Clinical EEG and neuroscience最新文献

{"title":"Simultaneous Infantile Spasms and Focal Seizures: A Rarely Reported Combined Seizure Phenomenon on Video Electroencephalogram (VEEG).","authors":"Katherine Horman, Sonal Bhatia","doi":"10.1177/15500594241289637","DOIUrl":"10.1177/15500594241289637","url":null,"abstract":"<p><p>Focal seizures (FS) have previously been described before or after infantile spasm (IS) clusters, but FS occurring simultaneously with an IS cluster has been rarely reported in the EEG literature. We present three cases where focal seizures (FS) occurred concurrently during an infantile spasm (IS) cluster on VEEG. On VEEG, onset of IS cluster preceded FS in all three patients; however, patient three was diagnosed with FS prior to the onset of IS. FS duration ranged from 10-90 s and was electrographic-only in two out of the three patients. Unfortunately, the first two patients are now deceased, and for patient two no etiology was ever identified. Currently, patient three is free of spasms as well as seizures but has global developmental delay; no definite etiology has been identified for their presentation. Concurrent FS with IS suggests that the seizure types may be generated in different brain areas with one seizure type potentially triggering the other and is generally reflective of multifocal or diffuse cerebral disease with a poor prognosis as was seen in at least two of our patients. Our three cases of IS where FS occurred concurrently contribute to the limited existing data describing this phenomenon on VEEG.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"286-290"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142483190","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":"Early Impairment of Face Perception in Post-Stroke Depression: An ERP Study.","authors":"Pingshu Zhang, Lingyun Cao, Jianxin Yuan, Changming Wang, Ya Ou, Jing Wang, Liqin Duan, Hongchun Qian, Qirong Ling, Xiaodong Yuan","doi":"10.1177/15500594241289473","DOIUrl":"10.1177/15500594241289473","url":null,"abstract":"<p><p><b>Objective:</b> Face recognition is an important cognitive function of the human brain. Post stroke depression (PSD) is a common mental complication after stroke, which has a serious impact on individual physical function recovery and quality of life. This study aims to explore the face perception characteristics of PSD through electrophysiological indicators N170 and VPP, and provide an objective basis for the early evaluation of facial cognitive dysfunction in PSD. <b>Methods:</b> 58 patients in the cerebral small vessel disease (CSVD) with depressive symptoms (PSD) and 188 patients in the pure CSVD (NPSD). At the same time, 30 healthy subjects were selected as the healthy controls (HC). The differences of N170 and VPP components between the three groups were compared under the stimulation of inverted faces and upright faces. <b>Results:</b> PSD patients exhibited significantly longer peak latency and lower amplitude of N170 and VPP under both inverted and upright face stimulation compared to HC and NPSD. These results suggest that PSD patients have defects in early face recognition, there are abnormalities in the early perception and structural encoding of face information, and both the \"overall mechanism\" and \"feature mechanism\" of face recognition are damaged. <b>Conclusions:</b> These findings provide neuroelectrophysiological evidence for impaired emotionless face recognition in PSD patients.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"239-248"},"PeriodicalIF":0.0,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142634586","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":"Measurement of Excitation-Inhibition Imbalance in Autism spectrum Disorder Using EEG Proxy Markers: A Pilot Study.","authors":"Jiannan Kang, Wenqin Mao, Juanmei Wu, Xiaoli Li","doi":"10.1177/15500594251333159","DOIUrl":"https://doi.org/10.1177/15500594251333159","url":null,"abstract":"<p><p>Autism Spectrum Disorder (ASD) is a severe neurodevelopmental disorder characterized primarily by social impairments and repetitive behaviors. Imbalance in excitatory-inhibitory (E/I) activity within the central nervous system may be a key mechanism underlying ASD. Electroencephalography (EEG) is a useful tool for recording brain electrical signals, reflecting the activity of cortical neuron populations, and estimating both global and regional E/I balance. Various EEG methods can estimate E/I balance, including non-periodic exponent, corrected alpha power, sample entropy, average spatial phase synchronization (ASPS), and detrended fluctuation analysis (DFA) based on E/I indices. However, research on using EEG proxy markers to assess E/I imbalance in autism is limited, and there is no study indicating which method is most sensitive. Therefore, this study employed a high-density EEG acquisition system to collect data from a relatively large sample of autistic and typically developing (TD) children. We computed EEG proxy markers and used the Coefficient of Variation (CV) to compare the sensitivity of five EEG markers between the two groups. The results indicated that non-periodic exponent based on power spectra and corrected alpha power from non-periodic neural activity were more advantageous. The findings may provide theoretical support for the exploration of EEG biomarkers based on E/I balance theory.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251333159"},"PeriodicalIF":0.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015204","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":"A Randomized, Double Blind, Sham-Controlled Clinical Trial to Evaluate the Efficacy of Electrical Vestibular Nerve Stimulation (VeNS), Compared to a Sham Control for Generalized Anxiety Disorder.","authors":"Sai Sailesh Kumar Goothy, Rohit S Chouhan, R Vijaya Raghavan, Wiktoria Ratajczak, Sinead Watson, Rachel Robinson, Shirin Macias, Jason Mckeown","doi":"10.1177/15500594251328080","DOIUrl":"https://doi.org/10.1177/15500594251328080","url":null,"abstract":"<p><p><b>Aims and Objectives:</b> It has been hypothesised that vestibular stimulation may have a modulatory effect on anxiety. The aim of this randomised, double blind, sham-controlled trial was to determine the efficacy and safety of a non-invasive electrical vestibular nerve stimulation (VeNS) device as a treatment for anxiety compared to a sham stimulation device. <b>Materials and methods:</b> A total of 60 participants (mean age [SD]: 35.6 [8.1]) with a generalized anxiety disorder assessment (GAD-7) score of ≥10 were randomised to receive either an active VeNS device (n = 34) or a sham control device (n = 26). Both groups were asked to complete 20 stimulation sessions (30 min duration) at a rate of 3-5 sessions per week at a research clinic. The primary outcome was change in GAD-7 score from baseline to the end of study (when each participant finished their 20 stimulation sessions). Secondary outcomes were change in Insomnia Severity Index (ISI), and the Short Form 36 Health Survey (SF-36) scores (8 domains). <b>Results:</b> One participant allocated to the sham group withdrew from the study. The mean (SD) number of weeks it took to complete the 20 stimulation sessions was 5.8. The active group had a statistically greater reduction in GAD-7 score compared to the sham group (-7.4 versus -2.2, <i>P </i>< .001; respectively). A total of 97% (n = 33) of the active group achieved a clinically meaningful reduction (defined as ≥4-point reduction) in GAD-7 from baseline to the follow up visit compared to 24% (n = 6) of the sham group (<i>P </i>< .001). Additionally, the active group showed a significant improvement in ISI (-4.9 versus 2.2, <i>P </i>< .001) and greater improvements on all eight SF36 domains (<i>P </i>< .001) compared with the sham group. There was no device related reported adverse events. <b>Conclusion:</b> Regular non-invasive electrical vestibular nerve stimulation appears to have a clinically meaningful benefit when used as an intervention for Generalized Anxiety Disorder.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251328080"},"PeriodicalIF":0.0,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701524","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":"Enhancing Schizophrenia Diagnosis Through Multi-View EEG Analysis: Integrating Raw Signals and Spectrograms in a Deep Learning Framework.","authors":"Hasan Zan","doi":"10.1177/15500594251328068","DOIUrl":"https://doi.org/10.1177/15500594251328068","url":null,"abstract":"<p><p><b>Objective:</b> Schizophrenia is a chronic mental disorder marked by symptoms such as hallucinations, delusions, and cognitive impairments, which profoundly affect individuals' lives. Early detection is crucial for improving treatment outcomes, but the diagnostic process remains complex due to the disorder's multifaceted nature. In recent years, EEG data have been increasingly investigated to detect neural patterns linked to schizophrenia. <b>Methods:</b> This study presents a deep learning framework that integrates both raw multi-channel EEG signals and their spectrograms. Our two-branch model processes these complementary data views to capture both temporal dynamics and frequency-specific features while employing depth-wise convolution to efficiently combine spatial dependencies across EEG channels. <b>Results:</b> The model was evaluated on two datasets, consisting of 84 and 28 subjects, achieving classification accuracies of 0.985 and 0.994, respectively. These results highlight the effectiveness of combining raw EEG signals with their time-frequency representations for precise and automated schizophrenia detection. Additionally, an ablation study assessed the contributions of different architectural components. <b>Conclusions:</b> The approach outperformed existing methods in the literature, underscoring the value of utilizing multi-view EEG data in schizophrenia detection. These promising results suggest that our framework could contribute to more effective diagnostic tools in clinical practice.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251328068"},"PeriodicalIF":0.0,"publicationDate":"2025-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694397","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 Bioimaging: Quantum Computing-Inspired Bald Eagle Search Optimization for Motor Imaging EEG Feature Selection.","authors":"Chandan Choubey, M Dhanalakshmi, S Karunakaran, Gaurav Vishnu Londhe, Vrince Vimal, M K Kirubakaran","doi":"10.1177/15500594251325273","DOIUrl":"https://doi.org/10.1177/15500594251325273","url":null,"abstract":"<p><p>One of the most important objectives in brain-computer interfaces (BCI) is to identify a subset of characteristics that represents the electroencephalographic (EEG) signal while eliminating elements that are duplicate or irrelevant. Neuroscientific research is advanced by bioimaging, especially in the field of BCI. In this work, a novel quantum computing-inspired bald eagle search optimization (QC-IBESO) method is used to improve the effectiveness of motor imagery EEG feature selection. This method can prevent the dimensionality curse and improve the classification accuracy of the system by lowering the dimensionality of the dataset. The dataset that was used in the assessment is from BCI Competition-III IV-A. To normalize the EEG data, Z-score normalization is used in the preprocessing stage. Principal component analysis reduces dimensionality and preserves important information during feature extraction. In the context of motor imagery, the QC-IBESO approach is utilized to select certain EEG characteristics for bioimaging. This facilitates the exploration of intricate search spaces and improves the detection of critical EEG signals related to motor imagery. The study contrasts the suggested approach with conventional methods like neural networks, support vector machines and logistic regression. To evaluate the efficacy of the suggested strategy in contrast to current techniques, performance measures such as F1-score, precision, accuracy and recall are computed. This work advances the field of feature selection techniques in bioimaging and opens up a novel and intriguing direction for the investigation of quantum-inspired optimization in neuroimaging.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251325273"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143660102","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":"Event-Related Brain Oscillations Changes in Major Depressive Disorder Patients During Emotional Face Recognition.","authors":"Mengwei Wang, Sihong Wei, Yiyang Zhang, Min Jia, Chaolin Teng, Wei Wang, Jin Xu","doi":"10.1177/15500594241304490","DOIUrl":"https://doi.org/10.1177/15500594241304490","url":null,"abstract":"<p><p>Major depressive disorder (MDD) is a disorder with multiple impairments, among which emotion disorder is the most main one. Nowadays, evoked activity (EA), such as event-related potential (ERP), has mostly been studied for MDD, but induced activity (IA) analysis is still lacking. In this paper, EA, IA and event-related spectral perturbation (ERSP) were studied and compared between MDD patients and healthy controls (HC). Electroencephalogram (EEG) of 26 healthy controls and 21 MDD patients were recorded during three different facial expression (positive, neutral, negative) recognition tasks. Two phases of task execution process were studied, the early stage (0-200 ms after stimuli), and the late stage (200-500 ms after stimuli). ERSP, EA index and IA index of θ (4-7 Hz), α (8-13 Hz) and β (14-30 Hz) frequency bands were calculated and compared between two groups for two phases, respectively. In the early stage, the results indicated a decreased IA in α band in MDD compared to HC in frontal and parieto-occipital areas during neutral and negative face recognition. During the late stage, reduced IA and lower ERSP were also observed in α band in frontal and parieto-occipital areas in MDD during neutral and negative face recognition. Moreover, IA in θ band in MDD was lower than HC during negative face recognition. The findings reflected the abnormality of negative emotion processing in MDD, which could help to interpret the neural mechanism of depression.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594241304490"},"PeriodicalIF":0.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626862","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":"Quantitative Electroencephalogram Might Improve the Predictive Value of Prognosis 6 Months After Discharge in Acute Ischemic Stroke.","authors":"Haifeng Mao, Liwei Liu, Peiyi Lin, Xinran Meng, Timothy H Rainer, Qianyi Wu","doi":"10.1177/15500594251323119","DOIUrl":"https://doi.org/10.1177/15500594251323119","url":null,"abstract":"<p><p><i>Background:</i> As a leading cause of severe morbidity, acute ischemic stroke (AIS) necessitates precise prognostic evaluation to inform critical treatment strategies. Recent advancements have identified quantitative electroencephalography (qEEG) as a pivotal instrument in refining prognostic accuracy for AIS. This investigation aimed to construct a robust prognostic model, anchored in qEEG parameters, to enhance the precision of clinical prognosis 6 months after discharge in AIS patients. <i>Methods:</i> In a retrospective observational study, we analyzed AIS cases from January 2022 to March 2023. Data encompassing demographic profiles, clinical manifestations, qEEG findings, and modified Rankin Scale (mRS) assessments were evaluated for 109 patients with AIS. These metrics were instrumental in developing prognostic models, segregating outcomes into either favorable (mRS: 0-2) or unfavorable categories (mRS: 3-6) at 6 months post-discharge. Prognostic models were developed using clinical and qEEG parameters. <i>Results:</i> The formulation of two distinct prognostic models was predicated on an integration of baseline clinical data (age, unilateral limb weakness, ataxia and red blood cell count) and specific qEEG metrics (T3-P3 (TAR) and T4-P4 (TAR)). The synthesis of these models culminated in the Prognostic Model 3, which exhibited a marked enhancement in prognostic accuracy, as evidenced by an area under the curve (AUC) of 0.8227 (95% CI: 0.7409-0.9045), thereby signifying a superior prediction of AIS prognosis 6 months after discharge relative to the individual models. <i>Conclusion:</i> Quantitative EEG, especially increased theta/alpha power ratio (TAR), might improve the prediction of prognosis 6 months after discharge of acute ischemic stroke in clinical practice.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"15500594251323119"},"PeriodicalIF":0.0,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143544937","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":"Frontal Activity of Recent Suicide Attempters: EEG spectrum Power Performing Raven Task.","authors":"Nafee Rasouli, Seyed Kazem Malakouti, Masoumeh Bayat, Firouzeh Mahjoubnavaz, Niloofar Fallahinia, Reza Khosrowabadi","doi":"10.1177/15500594241273125","DOIUrl":"10.1177/15500594241273125","url":null,"abstract":"<p><p><i>Background:</i> Deficits in problem-solving may be related to vulnerability to suicidal behavior. We aimed to identify the electroencephalographic (EEG) power spectrum associated with the performance of the Raven as a reasoning/problem-solving task among individuals with recent suicide attempts. <i>Methods</i>: This study with the case-control method, consisted of 61 participants who were assigned to three groups: Suicide attempt + Major Depressive Disorder (SA + MDD), Major Depressive Disorder (MDD), and Healthy Control (HC). All participants underwent clinical evaluations and problem-solving abilities. Subsequently, EEG signals were recorded while performing the Raven task. <i>Results</i>: The SA + MDD and MDD groups were significantly different from the HC group in terms of anxiety, reasons for life, and hopelessness. Regarding brain oscillations in performing the raven task, increased theta, gamma, and betha power extending over the frontal areas, including anterior prefrontal cortex, dlPFC, pre-SMA, inferior frontal cortex, and medial prefrontal cortex, was significant in SA + MDD compared with other groups. The alpha wave was more prominent in the left frontal, particularly in dlPFC in SA + MDD. Compared to the MDD group, the SA + MDD group had a shorter reaction time, while their response accuracy did not differ significantly. <i>Conclusions</i>: Suicidal patients have more frontal activity in planning and executive function than the two other groups. Nevertheless, it seems that reduced activity in the left frontal region, which plays a crucial role in managing emotional distress, can contribute to suicidal tendencies among vulnerable individuals. <i>Limitation</i> The small sample size and chosen difficult trials for the Raven task were the most limitations of the study.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"140-149"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142082882","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":"Electroencephalography can Ubiquitously Delineate the Brain Dysfunction of Neurodegenerative Dementia by Both Visual and Automatic Analysis Methods: A Preliminary Study.","authors":"Kei Sato, Takefumi Hitomi, Katsuya Kobayashi, Masao Matsuhashi, Akihiro Shimotake, Akira Kuzuya, Ayae Kinoshita, Riki Matsumoto, Hajime Takechi, Takenao Sugi, Shigeto Nishida, Ryosuke Takahashi, Akio Ikeda","doi":"10.1177/15500594241283512","DOIUrl":"10.1177/15500594241283512","url":null,"abstract":"<p><p><b>Introduction:</b> The aim was to examine the differences in electroencephalography (EEG) findings by visual and automated quantitative analyses between Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) and Parkinson's disease with dementia (PDD). <b>Methods:</b> EEG data of 20 patients with AD and 24 with DLB/PDD (12 DLB and 12 PDD) were retrospectively analyzed. Based on the awake EEG, the posterior dominant rhythm frequency and proportion of patients who showed intermittent focal and diffuse slow waves (IDS) were visually and automatically compared between the AD and DLB/PDD groups. <b>Results:</b> On visual analysis, patients with DLB/PDD showed a lower PDR frequency than patients with AD. In patients with PDR <8 Hz and occipital slow waves or patients with PDR <8 Hz and IDS, DLB/PDD was highly suspected (PPV 100%) and AD was unlikely (PPV 0%). On automatic analysis, the findings of the PDR were similar to those on visual analysis. Comparisons between visual and automatic analysis showed an overlap in the focal slow wave commonly detected by both methods in 10 of 44 patients, and concordant presence or absence of IDS in 29 of 43 patients. With respect to PDR <8 Hz and the combination of PDR <8 Hz and IDS, PPV and NPV in DLB/PDD and AD were not different between visual and automatic analysis. <b>Conclusions:</b> As the noninvasive, widely available clinical tool of low expense, visual analysis of EEG findings provided highly sufficient information to delineate different brain dysfunction in AD and DLB/PDD, and automatic EEG analysis could support visual analysis especially about PD.</p>","PeriodicalId":93940,"journal":{"name":"Clinical EEG and neuroscience","volume":" ","pages":"185-196"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373816","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}