate that the one-to-one mapping between respiration and CO₂ recordings can be well predicted using fully convolutional networks (FCNs), achieving a Pearson correlation coefficient (r) of 0.946 ± 0.056 with the ground truth CO₂. Moreover, dynamic PETCO₂ can be successfully derived from the predicted CO₂, achieving r of 0.512 ± 0.269 with the ground truth. Importantly, the FCN-based methods outperform previously proposed analytical methods. In addition, we provide guidelines for quality assurance of respiration recordings for the purposes of CO₂ prediction.</p><p><strong>Discussion: </strong>Our results demonstrate that dynamic CO₂ can be obtained from respiration-volume using neural networks, complementing the still few reports in DL of physiological fMRI signals, and paving the way for further research in DL based bio-signal processing.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1119539"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406216/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9956737","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":"Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI.","authors":"Elisa Moya-Sáez,&nbsp;Rodrigo de Luis-García,&nbsp;Carlos Alberola-López","doi":"10.3389/fnimg.2023.1055463","DOIUrl":"https://doi.org/10.3389/fnimg.2023.1055463","url":null,"abstract":"<p><p>Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native -non CE- counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow.</p>","PeriodicalId":73094,"journal":{"name":"Frontiers in neuroimaging","volume":"2 ","pages":"1055463"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406200/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10337585","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}