NeuroImage最新文献

{"title":"Olfaction modulates cortical arousal independent of perceived odor intensity and pleasantness","authors":"","doi":"10.1016/j.neuroimage.2024.120843","DOIUrl":"10.1016/j.neuroimage.2024.120843","url":null,"abstract":"<div><p>Throughout history, various odors have been harnessed to invigorate or relax the mind. The mechanisms underlying odors’ diverse arousal effects remain poorly understood. We conducted five experiments (184 participants) to investigate this issue, using pupillometry, electroencephalography, and the attentional blink paradigm, which exemplifies the limit in attentional capacity. Results demonstrated that exposure to citral, compared to vanillin, enlarged pupil size, reduced resting-state alpha oscillations and alpha network efficiency, augmented beta-gamma oscillations, and enhanced the coordination between parietal alpha and frontal beta-gamma activities. In parallel, it attenuated the attentional blink effect. These effects were observed despite citral and vanillin being comparable in perceived odor intensity, pleasantness, and nasal pungency, and were unlikely driven by semantic biases. Our findings reveal that odors differentially alter the small-worldness of brain network architecture, and thereby brain state and arousal. Furthermore, they establish arousal as a unique dimension in olfactory space, distinct from intensity and pleasantness.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003409/pdfft?md5=5a04d8c2d3fc014c15661544df47684d&pid=1-s2.0-S1053811924003409-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142167522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The strength of anticipated distractors shapes EEG alpha and theta oscillations in a Working Memory task","authors":"","doi":"10.1016/j.neuroimage.2024.120835","DOIUrl":"10.1016/j.neuroimage.2024.120835","url":null,"abstract":"<div><p>Working Memory (WM) requires maintenance of task-relevant information and suppression of task-irrelevant/distracting information. Alpha and theta oscillations have been extensively investigated in relation to WM. However, studies that examine both theta and alpha bands in relation to distractors, encompassing not only power modulation but also connectivity modulation, remain scarce. Here, we depicted, at the EEG-source level, the increase in power and connectivity in theta and alpha bands induced by strong relative to weak distractors during a visual Sternberg-like WM task involving the encoding of verbal items. During retention, a strong or weak distractor was presented, predictable in time and nature. Analysis focused on the encoding and retention phases before distractor presentation. Theta and alpha power were computed in cortical regions of interest, and connectivity networks estimated via spectral Granger causality and synthetized using in/out degree indices. The following modulations were observed for strong vs. weak distractors. In theta band during encoding, the power in frontal regions increased, together with frontal-to-frontal and bottom-up occipital-to-temporal-to-frontal connectivity; even during retention, bottom-up theta connectivity increased. In alpha band during retention, but not during encoding, the power in temporal-occipital regions increased, together with top-down frontal-to-occipital and temporal-to-occipital connectivity. From our results, we postulate a proactive cooperation between theta and alpha mechanisms: the first would mediate enhancement of target representation both during encoding and retention, and the second would mediate increased inhibition of sensory areas during retention only, to suppress the processing of imminent distractor without interfering with the processing of ongoing target stimulus during encoding.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S105381192400332X/pdfft?md5=03fac5ee5a078d78b3f301023c1f4ed7&pid=1-s2.0-S105381192400332X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Local-structure-preservation and redundancy-removal-based feature selection method and its application to the identification of biomarkers for schizophrenia","authors":"","doi":"10.1016/j.neuroimage.2024.120839","DOIUrl":"10.1016/j.neuroimage.2024.120839","url":null,"abstract":"<div><p>Accurate diagnosis of mental disorders is expected to be achieved through the identification of reliable neuroimaging biomarkers with the help of cutting-edge feature selection techniques. However, existing feature selection methods often fall short in capturing the local structural characteristics among samples and effectively eliminating redundant features, resulting in inadequate performance in disorder prediction. To address this gap, we propose a novel supervised method named local-structure-preservation and redundancy-removal-based feature selection (LRFS), and then apply it to the identification of meaningful biomarkers for schizophrenia (SZ). LRFS method leverages graph-based regularization to preserve original sample similarity relationships during data transformation, thus retaining crucial local structure information. Additionally, it introduces redundancy-removal regularization based on interrelationships among features to exclude similar and redundant features from high-dimensional data. Moreover, LRFS method incorporates <span><math><msub><mi>l</mi><mrow><mn>2</mn><mo>,</mo><mn>1</mn></mrow></msub></math></span> sparse regularization that enables selecting a sparse and noise-robust feature subset. Experimental evaluations on eight public datasets with diverse properties demonstrate the superior performance of our method over nine popular feature selection methods in identifying discriminative features, with average classification accuracy gains ranging from 1.30 % to 9.11 %. Furthermore, the LRFS method demonstrates superior discriminability in four functional magnetic resonance imaging (fMRI) datasets from 708 healthy controls (HCs) and 537 SZ patients, with an average increase in classification accuracy ranging from 1.89 % to 9.24 % compared to other nine methods. Notably, our method reveals reproducible and significant changes in SZ patients relative to HCs across the four datasets, predominantly in the thalamus-related functional network connectivity, which exhibit a significant correlation with clinical symptoms. Convergence analysis, parameter sensitivity analysis, and ablation studies further demonstrate the effectiveness and robustness of our method. In short, our proposed feature selection method effectively identifies discriminative and reliable features that hold the potential to be biomarkers, paving the way for the elucidation of brain abnormalities and the advancement of precise diagnosis of mental disorders.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003367/pdfft?md5=0b8cd0a4d9a34d465f3d0af5fe8fbdaa&pid=1-s2.0-S1053811924003367-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142219214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Temporal neural dynamics of understanding communicative intentions from speech prosody","authors":"","doi":"10.1016/j.neuroimage.2024.120830","DOIUrl":"10.1016/j.neuroimage.2024.120830","url":null,"abstract":"<div><p>Understanding the correct intention of a speaker is critical for social interaction. Speech prosody is an important source for understanding speakers' intentions during verbal communication. However, the neural dynamics by which the human brain translates the prosodic cues into a mental representation of communicative intentions in real time remains unclear. Here, we recorded EEG (electroencephalograph) while participants listened to dialogues. The prosodic features of the critical words at the end of sentences were manipulated to signal either <em>suggestion, warning</em>, or <em>neutral</em> intentions. The results showed that suggestion and warning intentions evoked enhanced late positive event-related potentials (ERPs) compared to the neutral condition. Linear mixed-effects model (LMEM) regression and representational similarity analysis (RSA) analyses revealed that these ERP effects were distinctively correlated with prosodic acoustic analysis, emotional valence evaluation, and intention interpretation in different time windows; The onset latency significantly increased as the processing level of abstractness and communicative intentionality increased. Neural representations of intention and emotional information emerged and parallelly persisted over a long time window, guiding the correct identification of communicative intention. These results provide new insights into understanding the structural components of intention processing and their temporal neural dynamics underlying communicative intention comprehension from speech prosody in online social interactions.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003276/pdfft?md5=3a2be359d2b2a925eb22e28d4fb95e9e&pid=1-s2.0-S1053811924003276-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154709","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neurodevelopmental subtypes of functional brain organization in the ABCD study using a rigorous analytic framework","authors":"","doi":"10.1016/j.neuroimage.2024.120827","DOIUrl":"10.1016/j.neuroimage.2024.120827","url":null,"abstract":"<div><p>The current study demonstrates that an individual's resting-state functional connectivity (RSFC) is a dependable biomarker for identifying differential patterns of cognitive and emotional functioning during late childhood. Using baseline RSFC data from the Adolescent Brain Cognitive Development (ABCD) study, which includes children aged 9–11, we identified four distinct RSFC subtypes. We introduce an integrated methodological pipeline for testing the reliability and importance of these subtypes. In the Identification phase, Leiden Community Detection defined RSFC subtypes, with their reproducibility confirmed through a split-sample technique in the Validation stage. The Evaluation phase showed that distinct cognitive and mental health profiles are associated with each subtype, with the Predictive phase indicating that subtypes better predict various cognitive and mental health characteristics than individual RSFC connections. The Replication stage employed bootstrapping and down-sampling methods to substantiate the reproducibility of these subtypes further. This work allows future explorations of developmental trajectories of these RSFC subtypes.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003240/pdfft?md5=6a70f1dbad5b800fe9fb829a5307677d&pid=1-s2.0-S1053811924003240-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142154708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Low-intensity transcranial ultrasound stimulation improves memory behavior in an ADHD rat model by modulating cortical functional network connectivity","authors":"","doi":"10.1016/j.neuroimage.2024.120841","DOIUrl":"10.1016/j.neuroimage.2024.120841","url":null,"abstract":"<div><p>Working memory in attention deficit hyperactivity disorder (ADHD) is closely related to cortical functional network connectivity (CFNC), such as abnormal connections between the frontal, temporal, occipital cortices and with other brain regions. Low-intensity transcranial ultrasound stimulation (TUS) has the advantages of non-invasiveness, high spatial resolution, and high penetration depth and can improve ADHD memory behavior. However, how it modulates CFNC in ADHD and the CFNC mechanism that improves working memory behavior in ADHD remain unclear. In this study, we observed working memory impairment in ADHD rats, establishing a corresponding relationship between changes in CFNCs and the behavioral state during the working memory task. Specifically, we noted abnormalities in the information transmission and processing capabilities of CFNC in ADHD rats while performing working memory tasks. These abnormalities manifested in the network integration ability of specific areas, as well as the information flow and functional differentiation of CFNC. Furthermore, our findings indicate that TUS effectively enhances the working memory ability of ADHD rats by modulating information transmission, processing, and integration capabilities, along with adjusting the information flow and functional differentiation of CFNC. Additionally, we explain the CFNC mechanism through which TUS improves working memory in ADHD. In summary, these findings suggest that CFNCs are important in working memory behaviors in ADHD.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003380/pdfft?md5=275eaa15f7cde724f2287a0916ae5b57&pid=1-s2.0-S1053811924003380-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated registration-based skull stripping procedure for feline neuroimaging","authors":"","doi":"10.1016/j.neuroimage.2024.120826","DOIUrl":"10.1016/j.neuroimage.2024.120826","url":null,"abstract":"<div><p>Skull stripping is a fundamental preprocessing step in modern neuroimaging analyses that consists of removing non-brain voxels from structural images. When performed entirely manually, this laborious step can be rate-limiting for analyses, with the potential to influence the population size chosen. This emphasizes the need for a fully- or semi-automated masking procedure to decrease man-hours without an associated decline in accuracy. These algorithms are plentiful in human neuroimaging but are relatively lacking for the plethora of animal species used in research. Unfortunately, software designed for humans cannot be easily transformed for animal use due to the high amount of tailoring required to accurately account for the considerable degree of variation within the highly folded human cortex. As most animals have a relatively less complex cerebral morphology, intersubject variability is consequently decreased, presenting the possibility to simply warp the brain mask of a template image into subject space for the purpose of skull stripping. This study presents the use of the Cat Automated Registration-based Skull Stripper (CARSS) tool on feline structural images. Validation metrics revealed that this method was able to perform on par with manual raters on &gt;90 % of scans tested, and that its consistency across multiple runs was superior to that of masking performed by two independent raters. Additionally, CARSS outperformed three well-known skull stripping programs on the validation dataset. Despite a handful of manual interventions required, the presented tool reduced the man-hours required to skull strip 60 feline images over tenfold when compared to a fully manual approach, proving to be invaluable for feline neuroimaging studies, particularly those with large population sizes.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003239/pdfft?md5=a87488db29a02774326122f3e2038435&pid=1-s2.0-S1053811924003239-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-supplementary motor area strengthens reward sensitivity in intertemporal choice","authors":"","doi":"10.1016/j.neuroimage.2024.120838","DOIUrl":"10.1016/j.neuroimage.2024.120838","url":null,"abstract":"<div><p>Previous investigations on the causal neural mechanisms underlying intertemporal decision making focused on the dorsolateral prefrontal cortex as neural substrate of cognitive control. However, little is known, about the causal contributions of further parts of the frontoparietal control network to delaying gratification, including the pre-supplementary motor area (pre-SMA) and posterior parietal cortex (PPC). Conflicting previous evidence related pre-SMA and PPC either to evidence accumulation processes, choice biases, or response caution. To disentangle between these alternatives, we combined drift diffusion models of decision making with online transcranial magnetic stimulation (TMS) over pre-SMA and PPC during an intertemporal decision task. While we observed no robust effects of PPC TMS, perturbation of pre-SMA activity reduced preferences for larger over smaller rewards. A drift diffusion model of decision making suggests that pre-SMA increases the weight assigned to reward magnitudes during the evidence accumulation process without affecting choice biases or response caution. Taken together, the current findings reveal the computational role of the pre-SMA in value-based decision making, showing that pre-SMA promotes choices of larger, costly rewards by strengthening the sensitivity to reward magnitudes.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003355/pdfft?md5=3cfb3a47d615819fde8350b9f4737a86&pid=1-s2.0-S1053811924003355-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A whole-brain analysis of functional connectivity and immediate early gene expression reveals functional network shifts after operant learning","authors":"","doi":"10.1016/j.neuroimage.2024.120840","DOIUrl":"10.1016/j.neuroimage.2024.120840","url":null,"abstract":"<div><p>Previous studies of operant learning have addressed neuronal activities and network changes in specific brain areas, such as the striatum, sensorimotor cortex, prefrontal/orbitofrontal cortices, and hippocampus. However, how changes in the whole-brain network are caused by cellular-level changes remains unclear. We, therefore, combined resting-state functional magnetic resonance imaging (rsfMRI) and whole-brain immunohistochemical analysis of early growth response 1 (EGR1), a marker of neural plasticity, to elucidate the temporal and spatial changes in functional networks and underlying cellular processes during operant learning. We used an 11.7-Tesla MRI scanner and whole-brain immunohistochemical analysis of EGR1 in mice during the early and late stages of operant learning. In the operant training, mice received a reward when they pressed left and right buttons alternately, and were punished with a bright light when they made a mistake. A group of mice (<em>n</em> = 22) underwent the first rsfMRI acquisition before behavioral sessions, the second acquisition after 3 training-session-days (early stage), and the third after 21 training-session-days (late stage). Another group of mice (<em>n</em> = 40) was subjected to histological analysis 15 min after the early or late stages of behavioral sessions. Functional connectivity increased between the limbic areas and thalamus or auditory cortex after the early stage of training, and between the motor cortex, sensory cortex, and striatum after the late stage of training. The density of EGR1-immunopositive cells in the motor and sensory cortices increased in both the early and late stages of training, whereas the density in the amygdala increased only in the early stage of training. The subcortical networks centered around the limbic areas that emerged in the early stage have been implicated in rewards, pleasures, and fears. The connectivities between the motor cortex, somatosensory cortex, and striatum that consolidated in the late stage have been implicated in motor learning. Our multimodal longitudinal study successfully revealed temporal shifts in brain regions involved in behavioral learning together with the underlying cellular-level plasticity between these regions. Our study represents a first step towards establishing a new experimental paradigm that combines rsfMRI and immunohistochemistry to link macroscopic and microscopic mechanisms involved in learning.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003379/pdfft?md5=42404e2e727f2787404b813d849f9286&pid=1-s2.0-S1053811924003379-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced diversity on connector hubs following sleep deprivation: Evidence from diffusion and functional magnetic resonance imaging","authors":"","doi":"10.1016/j.neuroimage.2024.120837","DOIUrl":"10.1016/j.neuroimage.2024.120837","url":null,"abstract":"<div><p>Sleep deprivation has been demonstrated to exert widespread and intricate impacts on the brain network. The human brain network is a modular network composed of interconnected nodes. This network consists of provincial hubs and connector hubs, with provincial hubs having diverse connectivities within their own modules, while connector hubs distribute their connectivities across different modules. The latter is crucial for integrating information from various modules and ensuring the normal functioning of the modular brain. However, there has been a lack of systematic investigation into the impact of sleep deprivation on brain connector hubs. In this study, we utilized functional connectivity from resting-state functional magnetic resonance imaging, as well as structural connectivity from diffusion-weighted imaging, to systematically explore the variation of connector hub properties in the cerebral cortex after one night of sleep deprivation. The normalized participation coefficients (PCnorm) were utilized to identify connector hubs. In both the functional and structural networks, connector hubs exhibited a significant increase in average PCnorm, indicating the diversity enhancement of the connector hub following sleep deprivation. This enhancement is associated with increased network cost, reduced modularity, and decreased small-worldness, but enhanced global efficiency. This may potentially signify a compensatory mechanism within the brain following sleep deprivation. The significantly affected connector hubs were primarily observed in both the Control Network and Salience Network. We believe that the observed results reflect the increasing demand on the brain to invest more effort at preventing performance deterioration after sleep loss, in exchange for increased communication efficiency, especially involving systems responsible for neural resource allocation and cognitive control. These results have been replicated in an independent dataset. In conclusion, this study has enhanced our understanding of the compensatory mechanism in the brain response to sleep deprivation. This compensation is characterized by an enhancement in the connector hubs responsible for inter-modular communication, especially those related to neural resource and cognitive control. As a result, this compensation comes with a higher network cost but leads to an improvement in global communication efficiency, akin to a more random-like network manner.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924003343/pdfft?md5=d6c6c1478c0783bcad96a8654e349209&pid=1-s2.0-S1053811924003343-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142146073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}