Journal of Neuroscience Methods最新文献

{"title":"Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.","authors":"Zhuo Cai, Yunyuan Gao, Feng Fang, Yingchun Zhang, Shunlan Du","doi":"10.1016/j.jneumeth.2024.110332","DOIUrl":"10.1016/j.jneumeth.2024.110332","url":null,"abstract":"<p><p>In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110332"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769822","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":"Human pluripotent stem cell-derived microglia shape neuronal morphology and enhance network activity in vitro.","authors":"L M L Kok, K Helwegen, N F Coveña, V M Heine","doi":"10.1016/j.jneumeth.2024.110354","DOIUrl":"10.1016/j.jneumeth.2024.110354","url":null,"abstract":"<p><strong>Background: </strong>Microglia, the resident immune cells of the central nervous system, play a critical role in maintaining neuronal health, but are often overlooked in traditional neuron-focused in vitro models.</p><p><strong>New method: </strong>In this study, we developed a novel co-culture system of human pluripotent stem cell (hPSC)-derived microglia and neurons to investigate how hPSC-derived microglia influence neuronal morphology and network activity. Using high-content morphological analysis and multi-electrode arrays (MEA), we demonstrate that these microglia successfully incorporate into neuronal networks and modulate key aspects of neuronal function.</p><p><strong>Results: </strong>hPSC-derived microglia significantly reduced cellular debris and altered neuronal morphology by decreasing axonal and dendritic segments and reducing synapse density. Interestingly, despite the decrease in synapse density, neuronal network activity increased.</p><p><strong>Conclusion: </strong>Our findings underscore the importance of including hPSC-derived microglia in in vitro models to better simulate in vivo neuroglial interactions and provide a platform for investigating neuron-glia dynamics in health and disease.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110354"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142895532","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":"IDyOMpy: A new Python-based model for the statistical analysis of musical expectations.","authors":"Guilhem Marion, Fei Gao, Benjamin P Gold, Giovanni M Di Liberto, Shihab Shamma","doi":"10.1016/j.jneumeth.2024.110347","DOIUrl":"10.1016/j.jneumeth.2024.110347","url":null,"abstract":"<p><strong>Background: </strong>IDyOM (Information Dynamics of Music) is the statistical model of music the most used in the community of neuroscience of music. It has been shown to allow for significant correlations with EEG (Marion, 2021), ECoG (Di Liberto, 2020) and fMRI (Cheung, 2019) recordings of human music listening. The language used for IDyOM -Lisp- is not very familiar to the neuroscience community and makes this model hard to use and more importantly to modify.</p><p><strong>New method: </strong>IDyOMpy is a new Python re-implementation and extension of IDyOM. This new model allows for computing the information content and entropy for each melody note after training on a corpus of melodies. In addition to those features, two new features are presented: probability estimation of silences and enculturation modeling.</p><p><strong>Results: </strong>We first describe the mathematical details of the implementation. We extensively compare the two models and show that they generate very similar outputs. We also support the validity of IDyOMpy by using its output to replicate previous EEG and behavioral results that relied on the original Lisp version (Gold, 2019; Di Liberto, 2020; Marion, 2021). Finally, it reproduced the computation of cultural distances between two different datasets as described in previous studies (Pearce, 2018).</p><p><strong>Comparison with existing methods and conclusions: </strong>Our model replicates the previous behaviors of IDyOM in a modern and easy-to-use language -Python. In addition, more features are presented. We deeply think this new version will be of great use to the community of neuroscience of music.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110347"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872035","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":"STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity.","authors":"A Marasco, C A Lupascu, C Tribuzi","doi":"10.1016/j.jneumeth.2024.110324","DOIUrl":"10.1016/j.jneumeth.2024.110324","url":null,"abstract":"<p><strong>Background: </strong>In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods.</p><p><strong>New method: </strong>Four time-scale adaptive performance and similarity measures are proposed and implemented in the STSimM (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains.</p><p><strong>Results: </strong>The proposed ST-measures demonstrate enhanced sensitivity over Spike-contrast and SPIKE-distance in detecting spike train similarity, aligning closely with SPIKE-synchronization. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization.</p><p><strong>Comparison of existing method: </strong>The STSimM measures are compared with SPIKE-distance, SPIKE-synchronization and Spike-contrast using four spike train datasets with varying similarity levels.</p><p><strong>Conclusion: </strong>ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures - ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore - capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110324"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142791828","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":"Convolutional Neural Networks for the segmentation of hippocampal structures in postmortem MRI scans.","authors":"Anoop B N, Karl Li, Nicolas Honnorat, Tanweer Rashid, Di Wang, Jinqi Li, Elyas Fadaee, Sokratis Charisis, Jamie M Walker, Timothy E Richardson, David A Wolk, Peter T Fox, José E Cavazos, Sudha Seshadri, Laura E M Wisse, Mohamad Habes","doi":"10.1016/j.jneumeth.2024.110359","DOIUrl":"10.1016/j.jneumeth.2024.110359","url":null,"abstract":"<p><strong>Background: </strong>The hippocampus plays a crucial role in memory and is one of the first structures affected by Alzheimer's disease. Postmortem MRI offers a way to quantify the alterations by measuring the atrophy of the inner structures of the hippocampus. Unfortunately, the manual segmentation of hippocampal subregions required to carry out these measures is very time-consuming.</p><p><strong>New method: </strong>In this study, we explore the use of fully automated methods relying on state-of-the-art Deep Learning approaches to produce these annotations. More specifically, we propose a new segmentation framework made of a set of encoder-decoder blocks embedding self-attention mechanisms and atrous spatial pyramidal pooling to produce better maps of the hippocampus and identify four hippocampal regions: the dentate gyrus, the hippocampal head, the hippocampal body, and the hippocampal tail.</p><p><strong>Results: </strong>Trained using slices extracted from 15 postmortem T1-weighted, T2-weighted, and susceptibility-weighted MRI scans, our new approach produces hippocampus parcellations that are better aligned with the manually delineated parcellations provided by neuroradiologists.</p><p><strong>Comparison with existing methods: </strong>Four standard deep learning segmentation architectures: UNet, Double UNet, Attention UNet, and Multi-resolution UNet have been utilized for the qualitative and quantitative comparison of the proposed hippocampal region segmentation model.</p><p><strong>Conclusions: </strong>Postmortem MRI serves as a highly valuable neuroimaging technique for examining the effects of neurodegenerative diseases on the intricate structures within the hippocampus. This study opens the way to large sample-size postmortem studies of the hippocampal substructures.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110359"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142927256","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":"SSSort 2.0: A semi-automated spike detection and sorting system for single sensillum recordings.","authors":"Lydia Ellison, Georg Raiser, Alicia Garrido-Peña, György Kemenes, Thomas Nowotny","doi":"10.1016/j.jneumeth.2024.110351","DOIUrl":"10.1016/j.jneumeth.2024.110351","url":null,"abstract":"<p><strong>Background: </strong>Single-sensillum recordings are a valuable tool for sensory research which, by their nature, access extra-cellular signals typically reflecting the combined activity of several co-housed sensory neurons. However, isolating the contribution of an individual neuron through spike-sorting has remained a major challenge due to firing rate-dependent changes in spike shape and the overlap of co-occurring spikes from several neurons. These challenges have so far made it close to impossible to investigate the responses to more complex, mixed odour stimuli.</p><p><strong>New method: </strong>Here we present SSSort 2.0, a method and software addressing both problems through automated and semi-automated signal processing. We have also developed a method for more objective validation of spike sorting methods based on generating surrogate ground truth data and we have tested the practical effectiveness of our software in a user study.</p><p><strong>Results: </strong>We find that SSSort 2.0 typically matches or exceeds the performance of expert manual spike sorting. We further demonstrate that, for novices, accuracy is much better with SSSort 2.0 under most conditions.</p><p><strong>Conclusion: </strong>Overall, we have demonstrated that spike-sorting with SSSort 2.0 software can automate data processing of SSRs with accuracy levels comparable to, or above, expert manual performance.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110351"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872199","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":"Establishing In-vivo brain microdialysis for comparing concentrations of a variety of cortical neurotransmitters in the awake rhesus macaque between different cognitive states.","authors":"Stella Mayer, Pankhuri Saxena, Max Arwed Crayen, Stefan Treue","doi":"10.1016/j.jneumeth.2025.110361","DOIUrl":"10.1016/j.jneumeth.2025.110361","url":null,"abstract":"<p><strong>Background: </strong>Neuronal activity is modulated by behavior and cognitive processes. The combination of several neurotransmitter systems, acting directly or indirectly on specific populations of neurons, underlie such modulations. Most studies with non-human primates (NHPs) fail to capture this complexity, partly due to the lack of adequate methods for reliably and simultaneously measuring a broad spectrum of neurotransmitters while the animal engages in behavioral tasks.</p><p><strong>New method: </strong>To address this gap, we introduce a novel implementation of brain microdialysis (MD), employing semi-chronically implanted guides and probes in awake, behaving NHPs facilitated by removable insets within a standard recording chamber over extrastriate visual cortex (here, the visual middle temporal area (MT)). This approach allows flexible access to diverse brain regions, including areas deep within the sulcus.</p><p><strong>Results: </strong>Reliable concentration measurements of GABA, glutamate, norepinephrine, epinephrine, dopamine, serotonin, and choline were achieved from small sample volumes (<20 µl) using ultra-performance liquid chromatography with electrospray ionization-mass spectrometry (UPLC-ESI-MS). Comparing two behavioral states - 'active' and 'inactive', we observe subtle concentration variations between the two behavioral states and a greater variability of concentrations in the active state. Additionally, we find positively and negatively correlated concentration changes for neurotransmitter pairs between the behavioral states.</p><p><strong>Conclusions: </strong>Therefore, this MD setup allows insights into the neurochemical dynamics in awake primates, facilitating comprehensive investigations into the roles and the complex interplay of neurotransmitters in cognitive and behavioral functions.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110361"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142971275","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":"Generation and validation of a D1 dopamine receptor Flpo knock-in mouse.","authors":"Alexis M Oppman, William J Paradee, Nandakumar S Narayanan, Young-Cho Kim","doi":"10.1016/j.jneumeth.2024.110345","DOIUrl":"10.1016/j.jneumeth.2024.110345","url":null,"abstract":"<p><strong>Background: </strong>Dopamine is a powerful neuromodulator of diverse brain functions, including movement, motivation, reward, and cognition. D1-type dopamine receptors (D1DRs) are the most prevalently expressed dopamine receptors in the brain. Neurons expressing D1DRs are heterogeneous and involve several subpopulations. Although these neurons can be studied with BAC-transgenic rodents, these models have some limitations especially when considering their integration with conditional or intersectional genetic tools.</p><p><strong>New method: </strong>We developed a novel Drd1-P2A-Flpo (Drd1-Flpo) mouse line in which the Flpo gene was knocked in immediately after the Drd1 gene using CRISPR-Cas9. We validated the Drd1-Flpo line by confirming Flp expression and functionality specific to D1DR+ neurons with immunohistochemistry and in situ hybridization.</p><p><strong>Comparison with existing methods: </strong>The Drd1-Flpo line is a useful resource for studying subpopulations of D1DR+ neurons with intersectional genetic tools.</p><p><strong>Conclusions: </strong>We demonstrated brain-wide GFP expression driven by Drd1-Flpo, suggesting that this mouse line may be useful for comprehensive anatomical and functional studies in many brain regions. The Drd1-Flpo model will advance the study of dopaminergic signaling by providing a new tool for investigating the diverse roles of D1DR+ neurons and their subpopulations in brain disease.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110345"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142864540","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":"PIDGN: An explainable multimodal deep learning framework for early prediction of Parkinson's disease.","authors":"Wenjia Li, Quanrui Rao, Shuying Dong, Mengyuan Zhu, Zhen Yang, Xianggeng Huang, Guangchen Liu","doi":"10.1016/j.jneumeth.2025.110363","DOIUrl":"10.1016/j.jneumeth.2025.110363","url":null,"abstract":"<p><strong>Background: </strong>Parkinson's disease (PD), the second most common neurodegenerative disease in the world, is usually not diagnosed until the later stages of the disease, when patients might have already missed the best treatment period. Therefore, more effective prediction methods based on artificial intelligence (AI) are needed to assist physicians in timely diagnosis.</p><p><strong>New methods: </strong>An explainable deep learning-based early Parkinson's disease diagnostic model, Parkinson's Integrative Diagnostic Gated Network (PIDGN), was designed by fusing Single Nucleotide Polymorphism (SNP) and brain sMRI data. Firstly, unimodal internal information was extracted using EmsembleTree dimensionality reduction method, Transformer encoder and 3D ResNet. Secondly, gated attention fusion technique was utilized to explore the inter-modal interactions. Finally, the classification results were output through the fully connected layer. SHapley additive interpretation (SHAP) values and Gradient-weighted Class Activation Mapping (Grad-CAM) techniques were used to help explain the importance of SNPs and brain regions for PD.</p><p><strong>Results: </strong>The results showed that the PIDGN model achieved the best results with the accuracy of 0.858 and AUROC of 0.897. Top 20 SNPs and the brain regions near the midbrain potentially related to PD were identified using two explainable techniques via SHAP values and Grad-CAM respectively.</p><p><strong>Comparison with existing methods and conclusion: </strong>The PIDGN model trained by fusing genetic and imaging data outperforms 13 other commonly used unimodal or bimodal models. Explainable PIDGN model helps deepen understanding of several SNPs and sMRI key factors that may affect PD. This study provides a potentially effective solution for automated early diagnosis of PD using AI.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110363"},"PeriodicalIF":2.7,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143006822","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":"Developing a predictive value for Predicting Stroke Recovery Based on Transcranial Doppler Ultrasound Parameters.","authors":"Liu Yang, Xinyi Cai, Yanhong Yan, Pinjing Hui","doi":"10.1016/j.jneumeth.2025.110383","DOIUrl":"https://doi.org/10.1016/j.jneumeth.2025.110383","url":null,"abstract":"<p><strong>Background: </strong>One of the leading causes of disability and death is acute ischemic stroke (AIS) brought on by middle cerebral artery (MCA) obstruction. For the best patient care, it is essential to accurately anticipate the functional prognosis in the early stages of stroke. The ability of conventional clinical evaluations and imaging methods to deliver precise and timely prognostic information is frequently limited.</p><p><strong>New method: </strong>In this work, a predictive value for predicting functional outcome in patients with acute ischemic stroke caused by MCA blockage was developed utilizing transcranial Doppler (TCD) ultrasonography characteristics. Within 24hours after intravenous thrombolysis (IVT), TCD measures such as pulsatility index (PI), mean flow velocity (Vm), end-diastolic velocity (EDV), and peak systolic velocity (PSV) were assessed. Independent determinants of functional outcome, as determined by the modified Rankin Scale (mRS), were found using logistic regression analysis. These important factors were used to create a prediction model.</p><p><strong>Comparison with existing methods: </strong>Favorable functional outcomes were substantially correlated with a number of TCD characteristics, such as the ratio of pulsatility index to mean flow velocity (rPI) and peak systolic velocity to end-diastolic velocity (rPSV). At three months after a stroke, a logistic regression model that included these measures together with additional clinical indicators showed excellent accuracy in predicting functional prognosis.</p><p><strong>Conclusion: </strong>In individuals who have experienced an acute ischemic stroke as a result of MCA blockage, TCD ultrasonography parameters-in particular, rPSV and rPI-are useful prognostic indicators for forecasting functional prognosis. Early risk classification and individualized treatment plans can benefit from the creation of a quantitative model based on these criteria. Validating and improving this model in bigger and more varied patient groups should be the goal of future research.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110383"},"PeriodicalIF":2.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143074816","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}