NeuroImage最新文献

{"title":"The role of task on the human brain's responses to, and representation of, visual regularity defined by reflection and rotation","authors":"","doi":"10.1016/j.neuroimage.2024.120760","DOIUrl":"10.1016/j.neuroimage.2024.120760","url":null,"abstract":"<div><p>Identifying and segmenting objects in an image is generally achieved effortlessly and is facilitated by the presence of symmetry: a principle of perceptual organisation used to interpret sensory inputs from the retina into meaningful representations. However, while imaging studies show evidence of symmetry selective responses across extrastriate visual areas in the human brain, whether symmetry is processed automatically is still under debate. We used functional Magnetic Resonance Imaging (fMRI) to study the response to and representation of two types of symmetry: reflection and rotation. Dot pattern stimuli were presented to 15 human participants (10 female) under stimulus-relevant (symmetry) and stimulus-irrelevant (luminance) task conditions. Our results show that symmetry-selective responses emerge from area V3 and extend throughout extrastriate visual areas. This response is largely maintained when participants engage in the stimulus irrelevant task, suggesting an automaticity to processing visual symmetry. Our multi-voxel pattern analysis (MVPA) results extend these findings by suggesting that not only spatial organisation of responses to symmetrical patterns can be distinguished from that of non-symmetrical (random) patterns, but also that representation of reflection and rotation symmetry can be differentiated in extrastriate and object-selective visual areas. Moreover, task demands did not affect the neural representation of the symmetry information. Intriguingly, our MVPA results show an interesting dissociation: representation of luminance (stimulus irrelevant feature) is maintained in visual cortex only when task relevant, while information of the spatial configuration of the stimuli is available across task conditions. This speaks in favour of the automaticity for processing perceptual organisation: extrastriate visual areas compute and represent global, spatial properties irrespective of the task at hand.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S105381192400257X/pdfft?md5=a4150bf25ef7dd223af8688c1aef1f1d&pid=1-s2.0-S105381192400257X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788750","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 fMRI global signal and its association with the signal from cranial bone","authors":"","doi":"10.1016/j.neuroimage.2024.120754","DOIUrl":"10.1016/j.neuroimage.2024.120754","url":null,"abstract":"<div><p>The nature of the global signal, i.e. the average signal from sequential functional imaging scans of the brain or the cortex, is not well understood, but is thought to include vascular and neural components. Using resting state data, we report on the strong association between the global signal and the average signal from the part of the volume that includes the cranial bone and subdural vessels and venous collectors, separated from each other and the subdural space by multispectral segmentation procedures. While subdural vessels carried a signal with a phase delay relative to the cortex, the association with the cortical signal was strongest in the parts of the scan corresponding to the laminae of the cranial bone, reaching 80% shared variance in some individuals. These findings suggest that in resting state data vascular components may play a prominent role in the genesis of fluctuations of the global signal. Evidence from other studies on the existence of neural sources of the global signal suggests that it may reflect the action of multiple mechanisms (including cerebrovascular reactivity and autonomic control) concurrently acting to regulate global cerebral perfusion.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002519/pdfft?md5=f1e47aa38d4b0dba8dcba80a45b566ad&pid=1-s2.0-S1053811924002519-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141766888","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":"EEG alpha band functional brain network correlates of cognitive performance in children after perinatal stroke","authors":"","doi":"10.1016/j.neuroimage.2024.120743","DOIUrl":"10.1016/j.neuroimage.2024.120743","url":null,"abstract":"<div><p>Mechanisms underlying cognitive impairment after perinatal stroke could be explained through brain network alterations. With aim to explore this connection, we conducted a matched test-control study to find a correlation between functional brain network properties and cognitive functions in children after perinatal stroke. First, we analyzed resting-state functional connectomes in the alpha frequency band from a 64-channel resting state EEG in 24 children with a history of perinatal stroke (12 with neonatal arterial ischemic stroke and 12 with neonatal hemorrhagic stroke) and compared them to the functional connectomes of 24 healthy controls. Next, all participants underwent cognitive evaluation. We analyzed the differences in functional brain network properties and cognitive abilities between groups and studied the correlation between network characteristics and specific cognitive functions. Functional brain networks after perinatal stroke had lower modularity, higher clustering coefficient, higher interhemispheric strength, higher characteristic path length and higher small world index. Modularity correlated positively with the IQ and processing speed, while clustering coefficient correlated negatively with IQ. Graph metrics, reflecting network segregation (clustering coefficient and small world index) correlated positively with a tendency to impulsive decision making, which also correlated positively with graph metrics, reflecting stronger functional connectivity (characteristic path length and interhemispheric strength). Our study suggests that specific cognitive functions correlate with different brain network properties and that functional network characteristics after perinatal stroke reflect poorer cognitive functioning.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002404/pdfft?md5=556597656dbe245601ae30dba48b1a68&pid=1-s2.0-S1053811924002404-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788745","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":"Adaptive node feature extraction in graph-based neural networks for brain diseases diagnosis using self-supervised learning","authors":"","doi":"10.1016/j.neuroimage.2024.120750","DOIUrl":"10.1016/j.neuroimage.2024.120750","url":null,"abstract":"<div><p>Electroencephalography (EEG) has demonstrated significant value in diagnosing brain diseases. In particular, brain networks have gained prominence as they offer additional valuable insights by establishing connections between EEG signal channels. While brain connections are typically delineated by channel signal similarity, there lacks a consistent and reliable strategy for ascertaining node characteristics. Conventional node features such as temporal and frequency domain properties of EEG signals prove inadequate for capturing the extensive EEG information. In our investigation, we introduce a novel adaptive method for extracting node features from EEG signals utilizing a distinctive task-induced self-supervised learning technique. By amalgamating these extracted node features with fundamental edge features constructed using Pearson correlation coefficients, we showed that the proposed approach can function as a plug-in module that can be integrated to many common GNN networks (e.g., GCN, GraphSAGE, GAT) as a replacement of node feature selections module. Comprehensive experiments are then conducted to demonstrate the consistently superior performance and high generality of the proposed method over other feature selection methods in various of brain disorder prediction tasks, such as depression, schizophrenia, and Parkinson’s disease. Furthermore, compared to other node features, our approach unveils profound spatial patterns through graph pooling and structural learning, shedding light on pivotal brain regions influencing various brain disorder prediction based on derived features.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002477/pdfft?md5=219bec4ff624ee37047fc27229c630c8&pid=1-s2.0-S1053811924002477-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141766887","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":"Exploring the clinical diagnostic value of linguistic learning ability in patients with disorders of consciousness using electrooculography","authors":"","doi":"10.1016/j.neuroimage.2024.120753","DOIUrl":"10.1016/j.neuroimage.2024.120753","url":null,"abstract":"<div><p>For patients with disorders of consciousness (DoC), accurate assessment of residual consciousness levels and cognitive abilities is critical for developing appropriate rehabilitation interventions. In this study, we investigated the potential of electrooculography (EOG) in assessing language processing abilities and consciousness levels. Patients’ EOG data and related electrophysiological data were analysed before and after explicit language learning. The results showed distinct differences in vocabulary learning patterns among patients with varying levels of consciousness. While minimally conscious patients showed significant neural tracking of artificial words and notable learning effects similar to those observed in healthy controls, whereas patients with unresponsive wakefulness syndrome did not show such effects. Correlation analysis further indicated that EOG detected vocabulary learning effects with comparable validity to electroencephalography, reinforcing the credibility of EOG indicator as a diagnostic tool. Critically, EOG also revealed significant correlations between individual patients’ linguistic learning performance and their Oromotor/verbal function as assessed through behavioural scales. In conclusion, this study explored the differences in language processing abilities among patients with varying consciousness levels. By demonstrating the utility of EOG in evaluating consciousness and detecting vocabulary learning effects, as well as its potential to guide personalised rehabilitation, our findings indicate that EOG indicators show promise as a rapid, accurate and effective additional tool for diagnosing and managing patients with DoC.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002507/pdfft?md5=2b720d72b36d09d84cdb8cbf5ac519da&pid=1-s2.0-S1053811924002507-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760095","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":"Corrigendum to “Effect of CACNA1C rs1006737 on neural correlates of verbal fluency in healthy individuals” [NeuroImage volume 49 (2010) 1831-1836]","authors":"","doi":"10.1016/j.neuroimage.2024.120741","DOIUrl":"10.1016/j.neuroimage.2024.120741","url":null,"abstract":"","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002386/pdfft?md5=4b652ddf1ac1e0e82f6849e7bd2cbbdb&pid=1-s2.0-S1053811924002386-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760094","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":"Evolving brain network dynamics in early childhood: Insights from modular graph metrics","authors":"","doi":"10.1016/j.neuroimage.2024.120740","DOIUrl":"10.1016/j.neuroimage.2024.120740","url":null,"abstract":"<div><p>Modular dynamic graph theory metrics effectively capture the patterns of dynamic information interaction during human brain development. While existing research has employed modular algorithms to examine the overall impact of dynamic changes in community structure throughout development, there is a notable gap in understanding the cross-community dynamic changes within different functional networks during early childhood and their potential contributions to the efficiency of brain information transmission. This study seeks to address this gap by tracing the trajectories of cross-community structural changes within early childhood functional networks and modeling their contributions to information transmission efficiency. We analyzed 194 functional imaging scans from 83 children aged 2 to 8 years, who participated in passive viewing functional magnetic resonance imaging sessions. Utilizing sliding windows and modular algorithms, we evaluated three spatiotemporal metrics—temporal flexibility, spatiotemporal diversity, and within-community spatiotemporal diversity—and four centrality metrics: within-community degree centrality, eigenvector centrality, between-community degree centrality, and between-community eigenvector centrality. Mixed-effects linear models revealed significant age-related increases in the temporal flexibility of the default mode network (DMN), executive control network (ECN), and salience network (SN), indicating frequent adjustments in community structure within these networks during early childhood. Additionally, the spatiotemporal diversity of the SN also displayed significant age-related increases, highlighting its broad pattern of cross-community dynamic interactions. Conversely, within-community spatiotemporal diversity in the language network exhibited significant age-related decreases, reflecting the network's gradual functional specialization. Furthermore, our findings indicated significant age-related increases in between-community degree centrality across the DMN, ECN, SN, language network, and dorsal attention network, while between-community eigenvector centrality also increased significantly for the DMN, ECN, and SN. However, within-community eigenvector centrality remained stable across all functional networks during early childhood. These results suggest that while centrality of cross-community interactions in early childhood functional networks increases, centrality within communities remains stable. Finally, mediation analysis was conducted to explore the relationships between age, brain dynamic graph metrics, and both global and local efficiency based on community structure. The results indicated that the dynamic graph metrics of the SN primarily mediated the relationship between age and the decrease in global efficiency, while those of the DMN, language network, ECN, dorsal attention network, and SN primarily mediated the relationship between age and the increase in local efficiency. This pattern sugg","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002337/pdfft?md5=6128485238cd869fb4da6181b2e60248&pid=1-s2.0-S1053811924002337-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760056","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":"Brain age prediction using interpretable multi-feature-based convolutional neural network in mild traumatic brain injury","authors":"","doi":"10.1016/j.neuroimage.2024.120751","DOIUrl":"10.1016/j.neuroimage.2024.120751","url":null,"abstract":"<div><h3>Background</h3><p>Convolutional neural network (CNN) can capture the structural features changes of brain aging based on MRI, thus predict brain age in healthy individuals accurately. However, most studies use single feature to predict brain age in healthy individuals, ignoring adding information from multiple sources and the changes in brain aging patterns after mild traumatic brain injury (mTBI) were still unclear.</p></div><div><h3>Methods</h3><p>Here, we leveraged the structural data from a large, heterogeneous dataset (<em>N</em> = 1464) to implement an interpretable 3D combined CNN model for brain-age prediction. In addition, we also built an atlas-based occlusion analysis scheme with a fine-grained human Brainnetome Atlas to reveal the age-sstratified contributed brain regions for brain-age prediction in healthy controls (HCs) and mTBI patients. The correlations between brain predicted age gaps (brain-PAG) following mTBI and individual's cognitive impairment, as well as the level of plasma neurofilament light were also examined.</p></div><div><h3>Results</h3><p>Our model utilized multiple 3D features derived from T1w data as inputs, and reduced the mean absolute error (MAE) of age prediction to 3.08 years and improved Pearson's r to 0.97 on 154 HCs. The strong generalizability of our model was also validated across different centers. Regions contributing the most significantly to brain age prediction were the caudate and thalamus for HCs and patients with mTBI, and the contributive regions were mostly located in the subcortical areas throughout the adult lifespan. The left hemisphere was confirmed to contribute more in brain age prediction throughout the adult lifespan. Our research showed that brain-PAG in mTBI patients was significantly higher than that in HCs in both acute and chronic phases. The increased brain-PAG in mTBI patients was also highly correlated with cognitive impairment and a higher level of plasma neurofilament light, a marker of neurodegeneration. The higher brain-PAG and its correlation with severe cognitive impairment showed a longitudinal and persistent nature in patients with follow-up examinations.</p></div><div><h3>Conclusion</h3><p>We proposed an interpretable deep learning framework on a relatively large dataset to accurately predict brain age in both healthy individuals and mTBI patients. The interpretable analysis revealed that the caudate and thalamus became the most contributive role across the adult lifespan in both HCs and patients with mTBI. The left hemisphere contributed significantly to brain age prediction may enlighten us to be concerned about the lateralization of brain abnormality in neurological diseases in the future. The proposed interpretable deep learning framework might also provide hope for testing the performance of related drugs and treatments in the future.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002489/pdfft?md5=a3616a3c286ad6036d628d48d01719fb&pid=1-s2.0-S1053811924002489-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760093","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-frequency sounds combined with motor imagery elicits a transient disruption of force performance: A path to neuromotor reprogramming?","authors":"","doi":"10.1016/j.neuroimage.2024.120746","DOIUrl":"10.1016/j.neuroimage.2024.120746","url":null,"abstract":"<div><p>The effectiveness of motor imagery (MI) training on sports performance is now well-documented. Recently, it has been proposed that a single session of MI combined with low frequency sound (LFS) might enhance muscle activation. However, the neural mechanisms underlying this effect remain unknown. We set up a test-retest intervention over the course of 2 consecutive days to evaluate the effect of (i) MI training (MI, <em>n</em> = 20), (ii) MI combined with LFS (MI + LFS, <em>n</em> = 20), and (iii) a control condition (CTRL, <em>n</em> = 20) on force torque produced across repeated maximal voluntary contractions of the quadriceps before (<span>Pretest</span>), after (<span>Posttest</span>) and at +12 h (<span>Retention</span>) post-intervention. We collected the integrated electromyograms of the quadriceps muscles, as well as brain electrical potentials during each experimental intervention. In the CTRL group, total force torque decreased from <span>Pretest</span> to <span>Retention</span> and from <span>Posttest</span> to <span>Retention.</span> By contrast, there was an increase between <span>Posttest</span> and <span>Retention</span> in both MI + LFS and MI groups (both η_P² = 0.03, <em>p</em> &lt; 0.05). Regression analyses further revealed a negative relationship between force performance and EEG activity in the MI + LFS group only. The data support a transient interference of LFS on cortical activity underlying the priming effects of MI practice on force performance. Findings are discussed in relation to the potential for motor reprogramming through MI combined with LFS.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S105381192400243X/pdfft?md5=7a92fe8a0d5b7ceffb6bbf0e701e8a3f&pid=1-s2.0-S105381192400243X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141734670","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":"High-resolution diffusion magnetic resonance imaging and spatial-transcriptomic in developing mouse brain","authors":"","doi":"10.1016/j.neuroimage.2024.120734","DOIUrl":"10.1016/j.neuroimage.2024.120734","url":null,"abstract":"<div><p>Brain development is a highly complex process regulated by numerous genes at the molecular and cellular levels. Brain tissue exhibits serial microstructural changes during the development process. High-resolution diffusion magnetic resonance imaging (dMRI) affords a unique opportunity to probe these changes in the developing brain non-destructively. In this study, we acquired multi-shell dMRI datasets at 32 µm isotropic resolution to investigate the tissue microstructure alterations, which we believe to be the highest spatial resolution dMRI datasets obtained for postnatal mouse brains. We adapted the Allen Developing Mouse Brain Atlas (ADMBA) to integrate quantitative MRI metrics and spatial transcriptomics. Diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and neurite orientation dispersion and density imaging (NODDI) metrics were used to quantify brain development at different postnatal days. We demonstrated that the differential evolutions of fiber orientation distributions contribute to the distinct development patterns in white matter (WM) and gray matter (GM). Furthermore, the genes enriched in the nervous system that regulate brain structure and function were expressed in spatial correlation with age-matched dMRI. This study is the first one providing high-resolution dMRI, including DTI, DKI, and NODDI models, to trace mouse brain microstructural changes in WM and GM during postnatal development. This study also highlighted the genotype-phenotype correlation of spatial transcriptomics and dMRI, which may improve our understanding of brain microstructure changes at the molecular level.</p></div>","PeriodicalId":19299,"journal":{"name":"NeuroImage","volume":null,"pages":null},"PeriodicalIF":4.7,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1053811924002271/pdfft?md5=819826b725160f3f5c87e1c7affc4254&pid=1-s2.0-S1053811924002271-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141734669","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}