Journal of neural engineering最新文献

{"title":"Identification of modulated whole-brain dynamical models from nonstationary electrophysiological data.","authors":"Addison Schwamb, Zongxi Yu, ShiNung Ching","doi":"10.1088/1741-2552/ae0d32","DOIUrl":"10.1088/1741-2552/ae0d32","url":null,"abstract":"<p><p><i>Objective.</i>Understanding the mechanisms underlying brain dynamics is a long-held goal in neuroscience. However, these dynamics are both individualized and nonstationary, making modeling challenging. Here, we present a data-driven approach to modeling nonstationary dynamics based on principles of neuromodulation, at the level of individual subjects.<i>Approach.</i>Previously, we developed the mesoscale individualized neural dynamics (MINDy) modeling approach to capture individualized brain dynamics which do not change over time. Here, we extend the MINDy approach by adding a modulatory component which is multiplied by a set of baseline, stationary connectivity weights. We validate this model on both synthetic data and publicly available electroencephalography data in the context of anesthesia, a known modulator of neural dynamics.<i>Main results.</i>We find that our modulated MINDy approach is accurate, individualized, and reliable. Additionally, we find that our models yield biologically interpretable inferences regarding the effects of propofol anesthesia on mesoscale cortical networks, consistent with previous literature on the neuromodulatory effects of propofol.<i>Significance.</i>Ultimately, our data-driven modeling approach is reliable and scalable, and provides insight into mechanisms underlying observed brain dynamics. Our modeling methodology can be used to infer insights about modulation dynamics in the brain in a number of different contexts.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145194339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EEG workload estimation and classification: a systematic review.","authors":"Jahid Hassan, Shamim Reza, Syed Udoy Ahmed, Nazmul Haque Anik, Md Obaydullah Khan","doi":"10.1088/1741-2552/ad705e","DOIUrl":"10.1088/1741-2552/ad705e","url":null,"abstract":"<p><p><i>Objective.</i>Electroencephalography (EEG) has evolved into an indispensable instrument for estimating cognitive workload in various domains. Machine Learning (ML) and deep learning (DL) techniques have been increasingly employed to develop accurate workload estimation and classification models based on EEG data. The goal of this systematic review is to compile the body of research on EEG workload estimation and classification using ML and DL approaches.<i>Methods.</i>The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures were followed in conducting the review, searches were conducted through databases at SpringerLink, ACM Digital Library, IEEE Explore, PubMed, and Science Direct from the beginning to the end of 16 February 2024. Studies were selected based on predefined inclusion criteria. Data were extracted to capture study design, participant demographics, EEG features, ML/DL algorithms, and reported performance metrics.<i>Results.</i>Out of the 125 items that emerged, 33 scientific papers were fully evaluated. The study designs, participant demographics, and EEG workload measurement and categorization techniques used in the investigations differed. Support vector machine (SVM), convolutional neural network (CNN), and hybrid networks are examples of ML and DL approaches that were often used. Analyzing the accuracy scores achieved by different ML/DL models. Furthermore, a relationship was noted between sample frequency and model accuracy, with higher sample frequencies generally leading to improved performance. The percentage distribution of ML/DL methods revealed that SVMs, CNNs, and recurrent neural networks were the most commonly utilized techniques, reflecting their robustness in handling EEG data.<i>Significance.</i>The comprehensive review emphasizes how ML may be used to identify mental workload across a variety of disciplines using EEG data. Optimizing practical applications requires multimodal data integration, standardization efforts, and real-world validation studies. These systems will also be further improved by addressing ethical issues and investigating new EEG properties, which will improve human-computer interaction and performance assessment.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141997104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Aligning machines and minds: Neural encoding for high-level visual cortices based on image captioning task.","authors":"Xu Yin, Jiang Jiuchuan, Sheng Ge, John Qiang Gan, Haixian Wang","doi":"10.1088/1741-2552/ae1164","DOIUrl":"https://doi.org/10.1088/1741-2552/ae1164","url":null,"abstract":"<p><strong>Objective: </strong>Neural encoding of visual stimuli aims to predict brain responses in the visual cortex to different external inputs. Deep neural networks (DNNs) trained on relatively simple tasks such as image classification have been widely applied in neural encoding studies of early visual areas. However, due to the complex and abstract nature of semantic representations in high-level visual cortices, their encoding performance and interpretability remain limited.</p><p><strong>Approach: </strong>We propose a novel neural encoding model guided by the image captioning task (ICT). During image captioning, an attention module is employed to focus on key visual objects. In the neural encoding stage, a flexible receptive field (RF) module is designed to simulate voxel-level visual fields. To bridge the domain gap between these two processes, we introduce the Atten-RF module, which effectively aligns attention-guided visual representations with voxel-wise brain activity patterns.</p><p><strong>Main results: </strong>Experiments on the large-scale Natural Scenes Dataset (NSD) demonstrate that our method achieves superior average encoding performance across seven high-level visual cortices, with a mean squared error (MSE) of 0.765, Pearson correlation coefficient (PCC) of 0.443, and coefficient of determination (R²) of 0.245.</p><p><strong>Significance: </strong>By leveraging the guidance and alignment provided by a complex vision-language task, our model enhances the prediction of voxel activity in high-level visual cortex, offering a new perspective on the neural encoding problem. Furthermore, various visualization techniques provide deeper insights into the neural mechanisms underlying visual information processing.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145260341","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Source-free domain adaptation for SSVEP-based brain-computer interfaces.","authors":"Osman Berke Guney, Deniz Kucukahmetler, Huseyin Ozkan","doi":"10.1088/1741-2552/ae0c3d","DOIUrl":"10.1088/1741-2552/ae0c3d","url":null,"abstract":"<p><p><i>Objective.</i>Steady-state visually evoked potential-based Brain-computer interface (BCI) spellers assist individuals experiencing speech difficulties by enabling them to communicate at a fast rate. However, achieving a high information transfer rate (ITR) in most prominent methods requires an extensive calibration period before using the system, leading to discomfort for new users. We address this issue by proposing a novel method that adapts a powerful deep neural network (DNN) pre-trained on data from source domains (data from former users or participants of previous experiments), to the new user (target domain) using only unlabeled target data.<i>Approach.</i>Our method adapts the pre-trained DNN to the new user by minimizing our proposed custom loss function composed of self-adaptation and local-regularity terms. The self-adaptation term uses the pseudo-label strategy, while the novel local-regularity term exploits the data structure and forces the DNN to assign similar labels to adjacent instances.<i>Main results.</i>Our method achieves excellent ITRs of 201.15 bits min^-1and 145.02 bits min^-1on the benchmark and BETA datasets, respectively, and outperforms the state-of-the-art alternatives. Our code is available athttps://github.com/osmanberke/SFDA-SSVEP-BCI.<i>Significance.</i>The proposed method prioritizes user comfort by removing the burden of calibration while maintaining an excellent character identification accuracy and ITR. Because of these attributes, our approach could significantly accelerate the adoption of BCI systems into everyday life.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145180762","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statistical characterization of cortical-thalamic dynamics evoked by cortical stimulation in mice.","authors":"Diana Nigrisoli, Simone Russo, Ruggero Freddi, Nicolas Seseri, Stefania Corti, Linda Ottoboni, Riccardo Barbieri","doi":"10.1088/1741-2552/ae0966","DOIUrl":"10.1088/1741-2552/ae0966","url":null,"abstract":"<p><p><i>Objective.</i>Statistical models are powerful tools for describing biological phenomena such as neuronal spiking activity. Although these models have been widely used to study spontaneous and stimulated neuronal activity, they have not yet been applied to analyze responses to electrical cortical stimulation. In this study, we present an innovative approach to characterize neuronal responses to electrical stimulation in the mouse cortex, providing detailed insights into cortical-thalamic dynamics.<i>Approach.</i>Our method applies mixture models to analyze the Peri-Stimulus time histogram of each neuron, predicting the probability of spiking at specific latencies following the onset of electrical stimuli. By applying this approach, we investigated neuronal responses to cortical stimulation recorded from the motor cortex, somatosensory cortex, and sensorimotor-related thalamic nuclei in the mouse brain.<i>Main results.</i>The characterization approach achieved high goodness of fit, and the model features were leveraged by applying machine learning methods for stimulus intensity decoding and classification of brain regions to which a neuron belongs given its response to the stimulus. The random forest model demonstrated the highest<i>F</i>1 scores, achieving 92.86% for stimulus intensity decoding and 84.35% for brain zone classification.<i>Significance.</i>This study presents a novel statistical framework for characterizing neuronal responses to electrical cortical stimulation, providing quantitative insights into cortical-thalamic dynamics. Our approach achieves high accuracy in stimulus decoding and brain region classification, providing valuable contributions for neuroscience research and neuro-technology applications.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145092863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PyHFO 2.0: an open-source platform for deep learning-based clinical high-frequency oscillations analysis.","authors":"Yuanyi Ding, Yipeng Zhang, Chenda Duan, Atsuro Daida, Yun Zhang, Sotaro Kanai, Mingjian Lu, Shaun A Hussain, Richard J Staba, Hiroki Nariai, Vwani Roychowdhury","doi":"10.1088/1741-2552/ae10e0","DOIUrl":"https://doi.org/10.1088/1741-2552/ae10e0","url":null,"abstract":"<p><strong>Objective: </strong>Accurate detection and classification of high-frequency oscillations (HFOs) in electroencephalography (EEG) recordings have become increasingly important for identifying epileptogenic zones in patients with drug-resistant epilepsy. However, few open-source platforms offer both state-of-the-art computational methods and user-friendly interfaces to support practical clinical use.</p><p><strong>Approach: </strong>We present PyHFO 2.0, an enhanced open-source, Python-based platform that extends previous work by incorporating a more comprehensive set of detection methods and deep learning tools for HFO analysis. The platform now supports three commonly used detectors: Short-Term Energy (STE), Montreal Neurological Institute (MNI), and a newly integrated Hilbert transform-based detector. For HFO classification, PyHFO 2.0 includes deep learning models for artifact rejection, spike high-frequency oscillation (spkHFO) detection, and identification of epileptogenic HFOs (eHFOs). These models are integrated with the Hugging Face ecosystem for automatic loading and can be replaced with custom-trained alternatives. An interactive annotation module enables clinicians and researchers to inspect, verify, and reclassify events.</p><p><strong>Main results: </strong>All detection and classification modules were evaluated using clinical EEG datasets, supporting the applicability of the platform in both research and translational settings. Validation across multiple datasets demonstrated close alignment with expert-labeled annotations and standard tools such as RIPPLELAB.</p><p><strong>Significance: </strong>PyHFO 2.0 aims to simplify the use of computational neuroscience tools in both research and clinical environments by combining methodological rigor with a user-friendly graphical interface. Its scalable architecture and model integration capabilities support a range of applications in biomarker discovery, epilepsy diagnostics, and clinical decision support, bridging advanced computation and practical usability.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing real-time phase detection in diverse rhythmic biological signals for phase-specific neurostimulation.","authors":"Mengzhan Liufu, Zachary M Leveroni, Sameera Shridhar, Nan Zhou, Jai Y Yu","doi":"10.1088/1741-2552/ae10e1","DOIUrl":"https://doi.org/10.1088/1741-2552/ae10e1","url":null,"abstract":"<p><p>Objective&#xD; Closed-loop, phase-specific neurostimulation is a powerful method to modulate ongoing brain activity for clinical and research applications. Phase-specific stimulation relies on estimating the phase of an ongoing oscillation in real time and issuing a control command at a target phase. Phase detection algorithms based on the Fast Fourier transform (FFT) are widely used due to their computational efficiency and robustness. However, it is unclear how algorithm performance depends on the spectral properties of the input signal and how algorithm parameters can be optimized. &#xD;Approach&#xD;We evaluated the in silico performance of three phase detection algorithms (Endpoint-corrected Hilbert Transform, Hilbert Transform, and Phase Mapping) on three real-world biological signals with distinct spectral properties (rodent hippocampal theta potential, human EEG alpha, and human essential tremor) to identify the optima model and parameters. We then validated the algorithm performance for estimating theta phase in real-time using rats implanted with electrodes in the hippocampus. &#xD;Results&#xD;First, we found that signal amplitude and frequency variations strongly influence algorithm performance. Frequency-specific SNR was positively correlated with performance (mean R2 = 0.42 across metrics), while amplitude and frequency variability were negatively correlated (mean R2 = 0.50 across metrics). Second, we showed that the size of the data window used for phase estimation was the key parameter for optimal performance of FFT-based algorithms, where the optimal data window size corresponds to the period of the oscillation (~150 ms for hippocampal theta oscillations, ~100 ms for human EEG alpha, and ~125 ms for essential tremor). This data window length was validated in vivo for estimating the phase of theta oscillations from the hippocampus of freely behaving rats, where an input window size of one theta cycle yielded the best performance across all metrics compared with shorter or longer window sizes.&#xD.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145254169","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physiological activity within peripheral nerves influences neural output in response to electrical stimulation: an<i>in vivo</i>study.","authors":"Lauren R Madden, Richard Liu, Sergiu Ivanescu, Tim M Bruns","doi":"10.1088/1741-2552/ae09fe","DOIUrl":"10.1088/1741-2552/ae09fe","url":null,"abstract":"<p><p><i>Objective.</i>Neuromodulation therapies are often applied to peripheral nerves. These nerves can have physiological activity that interacts with the activity evoked by electrical stimulation, potentially influencing targeted neural output and clinical outcomes. Our goal was to quantify changes in sensory neural unit activity in response to variations in electrical stimulation frequency and amplitude.<i>Approach.</i>In a feline model, we applied cutaneous brushing to evoke pudendal nerve afferent activity with and without electrical stimulation via a pudendal nerve cuff electrode. We recorded neural output with microelectrode arrays implanted in ipsilateral sacral dorsal root ganglia (DRG).<i>Main results.</i>Combined inter-spike interval distributions for all DRG units showed ranges of flattening, increases, and shifts in response to electrical stimulation. These distributions and changes within them due to electrical stimulation were largely driven by a select few units. Mixed-effects models revealed that quicker firing units generally decreased in firing rate in response to electrical stimulation and, conversely, slower firing units increased in firing rate. A unit's underlying firing rate also drove the magnitude of change in mean output firing rate in response to stimulation. Further, the models reported a small, negative correlation between the output mean unit firing rate and the applied electrical stimulation frequency.<i>Significance.</i>These results demonstrate the potential impact of electrical stimulation on underlying neural firing activity and output. Peripheral neuromodulation may normalize abnormal firing patterns in nerves contributing to pathological disorders or alter unrelated physiological activity in off-target neurons. These factors should be considered when selecting neuromodulation settings in animal subjects and human patients.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Disentangling indirect versus direct effects of somatosensory cortex microstimulation on neurons in primary motor and ventral premotor cortex.","authors":"Brandon Ruszala, Kevin A Mazurek, Marc H Schieber","doi":"10.1088/1741-2552/ae087e","DOIUrl":"10.1088/1741-2552/ae087e","url":null,"abstract":"<p><p><i>Objective.</i>Intracortical microstimulation in the primary somatosensory cortex (S1-ICMS) is being developed to provide on-line feedback for bidirectional brain-machine interfaces. Because S1-ICMS can alter the discharge of the motor cortex neurons used to decode motor intent, successful application of S1-ICMS feedback requires understanding the modulation it produces in motor cortex neuron activity.<i>Approach.</i>We investigated the effects of S1-ICMS on neurons in both the primary motor cortex (M1) and the ventral premotor cortex (PMv) during a task in which some trials were instructed with visual cues and other trials with S1-ICMS.<i>Main results.</i>We observed both indirect modulation during and/or after ICMS trains, as well as direct modulation time-locked to the individual S1-ICMS pulses within trains, with all possible combinations of the two types of modulation found among the majority of M1 and PMv neurons. Indirect effects were more prevalent and larger than direct effects. When S1-ICMS produced both indirect and direct modulation in the same neuron, the effects could both be excitatory, both inhibitory, or one excitatory and the other inhibitory. By simulating direct effects, we isolated the concurrent indirect effects, revealing that isolated direct effects failed to account for isolated indirect effects. Furthermore, indirect effects could be present 1 s or more after ICMS trains had terminated, when no direct effects could have occurred. Although the performance of movement decoders trained on visually-instructed trials was poor when applied to ICMS-instructed trials, decoders trained on ICMS-instructed trials performed well on ICMS-instructed trials, indicating that S1-ICMS altered the discharge of M1 and PMv neurons but did not degrade the decodable information available.<i>Significance.</i>When decoding movement intent from neural activity in M1 and/or PMv, accounting for indirect and direct modulation may improve the ability of bidirectional brain-machine interfaces to incorporate artificial somatosensory feedback delivered with S1-ICMS and restore functional movement.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145082927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Neural network reconstruction and motor function recovery in traumatic brain injury rat models via a 3D collagen/heparan/bFGF/NGF scaffold combined with mesenchymal stem cells COMBINED SCAFFOLD-MSC THERAPY FOR TBI REPAIR.","authors":"Miao Chen, Yichao Ye, Tiezhu Wang, Xin Zhang, Jian Chen, Jian Zhang","doi":"10.1088/1741-2552/ae09fd","DOIUrl":"10.1088/1741-2552/ae09fd","url":null,"abstract":"<p><p><i>Objective.</i>To address the limited innate regenerative capacity of neural tissues following traumatic brain injury (TBI) by developing a novel therapeutic intervention.<i>Approach.</i>We engineered a composite scaffold using 3D bioprinting to integrate mesenchymal stem cells (MSCs) with collagen-heparan matrices supplemented with basic fibroblast growth factor (bFGF) and nerve growth factor (NGF), creating a 3D-CH-bFGF/NGF-MSCs construct.<i>Main results.</i>The engineered construct demonstrated favorable biomechanical characteristics and cytocompatibility. In rat TBI models, this intervention significantly enhanced cognitive recovery and sustained sensorimotor function restoration. Histopathological analyses revealed corresponding neural network regeneration through axonal regrowth, synaptogenesis reinforcement, and myelination enhancement at injury sites.<i>Significance</i>. This study demonstrates the therapeutic potential of a 3D-bioprinted, growth factor-enhanced MSC-scaffold construct to promote structural and functional neural repair after TBI, offering a promising strategy for neural tissue regeneration.</p>","PeriodicalId":94096,"journal":{"name":"Journal of neural engineering","volume":" ","pages":""},"PeriodicalIF":3.8,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145126220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}