1
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Li Z, Fang H, Fan W, Wu J, Cui J, Li BM, Wang C. Brain markers of subtraction and multiplication skills in childhood: task-based functional connectivity and individualized structural similarity. Cereb Cortex 2024; 34:bhae374. [PMID: 39329357 DOI: 10.1093/cercor/bhae374] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 08/20/2024] [Accepted: 08/30/2024] [Indexed: 09/28/2024] Open
Abstract
Arithmetic, a high-order cognitive ability, show marked individual difference over development. Despite recent advancements in neuroimaging techniques have enabled the identification of brain markers for individual differences in high-order cognitive abilities, it remains largely unknown about the brain markers for arithmetic. This study used a data-driven connectome-based prediction model to identify brain markers of arithmetic skills from arithmetic-state functional connectivity and individualized structural similarity in 132 children aged 8 to 15 years. We found that both subtraction-state functional connectivity and individualized SS successfully predicted subtraction and multiplication skills but multiplication-state functional connectivity failed to predict either skill. Among the four successful prediction models, most predictive connections were located in frontal-parietal, default-mode, and secondary visual networks. Further computational lesion analyses revealed the essential structural role of frontal-parietal network in predicting subtraction and the essential functional roles of secondary visual, language, and ventral multimodal networks in predicting multiplication. Finally, a few shared nodes but largely nonoverlapping functional and structural connections were found to predict subtraction and multiplication skills. Altogether, our findings provide new insights into the brain markers of arithmetic skills in children and highlight the importance of studying different connectivity modalities and different arithmetic domains to advance our understanding of children's arithmetic skills.
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Affiliation(s)
- Zheng Li
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
| | - Haifeng Fang
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
| | - Weiguo Fan
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
| | - Jiaoyu Wu
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
| | - Jiaxin Cui
- College of Education, Hebei Normal University, South Second Ring Road 20, Shijiazhuang 050016, China
| | - Bao-Ming Li
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
- Zhejiang Philosophy and Social Science Laboratory for Research in Early Development and Childcare, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
| | - Chunjie Wang
- Institute of Brain Science, School of Basic Medical Sciences, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Yuhangtang Road 2318, Yuhang District, Hangzhou 311121, China
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2
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Leota J, Faulkner P, Mazidi S, Simpson D, Nash K. Neural rhythms of narcissism: Facets of narcissism are associated with different neural sources in resting-state EEG. Eur J Neurosci 2024; 60:4907-4921. [PMID: 39073208 DOI: 10.1111/ejn.16479] [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: 09/07/2023] [Revised: 05/03/2024] [Accepted: 07/10/2024] [Indexed: 07/30/2024]
Abstract
Trait narcissism is characterized by significant heterogeneity across individuals. Despite advances in the conceptualization of narcissism, including the increasing recognition that narcissism is a multidimensional construct, the sources of this heterogeneity remain poorly understood. Here, we used a neural trait approach to help better understand "how," and shed light on "why," individuals vary in facets of trait narcissism. Participants (N = 58) first completed personality measures, including the Narcissistic Personality Inventory (NPI), and then in a second session sat passively while resting-state electroencephalography (rs-EEG) was recorded. We then regressed source-localized rs-EEG activity on the distinct facets of narcissism: Grandiose Exhibitionism (GE), Entitlement/Exploitativeness (EE), and Leadership/Authority (LA). Results revealed that each facet was associated with different (though sometimes overlapping) neural sources. Specifically, GE was associated with reduced activation in the dorsomedial prefrontal cortex (DMPFC). EE was associated with reduced activation in the DMPFC and right lateral PFC. LA was associated with increased activation in the left anterior temporal cortex. These findings support the idea that trait narcissism is a multidimensional construct undergirded by individual differences in neural regions related to social cognition (the DMPFC), self-regulation (right lateral PFC), and self-referential processing (left anterior temporal cortex).
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Affiliation(s)
- Josh Leota
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, Australia
- School of Psychological Sciences, Monash University, Melbourne, Australia
| | - Paige Faulkner
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - Shafa Mazidi
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - David Simpson
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
| | - Kyle Nash
- Department of Psychology, University of Alberta, Edmonton, AB, Canada
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3
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Ramduny J, Kelly C. Connectome-based fingerprinting: reproducibility, precision, and behavioral prediction. Neuropsychopharmacology 2024:10.1038/s41386-024-01962-8. [PMID: 39147868 DOI: 10.1038/s41386-024-01962-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 08/02/2024] [Accepted: 08/05/2024] [Indexed: 08/17/2024]
Abstract
Functional magnetic resonance imaging-based functional connectivity enables the non-invasive mapping of individual differences in brain functional organization to individual differences in a vast array of behavioral phenotypes. This flexibility has renewed the search for neuroimaging-based biomarkers that exhibit reproducibility, prediction, and precision. Functional connectivity-based measures that share these three characteristics are key to achieving this goal. Here, we review the functional connectome fingerprinting approach and discuss its value, not only as a simple and intuitive conceptualization of the "functional connectome" that provides new insights into how the connectome is altered in association with psychiatric symptoms, but also as a straightforward and interpretable method for indexing the reproducibility of functional connectivity-based measures. We discuss how these advantages provide new avenues for strengthening reproducibility, precision, and behavioral prediction for functional connectomics and we consider new directions toward discovering better biomarkers for neuropsychiatric conditions.
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Affiliation(s)
- Jivesh Ramduny
- Department of Psychology, Yale University, New Haven, CT, USA.
- Kavli Institute for Neuroscience, Yale University, New Haven, CT, USA.
| | - Clare Kelly
- School of Psychology, Trinity College Dublin, Dublin, Ireland.
- Department of Psychiatry, School of Medicine, Trinity College Dublin, Dublin, Ireland.
- Trinity College Institute of Neuroscience, Trinity College Dublin, Dublin, Ireland.
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4
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Li Y, Yang L, Hao D, Chen Y, Ye-Lin Y, Li CSR, Li G. Functional Networks of Reward and Punishment Processing and Their Molecular Profiles Predicting the Severity of Young Adult Drinking. Brain Sci 2024; 14:610. [PMID: 38928610 PMCID: PMC11201596 DOI: 10.3390/brainsci14060610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2024] [Revised: 06/15/2024] [Accepted: 06/16/2024] [Indexed: 06/28/2024] Open
Abstract
Alcohol misuse is associated with altered punishment and reward processing. Here, we investigated neural network responses to reward and punishment and the molecular profiles of the connectivity features predicting alcohol use severity in young adults. We curated the Human Connectome Project data and employed connectome-based predictive modeling (CPM) to examine how functional connectivity (FC) features during wins and losses are associated with alcohol use severity, quantified by Semi-Structured Assessment for the Genetics of Alcoholism, in 981 young adults. We combined the CPM findings and the JuSpace toolbox to characterize the molecular profiles of the network connectivity features of alcohol use severity. The connectomics predicting alcohol use severity appeared specific, comprising less than 0.12% of all features, including medial frontal, motor/sensory, and cerebellum/brainstem networks during punishment processing and medial frontal, fronto-parietal, and motor/sensory networks during reward processing. Spatial correlation analyses showed that these networks were associated predominantly with serotonergic and GABAa signaling. To conclude, a distinct pattern of network connectivity predicted alcohol use severity in young adult drinkers. These "neural fingerprints" elucidate how alcohol misuse impacts the brain and provide evidence of new targets for future intervention.
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Affiliation(s)
- Yashuang Li
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
| | - Lin Yang
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
| | - Dongmei Hao
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
| | - Yu Chen
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA (C.-S.R.L.)
| | - Yiyao Ye-Lin
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
- Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Chiang-Shan Ray Li
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT 06510, USA (C.-S.R.L.)
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06511, USA
- Interdepartmental Neuroscience Program, Yale University School of Medicine, New Haven, CT 06520, USA
- Wu Tsai Institute, Yale University, New Haven, CT 06511, USA
| | - Guangfei Li
- Department of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, 100 Pingleyuan, Beijing 100124, China; (Y.L.)
- Beijing International Science and Technology Cooperation Base for Intelligent Physiological Measurement and Clinical Transformation, Beijing 100124, China
- BJUT-UPV Joint Research Laboratory in Biomedical Engineering, 46022 Valencia, Spain
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Jornkokgoud K, Baggio T, Bakiaj R, Wongupparaj P, Job R, Grecucci A. Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits. Eur J Neurosci 2024; 59:3273-3291. [PMID: 38649337 DOI: 10.1111/ejn.16345] [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: 12/13/2023] [Revised: 03/11/2024] [Accepted: 03/24/2024] [Indexed: 04/25/2024]
Abstract
Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.
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Affiliation(s)
- Khanitin Jornkokgoud
- Department of Research and Applied Psychology, Faculty of Education, Burapha University, Chonburi, Thailand
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Teresa Baggio
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Richard Bakiaj
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Peera Wongupparaj
- Department of Psychology, Faculty of Humanities and Social Sciences, Burapha University, Chonburi, Thailand
| | - Remo Job
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
| | - Alessandro Grecucci
- Department of Psychology and Cognitive Science (DiPSCo), University of Trento, Rovereto, Italy
- Centre for Medical Sciences (CISMed), University of Trento, Trento, Italy
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6
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Chen Y, He H, Ding Y, Tao W, Guan Q, Krueger F. Connectome-based prediction of decreased trust propensity in older adults with mild cognitive impairment: A resting-state functional magnetic resonance imaging study. Neuroimage 2024; 292:120605. [PMID: 38615705 DOI: 10.1016/j.neuroimage.2024.120605] [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: 12/01/2023] [Revised: 03/19/2024] [Accepted: 04/08/2024] [Indexed: 04/16/2024] Open
Abstract
Trust propensity (TP) relies more on social than economic rationality to transform the perceived probability of betrayal into positive reciprocity expectations in older adults with normal cognition. While deficits in social rationality have been observed in older adults with mild cognitive impairment (MCI), there is limited research on TP and its associated resting-state functional connectivity (RSFC) mechanisms in this population. To measure TP and related psychological functions (affect, motivation, executive cognition, and social cognition), MCI (n = 42) and normal healthy control (NHC, n = 115) groups completed a one-shot trust game and additional assessments of related psychological functions. RSFC associated with TP was analyzed using connectome-based predictive modeling (CPM) and lesion simulations. Our behavioral results showed that the MCI group trusted less (i.e., had lower TP) than the NHC group, with lower TP associated with higher sensitivity to the probability of betrayal in the MCI group. In the MCI group, only negative CPM models (RSFC negatively correlated with TP) significantly predicted TP, with a high salience network (SN) contribution. In contrast, in the NHC group, positive CPM models (RSFC positively correlated with TP) significantly predicted TP, with a high contribution from the default mode network (DMN). In addition, the total network strength of the NHC-specific positive network was lower in the MCI group than in the NHC group. Our findings demonstrated a decrease in TP in the MCI group compared to the NHC group, which is associated with deficits in social rationality (social cognition, associated with DMN) and increased sensitivity to betrayal (affect, associated with SN) in a trust dilemma. In conclusion, our study contributes to understanding MCI-related alterations in trust and their underlying neural mechanisms.
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Affiliation(s)
- Yiqi Chen
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Department of Psychology, University of Mannheim, Mannheim 68131, Germany
| | - Hao He
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
| | - Yiyang Ding
- School of Psychology, Shenzhen University, Shenzhen 518060, China
| | - Wuhai Tao
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China.
| | - Qing Guan
- School of Psychology, Shenzhen University, Shenzhen 518060, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China.
| | - Frank Krueger
- Department of Psychology, University of Mannheim, Mannheim 68131, Germany; School of Systems Biology, George Mason University, Fair, VA, USA
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7
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Feng Q, Ren Z, Wei D, Liu C, Wang X, Li X, Tie B, Tang S, Qiu J. Connectome-based predictive modeling of Internet addiction symptomatology. Soc Cogn Affect Neurosci 2024; 19:nsae007. [PMID: 38334691 PMCID: PMC10878364 DOI: 10.1093/scan/nsae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 12/14/2023] [Accepted: 02/06/2024] [Indexed: 02/10/2024] Open
Abstract
Internet addiction symptomatology (IAS) is characterized by persistent and involuntary patterns of compulsive Internet use, leading to significant impairments in both physical and mental well-being. Here, a connectome-based predictive modeling approach was applied to decode IAS from whole-brain resting-state functional connectivity in healthy population. The findings showed that IAS could be predicted by the functional connectivity between prefrontal cortex with the cerebellum and limbic lobe and connections of the occipital lobe with the limbic lobe and insula lobe. The identified edges associated with IAS exhibit generalizability in predicting IAS within an independent sample. Furthermore, we found that the unique contributing network, which predicted IAS in contrast to the prediction networks of alcohol use disorder symptomatology (the range of symptoms and behaviors associated with alcohol use disorder), prominently comprised connections involving the occipital lobe and other lobes. The current data-driven approach provides the first evidence of the predictive brain features of IAS based on the organization of intrinsic brain networks, thus advancing our understanding of the neurobiological basis of Internet addiction disorder (IAD) susceptibility, and may have implications for the timely intervention of people potentially at risk of IAD.
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Affiliation(s)
- Qiuyang Feng
- Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University (SWU), Chongqing 400715, China
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
| | - Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Xianrui Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Bijie Tie
- Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China, Southwest University (SWU), Chongqing 400715, China
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
| | - Shuang Tang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China
- School of Psychology, Southwest University (SWU), Chongqing 400715, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing 100000, China
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8
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Liu X, Hu Y, Hao Y, Yang L. Individual differences in the neural architecture in semantic processing. Sci Rep 2024; 14:170. [PMID: 38168133 PMCID: PMC10761854 DOI: 10.1038/s41598-023-49538-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 12/09/2023] [Indexed: 01/05/2024] Open
Abstract
Neural mechanisms underlying semantic processing have been extensively studied by using functional magnetic resonance imaging, nevertheless, the individual differences of it are yet to be unveiled. To further our understanding of functional and anatomical brain organization underlying semantic processing to the level of individual humans, we used out-of-scanner language behavioral data, T1, resting-state, and story comprehension task-evoked functional image data in the Human Connectome Project, to investigate individual variability in the task-evoked semantic processing network, and attempted to predict individuals' language skills based on task and intrinsic functional connectivity of highly variable regions, by employing a machine-learning framework. Our findings first confirmed that individual variability in both functional and anatomical markers were heterogeneously distributed throughout the semantic processing network, and that the variability increased towards higher levels in the processing hierarchy. Furthermore, intrinsic functional connectivities among these highly variable regions were found to contribute to predict individual reading decoding abilities. The contributing nodes in the overall network were distributed in the left superior, inferior frontal, and temporo-parietal cortices. Our results suggested that the individual differences of neurobiological markers were heterogeneously distributed in the semantic processing network, and that neurobiological markers of highly variable areas are not only linked to individual variability in language skills, but can predict language skills at the individual level.
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Affiliation(s)
- Xin Liu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China.
| | - Yiwen Hu
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Yaokun Hao
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
| | - Liu Yang
- Air Force Medical Center, Air Force Medical University, No. 28, Fucheng Street, Haidian District, Beijing, 100142, China
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9
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Feng C, Tian X, Luo YJ. Neurocomputational Substrates Underlying the Effect of Identifiability on Third-Party Punishment. J Neurosci 2023; 43:8018-8031. [PMID: 37752000 PMCID: PMC10669760 DOI: 10.1523/jneurosci.0460-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 09/28/2023] Open
Abstract
The identifiable target effect refers to the preference for helping identified victims and punishing identifiable perpetrators compared with equivalent but unidentifiable counterparts. The identifiable target effect is often attributed to the heightened moral emotions evoked by identified targets. However, the specific neurocognitive processes that mediate and/or modulate this effect remain largely unknown. Here, we combined a third-party punishment game with brain imaging and computational modeling to unravel the neurocomputational underpinnings of the identifiable transgressor effect. Human participants (males and females) acted as bystanders and punished identified or anonymous wrongdoers. Participants were more punitive toward identified wrongdoers than anonymous wrongdoers because they took a vicarious perspective of victims and adopted lower reference points of inequity (i.e., more stringent norms) in the identified context than in the unidentified context. Accordingly, there were larger activity of the ventral anterior insula, more distinct multivariate neural patterns in the dorsal anterior insula and dorsal anterior cingulate cortex, and lower strength between ventral anterior insula and dorsolateral PFC and between dorsal anterior insula and ventral striatum connectivity in response to identified transgressors than anonymous transgressors. These findings implicate the interplay of expectancy violations, emotions, and self-interest in the identifiability effect. Last, individual differences in the identifiability effect were associated with empathic concern/social dominance orientation, activity in the precuneus/cuneus and temporo-parietal junction, and intrinsic functional connectivity of the dorsolateral PFC. Together, our work is the first to uncover the neurocomputational processes mediating identifiable transgressor effect and to characterize psychophysiological profiles modulating the effect.SIGNIFICANCE STATEMENT The identifiable target effect, more help to identified victims or stronger punishment to identifiable perpetrators, is common in daily life. We examined the neurocomputational mechanisms mediating/modulating the identifiability effect on third-party punishment by bridging literature from economics and cognitive neuroscience. Our findings reveal that identifiable transgressor effect is mediated by lower reference points of inequity (i.e., more stringent norms), which might be associated with a stronger involvement of the emotion processes and a weaker engagement of the analytic/deliberate processes. Furthermore, personality traits, altered brain activity, and intrinsic functional connectivity contribute to the individual variance in the identifiability effect. Overall, our study advances the understanding of the identifiability effect by shedding light on its component processes and modulating factors.
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Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences, South China Normal University, Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Xia Tian
- Key Laboratory of Brain, Cognition and Education Sciences, South China Normal University, Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Yue-Jia Luo
- The State Key Lab of Cognitive and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
- Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, 266113, China
- School of Psychology, Chengdu Medical College, Chengdu, 610500, China
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10
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Liu X, Yang L. Individual differences in the language task-evoked and resting-state functional networks. Front Hum Neurosci 2023; 17:1283069. [PMID: 38021226 PMCID: PMC10656779 DOI: 10.3389/fnhum.2023.1283069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Accepted: 09/29/2023] [Indexed: 12/01/2023] Open
Abstract
The resting state functional network is highly variable across individuals. However, inter-individual differences in functional networks evoked by language tasks and their comparison with resting state are still unclear. To address these two questions, we used T1 anatomical data and functional brain imaging data of resting state and a story comprehension task from the Human Connectome Project (HCP) to characterize functional network variability and investigate the uniqueness of the functional network in both task and resting states. We first demonstrated that intrinsic and task-induced functional networks exhibited remarkable differences across individuals, and language tasks can constrain inter-individual variability in the functional brain network. Furthermore, we found that the inter-individual variability of functional networks in two states was broadly consistent and spatially heterogeneous, with high-level association areas manifesting more significant variability than primary visual processing areas. Our results suggested that the functional network underlying language comprehension is unique at the individual level, and the inter-individual variability architecture of the functional network is broadly consistent in language task and resting state.
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Affiliation(s)
- Xin Liu
- Air Force Medical Center, Air Force Medical University, Beijing, China
| | - Liu Yang
- Air Force Medical Center, Air Force Medical University, Beijing, China
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11
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Ash S, Greenwood D, Keenan JP. The Neural Correlates of Narcissism: Is There a Connection with Desire for Fame and Celebrity Worship? Brain Sci 2023; 13:1499. [PMID: 37891865 PMCID: PMC10605183 DOI: 10.3390/brainsci13101499] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 10/13/2023] [Accepted: 10/20/2023] [Indexed: 10/29/2023] Open
Abstract
(1) Objective: Narcissism is characterized by emotional regulation deficits, a lack of empathy for others, and extreme self-focus. Narcissism has also been linked to an increased desire for fame and celebrity worship. Here, the neuroscience underlying narcissism is examined in order to determine what regions and networks of the brain are altered when non-narcissistic individuals are compared to participants with both grandiose and vulnerable narcissism. (2) Methods: The behavioral relationships between grandiose narcissism and desire for fame and vulnerable narcissism and celebrity worship are explored, along with a possible relationship at the neural level between these constructs. In this paper, we review research demonstrating that increased levels of grandiose narcissism are associated with an increase in obsession with fame, while vulnerable narcissism is associated with celebrity worship. (3) Results: Based on current data, the frontal regions underlie narcissism and also likely underlie celebrity worship and desire for fame. This tenuous conclusion is based on a limited number of studies. (4) Conclusions: The brain areas associated with grandiose narcissism may be associated with an intense desire for fame as well, while brain regions associated with vulnerable narcissism may be similar in celebrity worshipers. Future research studies on the brain that are specifically designed to test these relationships at a neurological level are needed.
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Affiliation(s)
- Sydney Ash
- Cognitive Neuroimaging Laboratory, Montclair State University, 320 Science Hall, Montclair, NJ 07043, USA
| | - Dara Greenwood
- Department of Psychology, Vassar College, Poughkeepsie, NY 12604, USA
| | - Julian Paul Keenan
- Cognitive Neuroimaging Laboratory, Montclair State University, 320 Science Hall, Montclair, NJ 07043, USA
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12
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Chen X, Dong D, Zhou F, Gao X, Liu Y, Wang J, Qin J, Tian Y, Xiao M, Xu X, Li W, Qiu J, Feng T, He Q, Lei X, Chen H. Connectome-based prediction of eating disorder-associated symptomatology. Psychol Med 2023; 53:5786-5799. [PMID: 36177890 DOI: 10.1017/s0033291722003026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.
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Affiliation(s)
- Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Junjie Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jingmin Qin
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yun Tian
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaofei Xu
- School of Computing Technologies, RMIT University, Melbourne, Australia
| | - Wei Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
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13
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Zhang Y, Wang F, Sui J. Decoding individual differences in self-prioritization from the resting-state functional connectome. Neuroimage 2023; 276:120205. [PMID: 37253415 DOI: 10.1016/j.neuroimage.2023.120205] [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: 01/13/2023] [Revised: 05/23/2023] [Accepted: 05/27/2023] [Indexed: 06/01/2023] Open
Abstract
Although the self has traditionally been viewed as a higher-order mental function by most theoretical frameworks, recent research advocates a fundamental self hypothesis, viewing the self as a baseline function of the brain embedded within its spontaneous activities, which dynamically regulates cognitive processing and subsequently guides behavior. Understanding this fundamental self hypothesis can reveal where self-biased behaviors emerge and to what extent brain signals at rest can predict such biased behaviors. To test this hypothesis, we investigated the association between spontaneous neural connectivity and robust self-bias in a perceptual matching task using resting-state functional magnetic resonance imaging (fMRI) in 348 young participants. By decoding whole-brain connectivity patterns, the support vector regression model produced the best predictions of the magnitude of self-bias in behavior, which was evaluated via a nested cross-validation procedure. The out-of-sample generalizability was further authenticated using an external dataset of older adults. The functional connectivity results demonstrated that self-biased behavior was associated with distinct connections between the default mode, cognitive control, and salience networks. Consensus network and computational lesion analyses further revealed contributing regions distributed across six networks, extending to additional nodes, such as the thalamus, whose role in self-related processing remained unclear. These results provide evidence that self-biased behavior derives from spontaneous neural connectivity, supporting the fundamental self hypothesis. Thus, we propose an integrated neural network model of this fundamental self that synthesizes previous theoretical models and portrays the brain mechanisms by which the self emerges at rest internally and regulates responses to the external environment.
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Affiliation(s)
- Yongfa Zhang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China
| | - Fei Wang
- Department of Psychology, School of Social Sciences, Tsinghua University, Beijing 100084, China; Laboratory of Brain and Intelligence, Tsinghua University, Beijing 100084, China; The Centre for Positive Psychology Research, Tsinghua University, Beijing 100084, China.
| | - Jie Sui
- School of Psychology, University of Aberdeen, Aberdeen AB24 3FX, Scotland, Great Britain
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14
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Zwir I, Arnedo J, Mesa A, Del Val C, de Erausquin GA, Cloninger CR. Temperament & Character account for brain functional connectivity at rest: A diathesis-stress model of functional dysregulation in psychosis. Mol Psychiatry 2023; 28:2238-2253. [PMID: 37015979 PMCID: PMC10611583 DOI: 10.1038/s41380-023-02039-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Revised: 03/11/2023] [Accepted: 03/15/2023] [Indexed: 04/06/2023]
Abstract
The human brain's resting-state functional connectivity (rsFC) provides stable trait-like measures of differences in the perceptual, cognitive, emotional, and social functioning of individuals. The rsFC of the prefrontal cortex is hypothesized to mediate a person's rational self-government, as is also measured by personality, so we tested whether its connectivity networks account for vulnerability to psychosis and related personality configurations. Young adults were recruited as outpatients or controls from the same communities around psychiatric clinics. Healthy controls (n = 30) and clinically stable outpatients with bipolar disorder (n = 35) or schizophrenia (n = 27) were diagnosed by structured interviews, and then were assessed with standardized protocols of the Human Connectome Project. Data-driven clustering identified five groups of patients with distinct patterns of rsFC regardless of diagnosis. These groups were distinguished by rsFC networks that regulate specific biopsychosocial aspects of psychosis: sensory hypersensitivity, negative emotional balance, impaired attentional control, avolition, and social mistrust. The rsFc group differences were validated by independent measures of white matter microstructure, personality, and clinical features not used to identify the subjects. We confirmed that each connectivity group was organized by differential collaborative interactions among six prefrontal and eight other automatically-coactivated networks. The temperament and character traits of the members of these groups strongly accounted for the differences in rsFC between groups, indicating that configurations of rsFC are internal representations of personality organization. These representations involve weakly self-regulated emotional drives of fear, irrational desire, and mistrust, which predispose to psychopathology. However, stable outpatients with different diagnoses (bipolar or schizophrenic psychoses) were highly similar in rsFC and personality. This supports a diathesis-stress model in which different complex adaptive systems regulate predisposition (which is similar in stable outpatients despite diagnosis) and stress-induced clinical dysfunction (which differs by diagnosis).
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Affiliation(s)
- Igor Zwir
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
- University of Granada, Department of Computer Science, Granada, Spain
- University of Texas, Rio Grande Valley School of Medicine, Institute of Neuroscience, Harlingen, TX, USA
| | - Javier Arnedo
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA
- University of Granada, Department of Computer Science, Granada, Spain
| | - Alberto Mesa
- University of Granada, Department of Computer Science, Granada, Spain
| | - Coral Del Val
- University of Granada, Department of Computer Science, Granada, Spain
| | - Gabriel A de Erausquin
- University of Texas, Long School of Medicine, Department of Neurology, San Antonio, TX, USA
- Laboratory of Brain Development, Modulation and Repair, Glenn Biggs Institute of Alzheimer's & Neurodegenerative Disorders, San Antonio, TX, USA
| | - C Robert Cloninger
- Washington University School of Medicine, Department of Psychiatry, St. Louis, MO, USA.
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15
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Ren Z, Sun J, Liu C, Li X, Li X, Li X, Liu Z, Bi T, Qiu J. Individualized prediction of trait self-control from whole-brain functional connectivity. Psychophysiology 2023; 60:e14209. [PMID: 36325626 DOI: 10.1111/psyp.14209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Revised: 09/24/2022] [Accepted: 09/27/2022] [Indexed: 11/06/2022]
Abstract
Self-control is a core psychological construct for human beings and it plays a crucial role in the adaptation to society and achievement of success and happiness for individuals. Although progress has been made in behavioral studies examining self-control, its neural mechanisms remain unclear. In this study, we employed a machine-learning approach-relevance vector regression (RVR) to explore the potential predictive power of intrinsic functional connections to trait self-control in a large sample (N = 390). We used resting-state functional MRI (fMRI) to explore whole-brain functional connectivity patterns characteristic of 390 healthy adults and to confirm the effectiveness of RVR in predicting individual trait self-control scores. A set of connections across multiple neural networks that significantly predicted individual differences were identified, including the classic control network (e.g., fronto-parietal network (FPN), salience network (SAL)), the sensorimotor network (Mot), and the medial frontal network (MF). Key nodes that contributed to the predictive model included the dorsolateral prefrontal cortex (dlPFC), middle frontal gyrus (MFG), anterior cingulate and paracingulate gyri, inferior temporal gyrus (ITG) that have been associated with trait self-control. Our findings further assert that self-control is a multidimensional construct rooted in the interactions between multiple neural networks.
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Affiliation(s)
- Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
- College of International Studies, Southwest University, Chongqing, China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyue Li
- Department of Psychology, University of Washington, Seattle, Washington, USA
| | - Xianrui Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Xinyi Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Zeqing Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Taiyong Bi
- Centre for Mental Health Research in School of Management, Zunyi Medical University, Zunyi, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
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16
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Fang H, Liao C, Fu Z, Tian S, Luo Y, Xu P, Krueger F. Connectome-based individualized prediction of reciprocity propensity and sensitivity to framing: a resting-state functional magnetic resonance imaging study. Cereb Cortex 2023; 33:3193-3206. [PMID: 35788651 DOI: 10.1093/cercor/bhac269] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 06/11/2022] [Accepted: 06/12/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND The social representation theory states that individual differences in reciprocity decisions are composed of a stable central core (i.e., reciprocity propensity, RP) and a contextual-dependent periphery (i.e., sensitivity to the framing effect; SFE, the effect by how the decision is presented). However, the neural underpinnings that explain RP and SFE are still unknown. METHOD Here, we employed prediction and lesion models to decode resting-state functional connectivity (RSFC) of RP and SFE for reciprocity decisions of healthy volunteers who underwent RS functional magnetic resonance imaging and completed one-shot trust (give frame) and distrust (take frame) games as trustees. RESULTS Regarding the central core, reciprocity rates were positively associated between the give and take frame. Neuroimaging results showed that inter-network RSFC between the default-mode network (DMN; associated with mentalizing) and cingulo-opercular network (associated with cognitive control) contributed to the prediction of reciprocity under both frames. Regarding the periphery, behavioral results demonstrated a significant framing effect-people reciprocated more in the give than in the take frame. Our neuroimaging results revealed that intra-network RSFC of DMN (associated with mentalizing) contributed dominantly to the prediction of SFE. CONCLUSION Our findings provide evidence for distinct neural mechanisms of RP and SFE in reciprocity decisions.
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Affiliation(s)
- Huihua Fang
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
- Department of Psychology, University of Mannheim, Mannheim 68131, Germany
| | - Chong Liao
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
- Department of Psychology, University of Mannheim, Mannheim 68131, Germany
| | - Zhao Fu
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
| | - Shuang Tian
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
| | - Yuejia Luo
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing 100875, China
| | - Frank Krueger
- Department of Psychology, University of Mannheim, Mannheim 68131, Germany
- School of Systems Biology, George Mason University, Fairfax, VA 22030, USA
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17
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Traynor JM, Wrege JS, Walter M, Ruocco AC. Dimensional personality impairment is associated with disruptions in intrinsic intralimbic functional connectivity. Psychol Med 2023; 53:1323-1333. [PMID: 34376260 DOI: 10.1017/s0033291721002865] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Recently proposed alternative dimensional models of personality disorder (PD) place the severity of impairments in self and interpersonal functioning at the core of personality pathology. However, associations of these impairments with disturbances in social, cognitive, and affective brain networks remain uninvestigated. METHODS The present study examined patterns of resting-state functional connectivity (rsFC) in a sample of 74 age- and sex-matched participants (45 inpatients with PD and 29 healthy controls). At a minimum, PD patients carried a diagnosis of borderline PD, although the majority of the sample had one or more additional PDs. rsFC patterns in the following networks were compared between groups and in association with dimensional personality impairments: default mode network (DMN)/core mentalization, frontolimbic, salience, and central executive. Further, the extent to which variation in rsFC was explained by levels of personality impairment as compared to typology-specific borderline PD symptom severity was explored. RESULTS Relative to controls, the PD group showed disruptions in rsFC within the DMN/core mentalization and frontolimbic networks. Among PD patients, greater severity of dimensional self-interpersonal impairment was associated with stronger intralimbic rsFC. In contrast, severity of borderline PD-specific typology was not associated with any rsFC patterns. CONCLUSIONS Disruptions in core mentalization and affective networks are present in PD. Higher intralimbic functional connectivity may underlie self-interpersonal personality impairment in PD regardless of diagnostic typology-specific PD symptoms, providing initial neurobiological evidence supporting alternative dimensional conceptualizations of personality pathology.
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Affiliation(s)
- Jenna M Traynor
- Department of Psychology, University of Toronto Scarborough, Toronto, Ontario, Canada
| | - Johannes S Wrege
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Marc Walter
- Department of Psychiatry (UPK), University of Basel, Basel, Switzerland
| | - Anthony C Ruocco
- Department of Psychology, University of Toronto Scarborough, Toronto, Ontario, Canada
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18
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Wu X, Yang Q, Xu C, Huo H, Seger CA, Peng Z, Chen Q. Connectome-based predictive modeling of compulsion in obsessive-compulsive disorder. Cereb Cortex 2023; 33:1412-1425. [PMID: 35443038 DOI: 10.1093/cercor/bhac145] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 03/22/2022] [Accepted: 03/23/2022] [Indexed: 11/13/2022] Open
Abstract
Compulsion is one of core symptoms of obsessive-compulsive disorder (OCD). Although many studies have investigated the neural mechanism of compulsion, no study has used brain-based measures to predict compulsion. Here, we used connectome-based predictive modeling (CPM) to identify networks that could predict the levels of compulsion based on whole-brain functional connectivity in 57 OCD patients. We then applied a computational lesion version of CPM to examine the importance of specific brain areas. We also compared the predictive network strength in OCD with unaffected first-degree relatives (UFDR) of patients and healthy controls. CPM successfully predicted individual level of compulsion and identified networks positively (primarily subcortical areas of the striatum and limbic regions of the hippocampus) and negatively (primarily frontoparietal regions) correlated with compulsion. The prediction power of the negative model significantly decreased when simulating lesions to the prefrontal cortex and cerebellum, supporting the importance of these regions for compulsion prediction. We found a similar pattern of network strength in the negative predictive network for OCD patients and their UFDR, demonstrating the potential of CPM to identify vulnerability markers for psychopathology.
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Affiliation(s)
- Xiangshu Wu
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Qiong Yang
- Affiliated Brain Hospital of Guangzhou Medical University, 510370 Guangzhou, China
| | - Chuanyong Xu
- Department of Child Psychiatry and Rehabilitation, Institute of Maternity and Child Medical Research, Affiliated Shenzhen Maternity & Child Healthcare Hospital, Southern Medical University, Shenzhen 518047, China
| | - Hangfeng Huo
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
| | - Carol A Seger
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Psychology, Colorado State University, Fort Collins, CO 80523, United States
| | - Ziwen Peng
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China.,Department of Child Psychiatry, Shenzhen Kangning Hospital, Shenzhen University School of Medicine, Shenzhen 518061, China
| | - Qi Chen
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510631, China
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19
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Anderson AJ, Perone S. Predicting individual differences in behavioral activation and behavioral inhibition from functional networks in the resting EEG. Biol Psychol 2023; 177:108483. [PMID: 36587892 DOI: 10.1016/j.biopsycho.2022.108483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 12/09/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
Abstract
The behavioral activation system (BAS) and behavioral inhibition system (BIS) are thought to underly affective dispositions and self-regulatory processes. The BAS is sensitive to reward and involved in approach behaviors, and the BIS is sensitive to punishment and involved in avoidance behaviors. Trait BAS and BIS relate to distinct behavioral profiles and neural activity, but little is known about how trait BAS and BIS relate to functional networks in EEG. We applied a data-driven method called connectome predictive modeling (CPM) to identify networks relating to trait BAS and BIS and tested whether the strength of those networks predicted trait BAS and BIS in novel subjects using a leave-one-out cross-validation procedure. Adult participants (N = 107) completed a resting state task with eyes closed and eyes open, and trait BAS and BIS were measured via Carver and White's (1994) BIS and BAS scales. We hypothesized distinct positive (more synchronization) and negative (less synchronization) networks would relate to trait BAS and BIS. For eyes closed, we identified two negative networks, one in theta and one in alpha predicted BIS. We identified three positive networks, one in theta and one in beta predicted Fun Seeking and one in theta predicted Drive. For eyes open, negative theta and alpha networks predicted BIS, a positive theta network predicted Fun Seeking, and a negative gamma network predicted mean BAS. Visualization of the networks are presented. Discussion centers on the observed networks and how to advance application of CPM to EEG, including with clinical implications.
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Affiliation(s)
- Alana J Anderson
- Department of Human Development, Washington State University, USA.
| | - Sammy Perone
- Department of Human Development, Washington State University, USA
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20
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Zhang D, Yu L, Chen Y, Shen J, Du L, Lin L, Wu J. Connectome-based predictive modeling predicts paranoid ideation in young men with paranoid personality disorder: a resting-state functional magnetic resonance imaging study. Cereb Cortex 2023:6992943. [PMID: 36657794 DOI: 10.1093/cercor/bhac531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 01/21/2023] Open
Abstract
Paranoid personality disorder (PPD), a mental disorder that affects interpersonal relationships and work, is frequently neglected during diagnosis and evaluation at the individual-level. This preliminary study aimed to investigate whether connectome-based predictive modeling (CPM) can predict paranoia scores of young men with PPD using whole-brain resting-state functional connectivity (rs-FC). College students with paranoid tendencies were screened using paranoia scores ≥60 derived from the Minnesota Multiphasic Personality Inventory; 18 participants were ultimately diagnosed with PPD according to the Diagnostic and Statistical Manual of Mental Disorders and subsequently underwent resting-state functional magnetic resonance imaging. Whole-brain rs-FC was constructed, and the ability of this rs-FC to predict paranoia scores was evaluated using CPM. The significance of the models was assessed using permutation tests. The model constructed based on the negative prediction network involving the limbic system-temporal lobe was observed to have significant predictive ability for paranoia scores, whereas the model constructed using the positive and combined prediction network had no significant predictive ability. In conclusion, using CPM, whole-brain rs-FC predicted the paranoia score of patients with PPD. The limbic system-temporal lobe FC pattern is expected to become an important neurological marker for evaluating paranoid ideation.
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Affiliation(s)
- Die Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China.,Department of Radiology, Shenzhen Third People's Hospital, Shenzhen 518000, China
| | - Lan Yu
- Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou 211166,China
| | - Yingying Chen
- Department of Radiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen 518172, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lina Du
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lin Lin
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
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21
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Yu F, Fang H, Zhang J, Wang Z, Ai H, Kwok VPY, Fang Y, Guo Y, Wang X, Zhu C, Luo Y, Xu P, Wang K. Individualized prediction of consummatory anhedonia from functional connectome in major depressive disorder. Depress Anxiety 2022; 39:858-869. [PMID: 36325748 DOI: 10.1002/da.23292] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Revised: 10/12/2022] [Accepted: 10/22/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Anhedonia is a key symptom of major depressive disorder (MDD) and other psychiatric diseases. The neural basis of anhedonia has been widely examined, yet the interindividual variability in neuroimaging biomarkers underlying individual-specific symptom severity is not well understood. METHODS To establish an individualized prediction model of anhedonia, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity profiles of MDD patients. RESULTS The CPM can successfully and reliably predict individual consummatory but not anticipatory anhedonia. The predictive model mainly included salience network (SN), frontoparietal network (FPN), default mode network (DMN), and motor network. Importantly, subsequent computational lesion prediction and consummatory-specific model prediction revealed that connectivity of the SN with DMN and FPN is essential and specific for the prediction of consummatory anhedonia. CONCLUSIONS This study shows that brain functional connectivity, especially the connectivity of SN-FPN and SN-DMN, can specifically predict individualized consummatory anhedonia in MDD. These findings suggest the potential of functional connectomes for the diagnosis and prognosis of anhedonia in MDD and other disorders.
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Affiliation(s)
- Fengqiong Yu
- Research Center for Translational Medicine, The Second Hospital of Anhui Medical University, Hefei, China.,School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China
| | - Huihua Fang
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China.,Department of Psychology, University of Mannheim, Mannheim, Germany
| | - Junfeng Zhang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Zhihao Wang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Hui Ai
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen, China
| | - Veronica P Y Kwok
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Ya Fang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.,Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, China
| | - Yaru Guo
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.,Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, China
| | - Xin Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.,Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, China
| | - Chunyan Zhu
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.,Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, China
| | - Yuejia Luo
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (BNU), Faculty of Psychology, Beijing Normal University, Beijing, China.,Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen, China
| | - Kai Wang
- School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, China.,Department of Neurology, The First Affiliated Hospital of Anhui Medical University, The School of Mental Health and Psychological Sciences, Anhui Medical University, Hefei, Anhui Province, China.,Anhui Province Key Laboratory of Cognition and Neuropsychiatric Disorders, Hefei, China.,Collaborative Innovation Center of Neuropsychiatric Disorders and Mental Health, Anhui Province, China
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22
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Li Y, Cai H, Li X, Qian Y, Zhang C, Zhu J, Yu Y. Functional connectivity of the central autonomic and default mode networks represent neural correlates and predictors of individual personality. J Neurosci Res 2022; 100:2187-2200. [PMID: 36069656 DOI: 10.1002/jnr.25121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Accepted: 08/24/2022] [Indexed: 01/07/2023]
Abstract
There is solid evidence for the prominent involvement of the central autonomic and default mode systems in shaping personality. However, whether functional connectivity of these systems can represent neural correlates and predictors of individual variation in personality traits is largely unknown. Resting-state functional magnetic resonance imaging data of 215 healthy young adults were used to construct the sympathetic (SN), parasympathetic (PN), and default mode (DMN) networks, with intra- and internetwork functional connectivity measured. Personality factors were assessed using the five-factor model. We examined the associations between personality factors and functional network connectivity, followed by performance of personality prediction based on functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. All personality factors (neuroticism, extraversion, conscientiousness, and agreeableness) other than openness were significantly correlated with intra- and internetwork functional connectivity of the SN, PN, and DMN. Moreover, the CPM models successfully predicted conscientiousness and agreeableness at the individual level using functional network connectivity. Our findings may expand existing knowledge regarding the neural substrates underlying personality.
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Affiliation(s)
- Yating Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Xueying Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Cun Zhang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei, China.,Research Center of Clinical Medical Imaging, Anhui Province, Hefei, China.,Anhui Provincial Institute of Translational Medicine, Hefei, China
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23
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Huang Y, Deng Y, Jiang X, Chen Y, Mao T, Xu Y, Jiang C, Rao H. Resting-state occipito-frontal alpha connectome is linked to differential word learning ability in adult learners. Front Neurosci 2022; 16:953315. [PMID: 36188469 PMCID: PMC9521374 DOI: 10.3389/fnins.2022.953315] [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] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022] Open
Abstract
Adult language learners show distinct abilities in acquiring a new language, yet the underlying neural mechanisms remain elusive. Previous studies suggested that resting-state brain connectome may contribute to individual differences in learning ability. Here, we recorded electroencephalography (EEG) in a large cohort of 106 healthy young adults (50 males) and examined the associations between resting-state alpha band (8-12 Hz) connectome and individual learning ability during novel word learning, a key component of new language acquisition. Behavioral data revealed robust individual differences in the performance of the novel word learning task, which correlated with their performance in the language aptitude test. EEG data showed that individual resting-state alpha band coherence between occipital and frontal regions positively correlated with differential word learning performance (p = 0.001). The significant positive correlations between resting-state occipito-frontal alpha connectome and differential world learning ability were replicated in an independent cohort of 35 healthy adults. These findings support the key role of occipito-frontal network in novel word learning and suggest that resting-state EEG connectome may be a reliable marker for individual ability during new language learning.
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Affiliation(s)
- Yan Huang
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
- School of Foreign Languages, East China University of Science and Technology, Shanghai, China
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
| | - Yao Deng
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Xiaoming Jiang
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
| | - Yiyuan Chen
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
| | - Tianxin Mao
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Yong Xu
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Caihong Jiang
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
| | - Hengyi Rao
- Center for Magnetic Resonance Imaging Research, Key Laboratory of Applied Brain and Cognitive Sciences, Shanghai International Studies University, Shanghai, China
- Institute of Linguistics, Shanghai International Studies University, Shanghai, China
- Center for Functional Neuroimaging, Department of Neurology, University of Pennsylvania, Philadelphia, PA, United States
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24
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Multi-modality connectome-based predictive modeling of individualized compulsions in obsessive-compulsive disorder. J Affect Disord 2022; 311:595-603. [PMID: 35662573 DOI: 10.1016/j.jad.2022.05.120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/24/2022] [Accepted: 05/27/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND While previous neuroimaging studies are mainly focused on dichotomous classification of obsessive-compulsive disorder (OCD) from controls, predicting continuous severity of specific symptom is also pivotal to clinical diagnosis and treatment. METHODS We applied a machine-learning approach, connectome-based predictive modeling, on functional and structural brain networks constructed from resting-state functional magnetic resonance imaging and diffusion tensor imaging data to decode compulsions and obsessions of fifty-four patients with OCD. RESULTS We successfully predicted individualized compulsions with a positive model of structural brain network and with a negative model of functional brain network. The structural predictive brain network comprises the motor cortex, cerebellum and limbic lobe, which are involved in basic motor control, motor execution and emotion processing, respectively. The functional predictive brain network is composed by the prefrontal and limbic systems which are related to cognitive and affective control. Computational lesion analysis shows that functional connectivity among the salience network (SN), the frontal parietal network and the default mode network, as well as structural connectivity within the SN are vital in the individualized prediction of compulsions in OCD. LIMITATIONS There was no external validation of large samples to test the robustness of our predictive model. CONCLUSIONS These findings provide the first evidence for the predictive role of the triple network model in individualized compulsions and have important implications in diagnosis, prognosis and treatment of patients with OCD.
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25
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Kabbara A, Robert G, Khalil M, Verin M, Benquet P, Hassan M. An electroencephalography connectome predictive model of major depressive disorder severity. Sci Rep 2022; 12:6816. [PMID: 35473962 PMCID: PMC9042869 DOI: 10.1038/s41598-022-10949-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 04/05/2022] [Indexed: 11/21/2022] Open
Abstract
Emerging evidence showed that major depressive disorder (MDD) is associated with disruptions of brain structural and functional networks, rather than impairment of isolated brain region. Thus, connectome-based models capable of predicting the depression severity at the individual level can be clinically useful. Here, we applied a machine-learning approach to predict the severity of depression using resting-state networks derived from source-reconstructed Electroencephalography (EEG) signals. Using regression models and three independent EEG datasets (N = 328), we tested whether resting state functional connectivity could predict individual depression score. On the first dataset, results showed that individuals scores could be reasonably predicted (r = 0.6, p = 4 × 10-18) using intrinsic functional connectivity in the EEG alpha band (8-13 Hz). In particular, the brain regions which contributed the most to the predictive network belong to the default mode network. We further tested the predictive potential of the established model by conducting two external validations on (N1 = 53, N2 = 154). Results showed statistically significant correlations between the predicted and the measured depression scale scores (r1 = 0.52, r2 = 0.44, p < 0.001). These findings lay the foundation for developing a generalizable and scientifically interpretable EEG network-based markers that can ultimately support clinicians in a biologically-based characterization of MDD.
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Affiliation(s)
- Aya Kabbara
- Lebanese Association for Scientific Research, Tripoli, Lebanon
- MINDig, F-35000, Rennes, France
| | - Gabriel Robert
- Academic Department of Psychiatry, Centre Hospitalier Guillaume Régnier, Rennes, France
- Empenn, U1228, IRISA, UMR 6074, Rennes, France
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
| | - Mohamad Khalil
- Azm Center for Research in Biotechnology and Its Applications, EDST, Tripoli, Lebanon
- CRSI Research Center, Faculty of Engineering, Lebanese University, Beirut, Lebanon
| | - Marc Verin
- Comportement et Noyaux Gris Centraux, EA 4712, CHU Rennes, Université de Rennes 1, 35000, Rennes, France
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Pascal Benquet
- Univ Rennes, Inserm, LTSI-U1099, F-35000, Rennes, France
| | - Mahmoud Hassan
- MINDig, F-35000, Rennes, France.
- School of Science and Engineering, Reykjavik University, Reykjavik, Iceland.
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26
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Wei L, Baeken C, Liu D, Zhang J, Wu GR. Functional connectivity–based prediction of global cognition and motor function in riluzole-naive amyotrophic lateral sclerosis patients. Netw Neurosci 2022; 6:161-174. [PMID: 35356196 PMCID: PMC8959121 DOI: 10.1162/netn_a_00217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 11/17/2021] [Indexed: 12/03/2022] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem disorder accompanied by cognitive changes. To date, no effective therapy is available for ALS patients, partly due to disease heterogeneity and an imperfect understanding of the underlying pathophysiological processes. Reliable models that can predict cognitive and motor deficits are needed to improve symptomatic treatment and slow down disease progression. This study aimed to identify individualized functional connectivity–based predictors of cognitive and motor function in ALS by using multiple kernel learning (MKL) regression. Resting-state fMRI scanning was performed on 34 riluzole-naive ALS patients. Motor severity and global cognition were separately measured with the revised ALS functional rating scale (ALSFRS-R) and the Montreal Cognitive Assessment (MoCA). Our results showed that functional connectivity within the default mode network (DMN) as well as between the DMN and the sensorimotor network (SMN), fronto-parietal network (FPN), and salience network (SN) were predictive for MoCA scores. Additionally, the observed connectivity patterns were also predictive for the individual ALSFRS-R scores. Our findings demonstrate that cognitive and motor impairments may share common connectivity fingerprints in ALS patients. Furthermore, the identified brain connectivity signatures may serve as novel targets for effective disease-modifying therapies. Amyotrophic lateral sclerosis is recognized as a multisystem disorder, and currently no effective therapy is available for this devastating disease. Reliable models that can predict disease progression may facilitate the development of more efficient symptomatic treatment. This study used multiple kernel learning algorithm to identify a potential functional connectivity–based marker for cognitive and motor functioning in ALS. The results show that cognitive decline and motor progression could be predicted by seed-based functional connectivity from the medial prefrontal cortex/posterior cingulate cortex to the sensorimotor network, fronto-parietal network, and salience network. The identified brain connectivity signatures may serve as novel targets for effective disease-modifying therapies.
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Affiliation(s)
- Luqing Wei
- School of Psychology, Jiangxi Normal University, Nanchang, China
| | - Chris Baeken
- Ghent Experimental Psychiatry Lab, Department of Head and Skin, UZ Gent/Universiteit Gent, Ghent, Belgium
- Department of Psychiatry, UZ Brussel/Free University of Brussels, Brussels, Belgium
- Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Daihong Liu
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing, China
| | - Guo-Rong Wu
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Southwest University, Chongqing, China
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27
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Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure. Brain Imaging Behav 2021; 16:1049-1064. [PMID: 34724163 PMCID: PMC8558548 DOI: 10.1007/s11682-021-00574-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 09/28/2021] [Indexed: 12/03/2022]
Abstract
Need for closure (NFC) reflects stable individual differences in the desire for a quick, definite, and stable answer to a question. A large body of research has documented the association between NFC and various cognitive, emotional and social processes. Despite considerable interest in psychology, little effort has been made to uncover the neural substrates of individual variations in NFC. Herein, we took a data-driven approach to predict NFC trait combining machine learning framework and the whole-brain grey matter volume (GMV) features, which represent a reliable brain imaging measure and have been commonly employed to explore neural basis underlying individual differences of cognition and behaviors. Brain regions contributing to the prediction were then subjected to functional connectivity and decoding analyses for a quantitative inference on their psychophysiological functions. Our results indicated that multivariate patterns of GMV derived from multiple regions across distributed brain systems predicted NFC at individual level. The contributing regions are distributed across the emotional processing network (e.g., striatum), cognitive control network (e.g., lateral prefrontal cortex), social cognition network (e.g., temporoparietal junction) and perceptual processing network (e.g., occipital cortex). The current study provided the first evidence that dispositional NFC is embodied in multiple large-scale brain networks, helping to delineate a more complete picture about the neuropsychological processes that support individual differences in NFC. Beyond these findings, the current interdisciplinary approach to constructing and interpreting neuroimaging-based prediction model of personality traits would be informative to a wide range of future studies on personality.
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28
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Fang K, Han S, Li Y, Ding J, Wu J, Zhang W. The Vital Role of Central Executive Network in Brain Age: Evidence From Machine Learning and Transcriptional Signatures. Front Neurosci 2021; 15:733316. [PMID: 34557071 PMCID: PMC8453084 DOI: 10.3389/fnins.2021.733316] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Accepted: 08/06/2021] [Indexed: 11/24/2022] Open
Abstract
Recent studies combining neuroimaging with machine learning methods successfully infer an individual’s brain age, and its discrepancy with the chronological age is used to identify age-related diseases. However, which brain networks play decisive roles in brain age prediction and the underlying biological basis of brain age remain unknown. To answer these questions, we estimated an individual’s brain age in the Southwest University Adult Lifespan Dataset (N = 492) from the gray matter volumes (GMV) derived from T1-weighted MRI scans by means of Gaussian process regression. Computational lesion analysis was performed to determine the importance of each brain network in brain age prediction. Then, we identified brain age-related genes by using prior brain-wide gene expression data, followed by gene enrichment analysis using Metascape. As a result, the prediction model successfully inferred an individual’s brain age and the computational lesion prediction results identified the central executive network as a vital network in brain age prediction (Steiger’s Z = 2.114, p = 0.035). In addition, the brain age-related genes were enriched in Gene Ontology (GO) processes/Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways grouped into numbers of clusters, such as regulation of iron transmembrane transport, synaptic signaling, synapse organization, retrograde endocannabinoid signaling (e.g., dopaminergic synapse), behavior (e.g., memory and associative learning), neurotransmitter secretion, and dendrite development. In all, these results reveal that the GMV of the central executive network played a vital role in predicting brain age and bridged the gap between transcriptome and neuroimaging promoting an integrative understanding of the pathophysiology of brain age.
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Affiliation(s)
- Keke Fang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Shaoqiang Han
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yuming Li
- Department of Radiotherapy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Jing Ding
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Jilian Wu
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
| | - Wenzhou Zhang
- Department of Pharmacy, Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital, Zhengzhou, China
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29
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Zarnowski O, Ziton S, Holmberg R, Musto S, Riegle S, Van Antwerp E, Santos-Nunez G. Functional MRI findings in personality disorders: A review. J Neuroimaging 2021; 31:1049-1066. [PMID: 34468063 DOI: 10.1111/jon.12924] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Revised: 08/11/2021] [Accepted: 08/13/2021] [Indexed: 11/28/2022] Open
Abstract
Personality disorders (PDs) have a prevalence of approximately 10% in the United States, translating to over 30 million people affected in just one country. The true prevalence of these disorders may be even higher, as the paucity of objective diagnostic criteria could be leading to underdiagnosis. Because little is known about the underlying neuropathologies of these disorders, patients are diagnosed using subjective criteria and treated nonspecifically. To better understand the neural aberrancies responsible for these patients' symptoms, a review of functional MRI literature was performed. The findings reveal that each PD is characterized by a unique set of activation changes corresponding to individual structures or specific neural networks. While unique patterns of neural activity are distinguishable within each PD, aberrations of the limbic/paralimbic structures and default mode network are noted across several of them. In addition to identifying valuable activation patterns, this review reveals a void in research pertaining to paranoid, schizoid, histrionic, narcissistic, and dependent PDs. By delineating patterns in PD neuropathology, we can more effectively direct future research efforts toward enhancing objective diagnostic techniques and developing targeted treatment modalities. Furthermore, understanding why patients are manifesting certain symptoms can advance clinical awareness and improve patient outcomes.
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Affiliation(s)
- Oskar Zarnowski
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Shirley Ziton
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Rylan Holmberg
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Sarafina Musto
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Sean Riegle
- Dr. Kiran C. Patel College of Osteopathic Medicine, Nova Southeastern University, Fort Lauderdale, Florida, USA
| | - Emily Van Antwerp
- West Virginia School of Osteopathic Medicine, Lewisburg, West Virginia, USA
| | - Gabriela Santos-Nunez
- University of Massachusetts Memorial Medical Center, Radiology Department, Worcester, Massachusetts, USA
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30
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Song H, Rosenberg MD. Predicting attention across time and contexts with functional brain connectivity. Curr Opin Behav Sci 2021. [DOI: 10.1016/j.cobeha.2020.12.007] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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31
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Widespread plasticity of cognition-related brain networks in single-sided deafness revealed by randomized window-based dynamic functional connectivity. Med Image Anal 2021; 73:102163. [PMID: 34303170 DOI: 10.1016/j.media.2021.102163] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Revised: 06/07/2021] [Accepted: 07/02/2021] [Indexed: 11/22/2022]
Abstract
As an extreme type of partial auditory deprivation, single-sided deafness (SSD) has been demonstrated to lead to extensive neural plasticity according to multimodal neuroimaging studies. Among them, resting-state functional magnetic resonance imaging (rs-fMRI) offers valuable information on functional connectivities (FCs). However, most previous SSD rs-fMRI studies assumed that the extracted FC remains stationary during the entire fMRI scan and neglected dynamic functional activities. Existing fixed window-based dynamic FC analysis also ignores dynamic functional activities under different temporal terms. Additionally, due to the cost constraints of using MRI machines, using data-driven methods for unbiased hypothesis investigations may require more effective sample data augmentation techniques. To tackle these challenges and problems together, in this study, we proposed a dynamic window with a random length and position to extract participants' dynamic characteristics under different temporal terms and to extract more information from the dataset. Then, we proposed a nodal efficiency-based correlation matrix to describe the relationships of synergism between regions as features and applied a linear support vector machine (SVM) model to learn the importance of the features, which helped to identify SSD patients and healthy controls. A total of 68 participants (including 23 with left SSD, 20 with right SSD and 25 healthy controls) were enrolled. Our proposed approach with a random window showed clear improvement compared with traditional static and fixed window-based dynamic FC by using the linear SVM model. FCs related to the frontoparietal, somatomotor, dorsal attention, limbic and default mode networks played significant roles in differentiating SSD patients from healthy controls. Additionally, FCs between the somatomotor and frontoparietal networks made the greatest contribution to the classification model. Regarding brain regions, FCs related to the superior frontal gyrus, superior parietal lobule, superior temporal gyrus, amygdala, and orbital gyrus played significant roles. These findings suggest that networks and regions related to higher-order cognitive functions showed the most significant FC alterations in SSD, which may represent a compensatory collaboration of cognitive resources in SSD.
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32
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Wang X, Li Q, Zhao Y, He Y, Ma B, Fu Z, Li S. Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method. Neuroimage 2021; 238:118252. [PMID: 34116155 DOI: 10.1016/j.neuroimage.2021.118252] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 05/31/2021] [Accepted: 06/07/2021] [Indexed: 10/21/2022] Open
Abstract
Resting-state functional connectivity (RSFC) can be used for mapping large-scale human brain networks during rest. There is considerable interest in distinguishing the individual-shared and individual-specific components in RSFC for the better identification of individuals and prediction of behavior. Therefore, we propose a multi-task learning based sparse convex alternating structure optimization (MTL-sCASO) method to decompose RSFC into individual-specific connectivity and individual-shared connectivity. We used synthetic data to validate the efficacy of the MTL-sCASO method. In addition, we verified that individual-specific connectivity achieves higher identification rates than the Pearson correlation (PC) method, and the individual-specific components observed in 886 individuals from the Human Connectome Project (HCP) examined in two sessions over two consecutive days might serve as individual fingerprints. Individual-specific connectivity has low inter-subject similarity (-0.005±0.023), while individual-shared connectivity has high inter-subject similarity (0.822±0.061). We also determined the anatomical locations (region or subsystem) related to individual attributes and common features. We find that individual-specific connectivity exhibits low degree centrality in the sensorimotor processing system but high degree centrality in the control system. Importantly, the individual-specific connectivity estimated by the MTL-sCASO method accurately predicts behavioral scores (improved by 9.4% compared to the PC method) in the cognitive dimension. The decomposition of individual-specific and individual-shared components from RSFC provides a new approach for tracing individual traits and group analysis using functional brain networks.
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Affiliation(s)
- Xuetong Wang
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Qiongling Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Yan Zhao
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Yirong He
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Baoqiang Ma
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Zhenrong Fu
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
| | - Shuyu Li
- School of Biological Science & Medical Engineering, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, 100083, China
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Jauk E, Kanske P. Can neuroscience help to understand narcissism? A systematic review of an emerging field. PERSONALITY NEUROSCIENCE 2021; 4:e3. [PMID: 34124536 PMCID: PMC8170532 DOI: 10.1017/pen.2021.1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/23/2020] [Revised: 03/23/2021] [Accepted: 03/30/2021] [Indexed: 12/12/2022]
Abstract
Narcissism is a Janusian personality construct, associated with both grandiose self-assuredness and dominance, as well as vulnerable insecurity and reactivity. Central questions of intra- and interpersonal functioning in narcissism are still a matter of debate. Neuroscience could help to understand the paradoxical patterns of experience and behavior beyond the limitations of self-reports. We provide a systematic review of 34 neuroscience studies on grandiose, vulnerable, pathological narcissism, and Narcissistic Personality Disorder (NPD), spanning experimental investigations of intra- and interpersonal mechanisms, research on neurophysiological and neuroendocrine aspects of baseline function, and brain structural correlates. While neuroscience has scarcely directly studied vulnerable narcissism, grandiose narcissism is associated with heightened vigilance to ego threat and stress responses following ego threat, as well as heightened stress indicators in baseline measures. Such responses are not commonly observed in self-reports, highlighting the potential of neuroscience to augment our understanding of self-regulatory dynamics in narcissism. Interpersonal functioning is characterized by deficits in social-affective processes. Both involve altered activity within the salience network, pointing to a double dissociation regarding the expression of narcissism and self/other oriented situational focus. Findings are summarized in an integrative model providing testable hypotheses for future research along with methodological recommendations.
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Affiliation(s)
- Emanuel Jauk
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
- Department of Psychology, University of Graz, Graz, Austria
| | - Philipp Kanske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Dresden, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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34
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Zhu Y, Zang F, Wang Q, Zhang Q, Tan C, Zhang S, Hu T, Qi L, Xu S, Ren Q, Xie C. Connectome-based model predicts episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. Behav Brain Res 2021; 411:113387. [PMID: 34048872 DOI: 10.1016/j.bbr.2021.113387] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 04/19/2021] [Accepted: 05/22/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVE To explore whether the whole brain resting-state functional connectivity (rs-FC) could predict episodic memory performance in individuals with subjective cognitive decline and amnestic mild cognitive impairment. METHOD This study included 33 cognitive normal (CN), 26 subjective cognitive decline (SCD) and 27 amnestic mild cognitive impairment (aMCI) patients, and all the participants completed resting-state fMRI (rs-fMRI) scan and neuropsychological scale test data. Connectome-based predictive modeling (CPM) based on the rs-FC data was used to predict the auditory verbal learning test-delayed recall (AVLT-DR) scores, which measured episodic memory in individuals. Pearson correlation between each brain connection in the connectivity matrices and AVLT-DR scores was computed across the patients in predementia stages of Alzheimer's disease (AD). The Pearson correlation coefficient values separated into a positive network and a negative network. Predictive networks were then defined and employed by calculating positive and negative network strengths. CPM with leave-one-out cross-validation (LOOCV) was conducted to train linear models to respectively relate positive and negative network strengths to AVLT-DR scores in the training set. During the testing procedure, each left-out testing subject's strengths of positive and negative network was normalized using the parameters acquired during training procedure, and then the trained models were used to predict the testing participant's AVLT-DR score. RESULTS The negative network predictive model tested LOOCV significantly predicted individual differences in episodic memory from rs-FC. Key nodes that brain regions contributed to the prediction model were mainly located in the prefrontal cortex, frontal cortex, parietal cortex and temporal lobe. CONCLUSION Our findings demonstrated that rs-FC among multiple neural systems could predict episodic memory at the individual level.
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Affiliation(s)
- Yao Zhu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Feifei Zang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Qianqian Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Chang Tan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Shaoke Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Tianjian Hu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Lingyu Qi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China
| | - Shouyong Xu
- Department of Radiology, Geriatric Hospital of Nanjing Medical University, Nanjing, Jiangsu, 210000, China.
| | - Qingguo Ren
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China.
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, 210000, China.
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35
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Jiang R, Calhoun VD, Cui Y, Qi S, Zhuo C, Li J, Jung R, Yang J, Du Y, Jiang T, Sui J. Multimodal data revealed different neurobiological correlates of intelligence between males and females. Brain Imaging Behav 2021; 14:1979-1993. [PMID: 31278651 DOI: 10.1007/s11682-019-00146-z] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Intelligence is a socially and scientifically interesting topic because of its prominence in human behavior, yet there is little clarity on how the neuroimaging and neurobiological correlates of intelligence differ between males and females, with most investigations limited to using either mass-univariate techniques or a single neuroimaging modality. Here we employed connectome-based predictive modeling (CPM) to predict the intelligence quotient (IQ) scores for 166 males and 160 females separately, using resting-state functional connectivity, grey matter cortical thickness or both. The identified multimodal, IQ-predictive imaging features were then compared between genders. CPM showed high out-of-sample prediction accuracy (r > 0.34), and integrating both functional and structural features further improved prediction accuracy by capturing complementary information (r = 0.45). Male IQ demonstrated higher correlations with cortical thickness in the left inferior parietal lobule, and with functional connectivity in left parahippocampus and default mode network, regions previously implicated in spatial cognition and logical thinking. In contrast, female IQ was more correlated with cortical thickness in the right inferior parietal lobule, and with functional connectivity in putamen and cerebellar networks, regions previously implicated in verbal learning and item memory. Results suggest that the intelligence generation of males and females may rely on opposite cerebral lateralized key brain regions and distinct functional networks consistent with their respective superiority in cognitive domains. Promisingly, understanding the neural basis of gender differences underlying intelligence may potentially lead to optimized personal cognitive developmental programs and facilitate advancements in unbiased educational test design.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Yue Cui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Tianjin Mental Health Center, Nankai University Affiliated Anding Hospital, Tianjin, 300222, China
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Rex Jung
- Department of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jian Yang
- Beijing Engineering Research Center of Mixed Reality and Advanced Display, School of Optics and Electronics, Beijing Institute of Technology, Beijing, 100081, China
| | - Yuhui Du
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, 30303, USA
| | - Tianzi Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,University of Chinese Academy of Sciences, Beijing, 100049, China.,University of Electronic Science and Technology of China, Chengdu, 610054, China.,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,University of Chinese Academy of Sciences, Beijing, 100049, China. .,Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, 100190, China.
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36
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Jin J, Delaparte L, Chen HW, DeLorenzo C, Perlman G, Klein DN, Mohanty A, Kotov R. Structural Connectivity Between Rostral Anterior Cingulate Cortex and Amygdala Predicts First Onset of Depressive Disorders in Adolescence. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2021; 7:249-255. [PMID: 33610811 DOI: 10.1016/j.bpsc.2021.01.012] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 12/24/2020] [Accepted: 01/21/2021] [Indexed: 01/23/2023]
Abstract
BACKGROUND Adolescent-onset depressive disorders (DDs) are associated with deficits in the regulation of negative affect across modalities (self-report, behavioral paradigms, and neuroimaging), which may manifest prior to first-onset DDs. Whether the neurocircuitry governing emotional regulation predates DDs is unclear. This study tested whether a critical pathway for emotion regulation (rostral anterior cingulate cortex-amygdala structural connectivity) predicts first-onset DDs in adolescent females. METHODS Diffusion tensor imaging data were acquired on adolescent females (n = 212) without a history of DDs and the cohort was reassessed for first-onset DDs over the next 27 months. RESULTS A total of 26 girls developed first onsets of DDs in the 27 months after imaging. Multivariate logistic regression showed that lower weighted average fractional anisotropy of uncinate fasciculus tracts between the rostral anterior cingulate cortex and amygdala prospectively predicted first onset of DDs (adjusted odds ratio = 0.44, p = .005), above and beyond established risk factors including baseline depression symptom severity, history of anxiety disorders, parental history of depression, parental education, and age. CONCLUSIONS This study provides evidence for the first time showing that aberrant structural connectivity between the rostral anterior cingulate cortex and amygdala prospectively predates first onset of DDs in adolescent females. These results highlight the importance of a well-established neural circuit implicated in the regulation of negative affect as a likely etiological factor and a promising target for intervention and prevention of DDs.
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Affiliation(s)
- Jingwen Jin
- Department of Psychology, The University of Hong Kong, Hong Kong SAR, China; The State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong SAR, China
| | - Lauren Delaparte
- The Department of Psychology, Stony Brook University, Stony Brook, New York, USA
| | - Hung Wei Chen
- Department of Psychological and Brain Science, University of Delaware, Newark, Delaware
| | | | - Greg Perlman
- The Department of Psychiatry, Stony Brook University, Stony Brook, New York
| | - Daniel N Klein
- The Department of Psychology, Stony Brook University, Stony Brook, New York, USA; The Department of Psychiatry, Stony Brook University, Stony Brook, New York
| | - Aprajita Mohanty
- The Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
| | - Roman Kotov
- The Department of Psychology, Stony Brook University, Stony Brook, New York, USA; The Department of Psychiatry, Stony Brook University, Stony Brook, New York
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37
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Youm Y, Kim J, Kwak S, Chey J. Neural and social correlates of attitudinal brokerage: using the complete social networks of two entire villages. Proc Biol Sci 2021; 288:20202866. [PMID: 33563127 PMCID: PMC7893238 DOI: 10.1098/rspb.2020.2866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
To avoid polarization and maintain small-worldness in society, people who act as attitudinal brokers are critical. These people maintain social ties with people who have dissimilar and even incompatible attitudes. Based on resting-state functional magnetic resonance imaging (n = 139) and the complete social networks from two Korean villages (n = 1508), we investigated the individual-level neural capacity and social-level structural opportunity for attitudinal brokerage regarding gender role attitudes. First, using a connectome-based predictive model, we successfully identified the brain functional connectivity that predicts attitudinal diversity of respondents' social network members. Brain regions that contributed most to the prediction included mentalizing regions known to be recruited in reading and understanding others’ belief states. This result was corroborated by leave-one-out cross-validation, fivefold cross-validation and external validation where the brain connectivity identified in one village was used to predict the attitudinal diversity in another independent village. Second, the association between functional connectivity and attitudinal diversity of social network members was contingent on a specific position in a social network, namely, the structural brokerage position where people have ties with two people who are not otherwise connected.
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Affiliation(s)
- Yoosik Youm
- Department of Sociology, Yonsei University, Seoul, Republic of Korea
| | - Junsol Kim
- Department of Sociology, Yonsei University, Seoul, Republic of Korea
| | - Seyul Kwak
- Seoul National University Seoul Metropolitan Government Boramae Medical Center, Seoul, Republic of Korea
| | - Jeanyung Chey
- Department of Psychology, Seoul National University, Seoul, Republic of Korea
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38
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Wang Z, Goerlich KS, Ai H, Aleman A, Luo YJ, Xu P. Connectome-Based Predictive Modeling of Individual Anxiety. Cereb Cortex 2021; 31:3006-3020. [DOI: 10.1093/cercor/bhaa407] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 12/16/2022] Open
Abstract
Abstract
Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational “lesion” approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.
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Affiliation(s)
- Zhihao Wang
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of Groningen, Groningen 9713 AW , the Netherlands
| | - Katharina S Goerlich
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of Groningen, Groningen 9713 AW , the Netherlands
| | - Hui Ai
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
| | - André Aleman
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of Groningen, Groningen 9713 AW , the Netherlands
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
| | - Yue-jia Luo
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
- Department of Psychology, Southern Medical University, Guangzhou 510515, China
- College of Teacher Education, Qilu Normal University, Jining 250200, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen 518106, China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen 518106, China
- Guangdong-Hong Kong-Macao Greater Bay Area Research Institute for Neuroscience and Neurotechnologies, Kwun Tong, Hong Kong, China
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Zhu Y, Li X, Sun Y, Wang H, Guo H, Sui J. Investigating Neural Substrates of Individual Independence and Interdependence Orientations via Efficiency-based Dynamic Functional Connectivity: A Machine Learning Approach. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3101643] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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40
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Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020; 88:818-828. [PMID: 32336400 PMCID: PMC7483317 DOI: 10.1016/j.biopsych.2020.02.016] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 02/13/2020] [Accepted: 02/17/2020] [Indexed: 01/08/2023]
Abstract
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
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Ren Z, Daker RJ, Shi L, Sun J, Beaty RE, Wu X, Chen Q, Yang W, Lyons IM, Green AE, Qiu J. Connectome-Based Predictive Modeling of Creativity Anxiety. Neuroimage 2020; 225:117469. [PMID: 33099006 DOI: 10.1016/j.neuroimage.2020.117469] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 08/04/2020] [Accepted: 10/12/2020] [Indexed: 12/16/2022] Open
Abstract
While a recent upsurge in the application of neuroimaging methods to creative cognition has yielded encouraging progress toward understanding the neural underpinnings of creativity, the neural basis of barriers to creativity are as yet unexplored. Here, we report the first investigation into the neural correlates of one such recently identified barrier to creativity: anxiety specific to creative thinking, or creativity anxiety (Daker et al., 2019). We employed a machine-learning technique for exploring relations between functional connectivity and behavior (connectome-based predictive modeling; CPM) to investigate the functional connections underlying creativity anxiety. Using whole-brain resting-state functional connectivity data, we identified a network of connections or "edges" that predicted individual differences in creativity anxiety, largely comprising connections within and between regions of the executive and default networks and the limbic system. We then found that the edges related to creativity anxiety identified in one sample generalize to predict creativity anxiety in an independent sample. We additionally found evidence that the network of edges related to creativity anxiety were largely distinct from those found in previous work to be related to divergent creative ability (Beaty et al., 2018). In addition to being the first work on the neural correlates of creativity anxiety, this research also included the development of a new Chinese-language version of the Creativity Anxiety Scale, and demonstrated that key behavioral findings from the initial work on creativity anxiety are replicable across cultures and languages.
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Affiliation(s)
- Zhiting Ren
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Richard J Daker
- Department of Psychology, Georgetown University, Washington, DC, 20057, USA
| | - Liang Shi
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Jiangzhou Sun
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Roger E Beaty
- Department of Psychology, Pennsylvania State University, State College, PA, 16801, USA
| | - Xinran Wu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China
| | - Ian M Lyons
- Department of Psychology, Georgetown University, Washington, DC, 20057, USA
| | - Adam E Green
- Department of Psychology, Georgetown University, Washington, DC, 20057, USA.
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, 400715, China.
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Cai H, Zhu J, Yu Y. Robust prediction of individual personality from brain functional connectome. Soc Cogn Affect Neurosci 2020; 15:359-369. [PMID: 32248238 PMCID: PMC7235956 DOI: 10.1093/scan/nsaa044] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Revised: 03/19/2020] [Accepted: 03/24/2020] [Indexed: 01/14/2023] Open
Abstract
Neuroimaging studies have linked inter-individual variability in the brain to individualized personality traits. However, only one or several aspects of personality have been effectively predicted based on brain imaging features. The objective of this study was to construct a reliable prediction model of personality in a large sample by using connectome-based predictive modeling (CPM), a recently developed machine learning approach. High-quality resting-state functional magnetic resonance imaging data of 810 healthy young participants from the Human Connectome Project dataset were used to construct large-scale brain networks. Personality traits of the five-factor model (FFM) were assessed by the NEO Five Factor Inventory. We found that CPM successfully and reliably predicted all the FFM personality factors (agreeableness, openness, conscientiousness and neuroticism) other than extraversion in novel individuals. At the neural level, we found that the personality-associated functional networks mainly included brain regions within default mode, frontoparietal executive control, visual and cerebellar systems. Although different feature selection thresholds and parcellation strategies did not significantly influence the prediction results, some findings lost significance after controlling for confounds including age, gender, intelligence and head motion. Our finding of robust personality prediction from an individual's unique functional connectome may help advance the translation of 'brain connectivity fingerprinting' into real-world personality psychological settings.
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Affiliation(s)
- Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
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43
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Jin J, Van Snellenberg JX, Perlman G, DeLorenzo C, Klein DN, Kotov R, Mohanty A. Intrinsic neural circuitry of depression in adolescent females. J Child Psychol Psychiatry 2020; 61:480-491. [PMID: 31512744 PMCID: PMC7065934 DOI: 10.1111/jcpp.13123] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/09/2019] [Indexed: 12/16/2022]
Abstract
BACKGROUND Adolescence is characterized by affective and cognitive changes that increase vulnerability to depression, especially in females. Neurodevelopmental models attribute adolescent depression to abnormal responses in amygdala, striatum, and prefrontal cortex (PFC). We examined whether the strength of functional brain networks involving these regions predicts depression symptoms in adolescent females. METHODS In this longitudinal study, we recorded resting-state functional connectivity (RSFC) in 174 adolescent females. Using a cross-validation strategy, we related RSFC profiles that included (a) a network consisting of amygdala, striatum, and PFC (within-circuit model), (b) connectivity of this network to the whole brain (extended-circuit model), and (c) a network consisting of the entire brain (whole-brain model) to depression symptoms assessed concurrently and 18 months later. RESULTS In testing subsets, the within-circuit RSFC profiles were associated with depression symptoms concurrently and 18 months later, while the extended-circuit and whole-brain model did not explain any additional variance in depression symptoms. Connectivity related to anterior cingulate and ventromedial prefrontal cortex contributed most to the association. CONCLUSIONS Our results demonstrate that RSFC-based brain networks that include amygdala, striatum, and PFC are stable neural signatures of concurrent and future depression symptoms, representing a significant step toward identifying the neural mechanism of depression in adolescence.
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Affiliation(s)
- Jingwen Jin
- Department of Psychology, Stony Brook University, Stony Brook, NY
| | - Jared X. Van Snellenberg
- Department of Psychology, Stony Brook University, Stony Brook, NY,Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY,Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
| | - Greg Perlman
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY
| | - Christine DeLorenzo
- Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY
| | - Daniel N Klein
- Department of Psychology, Stony Brook University, Stony Brook, NY,Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY
| | - Roman Kotov
- Department of Psychology, Stony Brook University, Stony Brook, NY,Department of Psychiatry and Behavioral Health, Stony Brook University, Stony Brook, NY
| | - Aprajita Mohanty
- Department of Psychology, Stony Brook University, Stony Brook, NY
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Tu Y, Zhang B, Cao J, Wilson G, Zhang Z, Kong J. Identifying inter-individual differences in pain threshold using brain connectome: a test-retest reproducible study. Neuroimage 2019; 202:116049. [PMID: 31349067 DOI: 10.1016/j.neuroimage.2019.116049] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2018] [Revised: 05/23/2019] [Accepted: 07/22/2019] [Indexed: 02/07/2023] Open
Abstract
Individuals are unique in terms of brain and behavior. Some are very sensitive to pain, while others have a high tolerance. However, how inter-individual intrinsic differences in the brain are related to pain is unknown. Here, we performed longitudinal test-retest analyses to investigate pain threshold variability among individuals using a resting-state fMRI brain connectome. Twenty-four healthy subjects who received four MRI sessions separated by at least 7 days were included in the data analysis. Subjects' pain thresholds were measured using two modalities of experimental pain (heat and pressure) on two different locations (heat pain: leg and arm; pressure pain: leg and thumbnail). Behavioral results showed strong inter-individual variability and strong within-individual stability in pain threshold. Resting state fMRI data analyses showed that functional connectivity profiles can accurately identify subjects across four sessions, indicating that an individual's connectivity profile may be intrinsic and unique. By using multivariate pattern analyses, we found that connectivity profiles could be used to predict an individual's pain threshold at both within-session and between-session levels, with the most predictive contribution from medial-frontal and frontal-parietal networks. These results demonstrate the potential of using a resting-state fMRI brain connectome to build a 'neural trait' for characterizing an individual's pain-related behavior, and such a 'neural trait' may eventually be used to personalize clinical assessments.
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Affiliation(s)
- Yiheng Tu
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Binlong Zhang
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Jin Cao
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Georgia Wilson
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Jian Kong
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA.
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45
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Lu X, Li T, Xia Z, Zhu R, Wang L, Luo Y, Feng C, Krueger F. Connectome-based model predicts individual differences in propensity to trust. Hum Brain Mapp 2019; 40:1942-1954. [PMID: 30633429 PMCID: PMC6865671 DOI: 10.1002/hbm.24503] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 11/15/2018] [Accepted: 12/02/2018] [Indexed: 12/12/2022] Open
Abstract
Trust constitutes a fundamental basis of human society and plays a pivotal role in almost every aspect of human relationships. Although enormous interest exists in determining the neuropsychological underpinnings of a person's propensity to trust utilizing task-based fMRI; however, little progress has been made in predicting its variations by task-free fMRI based on whole-brain resting-state functional connectivity (RSFC). Here, we combined a one-shot trust game with a connectome-based predictive modeling approach to predict propensity to trust from whole-brain RSFC. We demonstrated that individual variations in the propensity to trust were primarily predicted by RSFC rooted in the functional integration of distributed key nodes-caudate, amygdala, lateral prefrontal cortex, temporal-parietal junction, and the temporal pole-which are part of domain-general large-scale networks essential for the motivational, affective, and cognitive aspects of trust. We showed, further, that the identified brain-behavior associations were only evident for trust but not altruistic preferences and that propensity to trust (and its underlying neural underpinnings) were modulated according to the extent to which a person emphasizes general social preferences (i.e., horizontal collectivism) rather than general risk preferences (i.e., trait impulsiveness). In conclusion, the employed data-driven approach enables to predict propensity to trust from RSFC and highlights its potential use as an objective neuromarker of trust impairment in mental disorders.
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Affiliation(s)
- Xiaping Lu
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- Brain, Mind & Markets Laboratory, Department of FinanceThe University of MelbourneMelbourneVictoriaAustralia
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Ting Li
- Collaborative Innovation Center of Assessment toward Basic Education QualityBeijing Normal UniversityBeijingChina
| | - Zhichao Xia
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Ruida Zhu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Li Wang
- Collaborative Innovation Center of Assessment toward Basic Education QualityBeijing Normal UniversityBeijingChina
| | - Yue‐Jia Luo
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- Center for Emotion and BrainShenzhen Institute of NeuroscienceShenzhenChina
- Medical SchoolKunming University of Science and TechnologyKunmingChina
| | - Chunliang Feng
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
| | - Frank Krueger
- School of Systems BiologyGeorge Mason UniversityFairfaxVirginia
- Department of PsychologyUniversity of MannheimMannheimGermany
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Feng C, Yuan J, Geng H, Gu R, Zhou H, Wu X, Luo Y. Individualized prediction of trait narcissism from whole-brain resting-state functional connectivity. Hum Brain Mapp 2018; 39:3701-3712. [PMID: 29749072 DOI: 10.1002/hbm.24205] [Citation(s) in RCA: 44] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 04/05/2018] [Accepted: 04/23/2018] [Indexed: 01/16/2023] Open
Abstract
Narcissism is one of the most fundamental personality traits in which individuals in general population exhibit a large heterogeneity. Despite a surge of interest in examining behavioral characteristics of narcissism in the past decades, the neurobiological substrates underlying narcissism remain poorly understood. Here, we addressed this issue by applying a machine learning approach to decode trait narcissism from whole-brain resting-state functional connectivity (RSFC). Resting-state functional MRI (fMRI) data were acquired for a large sample comprising 155 healthy adults, each of whom was assessed for trait narcissism. Using a linear prediction model, we examined the relationship between whole-brain RSFC and trait narcissism. We demonstrated that the machine-learning model was able to decode individual trait narcissism from RSFC across multiple neural systems, including functional connectivity between and within limbic and prefrontal systems as well as their connectivity with other networks. Key nodes that contributed to the prediction model included the amygdala, prefrontal and anterior cingulate regions that have been linked to trait narcissism. These findings remained robust using different validation procedures. Our findings thus demonstrate that RSFC among multiple neural systems predicts trait narcissism at the individual level.
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Affiliation(s)
- Chunliang Feng
- College of Information Science and Technology, Beijing Normal University, Beijing, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China
| | - Jie Yuan
- State Key Laboratory of Brain and Cognitive Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Haiyang Geng
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China
| | - Ruolei Gu
- Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - Hui Zhou
- Department of Psychology, Sun Yat-sen University, Guangzhou, China
| | - Xia Wu
- College of Information Science and Technology, Beijing Normal University, Beijing, China
| | - Yuejia Luo
- Shenzhen Key Laboratory of Affective and Social Cognitive Science, Shenzhen University, Shenzhen, China
- Center for Emotion and Brain, Shenzhen Institute of Neuroscience, Shenzhen, China
- Depatment of Psychology, Southern Medical University, Guangzhou, China
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