NeuroImage最新文献

{"title":"Context-dependent PSIICOS: A novel framework for functional connectivity estimation accounting for task-related power leakage.","authors":"Daria Kleeva, Alexei Ossadtchi","doi":"10.1016/j.neuroimage.2025.121268","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2025.121268","url":null,"abstract":"<p><p>Functional connectivity (FC) analysis using non-invasive neuroimaging methods, such as MEG and EEG, is often confounded by artifacts from spatial leakage and task-related power modulations. To address these limitations, we present Context-Dependent PSIICOS (CD-PSIICOS), a novel framework that improves the estimation of FC by incorporating task-specific cortical power distributions into the projection operator applied to the vectorized sensor-space cross-spectrum. Unlike the original PSIICOS (Phase Shift Invariant Imaging of Coherent Sources) approach, designed to suppress spatial leakage from all the sources, CD-PSIICOS dynamically adjusts the projection based on the active source distribution, enabling more accurate suppression of spatial leakage while preserving true zero-phase interactions. We validated CD-PSIICOS using realistic simulations and a multi-subject MEG dataset. The results demonstrate that CD-PSIICOS outperforms the original PSIICOS in suppressing artifacts at the lower projection ranks, maintaining robust detection of functional networks across theta and gamma frequency bands. By requiring lower projection ranks for optimal performance, CD-PSIICOS facilitates the reconstruction of physiologically relevant networks with improved sensitivity and stability.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"121268"},"PeriodicalIF":4.7,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144187455","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diffusion based multi-domain neuroimaging harmonization method with preservation of anatomical details.","authors":"Haoyu Lan, Bino A Varghese, Nasim Sheikh-Bahaei, Farshid Sepehrband, Arthur W Toga, Jeiran Choupan","doi":"10.1016/j.neuroimage.2025.121297","DOIUrl":"https://doi.org/10.1016/j.neuroimage.2025.121297","url":null,"abstract":"<p><p>In multi-center neuroimaging studies, the technical variability caused by the batch differences could hinder the ability to aggregate data across sites, and negatively impact the reliability of study-level results. Recent efforts in neuroimaging harmonization have aimed to minimize these technical gaps and reduce technical variability across batches. While Generative Adversarial Networks (GAN) has been a prominent method for addressing harmonization tasks, GAN-harmonized images suffer from artifacts or anatomical distortions. Given the advancements of denoising diffusion probabilistic model which produces high-fidelity images, we have assessed the efficacy of the diffusion model for neuroimaging harmonization. While GAN-based methods intrinsically transform imaging styles between two domains per model, we have demonstrated the diffusion model's superior capability in harmonizing images across multiple domains with single model. Our experiments highlight that the learned domain invariant anatomical condition reinforces the model to accurately preserve the anatomical details while differentiating batch differences at each diffusion step. Our proposed method has been tested using T1-weighted MRI images from two public neuroimaging datasets of ADNI1 and ABIDE II, yielding harmonization results with consistent anatomy preservation and superior FID score compared to the GAN-based methods. We have conducted multiple analyses including extensive quantitative and qualitative evaluations against the baseline models, ablation study showcasing the benefits of the learned domain invariant conditions, and improvements in the consistency of perivascular spaces segmentation analysis and volumetric analysis through harmonization.</p>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":" ","pages":"121297"},"PeriodicalIF":4.7,"publicationDate":"2025-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144173167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of gut microbiota in modulating brain structure and psychiatric disorders: A Mendelian randomization study","authors":"Zheng Ye ,&nbsp;Yingying Gao ,&nbsp;Jiaqi Yuan ,&nbsp;Feng Chen ,&nbsp;Peng Xu ,&nbsp;Wenbin Liu","doi":"10.1016/j.neuroimage.2025.121292","DOIUrl":"10.1016/j.neuroimage.2025.121292","url":null,"abstract":"<div><div>The influence of the gut microbiome on the human brain, especially its associations with psychiatric disorders, has emerged as a focal area in contemporary neuroscience and psychiatry research. In this study, we employed a mediation Mendelian randomization approach to delve into the potential causal relationships between gut microbiota and psychiatric disorders, with a focus on the mediating role of brain structural changes. We harnessed genetic data from large - scale genome - wide association studies to analyze how 196 gut microbiota taxa affect ten psychiatric disorders via alterations in 3143 brain structures.</div><div>Our key findings revealed significant bidirectional causal relationships. In the gut microbiota - brain structure relationship, certain gut microbiota taxa, such as Bacteroides and Marvinbryantia, were associated with changes in brain activity and white matter integrity respectively. Conversely, brain structures like the right hippocampus and left superior cerebellar peduncle influenced gut microbiota composition.</div><div>Regarding gut microbiota and psychiatric disorders, we identified numerous associations. For example, the genus Prevotellaceae was significantly associated with an increased risk of Autism Spectrum Disorder, while Ruminococcaceae UCG005 showed a protective effect. In Panic Disorder, Alistipes was positively associated, and for Schizophrenia, both protective (Barnesiella) and risk - associated (Phascolarctobacterium) genera were found.</div><div>Moreover, through mediation analysis, we found that brain structures mediated the effects of gut microbiota on five psychiatric disorders, including bipolar disorder and anorexia nervosa. In these cases, the influence of gut microbiota on the disorders was fully transmitted through changes in brain structure.</div><div>Overall, our research clarifies the role of the microbiota - gut - brain axis in mental health. It offers a new perspective on how intestinal microbes impact brain physiology and psychiatric pathology. These findings not only deepen our understanding of the biological interactions between the gut and brain but also suggest that targeted gut microbiota modifications could be novel therapeutic strategies for mental health disorders.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121292"},"PeriodicalIF":4.7,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144160672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Data integration with Fusion Searchlight: Classifying brain states from resting-state fMRI","authors":"Simon Wein,&nbsp;Marco Riebel,&nbsp;Lisa-Marie Brunner,&nbsp;Caroline Nothdurfter,&nbsp;Rainer Rupprecht,&nbsp;Jens V. Schwarzbach","doi":"10.1016/j.neuroimage.2025.121263","DOIUrl":"10.1016/j.neuroimage.2025.121263","url":null,"abstract":"<div><div>Resting-state fMRI captures spontaneous neural activity characterized by complex spatiotemporal dynamics. Various metrics, such as local and global brain connectivity and low-frequency amplitude fluctuations, quantify distinct aspects of these dynamics. However, these measures are typically analyzed independently, overlooking their interrelations and potentially limiting analytical sensitivity. Here, we introduce the Fusion Searchlight (FuSL) framework, which integrates complementary information from multiple resting-state fMRI metrics. We demonstrate that combining these metrics enhances the accuracy of pharmacological treatment prediction from rs-fMRI data, enabling the identification of additional brain regions affected by sedation with alprazolam. Furthermore, we leverage explainable AI to delineate the differential contributions of each metric, which additionally improves spatial specificity of the searchlight analysis. Moreover, this framework can be adapted to combine information across imaging modalities or experimental conditions, providing a versatile and interpretable tool for data fusion in neuroimaging.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121263"},"PeriodicalIF":4.7,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144151213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Musical expertise modulates embodied processing of biological motion and audiovisual-motor integration in rhythmic hand tapping","authors":"Alice Mado Proverbio ,&nbsp;Matteo Valtolina","doi":"10.1016/j.neuroimage.2025.121287","DOIUrl":"10.1016/j.neuroimage.2025.121287","url":null,"abstract":"<div><div>Musical training enhances sensorimotor integration and multimodal processing, particularly in rhythm perception and execution. However, the neural mechanisms underlying the audiovisual-motor integration of non-musical rhythmic gestures remain poorly understood. This study investigated differences between professional musicians and musically untrained individuals in processing audiovisual rhythmic incongruence, focusing on the anterior <em>error-related negativity</em> (ERN) and the preceding posterior P300 as neurophysiological markers of sensitivity to audio-visuomotor misalignment. We recorded EEG responses from 29 participants (15 musicians, 14 controls) while they observed video recordings of finger tapping synchronized or desynchronized with percussive sounds. Stimuli varied in rhythmic complexity and frequency. Musicians significantly outperformed controls in an independent rhythmic ability test, demonstrating superior auditory-motor encoding. ERP analysis revealed a larger P300 and ERN amplitude for incongruent stimuli, with musicians exhibiting stronger differentiation between conditions. Source localization indicated distinct neural substrates: while non-musicians primarily engaged visual areas, musicians recruited the superior frontal gyrus (BA 10), basal ganglia, and cingulate cortex—structures implicated in rhythmic motor coordination and error monitoring. These findings suggest that musical expertise facilitates embodied cognition of rhythm, refining the integration of auditory, visual, and motor representations. Enhanced activation in prefrontal and subcortical networks in musicians highlights their heightened sensitivity to audiovisual rhythmic discrepancies, underscoring the role of training-induced neuroplasticity in multimodal temporal processing.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121287"},"PeriodicalIF":4.7,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143053","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data","authors":"Ailing Zhang ,&nbsp;Haobo Zhang","doi":"10.1016/j.neuroimage.2025.121285","DOIUrl":"10.1016/j.neuroimage.2025.121285","url":null,"abstract":"<div><div>Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) techniques and longitudinal data from the Beck Depression Inventory, structural MRI (sMRI), and resting-state functional MRI (rs-fMRI). Feature selection methods, including the least absolute shrinkage and selection operator (LASSO), Boruta, and VSURF, were applied to identify MRI features associated with depression. Support vector machine and random forest algorithms were then used to construct prediction models. Eight MRI features were identified as predictive of depression, including brain regions in the Orbital Gyrus, Superior Frontal Gyrus, Middle Frontal Gyrus, Parahippocampal Gyrus, Cingulate Gyrus, and Inferior Parietal Lobule. The overlaps and the differences between selected features and brain regions with significant between-group differences in <em>t</em>-tests suggest that ML provides a unique perspective on the neural changes associated with depression. Six pairs of prediction models demonstrated varying performance, with accuracies ranging from 0.68 to 0.85 and areas under the curve (AUC) ranging from 0.57 to 0.81. The best-performing model achieved an accuracy of 0.85 and an AUC of 0.80, highlighting the potential of combining sMRI and rs-fMRI features with ML for early depression detection while revealing the potential of overfitting in small-sample and high-dimensional settings. This study necessitates further research to (1) replicate findings in independent larger datasets to address potential overfitting and (2) utilize different advanced ML techniques and multimodal data fusion to improve model performance.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121285"},"PeriodicalIF":4.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138977","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Atypical visual selective attention in children with dyslexia: evidence from N2pc and PD","authors":"Hongyu Liu ,&nbsp;Yulu Sun ,&nbsp;Zimo Wang ,&nbsp;Jialiang Guo ,&nbsp;Yan Song ,&nbsp;Xiangzhi Meng","doi":"10.1016/j.neuroimage.2025.121281","DOIUrl":"10.1016/j.neuroimage.2025.121281","url":null,"abstract":"<div><div>Efficient visual attention is fundamental to the development of reading abilities. Previous studies have identified visual attention impairments in individuals with dyslexia, particularly in visual attention span and sluggish attention shifting. However, findings regarding basic visual selective attention remain controversial. To address this issue, the present study provides event-related potential (ERP) evidence to verify whether children with developmental dyslexia (DD) suffered from deficits in visual selective attention. A pop-out visual search task was used to examine visual attentional patterns to a lateral target in 66 children (33 children with DD and 33 typically developing (TD) children) using electroencephalography. Compared to TD children, children with DD showed a larger and prolonged P1 component, as well as a larger target-evoked N2-posterior contralateral (N2pc) component. The larger P1 amplitude was associated with lower reading fluency while the larger N2pc amplitude was associated with poor reading comprehension performance. Interestingly, children with DD were characterised by a larger target-elicited N2pc followed by a diminished distractor positivity (P_D) component, as well as a robust correlation between N2pc and P_D, compared to TD children. Children with DD show immature early attentional processing and may have an imbalance in the regulation of visual selective attention between attentional selection and suppression. Our findings provide ERP evidence for interpreting the underlying neural mechanism of visual selective attention in children with DD and may offer a new perspective for exploring the mechanism of visual attention deficits in dyslexia.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121281"},"PeriodicalIF":4.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144143095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The neural dynamics of integrating prior and kinematic information during action anticipation in sport","authors":"Danlei Wang ,&nbsp;Dongwei Li ,&nbsp;Arash Mirifar ,&nbsp;Chenglin Zhou ,&nbsp;Mengkai Luan","doi":"10.1016/j.neuroimage.2025.121291","DOIUrl":"10.1016/j.neuroimage.2025.121291","url":null,"abstract":"<div><div>Effective action anticipation in sports hinges on the integration of prior knowledge and kinematic cues, enabling athletes to respond swiftly and accurately in real-time scenarios. However, the neural mechanisms supporting this integrative process remain insufficiently understood. This study addressed this gap by using electroencephalography (EEG), combined with both multivariate and univariate analyses, to investigate how expert basketball players and non-athlete controls process prior and kinematic information during a sport-specific action anticipation task. Eighty-five participants (44 experts and 41 controls) were asked to predict the outcomes of basketball free throws presented via video clips, either with or without outcome-based prior information cues. Multivariate pattern classification and contingent negative variation (CNV) analyses revealed distinct anticipatory strategies between groups, with experts predominantly relying on kinematic information, whereas controls showed greater sensitivity to prior information. Additionally, time–frequency analysis of alpha-band activity indicated stronger desynchronization in experts, reflecting enhanced cortical engagement during kinematic processing. Notably, alpha ERD was significantly stronger for incongruent trials in the later phase of the task, suggesting increased cortical engagement when resolving conflicts between prior expectations and observed actions. These findings advance our understanding of the temporal dynamics and neural mechanisms underlying action anticipation, and highlight the value of combining EEG with multivariate decoding approaches to characterize individual differences in predictive processing.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121291"},"PeriodicalIF":4.7,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive mechanisms and temporal dynamics of negative emotion in facilitating congruency judgments","authors":"Yiheng Chen ,&nbsp;Feier Fu ,&nbsp;Qiwei Zhao ,&nbsp;Yueyi Ding ,&nbsp;Yingzhi Lu","doi":"10.1016/j.neuroimage.2025.121276","DOIUrl":"10.1016/j.neuroimage.2025.121276","url":null,"abstract":"<div><div>Although it is well-established that negative emotions facilitate congruency judgments, the underlying cognitive mechanisms remain unclear. Traditional event-related potential (ERP) markers blur the temporal dynamics between emotion-driven and conflict-driven processes during emotion-conflict interactions. We used a congruency judgment task involving table tennis action outcome prediction and emotional image processing to explore the temporal dynamics and cognitive mechanisms underlying the influence of negative emotions on congruency judgments. Behavioral and hierarchical drift-diffusion model results showed that negative emotions enhanced judgments by accelerating evidence accumulation and improving incongruency detection. ERP analysis revealed larger P1 and late positive potential (LPP) components in response to negative emotions, which indicated stronger early attention capture and sustained emotional processing. Furthermore, multivariate pattern analysis showed that neural responses to emotional stimuli were evoked as early as 120 ms from stimulus onset, and the responses continued throughout the task, with a temporal separation between early emotion processing and late congruency judgments. These results suggest that during emotion-conflict interactions, negative emotions modulate both early stimulus-driven attention and later goal-directed conflict resolution.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121276"},"PeriodicalIF":4.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144131038","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anatomical heterogeneity in low-grade and high-grade gliomas: A multiscale perspective","authors":"Shengpeng Liang ,&nbsp;Nuo Dong ,&nbsp;Yumin Chen ,&nbsp;Yang Yang ,&nbsp;Haibing Xu","doi":"10.1016/j.neuroimage.2025.121289","DOIUrl":"10.1016/j.neuroimage.2025.121289","url":null,"abstract":"<div><h3>Background</h3><div>Low-grade gliomas (LGGs) and high-grade gliomas (HGGs) often exhibit distinct spatial distributions, a phenomenon that remains incompletely understood. Based on previous research, we hypothesized that functional networks, neurotransmitters, and isocitrate dehydrogenase-1 (IDH-1) status characterize the spatial patterns of LGG and HGG.</div></div><div><h3>Methods</h3><div>We analyzed 399 patients diagnosed with primary gliomas. First, we generated glioma frequency maps based on tumor grade, neurotransmitters, and IDH-1 status and constructed a brain functional connectivity network to explore heterogeneity in glioma location. Second, all tumor masks were mirror-symmetrized onto the brain’s left hemisphere to facilitate feature extraction. We performed independent component analysis on merged four-dimensional files using Multivariate Exploratory Linear Optimized Decomposition into Independent Component (MELODIC), identifying four IDH-1 wild-type lesion covariance networks (IDHwt-LCNs) and three IDH-1 mutant lesion covariance networks (IDHmut-LCNs) with distinct spatial distributions, and analyzing correlation between the neurotransmitter levels and the IDH-wt/mut specific LCNs. Finally, we compared 42 white matter fibers extracted using XTRACT with 39 functional brain connectivity networks from the multi-subject dictionary learning (MSDL) atlas, revealing significant associations among the frontal aslant tract (FAT) and the intraparietal sulcus (IPS).</div></div><div><h3>Results</h3><div>Our findings revealed high anatomical heterogeneity between LGG and HGG. Moreover, the high node strength played a critical role in the distinct spatial distribution of glioma. Significant correlations were observed between glioma frequency maps and dopaminergic, cholinergic, μ-opioid, and serotonergic neurotransmission. Furthermore, IDHwt/mut-LCNs analysis demonstrated that IDH-1 status influences glioma distribution, involving key brain structures. Lastly, we also found significant correlations between IDHwt/mut-LCNs and the neurotransmission of dopaminergic, cholinergic, μ-opioid, and serotonergic systems.</div></div><div><h3>Conclusion</h3><div>Our study highlighted the mechanisms by which functional networks, neurotransmitter systems, and IDH-1 status collectively contribute to the anatomical heterogeneity observed in LGG and HGG.</div></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":"315 ","pages":"Article 121289"},"PeriodicalIF":4.7,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144132474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}