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Zhang Z, Chen T, Liu Y, Wang C, Zhao K, Liu CH, Fu X. Decoding the temporal representation of facial expression in face-selective regions. Neuroimage 2023; 283:120442. [PMID: 37926217 DOI: 10.1016/j.neuroimage.2023.120442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 10/23/2023] [Accepted: 11/02/2023] [Indexed: 11/07/2023] Open
Abstract
The ability of humans to discern facial expressions in a timely manner typically relies on distributed face-selective regions for rapid neural computations. To study the time course in regions of interest for this process, we used magnetoencephalography (MEG) to measure neural responses participants viewed facial expressions depicting seven types of emotions (happiness, sadness, anger, disgust, fear, surprise, and neutral). Analysis of the time-resolved decoding of neural responses in face-selective sources within the inferior parietal cortex (IP-faces), lateral occipital cortex (LO-faces), fusiform gyrus (FG-faces), and posterior superior temporal sulcus (pSTS-faces) revealed that facial expressions were successfully classified starting from ∼100 to 150 ms after stimulus onset. Interestingly, the LO-faces and IP-faces showed greater accuracy than FG-faces and pSTS-faces. To examine the nature of the information processed in these face-selective regions, we entered with facial expression stimuli into a convolutional neural network (CNN) to perform similarity analyses against human neural responses. The results showed that neural responses in the LO-faces and IP-faces, starting ∼100 ms after the stimuli, were more strongly correlated with deep representations of emotional categories than with image level information from the input images. Additionally, we observed a relationship between the behavioral performance and the neural responses in the LO-faces and IP-faces, but not in the FG-faces and lpSTS-faces. Together, these results provided a comprehensive picture of the time course and nature of information involved in facial expression discrimination across multiple face-selective regions, which advances our understanding of how the human brain processes facial expressions.
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Affiliation(s)
- Zhihao Zhang
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tong Chen
- Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing, Southwest University, Chongqing 400715, China; Chongqing Key Laboratory of Artificial Intelligence and Service Robot Control Technology, Chongqing 400715, China
| | - Ye Liu
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Chongyang Wang
- Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
| | - Ke Zhao
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Chang Hong Liu
- Department of Psychology, Bournemouth University, Dorset, United Kingdom
| | - Xiaolan Fu
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China; Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China.
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Fairchild G, Sully K, Passamonti L, Staginnus M, Darekar A, Sonuga-Barke EJS, Toschi N. Neuroanatomical markers of familial risk in adolescents with conduct disorder and their unaffected relatives. Psychol Med 2023; 53:1721-1731. [PMID: 34607618 DOI: 10.1017/s0033291721003202] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Previous studies have reported brain structure abnormalities in conduct disorder (CD), but it is unclear whether these neuroanatomical alterations mediate the effects of familial (genetic and environmental) risk for CD. We investigated brain structure in adolescents with CD and their unaffected relatives (URs) to identify neuroanatomical markers of familial risk for CD. METHODS Forty-one adolescents with CD, 24 URs of CD probands, and 38 healthy controls (aged 12-18), underwent structural magnetic resonance imaging. We performed surface-based morphometry analyses, testing for group differences in cortical volume, thickness, surface area, and folding. We also assessed the volume of key subcortical structures. RESULTS The CD and UR groups both displayed structural alterations (lower surface area and folding) in left inferior parietal cortex compared with controls. In contrast, CD participants showed lower insula and pars opercularis volume than controls, and lower surface area and folding in these regions than controls and URs. The URs showed greater folding in rostral anterior cingulate and inferior temporal cortex than controls and greater medial orbitofrontal folding than CD participants. The surface area and volume differences were not significant when controlling for attention-deficit/hyperactivity disorder comorbidity. There were no group differences in subcortical volumes. CONCLUSIONS These findings suggest that alterations in inferior parietal cortical structure partly mediate the effects of familial risk for CD. These structural changes merit investigation as candidate endophenotypes for CD. Neuroanatomical changes in medial orbitofrontal and anterior cingulate cortex differentiated between URs and the other groups, potentially reflecting neural mechanisms of resilience to CD.
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Affiliation(s)
| | - Kate Sully
- School of Psychology, University of Southampton, Southampton, UK
| | - Luca Passamonti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Institute of Bioimaging and Molecular Physiology, National Research Council, Milan, Italy
| | | | - Angela Darekar
- Department of Medical Physics, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | | | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome "Tor Vergata", Rome, Italy
- Martinos Center for Biomedical Imaging, Boston, USA
- Harvard Medical School, Boston, USA
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Kuang QJ, Zhou SM, Liu Y, Wu HW, Bi TY, She SL, Zheng YJ. Prediction of Facial Emotion Recognition Ability in Patients With First-Episode Schizophrenia Using Amplitude of Low-Frequency Fluctuation-Based Support Vector Regression Model. Front Psychiatry 2022; 13:905246. [PMID: 35911229 PMCID: PMC9326045 DOI: 10.3389/fpsyt.2022.905246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 06/06/2022] [Indexed: 11/13/2022] Open
Abstract
Objective There were few studies that had attempted to predict facial emotion recognition (FER) ability at the individual level in schizophrenia patients. In this study, we developed a model for the prediction of FER ability in Chinese Han patients with the first-episode schizophrenia (FSZ). Materials and Methods A total of 28 patients with FSZ and 33 healthy controls (HCs) were recruited. All subjects underwent resting-state fMRI (rs-fMRI). The amplitude of low-frequency fluctuation (ALFF) method was selected to analyze voxel-level spontaneous neuronal activity. The visual search experiments were selected to evaluate the FER, while the support vector regression (SVR) model was selected to develop a model based on individual rs-fMRI brain scan. Results Group difference in FER ability showed statistical significance (P < 0.05). In FSZ patients, increased mALFF value were observed in the limbic lobe and frontal lobe, while decreased mALFF value were observed in the frontal lobe, parietal lobe, and occipital lobe (P < 0.05, AlphaSim correction). SVR analysis showed that abnormal spontaneous activity in multiple brain regions, especially in the right posterior cingulate, right precuneus, and left calcarine could effectively predict fearful FER accuracy (r = 0.64, P = 0.011) in patients. Conclusion Our study provides an evidence that abnormal spontaneous activity in specific brain regions may serve as a predictive biomarker for fearful FER ability in schizophrenia.
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Affiliation(s)
- Qi-Jie Kuang
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Su-Miao Zhou
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Yi Liu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Hua-Wang Wu
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Tai-Yong Bi
- Centre for Mental Health Research in School of Management, Zunyi Medical University, Zunyi, China
| | - Sheng-Lin She
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Ying-Jun Zheng
- Department of Psychiatry, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
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Haeger A, Pouzat C, Luecken V, N’Diaye K, Elger C, Kennerknecht I, Axmacher N, Dinkelacker V. Face Processing in Developmental Prosopagnosia: Altered Neural Representations in the Fusiform Face Area. Front Behav Neurosci 2021; 15:744466. [PMID: 34867227 PMCID: PMC8636799 DOI: 10.3389/fnbeh.2021.744466] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Accepted: 10/20/2021] [Indexed: 11/13/2022] Open
Abstract
Rationale: Face expertise is a pivotal social skill. Developmental prosopagnosia (DP), i.e., the inability to recognize faces without a history of brain damage, affects about 2% of the general population, and is a renowned model system of the face-processing network. Within this network, the right Fusiform Face Area (FFA), is particularly involved in face identity processing and may therefore be a key element in DP. Neural representations within the FFA have been examined with Representational Similarity Analysis (RSA), a data-analytical framework in which multi-unit measures of brain activity are assessed with correlation analysis. Objectives: Our study intended to scrutinize modifications of FFA-activation during face encoding and maintenance based on RSA. Methods: Thirteen participants with DP (23-70 years) and 12 healthy control subjects (19-62 years) participated in a functional MRI study, including morphological MRI, a functional FFA-localizer and a modified Sternberg paradigm probing face memory encoding and maintenance. Memory maintenance of one, two, or four faces represented low, medium, and high memory load. We examined conventional activation differences in response to working memory load and applied RSA to compute individual correlation-matrices on the voxel level. Group correlation-matrices were compared via Donsker's random walk analysis. Results: On the functional level, increased memory load entailed both a higher absolute FFA-activation level and a higher degree of correlation between activated voxels. Both aspects were deficient in DP. Interestingly, control participants showed a homogeneous degree of correlation for successful trials during the experiment. In DP-participants, correlation levels between FFA-voxels were significantly lower and were less sustained during the experiment. In behavioral terms, DP-participants performed poorer and had longer reaction times in relation to DP-severity. Furthermore, correlation levels were negatively correlated with reaction times for the most demanding high load condition. Conclusion: We suggest that participants with DP fail to generate robust and maintained neural representations in the FFA during face encoding and maintenance, in line with poorer task performance and prolonged reaction times. In DP, alterations of neural coding in the FFA might therefore explain curtailing in working memory and contribute to impaired long-term memory and mental imagery.
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Affiliation(s)
- Alexa Haeger
- JARA-BRAIN, Jülich, Germany
- Forschungszentrum Jülich GmbH, Institute of Neuroscience and Medicine (INM-11), Jülich, Germany
- Department of Neurology, RWTH Aachen University, Aachen, Germany
| | | | | | - Karim N’Diaye
- Institut du Cerveau et de la Moelle épinière, ICM, Inserm U 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | | | - Ingo Kennerknecht
- Institute of Human Genetics, Westfaelische Wilhelms-Universitaet Muenster, Muenster, Germany
| | - Nikolai Axmacher
- Department of Neuropsychology, Ruhr University Bochum, Bochum, Germany
| | - Vera Dinkelacker
- Neurology Department, Hautepierre Hospital, University of Strasbourg, Strasbourg, France
- Rothschild Foundation, Neurology Department, Paris, France
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Wu C, Zhen Z, Huang L, Huang T, Liu J. COMT-Polymorphisms Modulated Functional Profile of the Fusiform Face Area Contributes to Face-Specific Recognition Ability. Sci Rep 2020; 10:2134. [PMID: 32034175 PMCID: PMC7005682 DOI: 10.1038/s41598-020-58747-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2019] [Accepted: 01/15/2020] [Indexed: 12/03/2022] Open
Abstract
Previous studies have shown that face-specific recognition ability (FRA) is heritable; however, the neural basis of this heritability is unclear. Candidate gene studies have suggested that the catechol-O-methyltransferase (COMT) rs4680 polymorphism is related to face perception. Here, using a partial least squares (PLS) method, we examined the multivariate association between 12 genotypes of 4 COMT polymorphisms (rs6269-rs4633-rs4818-rs4680) and multimodal MRI phenotypes in the human fusiform face area (FFA), which selectively responds to face stimuli, in 338 Han Chinese adults (mean age 20.45 years; 135 males). The MRI phenotypes included gray matter volume (GMV), resting-state fractional amplitude of low-frequency fluctuations (fALFF), and face-selective blood-oxygen-level-dependent (BOLD) responses (FS). We found that the first COMT-variant component (PLS1) was positively associated with the FS but negatively associated with the fALFF in the FFA. Moreover, participants with the COMT heterozygous-HEA-haplotype showed higher PLS1 FFA-MRI scores, which were positively associated with the FRA in an old/new face recognition task, than those with the COMT homozygous HEA haplotype and HEA non-carriers, suggesting that individuals with an appropriate (intermediate) level of dopamine activity in the FFA might have better FRA. In summary, our study provides empirical evidence for the genetic and neural basis for the heritability of face recognition and informs the formation of neural module functional specificity.
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Affiliation(s)
- Chao Wu
- School of Nursing, Peking University Health Science Centre, Beijing, 100191, China
| | - Zonglei Zhen
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
| | - Lijie Huang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Taicheng Huang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China
| | - Jia Liu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
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Wang X, Zhu Q, Song Y, Liu J. Developmental Reorganization of the Core and Extended Face Networks Revealed by Global Functional Connectivity. Cereb Cortex 2019; 28:3521-3530. [PMID: 28968833 DOI: 10.1093/cercor/bhx217] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 07/30/2017] [Indexed: 12/15/2022] Open
Abstract
Prior studies on development of functional specialization in human brain mainly focus on age-related increases in regional activation and connectivity among regions. However, a few recent studies on the face network demonstrate age-related decrease in face-specialized activation in the extended face network (EFN), in addition to increase in activation in the core face network (CFN). Here we used a voxel-based global brain connectivity approach to investigate whether development of the face network exhibited both increase and decrease in network connectivity. We found the voxel-wise resting-state functional connectivity (FC) within the CFN increased with age in bilateral posterior superior temporal sulcus, suggesting the integration of the CFN during development. Interestingly, the FC of the voxels in the EFN to the right fusiform face area and occipital face area decreased with age, suggesting that the CFN segregated from the EFN during development. Moreover, the age-related connectivity in the CFN was related to behavioral performance in face processing. Overall, our study demonstrated developmental reorganization of the face network achieved by both integration within the CFN and segregation of the CFN from the EFN, which may account for the simultaneous increases and decreases in neural activation during the development of the face network.
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Affiliation(s)
- Xu Wang
- School of Life Sciences, Beijing University of Chinese Medicine, Beijing, China
| | - Qi Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yiying Song
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jia Liu
- Beijing Key Laboratory of Applied Experimental Psychology & National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, China
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How the depth of processing modulates emotional interference – evidence from EEG and pupil diameter data. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2019; 19:1231-1246. [DOI: 10.3758/s13415-019-00732-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Schreiter ML, Chmielewski W, Beste C. Neurophysiological processes and functional neuroanatomical structures underlying proactive effects of emotional conflicts. Neuroimage 2018. [DOI: 10.1016/j.neuroimage.2018.03.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
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The neural network for face recognition: Insights from an fMRI study on developmental prosopagnosia. Neuroimage 2017; 169:151-161. [PMID: 29242103 DOI: 10.1016/j.neuroimage.2017.12.023] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 11/23/2017] [Accepted: 12/10/2017] [Indexed: 12/18/2022] Open
Abstract
Face recognition is supported by collaborative work of multiple face-responsive regions in the brain. Based on findings from individuals with normal face recognition ability, a neural model has been proposed with the occipital face area (OFA), fusiform face area (FFA), and face-selective posterior superior temporal sulcus (pSTS) as the core face network (CFN) and the rest of the face-responsive regions as the extended face network (EFN). However, little is known about how these regions work collaboratively for face recognition in our daily life. Here we focused on individuals suffering developmental prosopagnosia (DP), a neurodevelopmental disorder specifically impairing face recognition, to shed light on the infrastructure of the neural model of face recognition. Specifically, we used a variant of global brain connectivity method to comprehensively explore resting-state functional connectivity (FC) among face-responsive regions in a large sample of DPs (N = 64). We found that both the FCs within the CFN and those between the CFN and EFN were largely reduced in DP. Importantly, the right OFA and FFA served as the dysconnectivity hubs within the CFN, i.e., FCs concerning these two regions within the CFN were largely disrupted. In addition, DPs' right FFA also showed reduced FCs with the EFN. Moreover, these disrupted FCs were related to DP's behavioral deficit in face recognition, with the FCs from the FFA to the anterior temporal lobe (ATL) and pSTS the most predictive. Based on these findings, we proposed a revised neural model of face recognition demonstrating the relatedness of interactions among face-responsive regions to face recognition.
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Li J, Huang L, Song Y, Liu J. Dissociated neural basis of two behavioral hallmarks of holistic face processing: The whole-part effect and composite-face effect. Neuropsychologia 2017; 102:52-60. [PMID: 28552781 DOI: 10.1016/j.neuropsychologia.2017.05.026] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2016] [Revised: 05/05/2017] [Accepted: 05/24/2017] [Indexed: 12/19/2022]
Abstract
It has been long proposed that our extraordinary face recognition ability stems from holistic face processing. Two widely-used behavioral hallmarks of holistic face processing are the whole-part effect (WPE) and composite-face effect (CFE). However, it remains unknown whether these two effects reflect similar or different aspects of holistic face processing. Here we investigated this question by examining whether the WPE and CFE involved shared or distinct neural substrates in a large sample of participants (N=200). We found that the WPE and CFE showed hemispheric dissociation in the fusiform face area (FFA), that is, the WPE was correlated with face selectivity in the left FFA, while the CFE was correlated with face selectivity in the right FFA. Further, the correlation between the WPE and face selectivity was largely driven by the FFA response to faces, whereas the association between the CFE and face selectivity resulted from suppressed response to objects in the right FFA. Finally, we also observed dissociated correlation patterns of the WPE and CFE in other face-selective regions and across the whole brain. These results suggest that the WPE and CFE may reflect different aspects of holistic face processing, which shed new light on the behavioral dissociations of these two effects demonstrated in literature.
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Affiliation(s)
- Jin Li
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Lijie Huang
- Research Center for Brain-inspired Intelligence, Institute of Automation, Chinese Academy of Sciences, Beijing 100875, China
| | - Yiying Song
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
| | - Jia Liu
- Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, Beijing 100875, China.
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A study in facial features saliency in face recognition: an analytic hierarchy process approach. Soft comput 2016. [DOI: 10.1007/s00500-016-2305-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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