Brain Topography最新文献

{"title":"Classification of Imagined Speech Signals Using Functional Connectivity Graphs and Machine Learning Models.","authors":"Anand Mohan, R S Anand","doi":"10.1007/s10548-025-01100-7","DOIUrl":"10.1007/s10548-025-01100-7","url":null,"abstract":"<p><p>EEG involves recording electrical activity generated by the brain through electrodes placed on the scalp. Imagined speech classification has emerged as an essential area of research in brain-computer interfaces (BCIs). Despite significant advances, accurately classifying imagined speech signals remains challenging due to their complex and non-stationary nature. Existing approaches often struggle with low signal-to-noise ratios and high inter-subject variability. A proposed method named imagined speech functional connectivity graph (ISFCG) is implemented to deal with these issues. The functional connectivity graphs capture the complex relationships between brain regions during imagined speech tasks. These graphs are then used to extract features that serve as inputs to various machine-learning models. The ISFCG provides an alternative representation of imagined speech signals, focusing on brain connectivity features to enhance the analysis and classification process. Also, a convolutional neural network (CNN) is proposed to learn features from these complex graphs, leading to improved classification accuracy. Experimental results on a benchmark dataset demonstrate the effectiveness of our method.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 2","pages":"25"},"PeriodicalIF":2.3,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143054320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Relational Integration Training Modulated the Frontoparietal Network for Fluid Intelligence: An EEG Microstates Study.","authors":"Zhidong Wang, Tie Sun, Feng Xiao","doi":"10.1007/s10548-024-01099-3","DOIUrl":"10.1007/s10548-024-01099-3","url":null,"abstract":"<p><p>Relational integration is a key subcomponent of working memory and a strong predictor of fluid intelligence. Both relational integration and fluid intelligence share a common neural foundation, particularly involving the frontoparietal network. This study utilized a randomized controlled experiment to examine the effect of relational integration training on brain networks using electroencephalogram (EEG) and microstate analysis. Participants were randomly assigned to either a relational integration training group (n = 29) or an active control group (n = 28) for one month. The Sandia matrices task assessed fluid intelligence, while rest-EEG was recorded during pre- and post-tests. Microstate analysis revealed that, for microstate D, the training group demonstrated a significant increase in occurrence and contribution following the intervention compared to the control group. Additionally, microstate D occurrence was negatively correlated with reaction times (RTs). Post-training, the training group showed a lower occurrence and contribution of microstate C compared to the control group. Regarding transfer probability, the training group exhibited a decrease between microstates A and B, and an increase between microstates C and D. In contrast, the control group showed increased transfer probability between microstates A, B, and C, and a decrease between microstate D and other microstates (B and A). These findings indicate that relational integration training influences frontoparietal networks associated with fluid intelligence. The current study suggests that relational integration training is an effective intervention for enhancing fluid intelligence.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 2","pages":"24"},"PeriodicalIF":2.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eeg Microstates and Balance Parameters for Stroke Discrimination: A Machine Learning Approach.","authors":"Eloise de Oliveira Lima, José Maurício Ramos de Souza Neto, Felipe Leonardo Seixas Castro, Letícia Maria Silva, Rebeca Andrade Laurentino, Vitória Ferreira Calado, Isolda Maria Barros Torquato, Karen Lúcia de Araújo Freitas Moreira, Suellen Marinho Andrade","doi":"10.1007/s10548-024-01093-9","DOIUrl":"10.1007/s10548-024-01093-9","url":null,"abstract":"<p><p>Electroencephalography microstates (EEG-MS) show promise to be a neurobiological biomarker in stroke. Thus, the aim of the study was to identify biomarkers to discriminate stroke patients from healthy individuals based on EEG-MS and clinical features using a machine learning approach. Fifty-four participants (27 stroke patients and 27 healthy age and sex-matched controls) were recruited. We recorded EEG-MS using 32 channels during eyes-closed and eyes-open conditions and analyzed the four classical EEG-MS maps (A, B, C, D). Clinical information and motor aspects were evaluated. A machine learning method using k-means algorithms to discriminate stroke patients from healthy subjects showed that the most influential parameters in clustering were balance scores and microstate parameters (duration and coverage of microstate A, duration, coverage and occurrence of microstates C and global variance explained). To evaluate the quality of clustering, the Silhouette score was applied and the score was close to 0.20, indicating that the clusters overlap. These results are encouraging and support the usefulness of these methods for classifying stroke patients in order to contribute to the development of therapeutic strategies, improve the clinical management of these patients, and consequently reduce the associated costs.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 2","pages":"23"},"PeriodicalIF":2.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Individuals' Food Preferences can be Influenced by the Music Styles: An ERP Study.","authors":"Dingyue Tian, Ziyuan Xu, Han Yan, Bijie Tie, Wen Zhao, Yuanluo Jing, Yazhi Pang, Xiaolin Liu, Jia Zhao, Yong Liu","doi":"10.1007/s10548-024-01097-5","DOIUrl":"10.1007/s10548-024-01097-5","url":null,"abstract":"<p><p>Studies have shown that a cross-modal association between listening to music and eating. This study aims to explore the influence of music style on individuals' food preferences and provide evidence for understanding multi-sensory research. Twenty-seven participants participated in the experiment which consisted of two parts. First, participants completed basic demographic information, followed by a food choice task after exposure to four different music styles: classical, jazz, rap, and rock, with ERP data recorded simultaneously. The behavioral results showed that participants selected more high-calorie foods after exposure to jazz and rock music compared to low-calorie foods. Additionally, during jazz and rock music, participants selected more high-calorie foods, while they favored low-calorie foods during classical music. The ERP results showed that the N1 amplitudes were smallest during the food choice task following the classical music and greatest during the food choice task following the rock music, while the N450 amplitudes were smallest during the food choice task following the jazz music. P2 amplitudes were smallest during the food choice task following the rock music and greatest during the food choice task following the classical music, and P3 amplitudes during the food choice task following jazz music were the greatest. The aforementioned ERP differences were observed irrespective of food choices. However, we did not find a significant interaction between foods (high and low-calorie) and music. Pearson correlation analysis revealed a positive relationship between body satisfaction and P3 amplitudes for classical, jazz, and rock music, with BMI negatively correlated with body satisfaction. This study provides innovative practical perspectives for healthy eating.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 2","pages":"22"},"PeriodicalIF":2.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143025012","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke.","authors":"Hao Liu, Xin Huang, Yu-Xin Yang, Ri-Bo Chen","doi":"10.1007/s10548-024-01095-7","DOIUrl":"10.1007/s10548-024-01095-7","url":null,"abstract":"<p><p>Stroke is a condition characterized by damage to the cerebral vasculature from various causes, resulting in focal or widespread brain tissue damage. Prior neuroimaging research has demonstrated that individuals with stroke present structural and functional brain abnormalities, evident through disruptions in motor, cognitive, and other vital functions. Nevertheless, there is a lack of studies on alterations in static and dynamic functional network connectivity in the brains of stroke patients. Fifty stroke patients and 50 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (rs-fMRI) scanning. Initially, the independent component analysis (ICA) method was utilized to extract the resting-state network (RSN). Subsequently, the disparities in static functional network connectivity both within and between networks among the two groups were computed and juxtaposed. Following this, five consistent and robust dynamic functional network connectivity (dFNC) states were derived by integrating the sliding time window method with k-means cluster analysis, and the distinctions in dFNC between the groups across different states, along with the intergroup variations in three dynamic temporal metrics, were assessed. Finally, a support vector machine (SVM) approach was employed to discriminate stroke patients from HCs using FC and FNC as classification features. Comparing the stroke group to the healthy control (HC) group, the stroke group exhibited reduced intra-network functional connectivity (FC) in the right superior temporal gyrus of the ventral attention network (VAN), the left calcarine of the visual network (VN), and the left precuneus of the default mode network (DMN). Regarding static functional network connectivity (FNC), we identified increased connectivity between the executive control network (ECN) and dorsal attention network (DAN), salience network (SN) and DMN, SN-ECN, and VN-ECN, along with decreased connectivity between DAN-DAN, ECN-SN, SN-SN, and DAN-VN between the two groups. Noteworthy differences in dynamic FNC (dFNC) were observed between the groups in states 3 to 5. Moreover, stroke patients demonstrated a significantly higher proportion of time and longer mean dwell time in state 4, alongside a decreased proportion of time in state 5 compared to HC. Finally, utilizing FC and FNC as features, stroke patients could be distinguished from HC with an accuracy exceeding 70% and an area under the curve ranging from 0.8284 to 0.9364. In conclusion, our study reveals static and dynamic changes in large-scale brain networks in stroke patients, potentially linked to abnormalities in visual, cognitive, and motor functions. This investigation offers valuable insights into the neural mechanisms underpinning the functional deficits observed in stroke, thereby aiding in the diagnosis and development of targeted therapeutic interventions for affected individuals.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 2","pages":"21"},"PeriodicalIF":2.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142959191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG Signals Classification Related to Visual Objects Using Long Short-Term Memory Network and Nonlinear Interval Type-2 Fuzzy Regression.","authors":"Hajar Ahmadieh, Farnaz Ghassemi, Mohammad Hassan Moradi","doi":"10.1007/s10548-024-01080-0","DOIUrl":"10.1007/s10548-024-01080-0","url":null,"abstract":"<p><p>By gaining insights into how brain activity is encoded and decoded, we enhance our understanding of brain function. This study introduces a method for classifying EEG signals related to visual objects, employing a combination of an LSTM network and nonlinear interval type-2 fuzzy regression (NIT2FR). Here, ResNet is utilized for feature extraction from images, the LSTM network for feature extraction from EEG signals, and NIT2FR for mapping image features to EEG signal features. The application of type-2 fuzzy logic addresses uncertainties arising from EEG signal nonlinearity, noise, limited data sample size, and diverse mental states among participants. The Stanford database was used for implementation, evaluating effectiveness through metrics like classification accuracy, precision, recall, and F1 score. According to the findings, the LSTM network achieved an accuracy of 55.83% in categorizing images using raw EEG data. When compared to other methods like linear type-2, linear/nonlinear type-1 fuzzy, neural network, and polynomial regression, NIT2FR coupled with an SVM classifier outperformed with a 68.05% accuracy. Thus, NIT2FR demonstrates superiority in handling high uncertainty environments. Moreover, the 6.03% improvement in accuracy over the best previous study using the same dataset underscores its effectiveness. Precision, recall, and F1 score results for NIT2FR were 68.93%, 68.08%, and 68.49% respectively, surpassing outcomes from linear type-2, linear/nonlinear type-1 fuzzy regression methods.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 2","pages":"20"},"PeriodicalIF":2.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143460881","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Network Abnormalities in Ischemic Stroke: A Meta-analysis of Resting-State Functional Connectivity.","authors":"Zheng Zhang","doi":"10.1007/s10548-024-01096-6","DOIUrl":"10.1007/s10548-024-01096-6","url":null,"abstract":"<p><p>Aberrant large-scale resting-state functional connectivity (rsFC) has been frequently documented in ischemic stroke. However, it remains unclear about the altered patterns of within- and across-network connectivity. The purpose of this meta-analysis was to identify the altered rsFC in patients with ischemic stroke relative to healthy controls, as well as to reveal longitudinal changes of network dysfunctions across acute, subacute, and chronic phases. A total of 24 studies were identified as eligible for inclusion in the present meta-analysis. These studies included 269 foci observed in 58 contrasts (558 patients with ischemic stroke; 526 healthy controls; 38.84% female). The results showed: (1) within-network hypoconnectivity in the sensorimotor network (SMN), default mode network (DMN), frontoparietal network (FPN), and salience network (SN), respectively; (2) across-network hypoconnectivity between the SMN and both of the SN and visual network, and between the FPN and both of the SN and DMN; and (3) across-network hyperconnectivity between the SMN and both of the DMN and FPN, and between the SN and both of the DMN and FPN. Meta-regression showed that hypoconnectivity between the DMN and the FPN became less pronounced as the ischemic stroke phase progressed from the acute to the subacute and chronic phases. This study provides the first meta-analytic evidence of large-scale rsFC dysfunction in ischemic stroke. These dysfunctional biomarkers could help identify patients with ischemic stroke at risk for cognitive, sensory, motor, and emotional impairments and further provide potential insight into developing diagnostic models and therapeutic interventions for rehabilitation and recovery.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 2","pages":"19"},"PeriodicalIF":2.3,"publicationDate":"2025-01-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142928334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Distinctive Neural Substrates of low and high Risky Decision Making: Evidence from the Balloon Analog Risk Task.","authors":"Zhenlan Jin, Simeng Li, Changan Wang, Xiaoqian Chai, Junjun Zhang, Ling Li","doi":"10.1007/s10548-024-01094-8","DOIUrl":"10.1007/s10548-024-01094-8","url":null,"abstract":"<p><p>Human beings exhibit varying risk-taking behaviors in response to different risk levels. Despite numerous studies on risk-taking in decision-making, the neural mechanisms of decision-making regarding risk levels remains unclear. To investigate the neural correlates of individual differences in risk-taking under different risk-levels, we analyzed behavioral data of the Balloon Analogue Risk Task (BART) and resting-state functional Magnetic Resonance Imaging (rs-fMRI) data of healthy participants (22-39 years, N = 93) from the University of California, Los Angeles Consortium for Neuropsychiatric Phenomics dataset. In the BART, the participants decided to pump for more points or stop pumping to avoid explosion of the balloons, where the risk level was manipulated by the explosion likelihood which was distinguished by the balloon color (blue for low-, red for high- risk condition). Compared with low-risk condition, the participants pumped less number, exploded more balloons, and showed more variability in pump numbers in high-risk condition, demonstrating the effective manipulation of the risky level. Next, resting state features and functional connectivity (rsFC) strength were associated with behavioral measures in low- and high-risk conditions. We found that the explosion number of balloons were correlated with the low frequency fluctuations (ALFF) in the left dorsolateral prefrontal cortex (L. DLPFC), the rsFC strength between L. DLPFC and the left anterior orbital gyrus in the low-risk condition. In the high-risk condition, we found variability in pump numbers was correlated with the ALFF in the left middle/superior frontal gyrus, the fractional ALFF (fALFF) in the medial segment of precentral gyrus (M. PrG), and the rsFC strength between the M. PrG and bilateral precentral gyrus. Our results highlighted significance of the L. DLPFC in lower risky decision making and the precentral gyrus in higher risky decision making, suggesting that distinctive neural correlates underlie the individual differences of decision-making under different risk level.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 1","pages":"18"},"PeriodicalIF":2.3,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multivariate and Network Analysis Uncovers a Long-Term Influence of Exclusive Breastfeeding on the Development of Brain Morphology and Structural Connectivity.","authors":"Fabrizio Parente, Tiziana Pedale, Camilla Rossi-Espagnet, Daniela Longo, Antonio Napolitano, Simone Gazzellini, Simone Macrì, Valerio Santangelo","doi":"10.1007/s10548-024-01091-x","DOIUrl":"10.1007/s10548-024-01091-x","url":null,"abstract":"<p><p>Exclusive breastfeeding (eBF) in infancy appears to offer a developmental advantage for children's brains compared to formula-fed counterparts. Existing research has predominantly focused on global brain measures (i.e., total white/grey matter volumes) or on limited sets of specific brain regions, in selected age groups, leaving uncertainties about the impact of eBF on the overall structural connectomes. In this cross-sectional study encompassing participants from childhood to adulthood, partial least squares correlations (PLSC) were employed to assess white and grey matter volumes. Furthermore, a network analytic approach was used to estimate the structural connectome based on cortical thickness data. The results revealed that eBF duration correlated with increased white matter volumes in children and with the volume of the medial orbital gyrus in adults. Structural connectome analyses demonstrated heightened anatomical connectivity in eBF children, evidenced by enhanced network density and local/global efficiency, along with increased node degree and local efficiency in frontal and temporal lobes. Similarly, eBF in adults was associated to an improved node connectivity in the frontal lobe. These findings imply a lasting impact of eBF on brain morphometry and structural connectivity. Childhood benefits include heightened white matter development, while in adulthood, eBF may contribute to reduced neural loss associated with aging and enhanced connectivity, particularly in frontal regions.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 1","pages":"16"},"PeriodicalIF":2.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Brain Function and Structure Changes in the Prognosis Prediction of Prolonged Disorders of Consciousness.","authors":"Weiguan Chen, Ye Zhang, Aisong Guo, Xuejun Zhou, Weiqun Song","doi":"10.1007/s10548-024-01087-7","DOIUrl":"10.1007/s10548-024-01087-7","url":null,"abstract":"<p><strong>Objectives: </strong>To observe the functional differences in the key brain areas in patients with different levels of consciousness after severe brain injury, and provide reference for confirming the objective diagnosis indicators for prolonged disorders of consciousness (pDoCs).</p><p><strong>Methods: </strong>This prospective study enrolled patients with pDoCs hospitalized in the department of rehabilitation medicine of our Hospital. Levels of consciousness and clinical outcomes were assessed according to diagnostic criteria and behavioral scales. Resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) of 30 patients with different levels of consciousness was performed. The patients were grouped as conscious or unconscious according to whether they regained consciousness during the 12-month follow-up.</p><p><strong>Results: </strong>Thirty patients were enrolled, including eight with unresponsive wakefulness syndrome/vegetative state, eight with minimally conscious state, six with emergence from the minimally conscious state, and eight with a locked-in syndrome. There were 19 and 11 patients in the conscious and unconscious groups. Compared with the unconscious group, the left basal nucleus was activated in the conscious group, and there were significant differences in white matter fiber bundles. Correlations were observed between the regional homogeneity (ReHo) value of the cerebellum and the Glasgow coma scale score (r = 0.387, P = 0.038) and between the ReHo value of the left temporal and the coma recovery scale-revised score (r = 0.394, P = 0.035).</p><p><strong>Conclusions: </strong>The left insula and cerebellum might be important for regaining consciousness. The brain function activity and structural remodeling of the key brain regions and the activation level of the cerebellum are correlated with clinical behaviors and have potential application value for the prognosis prediction of pDoCs patients.</p>","PeriodicalId":55329,"journal":{"name":"Brain Topography","volume":"38 1","pages":"17"},"PeriodicalIF":2.3,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142711828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}