Frontiers in Computational Neuroscience最新文献

{"title":"The two dragons of cognition: recursive condensation for predictive processing.","authors":"Xin Li","doi":"10.3389/fncom.2026.1778902","DOIUrl":"https://doi.org/10.3389/fncom.2026.1778902","url":null,"abstract":"<p><p>Computation separates time from space: nondeterministic problems are exponential in time (the \"Time Dragon\") but polynomially simulable in space (the \"Space Dragon\"), as formalized by Savitch's theorem (NPSPACE⊆PSPACE). We propose that the brain physically instantiates this theorem through Recursive Condensation, a topological mechanism that converts intractable high-dimensional search into efficient low-dimensional navigation. Drawing on Urysohn's Lemma, we demonstrate that separability is a property of connectivity, not volume; a stable decision boundary exists independent of ambient dimension provided the underlying manifolds are topologically disjoint. To manufacture this disjointness, the cortex employs a parity alternation strategy: it alternates between odd-parity metric expansion (exploratory search) to untangle local geometry, and even-parity topological contraction (closure/condensation) to lock in validated invariants. This cycle acts as a biological \"Savitch Machine,\" mediating a Topological Trinity Transformation (TTT), <i>Search</i>→<i>Closure</i>→<i>Navigation</i>, that compiles high-entropy exploration paths into low-energy quotient tokens. Under Memory-Amortized Inference (MAI), the cortex slays the Space Dragon by collapsing vast state spaces into compact metric singularities, and tames the Time Dragon by memoizing these traversals as structural priors. Evolution's \"cheat code,\" linear cortical growth yielding exponential cognitive gain, emerges as a physical law of topological inference: exponential search in time becomes polynomial reuse in space via recursive metric collapse.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1778902"},"PeriodicalIF":2.3,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13050847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147632344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pre-movement EEG microstates reflect intended lifted load of volitional movement.","authors":"Rohit Kumar Yadav, Sutirtha Ghosh, Lalan Kumar, Shubhendu Bhasin, Sitikantha Roy, Ratna Sharma, Suriya Prakash Muthukrishnan","doi":"10.3389/fncom.2026.1784913","DOIUrl":"https://doi.org/10.3389/fncom.2026.1784913","url":null,"abstract":"<p><strong>Introduction: </strong>Load estimation is one of the essential parameters for assistive robotic control in cases of rehabilitation. The high temporal resolution of the Electroencephalography (EEG) technique makes it the best tool to resolve the temporal dynamics of movement intention and planning. The quasi-stable scalp electrical potential topography represented by the EEG microstates could assess the real-time information processing in the brain for controlling assistive devices. We hypothesize that the EEG microstate preceding the movement could reflect the increasing load during a biceps curl movement.</p><p><strong>Methods: </strong>Ten healthy participants performed biceps curl movements, while their brain activity and muscle activation was recorded using EEG and EMG.</p><p><strong>Results: </strong>Eight microstate maps were found to represent the functional brain state before the movements. Two pre-movement microstate maps were found to reflect the load increments. The source maxima of these two reflective microstates maps were localized at the right insula and cingulate gyrus.</p><p><strong>Discussion: </strong>Our results imply that the load increments of volitional movement could be reflected by the pre-movement EEG microstates.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1784913"},"PeriodicalIF":2.3,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13050944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147632322","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wave turbulence and cortical dynamics.","authors":"Gerald K Cooray","doi":"10.3389/fncom.2026.1682176","DOIUrl":"https://doi.org/10.3389/fncom.2026.1682176","url":null,"abstract":"<p><p>Cortical activity recorded through EEG and MEG reflects complex dynamics that span multiple temporal and spatial scales. Spectral analyses of these signals consistently reveal power-law behavior, a hallmark of turbulent systems. In this paper, we derive a kinetic equation for neural field activity based on wave turbulence theory, highlighting how quantities such as energy and pseudo-particle density flow through wave-space (<i>k</i>-space) via direct and inverse cascades. We explore how different forms of nonlinearity-particularly 3-wave and 4-wave interactions-shape spectral features, including harmonic generation, spectral dispersion, and transient dynamics. While the observed power-law decays in empirical data are broadly consistent with turbulent cascades, variations across studies-such as the presence of dual decay rates or harmonic structures-point to a diversity of underlying mechanisms. We argue that although no single model fully explains all spectral observations, key constraints emerge: namely, that cortical dynamics exhibit features consistent with turbulent wave systems involving both single and dual cascades and a mixture of 3- and 4-wave interactions. This turbulence-based framework offers a principled and unifying approach to interpreting large-scale brain activity, including state transitions and seizure dynamics.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1682176"},"PeriodicalIF":2.3,"publicationDate":"2026-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13047063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147622189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SBR-YOLO: context-position attention and adaptive feature fusion for student behavior recognition.","authors":"Yunming Zhang","doi":"10.3389/fncom.2026.1804422","DOIUrl":"https://doi.org/10.3389/fncom.2026.1804422","url":null,"abstract":"<p><strong>Introduction: </strong>In classroom scenarios, student behaviors exhibit high intra-class variance and subtle inter-class differences, while complex backgrounds and severe occlusions pose significant challenges for accurate behavior recognition.</p><p><strong>Methods: </strong>SBR-YOLO is proposed as a student behavior detection framework for accurate and robust recognition in complex classroom environments. To address the challenges posed by visually similar behaviors and non-uniform spatial distributions of targets, a Behavior-aware Context-Position Attention module is designed, which leverages learnable positional encoding and inter-head interaction mechanisms to capture spatial dependencies among behavioral regions and enable discriminative feature learning. To handle substantial scale variations between front-row and back-row students, an Adaptive Spatial Feature Fusion mechanism is introduced at each output level of the neck, prior to the detection heads, which adaptively learns fusion weights for cross-scale feature integration. A Class-Aware Discriminative Loss function is further introduced to enhance fine-grained discrimination by enforcing intra-class compactness and inter-class separation constraints.</p><p><strong>Results: </strong>Experiments on SCB-Dataset3 demonstrate that SBR-YOLO achieves 74.2% mAP@50, representing a 6.4 percentage point improvement over the YOLOv8n baseline, with the parameter count increasing moderately from 3.0 M to 4.6 M.</p><p><strong>Discussion: </strong>Comprehensive ablation studies and comparative experiments with state-of-the-art methods confirm the effectiveness of SBR-YOLO for student behavior recognition in complex smart classroom environments.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1804422"},"PeriodicalIF":2.3,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13038954/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147608152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based approach for Behavior classification in diagnoses of Autism Spectrum Disorder using naturalistic videos.","authors":"Usama Jabbar, Muhammad Waseem Iqbal, Alexandru Nechifor, Mohammed Abaker, Mohammed Ahmed Khairalseed, Valentin Marian Antohi, Costinela Fortea, Catalin Aurelian Stefanescu","doi":"10.3389/fncom.2026.1626315","DOIUrl":"https://doi.org/10.3389/fncom.2026.1626315","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder that is marked by a lack of communication skills in social situations and repetitive and stereotypical Behaviors. The most widespread form of diagnosing ASD among children is based on psychological screening test along with monitoring of the Behavioral pattern, especially repetitive Behaviors. Some of these Behaviors include hand-flapping, head banging and spinning which are common among ASD children. In our research, we examine abnormal Behavioral patterns that may reflect ASD through the videos of children engaged in the everyday activities in the unstructured settings. A publicly available multiclass Self-Stimulatory Behavior Dataset (SSBD) is use in classify autistic Behavior. Before training the model, the dataset is thoroughly pre-processed (region-of-interest (ROI) detection and image cropping to eliminate irrelevant background objects). Moreover, information-augmenting methods are used to reduce overfitting and increase training efficiency and generalization effectiveness. In order to obtain spatiotemporal details successfully, a number of deep learning models are tested, such as studied CNN-GRU model, 3D-CNN + LSTM, MobileNet, VGG16, and EfficientNet-B7. The findings of the experiment prove that the proposed CNN-GRU model is superior to all competing methods. The model with a k-fold cross-validation provides a steady accuracy of 0.9284 ± 0.0039-0.9294 ± 0.0038, which means that the model is robust and consistent across the folds. The effectiveness of the proposed approach is additionally justified by the comparisons with state-of-the-art methods. The results show that the systems based on the action recognition can help clinicians monitor the Behavioral trends and facilitate the quick, accurate, and effective screening of ASD. The proposed approach works effectively in predicting Behavior in real-life, uncontrolled videos and shows tremendous potential for real-world clinical implementation as a decision-support tool.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1626315"},"PeriodicalIF":2.3,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13039029/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147608137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of spinal sensorimotor circuits in triphasic muscle command: a simulation approach using goal exploration process.","authors":"Daniel Cattaert, Matthieu Guemann, Florent Paclet, Luca Lemarchand, Bryce Chung, Pierre-Yves Oudeyer, Aymar de Rugy","doi":"10.3389/fncom.2026.1745836","DOIUrl":"10.3389/fncom.2026.1745836","url":null,"abstract":"<p><p>During rapid voluntary elbow movement on horizontal plane, a stereotyped triphasic pattern is typically observed in the electromyograms (EMGs) of antagonistic muscles acting at this joint. To explain the origin of such triphasic commands, two types of theories have been proposed. Peripheral theories consider that triphasic commands result from sensorimotor spinal networks, either through a combination of reflexes or through a spinal central pattern generator. Central theories consider that the triphasic command is elaborated in the brain. Although both theories were partially supported by physiological data, there is still no consensus about how exactly triphasic commands are elaborated. Moreover, capacities of simple spinal sensorimotor circuits to elaborate triphasic commands on their own have not been tested yet. In order to test this, we modelled arm musculoskeletal system operating in the absence of gravity, muscle activation dynamics, proprioceptive spindle and Golgi afferent activities and spinal sensorimotor circuits. Step commands were designed to modify the activity of spinal neurons and the strength of their synapses, either to prepare (SET) the network before movement onset, or to launch the movement (GO). Since these step commands do not contain any dynamics, changes in muscle activities responsible for arm movement rest entirely upon interactions between the spinal network and the musculoskeletal system. Critically, we selected step commands using a Goal Exploration Process inspired from baby babbling during development. In this task, the Goal Exploration Process proved very efficient at discovering step commands that enabled spinal circuits to handle a broad spectrum of functional behaviors, displayed in a behavioral space characterized by movement amplitude and maximal speed. All over the behavioral space, specific SET and GO commands elicited natural triphasic commands, thereby substantiating the inherent capacity of the spinal network in generating them.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1745836"},"PeriodicalIF":2.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13015193/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147519965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information flow drives localized morphological differences across neuronal and glial cell types.","authors":"Paheli Desai-Chowdhry, Alexander B Brummer, Samhita Mallavarapu, Masai Oakes, Van M Savage","doi":"10.3389/fncom.2026.1771227","DOIUrl":"10.3389/fncom.2026.1771227","url":null,"abstract":"<p><p>Neuron processes-axons and dendrites-have distinct branching patterns related to their biological function in the brain and body. Other non-neuronal cells in the nervous system, glia, also have characteristic branching morphologies. Our previous work has used biological scaling theory to connect branching patterns in neurons to biophysical function such as energy or conduction time minimization and material constrants in a compact, unifying mathematical model. Here, we use functionally relevant structural parameters related to asymmetric branching patterns extracted from our model as features in machine-learning classification methods to highlight differences between different types of neurons and glia as well as between healthy and diseased cells. Notably, we find that parameters related to information flow vary with position in the cell-that is, relative proximity of each branching junction to the soma (cell body) or synapses. We find that for some neuronal and glial cell type comparisons, such as comparisons between medium spiny neuron (MSN) dendrites, incorporating relative branching junction location significantly improves the performance of machine-learning classification methods. Our results imply that differences in information flow across cells drive specific morphological changes that correspond to localized regions of neuronal and glial cells. The promise of our methods and results lay foundation for future studies classifying neuronal and glial cells based on pathology, using our asymmetric scale factors and relative branching junction location as potential biomarkers to identify particular diseases based on both structural differences and the underlying differences in function.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1771227"},"PeriodicalIF":2.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13013023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147519995","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Editorial: Unraveling information encoding and representation in memory formation and learning.","authors":"Fernando Montani","doi":"10.3389/fncom.2026.1812259","DOIUrl":"10.3389/fncom.2026.1812259","url":null,"abstract":"","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1812259"},"PeriodicalIF":2.3,"publicationDate":"2026-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13013528/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147519919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimized facial landmark modeling with medical aesthetic constraints by a multi-objective genetic algorithm.","authors":"Yuan Ye, Gangxing Yan, Di Wen, Meijun Tan","doi":"10.3389/fncom.2026.1705259","DOIUrl":"https://doi.org/10.3389/fncom.2026.1705259","url":null,"abstract":"<p><p>\"Facial Beauty\" is not an absolute physical attribute but a subjective social and cultural construct. Facial beauty assessment is an interdisciplinary field that integrates computer vision and medical aesthetics (MAs) to quantify personal judgment regarding facial attractiveness. In this study, the beauty assessment we adopted was based on the scores given by plastic surgeons; this method is more professional and is supported by a theoretical basis. We derived a set of MA features that encompass global traits, local details, and curvature aspects from established aesthetic principles. Incorporating these features enhances predictive accuracy in facial beauty. Furthermore, we propose a feature selection algorithm with aesthetic-driven initialization embedded in a multi-objective evolutionary framework. Additionally, we introduce an MA facial landmark model that provides explicit annotation of bilateral zygomatic, orbital, and nasal points for precise attractiveness scoring. Experimental results on the South China University of Technology-Facial Beauty Perception (SCUT-FBP) and SCUT-FBP5500 datasets and the Chicago Face Dataset demonstrate superior performance (Pearson's correlation coefficient = 0.8216, mean absolute error = 0.2638, and root mean square error = 0.3743) over state-of-the-art methods, validating its clinical relevance. This study provides a practical tool for beauty evaluation, where the selected features align with professional judgments, enabling transparent and explainable outcomes in both clinical and cosmetic applications.</p>","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1705259"},"PeriodicalIF":2.3,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12975933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147442388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population-level neural rejuvenation dynamics in addiction: a computational framework for understanding developmental plasticity reactivation.","authors":"Mehdi Borjkhani, Hadi Borjkhani, Morteza A Sharif","doi":"10.3389/fncom.2026.1753417","DOIUrl":"https://doi.org/10.3389/fncom.2026.1753417","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;The neural rejuvenation hypothesis proposes that drugs of abuse reactivate developmental plasticity mechanisms to create abnormally persistent addiction memories. While individual molecular components have been characterized experimentally, the population-level dynamics and their collective contribution to addiction pathophysiology remain poorly understood.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objectives: &lt;/strong&gt;To develop a computational framework tracking theoretical synaptic population dynamics during simulated drug exposure and withdrawal, and to demonstrate how coordinated population-level transitions could account for key experimental observations in addiction neuroscience.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We constructed a mathematical model tracking four theoretical synaptic populations (adult, juvenile, silent, and matured synapses) using differential equations. The model incorporates two distinct processes: (1) rejuvenation of existing synapses through receptor composition switching, and (2) &lt;i&gt;de novo&lt;/i&gt; generation of silent synapses during drug exposure. Critically, the total synapse population is dynamic, increasing during drug exposure due to synaptogenesis and decreasing during withdrawal due to pruning. State transitions are explicitly phase-gated: silent synapse generation occurs only during exposure, while maturation and pruning occur predominantly during withdrawal. Rate constants were derived from experimental time scales reported in the literature, with explicit biological time mapping (1 time unit = 2 h). Simulations involved five intermittent exposures followed by extended withdrawal, with comprehensive parameter sensitivity analysis to assess model robustness across ±50% parameter variations. Initial conditions were fixed to represent the experimentally motivated baseline (adult synapses only); alternative initial states were also tested and did not change qualitative conclusions.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The model demonstrated coordinated synaptic population transformations that qualitatively paralleled experimental observations. In simulation, results revealed distinct phases of neural rejuvenation with characteristic population dynamics: adult-to-juvenile conversion during exposure (reaching ~500 juvenile synapses in the model), &lt;i&gt;de novo&lt;/i&gt; silent synapse generation (~400 synapses), and progressive maturation during withdrawal (~300 matured synapses). The modeled total synapse population increased dynamically from baseline (1,000) to ~1,400 during exposure due to &lt;i&gt;de novo&lt;/i&gt; synaptogenesis, then decreased to ~1,300 during withdrawal due to pruning. NMDA receptor composition shifted from 80% GluN2A to 80% GluN2B during simulated exposure. Memory strength increased continuously through biphasic mechanisms: during exposure, memory formation was driven by enhanced plasticity capacity; during withdrawal, memory strengthening was driven by the maturation flux (the rate of CP-AMPAR recruitment into silen","PeriodicalId":12363,"journal":{"name":"Frontiers in Computational Neuroscience","volume":"20 ","pages":"1753417"},"PeriodicalIF":2.3,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12968142/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147431743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}