Journal of Neuroscience Methods最新文献

{"title":"Detecting fast-ripples on both micro- and macro-electrodes in epilepsy: a wavelet-based CNN detector.","authors":"Ludovic Gardy, Jonathan Curot, Luc Valton, Louis Berthier, Emmanuel J Barbeau, Christophe Hurter","doi":"10.1016/j.jneumeth.2024.110350","DOIUrl":"https://doi.org/10.1016/j.jneumeth.2024.110350","url":null,"abstract":"<p><strong>Background: </strong>Fast-ripples (FR) are short (~10 ms) high-frequency oscillations (HFO) between 200-600Hz that are helpful in epilepsy to identify the epileptogenic zone. Our aim is to propose a new method to detect FR that had to be efficient for intracerebral EEG (iEEG) recorded from both usual clinical macro-contacts (millimeter scale) and microwires (micrometer scale).</p><p><strong>New method: </strong>Step 1 of the detection method is based on a convolutional neural network (CNN) trained using a large database of >11,000 FR recorded from the iEEG of 38 patients with epilepsy from both macro-contacts and microwires. The FR and non-FR events were fed to the CNN as normalized time-frequency maps. Step 2 is based on feature-based control techniques in order to reject false positives. In step 3, the human is reinstated in the decision-making process for final validation using a graphical user interface.</p><p><strong>Results: </strong>WALFRID achieved high performance on the realistically simulated data with sensitivity up to 99.95% and precision up to 96.51%. The detector was able to adapt to both macro and micro-EEG recordings. The real data was used without any pre-processing step such as artefact rejection. The precision of the automatic detection was of 57.5. Step 3 helped eliminating remaining false positives in a few minutes per subject.</p><p><strong>Comparison with existing methods: </strong>WALFRID performed as well or better than 6 other existing methods.</p><p><strong>Conclusion: </strong>Since WALFRID was created to mimic the work-up of the neurologist, clinicians can easily use, understand, interpret and, if necessary, correct the output.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110350"},"PeriodicalIF":2.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Using a mixed-reality headset to elicit and track clinically relevant movement in the clinic.","authors":"Dylan Calame, Evan Lester, Phil Chiu, Lauren Seeberger","doi":"10.1016/j.jneumeth.2024.110349","DOIUrl":"https://doi.org/10.1016/j.jneumeth.2024.110349","url":null,"abstract":"<p><strong>Background: </strong>21st century neurology will require scalable and quantitative tools that can improve neurologic evaluations over telehealth and expand access to care. Commercially available mixed-reality headsets allow for simultaneous presentation of stimuli via holograms projected into the real world and objective and quantitative measurement of hand movement, eye movement, and phonation.</p><p><strong>New method: </strong>We created 6 tasks designed to mimic standard neurologic assessments and administered them to a single participant via the Microsoft HoloLens 2 mixed-reality headset. The tasks assessed postural hand tremor, finger tapping, pronation and supination of hands, hand and eye tracking of a center-out task, hand and eye tracking of a random motion task, and vocal assessment.</p><p><strong>Results: </strong>We show the utility of the HoloLens for commonly used neurological exams. First, we demonstrate that headset-derived holograms can project hand movements and objects in 3D space, providing a method to accurately and reproducibly present test stimuli to reduce test-test variability. Second, we found that participant hand movements closely matched holographic stimuli using a variety of metrics calculated on recorded movement data. Third, we showed that the HoloLens can record and playback exam tasks for visual inspection, sharing with other medical providers, and future analysis. Fourth, we showed that vocal recordings and analysis could be used to profile vocal characteristics over time. Together, this demonstrates the versatility of mixed reality headsets and possible applications for neurological assessment.</p><p><strong>Conclusions: </strong>Administering components of the neurologic exam via a self-contained and commercially available mixed-reality headset has numerous benefits including detailed kinematic quantification, reproducible stimuli presentation from test to test, and can be self-administered expanding access to neurological care and saving hospital time and money.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110349"},"PeriodicalIF":2.7,"publicationDate":"2024-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142829008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Studying decision making in rats using a contextual visual discrimination task: Detection and prevention of alternative behavioral strategies.","authors":"Alon Amir, Drew B Headley, Mohammad M Herzallah, Asriya Karki, Ian T Kim, Denis Paré","doi":"10.1016/j.jneumeth.2024.110346","DOIUrl":"10.1016/j.jneumeth.2024.110346","url":null,"abstract":"<p><strong>Background: </strong>The neural bases of decision-making and contextual sensory discriminations have traditionally been studied in primates, highlighting the role of the prefrontal cortex in cognitive control and flexibility. With the advent of molecular tools to manipulate and monitor neuronal activity, these processes have increasingly been studied in rodents. However, rodent tasks typically consist of two-alternative forced choice paradigms that usually feature coarse sensory discriminations and no contextual dependence, limiting prefrontal involvement in task performance.</p><p><strong>New method: </strong>To circumvent these limitations, we developed a novel contextual visual discrimination task that lends itself to rigorous psychophysical analyses. In this task, rats learn to detect left-right differences in one dimension (e.g. luminance or speed) depending on context while ignoring another (e.g. speed or luminance, respectively). Depending on trials, speed and luminance can be greater on the same side (congruent trials) or on opposite sides (incongruent trials).</p><p><strong>Results: </strong>Rats learned the task in four phases: nose-poking and lever-pressing (∼7 days), discriminating left-right differences in one dimension (∼20 days), discriminating left-right differences in a second dimension (∼10 days), and discriminating left-right differences in one of the two dimensions depending on context (∼2.5 months). A 20:80 ratio of congruent to incongruent trials is used to prevent rats from adopting alternative strategies.</p><p><strong>Comparison with existing methods: </strong>This task is comparable to contextual sensory discrimination tasks used in monkeys. Few equivalent tasks exist in rodents.</p><p><strong>Conclusions: </strong>This task will allow investigators to use the full neuroscientific armamentarium to study contextual neural coding in the rat prefrontal cortex.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110346"},"PeriodicalIF":2.7,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142818322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fractal analysis to assess the differentiation state of oligodendroglia in culture.","authors":"J Pascual-Guerra, M Torres-Rico, C L Paíno, J A Rodríguez-Navarro, A G García","doi":"10.1016/j.jneumeth.2024.110336","DOIUrl":"10.1016/j.jneumeth.2024.110336","url":null,"abstract":"<p><strong>Background: </strong>Oligodendroglial development is accompanied by increased cell complexity. A simple and cost-effective evaluation of the pro-myelinating activity of different drugs and/or treatments would be of great interest. In cultured oligodendroglia, an evaluation of the pro-myelinating activity of different drugs and/or treatments can be achieved through fractal analysis, which allows measuring cell complexity.</p><p><strong>New method: </strong>Fractal dimension was assessed in two O4⁺ cell types (neural stem cell-derived and lineage-converted adipose tissue mesenchymal cells) under proliferating or differentiating conditions.</p><p><strong>Comparison with existing methods: </strong>This analysis, which was originally developed to analyze microglia, assigns a quantitative value (fractal dimension) to cellular profiles, obtaining higher coefficients as cells increase in size and arborizations instead of mRNA or protein quantification of mature oligodendroglial markers, such as MBP, MAG, O1 or PLP1/DM20.</p><p><strong>Results: </strong>This article describes a methodology to perform fractal analysis in immunofluorescent images of O4-positive (O4⁺) oligodendroglia using the FracLac plugin of ImageJ software. Pro-myelinating drug Benztropine-treated O4⁺ cells exhibit higher fractal dimension than control group.</p><p><strong>Conclusions: </strong>The results demonstrated the effectiveness and sensitivity of the fractal dimension coefficient provided by FracLac software to assess the effects of treatments on oligodendroglial differentiation.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110336"},"PeriodicalIF":2.7,"publicationDate":"2024-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"STSimM: A new tool for evaluating neuron model performance and detecting spike trains similarity.","authors":"A Marasco, C A Lupascu, C Tribuzi","doi":"10.1016/j.jneumeth.2024.110324","DOIUrl":"10.1016/j.jneumeth.2024.110324","url":null,"abstract":"<p><strong>Background: </strong>In computational neuroscience, performance measures are essential for quantitatively assessing the predictive power of neuron models, while similarity measures are used to estimate the level of synchrony between two or more spike trains. Most of the measures proposed in the literature require setting an appropriate time-scale and often neglect silent periods.</p><p><strong>New method: </strong>Four time-scale adaptive performance and similarity measures are proposed and implemented in the STSimM (Spike Trains Similarity Measures) Python tool. These measures are designed to accurately capture both the precise timing of individual spikes and shared periods of inactivity among spike trains.</p><p><strong>Results: </strong>The proposed ST-measures demonstrate enhanced sensitivity over Spike-contrast and SPIKE-distance in detecting spike train similarity, aligning closely with SPIKE-synchronization. Correlations among all similarity measures were observed in Poisson datasets, whereas in vivo-like synaptic stimulations showed correlations only between ST-measures and SPIKE-synchronization.</p><p><strong>Comparison of existing method: </strong>The STSimM measures are compared with SPIKE-distance, SPIKE-synchronization and Spike-contrast using four spike train datasets with varying similarity levels.</p><p><strong>Conclusion: </strong>ST-measures appear more suitable for detecting both the precise timing of single spikes and shared periods of inactivity among spike trains compared to those considered in this work. Their flexibility originates from two primary factors: firstly, the inclusion of four key measures - ST-Accuracy, ST-Precision, ST-Recall, ST-Fscore - capable of discerning similarity levels across neuronal activity, whether interleaved with silent periods or solely focusing on spike timing accuracy; secondly, the integration of three model parameters that govern both precise spike detection and the weighting of silent periods.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110324"},"PeriodicalIF":2.7,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142791828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrode configurations for sensitive and specific detection of compound muscle action potentials to the tibialis anterior muscle after peroneal nerve injury in rats.","authors":"JuliAnne Allgood, Sam James, Lillian Laird, Albert Allotey, Jared Bushman","doi":"10.1016/j.jneumeth.2024.110335","DOIUrl":"10.1016/j.jneumeth.2024.110335","url":null,"abstract":"<p><strong>Background: </strong>Quantifying peripheral nerve regeneration via electrophysiology is a commonly used technique, but it can be complicated by spurious electrical activity. This study sought to compare electrode configurations for measuring compound muscle action potential (CMAP) of the tibialis anterior (TA) muscle in a rat model for specific and sensitive detection of regeneration of peroneal nerve to the TA.</p><p><strong>New method: </strong>10 Sprague-Dawley rats underwent a peroneal nerve transection with direct microsuture repair. CMAPs were conducted with different placements and types of electrodes. Compound action potentials (CAPs) and gait analysis were regularly collected up to 70 days (d) post operation (PO). Nerve sections were harvested at 49 d (n = 4) and 70 d (n = 6) PO and stained with toluidine blue to assess nerve morphometry.</p><p><strong>Results: </strong>Of the tested configurations for CMAPs, a concentric recording/reference electrode in combination with stimulation from the sciatic notch showed the least background and highest sensitivity, while some configurations showed significant noise and did not detect changes in CMAPs within the 70 d recording period following injury. CAPs, gait analysis, morphometry, and muscle mass support the extent of regeneration indicated by CMAPs collected with concentric electrodes.</p><p><strong>Conclusion: </strong>Collateral innervation patterns can complicate CMAP recordings as signals from adjacent muscles can be detected and misinterpreted as regeneration. The outcome of this study shows how differences in configurations and electrodes have significant effects on CMAP for the TA. The results identify methods using concentric electrodes that provide high specificity and sensitivity capable of detecting evidence of regeneration early after injury.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110335"},"PeriodicalIF":2.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhancing fMRI quality control.","authors":"Lennard van den Berg, Nick Ramsey, Mathijs Raemaekers","doi":"10.1016/j.jneumeth.2024.110337","DOIUrl":"10.1016/j.jneumeth.2024.110337","url":null,"abstract":"<p><strong>Background: </strong>fMRI in clinical settings faces challenges affecting activity maps. Template matching can screen for abnormal results by providing an objective metric of activity map quality. This research tests how sample size, age, or gender-specific templates, and unilateral templates affect template matching results.</p><p><strong>New method: </strong>We used an fMRI database of 76 healthy subjects performing 7 tasks assessing motor, language, and working memory functions. Templates were created with varying numbers of subjects, genders, and ages. Individual subjects were compared to templates using leave-one-out cross validation. We also compared unilateral and bilateral templates.</p><p><strong>Results: </strong>Increasing sample size improved template matches, with diminishing returns for larger sample sizes. Gender and age-specific templates increased correlations for some tasks, with age having a larger effect than gender. Generally, templates including all subjects provided the highest correlations, indicating that age and gender effects did not outweigh the benefits of larger sample sizes. Unilateral templates of the task-dominant hemisphere increased template correlations.</p><p><strong>Conclusions: </strong>Age and gender affect templates, but the benefits depend on the database size. When the database is large enough, age and gender effects are beneficial. Unilateral templates enhance template matching, but practical benefits depend on the severity of neurological abnormalities in patients.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110337"},"PeriodicalIF":2.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-layer transfer learning algorithm based on improved common spatial pattern for brain-computer interfaces.","authors":"Zhuo Cai, Yunyuan Gao, Feng Fang, Yingchun Zhang, Shunlan Du","doi":"10.1016/j.jneumeth.2024.110332","DOIUrl":"10.1016/j.jneumeth.2024.110332","url":null,"abstract":"<p><p>In the application of brain-computer interface, the differences in imaging methods and brain structure between subjects hinder the effectiveness of decoding algorithms when applied on different subjects. Transfer learning has been designed to solve this problem. There have been many applications of transfer learning in motor imagery (MI), however the effectiveness is still limited due to the inconsistent domain alignment, lack of prominent data features and allocation of weights in trails. In this paper, a Multi-layer transfer learning algorithm based on improved Common Spatial Patterns (MTICSP) was proposed to solve these problems. Firstly, the source domain data and target domain data were aligned by Target Alignment (TA)method to reduce distribution differences between subjects. Secondly, the mean covariance matrix of the two classes was re-weighted by calculating the distance between the covariance matrix of each trial in the source domain and the target domain. Thirdly, the improved Common Spatial Patterns (CSP) by introducing regularization coefficient was proposed to further reduce the difference between source domain and target domain to extract features. Finally, the feature blocks of the source domain and target domain were aligned again by Joint Distribution Adaptation (JDA) method. Experiments on two public datasets in two transfer paradigms multi-source to single-target (MTS) and single-source to single-target (STS) verified the effectiveness of our proposed method. The MTS and STS in the 5-person dataset were 80.21% and 77.58%, respectively, and 80.10% and 73.91%, respectively, in the 9-person dataset. Experimental results also showed that the proposed algorithm was superior to other state-of-the-art algorithms. In addition, the generalization ability of our algorithm MTICSP was validated on the fatigue EEG dataset collected by ourselves, and obtained 94.83% and 87.41% accuracy in MTS and STS experiments respectively. The proposed method combines improved CSP with transfer learning to extract the features of source and target domains effectively, providing a new method for combining transfer learning with motor imagination.</p>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":" ","pages":"110332"},"PeriodicalIF":2.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal predictive modeling: Scalable imaging informed approaches to predict future brain health","authors":"Meenu Ajith ,&nbsp;Jeffrey S. Spence ,&nbsp;Sandra B. Chapman ,&nbsp;Vince D. Calhoun","doi":"10.1016/j.jneumeth.2024.110322","DOIUrl":"10.1016/j.jneumeth.2024.110322","url":null,"abstract":"<div><h3>Background:</h3><div>Predicting future brain health is a complex endeavor that often requires integrating diverse data sources. The neural patterns and interactions identified through neuroimaging serve as the fundamental basis and early indicators that precede the manifestation of observable behaviors or psychological states.</div></div><div><h3>New Method:</h3><div>In this work, we introduce a multimodal predictive modeling approach that leverages an imaging-informed methodology to gain insights into future behavioral outcomes. We employed three methodologies for evaluation: an assessment-only approach using support vector regression (SVR), a neuroimaging-only approach using random forest (RF), and an image-assisted method integrating the static functional network connectivity (sFNC) matrix from resting-state functional magnetic resonance imaging (rs-fMRI) alongside assessments. The image-assisted approach utilized a partially conditional variational autoencoder (PCVAE) to predict brain health constructs in future visits from the behavioral data alone.</div></div><div><h3>Results:</h3><div>Our performance evaluation indicates that the image-assisted method excels in handling conditional information to predict brain health constructs in subsequent visits and their longitudinal changes. These results suggest that during the training stage, the PCVAE model effectively captures relevant information from neuroimaging data, thereby potentially improving accuracy in making future predictions using only assessment data.</div></div><div><h3>Comparison with Existing Methods:</h3><div>The proposed image-assisted method outperforms traditional assessment-only and neuroimaging-only approaches by effectively integrating neuroimaging data with assessment factors.</div></div><div><h3>Conclusion:</h3><div>This study underscores the potential of neuroimaging-informed predictive modeling to advance our comprehension of the complex relationships between cognitive performance and neural connectivity.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110322"},"PeriodicalIF":2.7,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-channel electroencephalography decomposition by detector-atom network and its pre-trained model","authors":"Hiroshi Higashi","doi":"10.1016/j.jneumeth.2024.110323","DOIUrl":"10.1016/j.jneumeth.2024.110323","url":null,"abstract":"<div><div>Signal decomposition techniques utilizing multi-channel spatial features are critical for analyzing, denoising, and classifying electroencephalography (EEG) signals. To facilitate the decomposition of signals recorded with limited channels, this paper presents a novel single-channel decomposition approach that does not rely on multi-channel features. Our model posits that an EEG signal comprises short, shift-invariant waves, referred to as atoms. We design a decomposer as an artificial neural network aimed at estimating these atoms and detecting their time shifts and amplitude modulations within the input signal. The efficacy of our method was validated across various scenarios in brain–computer interfaces and neuroscience, demonstrating enhanced performance. Additionally, cross-dataset validation indicates the feasibility of a pre-trained model, enabling a plug-and-play signal decomposition module.</div></div>","PeriodicalId":16415,"journal":{"name":"Journal of Neuroscience Methods","volume":"414 ","pages":"Article 110323"},"PeriodicalIF":2.7,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142716230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}