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Lee ZL, Siew SKH, Yu J. Intrinsic functional connectivity mediates the effect of personality traits on depressive symptoms. PLoS One 2024; 19:e0300462. [PMID: 38985695 PMCID: PMC11236141 DOI: 10.1371/journal.pone.0300462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/27/2024] [Indexed: 07/12/2024] Open
Abstract
BACKGROUND Personality traits have been proposed as risk factors for depressive symptoms. However, the neural mechanism behind these relationships is unclear. This study examined the possible mediating effect of resting-state functional connectivity networks on these relationships. METHODS Data from 153 healthy Germans were obtained from the MPI-Leipzig Mind-Brain-Body: Neuroanatomy & Connectivity Protocol database. Network-based statistics were used to identify significant functional connectivity networks that were positively and negatively associated with the personality traits of neuroticism, conscientiousness, and extraversion, with and without demographical covariates. Mediation analyses were performed for each personality trait and depressive symptoms with the significant positive and negative network strengths of the respective personality traits as mediators. RESULTS Neuroticism, conscientiousness, and extraversion were significantly correlated with depressive symptoms. Network-based statistics identified patterns of functional connectivity that were significantly associated with neuroticism and conscientiousness. After controlling for demographical covariates, significant conscientiousness-associated and extraversion-associated networks emerged. Mediation analysis concluded that only the neuroticism-positive network mediated the effect of neuroticism on depressive symptoms. When age and sex were controlled, the extraversion-positive network completely mediated the effect of extraversion on depressive symptoms. CONCLUSIONS These findings revealed that patterns of intrinsic functional networks predict personality traits and suggest that the relationship between personality traits and depressive symptoms may in part be due to their common patterns of intrinsic functional networks.
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Affiliation(s)
- Zheng Long Lee
- School of Social Sciences, Psychology, Nanyang Technological University, Singapore, Singapore
| | - Savannah Kiah Hui Siew
- School of Social Sciences, Psychology, Nanyang Technological University, Singapore, Singapore
| | - Junhong Yu
- School of Social Sciences, Psychology, Nanyang Technological University, Singapore, Singapore
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2
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Yi H, Xiao M, Chen X, Yan Q, Yang Y, Liu Y, Song S, Gao X, Chen H. Resting-state functional network connectivity underlying conscientiousness in school-aged children. Child Neuropsychol 2024; 30:486-502. [PMID: 37278282 DOI: 10.1080/09297049.2023.2221757] [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: 08/28/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
Conscientiousness is a personality trait that matures from early childhood to late adolescence, yet little is known about its underlying brain mechanisms during this period. To investigate this, our study examined the resting-state functional network connectivity (rsFNC) of 69 school-aged children (mean age = 10.12 years, range = 9-12) using a whole-brain region-of-interest (ROI) based analysis, based on functional magnetic resonance imaging (fMRI). The results indicated a positive association between conscientiousness and the rsFNC between the fronto-parietal network (FPN) and two brain networks: the somatosensory motor-hand network (SMHN) and the auditory network (AN). However, conscientiousness was negatively associated with the rsFNC between FPN and two other networks: the salience network (SN); the default mode network (DMN). Moreover, our results suggest that the FPN may play a hub role in the neural performance of children's conscientiousness. Intrinsic brain networks, particularly those involved in higher-order cognitive functions, impact children's conscientiousness. Therefore, FPN plays an important role in the development of children's personality, providing insight into the neural mechanisms underlying children's personality.
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Affiliation(s)
- Haijing Yi
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Ximei Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Qiaoling Yan
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yue Yang
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shiqing Song
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
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3
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Wang G, Zeng M, Li J, Liu Y, Wei D, Long Z, Chen H, Zang X, Yang J. Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality. Neuroscience 2023; 530:66-78. [PMID: 37619767 DOI: 10.1016/j.neuroscience.2023.08.017] [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: 02/14/2023] [Revised: 08/01/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
INTRODUCTION Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear. METHODS We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset. RESULTS SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control. CONCLUSION Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.
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Affiliation(s)
- Guangtong Wang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Mei Zeng
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Jiwen Li
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Yadong Liu
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Dongtao Wei
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Zhiliang Long
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Haopeng Chen
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xinlei Zang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Juan Yang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China.
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Frank LE, Zeithamova D. Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs. Brain Behav 2023; 13:e3015. [PMID: 37062880 PMCID: PMC10275534 DOI: 10.1002/brb3.3015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 04/18/2023] Open
Abstract
INTRODUCTION Resting-state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large-scale brain networks and brain-behavior relationships. Furthermore, idiosyncratic patterns of resting-state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task-based fMRI can be similarly leveraged for individual differences analyses. METHOD Here, we tested the degree to which functional interactions occurring in the background of a task during slow event-related fMRI parallel or differ from those captured during resting-state fMRI. We compared two approaches for removing task-evoked activity from task-based fMRI: (1) applying a low-pass filter to remove task-related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task-evoked responses. RESULT We found that the organization of large-scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task-based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low-pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low-pass-filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. CONCLUSION Together, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.
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Affiliation(s)
- Lea E. Frank
- Department of PsychologyUniversity of OregonEugeneOregonUSA
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5
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Talaei N, Ghaderi A. Integration of structural brain networks is related to openness to experience: A diffusion MRI study with CSD-based tractography. Front Neurosci 2022; 16:1040799. [PMID: 36570828 PMCID: PMC9775296 DOI: 10.3389/fnins.2022.1040799] [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: 09/23/2022] [Accepted: 11/23/2022] [Indexed: 12/13/2022] Open
Abstract
Openness to experience is one of the big five traits of personality which recently has been the subject of several studies in neuroscience due to its importance in understanding various cognitive functions. However, the neural basis of openness to experience is still unclear. Previous studies have found largely heterogeneous results, suggesting that various brain regions may be involved in openness to experience. Here we suggested that performing structural connectome analysis may shed light on the neural underpinnings of openness to experience as it provides a more comprehensive look at the brain regions that are involved in this trait. Hence, we investigated the involvement of brain network structural features in openness to experience which has not yet been explored to date. The magnetic resonance imaging (MRI) data along with the openness to experience trait score from the self-reported NEO Five-Factor Inventory of 100 healthy subjects were evaluated from Human Connectome Project (HCP). CSD-based whole-brain probabilistic tractography was performed using diffusion-weighted images as well as segmented T1-weighted images to create an adjacency matrix for each subject. Using graph theoretical analysis, we computed global efficiency (GE) and clustering coefficient (CC) which are measures of two important aspects of network organization in the brain: functional integration and functional segregation respectively. Results revealed a significant negative correlation between GE and openness to experience which means that the higher capacity of the brain in combining information from different regions may be related to lower openness to experience.
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Affiliation(s)
- Nima Talaei
- Department of Psychology, Faculty of Literature and Human Sciences, Shahid Bahonar University, Kerman, Iran,*Correspondence: Nima Talaei,
| | - Amirhossein Ghaderi
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada,Department of Psychology, University of Calgary, Calgary, AB, Canada
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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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7
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Adamovich T, Zakharov I, Tabueva A, Malykh S. The thresholding problem and variability in the EEG graph network parameters. Sci Rep 2022; 12:18659. [PMID: 36333413 PMCID: PMC9636266 DOI: 10.1038/s41598-022-22079-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Graph thresholding is a frequently used practice of eliminating the weak connections in brain functional connectivity graphs. The main aim of the procedure is to delete the spurious connections in the data. However, the choice of the threshold is arbitrary, and the effect of the threshold choice is not fully understood. Here we present the description of the changes in the global measures of a functional connectivity graph depending on the different proportional thresholds based on the 146 resting-state EEG recordings. The dynamics is presented in five different synchronization measures (wPLI, ImCoh, Coherence, ciPLV, PPC) in sensors and source spaces. The analysis shows significant changes in the graph's global connectivity measures as a function of the chosen threshold which may influence the outcome of the study. The choice of the threshold could lead to different study conclusions; thus it is necessary to improve the reasoning behind the choice of the different analytic options and consider the adoption of different analytic approaches. We also proposed some ways of improving the procedure of thresholding in functional connectivity research.
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Affiliation(s)
- Timofey Adamovich
- Psychological Institute of Russian Academy of Education, Moscow, Russia.
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia.
| | - Ilya Zakharov
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Anna Tabueva
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
| | - Sergey Malykh
- Psychological Institute of Russian Academy of Education, Moscow, Russia
- Ural Federal University Named After the First President of Russia B. N. Yeltsin, Yekaterinburg, Russia
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8
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Yang Y, Li J, Zhang J, Zhou K, Kao HSR, Bi H, Xu M. Personality traits modulate the neural responses to handwriting processing. Ann N Y Acad Sci 2022; 1516:222-233. [PMID: 35899373 PMCID: PMC9796404 DOI: 10.1111/nyas.14871] [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: 01/01/2023]
Abstract
Handwriting is a vital skill for everyday human activities. It has a wealth of information about writers' characteristics and can hint toward underlying neurological conditions, such as Parkinson's disease, autism, dyslexia, and attention-deficit/hyperactivity disorder (ADHD). Many previous studies have reported a link between personality and individual differences in handwriting, but the evidence for the relationship tends to be anecdotal in nature. Using functional magnetic resonance imaging (fMRI), we examined whether the association between personality traits and handwriting was instantiated at the neural level. Results showed that the personality trait of conscientiousness modulated brain activation in the left premotor cortex and right inferior/middle frontal gyrus, which may reflect the impact of personality on orthography-to-grapheme transformation and executive control involved in handwriting. Such correlations were not observed in symbol-drawing or word-reading tasks, suggesting the specificity of the link between conscientiousness and handwriting in these regions. Moreover, using a connectome-based predictive modeling approach, we found that individuals' conscientiousness scores could be predicted based on handwriting-related functional brain networks, suggesting that the influence of personality on handwriting may occur within a broader network. Our findings provide neural evidence for the link between personality and handwriting processing, extending our understanding of the nature of individual differences in handwriting.
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Affiliation(s)
- Yang Yang
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning DifficultiesInstitute of Psychology, Chinese Academy of SciencesBeijingChina,Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Junjun Li
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning DifficultiesInstitute of Psychology, Chinese Academy of SciencesBeijingChina,Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Jun Zhang
- College of EducationCapital Normal UniversityBeijingChina
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, School of PsychologyBeijing Normal UniversityBeijingChina
| | - Henry S. R. Kao
- Department of PsychologyUniversity of Hong KongHong KongChina
| | - Hong‐Yan Bi
- CAS Key Laboratory of Behavioral Science, Center for Brain Science and Learning DifficultiesInstitute of Psychology, Chinese Academy of SciencesBeijingChina,Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
| | - Min Xu
- Center for Brain Disorders and Cognitive Sciences, School of PsychologyShenzhen UniversityShenzhenChina
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Xie H, Zhang F, Cao Y, Long X, Wu B, Gong Q, Jia Z. Brain default mode network mediates the association between negative perfectionism and exercise dependence. J Behav Addict 2022; 11:928-940. [PMID: 36112490 PMCID: PMC9872538 DOI: 10.1556/2006.2022.00067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Revised: 07/11/2022] [Accepted: 08/19/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND AND AIMS Perfectionism is correlated with the occurrence of exercise dependence. We aim to reveal the role of functional connectivity (FC) between gray matter (GM) and white matter (WM) networks in the association between perfectionism and exercise dependence. METHODS In this cross-sectional study, one hundred ten participants with exercise dependence underwent behavioral evaluation and resting-state functional magnetic resonance imaging. Perfectionism and exercise dependence were quantified using the Frost Multidimensional Perfectionism Scale (FMPS) and Exercise Dependence Scale (EDS). We used a K-means clustering algorithm to identify functional GM and WM networks and obtained the FCs of the GM-GM, GM-WM, and WM-WM networks. Partial correlation and mediation analyses were performed to explore the relationships among FCs, FMPS, and EDS. RESULTS We identified ten stable GM networks and nine WM networks. Of these, FCs existed between the corona radiata network (WM1) and default mode network (DMN, GM8), WM1 network and WM DMN (WM4), WM1 network and midbrain WM network (WM7), and WM4 network and inferior longitudinal fasciculus network (WM9). The WM1-GM8 and WM1-WM4 FCs were positively correlated with the EDS and negative FMPS. The mediating effects of the WM1-GM8 and WM1-WM4 FCs were established in the association between the negative dimensional FMPS and EDS. DISCUSSION AND CONCLUSIONS The WM1 network anatomically linked the subregions within the GM8 and WM4 networks, and WM1-GM8 and WM1-WM4 FCs mediated the association between negative dimensional FMPS and EDS. These findings indicated that DMN function might be involved in the increased risks of exercise dependence promoted by negative perfectionism.
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Affiliation(s)
- Hongsheng Xie
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041, China,Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Feifei Zhang
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China,Department of Radiology, First Hospital of Shanxi Medical University, Taiyuan, 030001, Shanxi Province, China
| | - Yuan Cao
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041, China,Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China
| | - Xipeng Long
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041, China,Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Baolin Wu
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China,Functional and Molecular Imaging Key Laboratory of Sichuan University, West China Hospital of Sichuan University, Chengdu, 610041, China
| | - Qiyong Gong
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, 610041, China,Department of Radiology, West China Xiamen Hospital of Sichuan University, Xiamen, 361021, Fujian Province, China,Corresponding authors. E-mail: ,
| | - Zhiyun Jia
- Department of Nuclear Medicine, West China Hospital of Sichuan University, Chengdu, 610041, China,Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, 610041, China,Functional and Molecular Imaging Key Laboratory of Sichuan University, West China Hospital of Sichuan University, Chengdu, 610041, China,Corresponding authors. E-mail: ,
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10
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Safron A, Klimaj V, Hipólito I. On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances. Front Syst Neurosci 2022; 15:688424. [PMID: 35126062 PMCID: PMC8814434 DOI: 10.3389/fnsys.2021.688424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022] Open
Abstract
In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kinsey Institute, Indiana University, Bloomington, IN, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
| | - Victoria Klimaj
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Complex Networks and Systems, Informatics Department, Indiana University, Bloomington, IN, United States
| | - Inês Hipólito
- Department of Philosophy, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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11
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Yokoyama C, Autio JA, Ikeda T, Sallet J, Mars RB, Van Essen DC, Glasser MF, Sadato N, Hayashi T. Comparative connectomics of the primate social brain. Neuroimage 2021; 245:118693. [PMID: 34732327 PMCID: PMC9159291 DOI: 10.1016/j.neuroimage.2021.118693] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/27/2021] [Accepted: 10/29/2021] [Indexed: 01/13/2023] Open
Abstract
Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior.
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Affiliation(s)
- Chihiro Yokoyama
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan.
| | - Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Takuro Ikeda
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, Oxford University, Oxford, United Kingdom; University of Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - David C Van Essen
- Departments of Neuroscience, Washington University Medical School, St Louis, MO, United States of America
| | - Matthew F Glasser
- Departments of Neuroscience, Washington University Medical School, St Louis, MO, United States of America; Department of Radiology, Washington University Medical School, St Louis, MO, United States of America
| | - Norihiro Sadato
- National Institute for Physiological Sciences, Okazaki, Japan; The Graduate University for Advanced Studies (SOKENDAI), Kanagawa, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan; School of Medicine, Kyoto University, Kyoto, Japan.
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12
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Hilger K, Markett S. Personality network neuroscience: Promises and challenges on the way toward a unifying framework of individual variability. Netw Neurosci 2021; 5:631-645. [PMID: 34746620 PMCID: PMC8567832 DOI: 10.1162/netn_a_00198] [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: 12/24/2020] [Accepted: 04/22/2021] [Indexed: 11/21/2022] Open
Abstract
We propose that the application of network theory to established psychological personality conceptions has great potential to advance a biologically plausible model of human personality. Stable behavioral tendencies are conceived as personality “traits.” Such traits demonstrate considerable variability between individuals, and extreme expressions represent risk factors for psychological disorders. Although the psychometric assessment of personality has more than hundred years tradition, it is not yet clear whether traits indeed represent “biophysical entities” with specific and dissociable neural substrates. For instance, it is an open question whether there exists a correspondence between the multilayer structure of psychometrically derived personality factors and the organizational properties of traitlike brain systems. After a short introduction into fundamental personality conceptions, this article will point out how network neuroscience can enhance our understanding about human personality. We will examine the importance of intrinsic (task-independent) brain connectivity networks and show means to link brain features to stable behavioral tendencies. Questions and challenges arising from each discipline itself and their combination are discussed and potential solutions are developed. We close by outlining future trends and by discussing how further developments of network neuroscience can be applied to personality research.
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Affiliation(s)
- Kirsten Hilger
- Department of Psychology I, Julius-Maximilians University Würzburg, Würzburg, Germany
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13
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Kajimura S, Ito A, Izuma K. Brain Knows Who Is on the Same Wavelength: Resting-State Connectivity Can Predict Compatibility of a Female-Male Relationship. Cereb Cortex 2021; 31:5077-5089. [PMID: 34145453 PMCID: PMC8491675 DOI: 10.1093/cercor/bhab143] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 04/15/2021] [Accepted: 05/03/2021] [Indexed: 12/03/2022] Open
Abstract
Prediction of the initial compatibility of heterosexual individuals based on self-reported traits and preferences has not been successful, even with significantly developed information technology. To overcome the limitations of self-reported measures and predict compatibility, we used functional connectivity profiles from resting-state functional magnetic resonance imaging (fMRI) data that carry rich individual-specific information sufficient to predict psychological constructs and activation patterns during social cognitive tasks. Several days after collecting data from resting-state fMRIs, participants undertook a speed-dating experiment in which they had a 3-min speed date with every other opposite-sex participant. Our machine learning algorithm successfully predicted whether pairs in the experiment were compatible or not using (dis)similarity of functional connectivity profiles obtained before the experiment. The similarity and dissimilarity of functional connectivity between individuals and these multivariate relationships contributed to the prediction, hence suggesting the importance of complementarity (observed as dissimilarity) as well as the similarity between an individual and a potential partner during the initial attraction phase. The result indicates that the salience network, limbic areas, and cerebellum are especially important for the feeling of compatibility. This research emphasizes the utility of neural information to predict complex phenomena in a social environment that behavioral measures alone cannot predict.
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Affiliation(s)
- Shogo Kajimura
- Faculty of Information and Human Sciences, Kyoto Institute of Technology, Kyoto 606-8585, Japan
| | - Ayahito Ito
- Research Institute for Future Design, Kochi University of Technology, Kochi 780-8515, Japan
- Department of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom
- Faculty of Health Sciences, Hokkaido University, Hokkaido 060-0812, Japan
| | - Keise Izuma
- Research Institute for Future Design, Kochi University of Technology, Kochi 780-8515, Japan
- Department of Psychology, University of Southampton, Southampton SO17 1BJ, United Kingdom
- School of Economics & Management, Kochi University of Technology, Kochi 780-8515, Japan
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14
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Altinok DCA, Rajkumar R, Nießen D, Sbaihat H, Kersey M, Shah NJ, Veselinović T, Neuner I. Common neurobiological correlates of resilience and personality traits within the triple resting-state brain networks assessed by 7-Tesla ultra-high field MRI. Sci Rep 2021; 11:11564. [PMID: 34079001 PMCID: PMC8172832 DOI: 10.1038/s41598-021-91056-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2020] [Accepted: 05/10/2021] [Indexed: 11/28/2022] Open
Abstract
Despite numerous studies investigating resilience and personality trials, a paucity of information regarding their neurobiological commonalities at the level of the large resting-state networks (rsNWs) remains. Here we address this topic using the advantages of ultra-high-field (UHF) 7T-MRI, characterized by higher signal-to-noise ratio and increased sensitivity. The association between resilience, personality traits and three fMRI measures (fractional amplitude of low-frequency fluctuations (fALFF), degree centrality (DC) and regional homogeneity (ReHo)) determined for three core rsNWs (default mode (DMN), salience (SN), and central executive network (CEN)) were examined in 32 healthy volunteers. The investigation revealed a significant role of SN in both resilience and personality traits and a tight association of the DMN with resilience. DC in CEN emerged as a significant moderator for the correlations of resilience with the personality traits of neuroticism and extraversion. Our results indicate that the common neurobiological basis of resilience and the Big Five personality traits may be reflected at the level of the core rsNWs.
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Affiliation(s)
- Dilsa Cemre Akkoc Altinok
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Ravichandran Rajkumar
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
- JARA - BRAIN - Translational Medicine, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
| | - Dominik Nießen
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
| | - Hasan Sbaihat
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
- Department of Medical Imaging, Arab-American University Palestine, AAUP, Jenin, Palestine
| | - Margo Kersey
- Department of Mathematics, University of California, Los Angeles, CA, 90095, USA
| | - N Jon Shah
- JARA - BRAIN - Translational Medicine, Pauwelsstraße 30, 52074, Aachen, Germany
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany
- Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Jülich, Germany
- Department of Neurology, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Tanja Veselinović
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany
| | - Irene Neuner
- Department of Psychiatry, Psychotherapy and Psychosomatics, RWTH Aachen University, Pauwelsstraße 30, 52074, Aachen, Germany.
- JARA - BRAIN - Translational Medicine, Pauwelsstraße 30, 52074, Aachen, Germany.
- Institute of Neuroscience and Medicine, INM-4, Forschungszentrum Jülich GmbH, Wilhelm-Johnen-Straße, 52428, Jülich, Germany.
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15
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Mechanisms of the Effects of Parental Emotional Warmth on Extraversion in Children and Adolescents. Neuroscience 2021; 467:134-141. [PMID: 34038771 DOI: 10.1016/j.neuroscience.2021.05.021] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 04/30/2021] [Accepted: 05/19/2021] [Indexed: 01/06/2023]
Abstract
The purpose of this study is to probe into the influence mechanism of parental emotional warmth (PEW) on extraversion for children and adolescents, as well as the moderating and mediating role of brain functional activity. Thirty-two children and adolescents underwent functional magnetic resonance imaging (fMRI) scans and completed Egna Minnen av Barndoms Uppfostran (EMBU) and Eysenck Personality Questionnaire (EPQ). Small-worldness (SW) of brain networks, fractional amplitude of low-frequency fluctuations (fALFF), and region-of-interest to region-of-interest (ROI-ROI) functional connectivity were calculated to study intrinsic neuronal activity. We found that PEW had a positive direct effect on extraversion, and all participants in the current study showed an efficient small-world structure. The positive association between PEW and extraversion was mediated by SW. Furthermore, the fALFF and extraversion were significantly and negatively correlated in the right precuneus and dorsolateral superior frontal gyrus. The mediating effect of SW was moderated by the functional connectivity between the right precuneus and the right dorsolateral superior frontal gyrus. The indirect effect was significant with lower level of the functional connectivity between the right precuneus and the right dorsolateral superior frontal gyrus. These findings indicate that SW of brain networks may be a key factor that accounts for the positive association between PEW and extraversion in children and adolescents and the level of the functional connectivity between the right precuneus and the right dorsolateral superior frontal gyrus could moderate the relationship.
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16
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Marstrand-Joergensen MR, Madsen MK, Stenbæk DS, Ozenne B, Jensen PS, Frokjaer VG, Knudsen GM, Fisher PM. Default Mode Network Functional Connectivity Negatively Associated with Trait Openness to Experience. Soc Cogn Affect Neurosci 2021; 16:950-961. [PMID: 33891043 PMCID: PMC8610093 DOI: 10.1093/scan/nsab048] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 03/30/2021] [Accepted: 04/23/2021] [Indexed: 11/12/2022] Open
Abstract
Evaluating associations between the five-factor personality domains and resting-state functional connectivity networks (e.g., default mode network, DMN) highlights distributed neurobiological systems linked to behaviorally relevant phenotypes. Establishing these associations can highlight a potential underlying role for these neural pathways in related clinical illness and treatment response. Here we examined associations between within- and between-network resting-state functional connectivity with functional magnetic resonance imaging (fMRI) and the five-factor personality domains: Openness to experience (Openness), Extraversion, Neuroticism, Agreeableness and Conscientiousness. We included data from 470 resting-state scan sessions and personality assessments in 295 healthy participants. Within- and between-network functional connectivity from 32 a priori defined regions was computed across seven resting-state networks. The association between functional connectivity and personality traits was assessed using generalized least squares. Within-network DMN functional connectivity was significantly negatively associated with trait Openness (regression coefficient= -0.0010; [95% CI] = [-0.0017, -0.0003]; pFWER = 0.033), seemingly driven by association with the Fantasy subfacet. Trait Extraversion was significantly negatively associated with functional connectivity between the visual and dorsal attention networks and positively associated with functional connectivity between the frontoparietal and language networks. Our findings provide evidence that resting-state DMN is associated with trait Openness and gives insight into personality neuroscience.
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Affiliation(s)
- Maja Rou Marstrand-Joergensen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Martin K Madsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dea S Stenbæk
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark.,Department of Public Health, Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark
| | - Peter S Jensen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark
| | - Vibe G Frokjaer
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Department of Psychiatry Copenhagen, Mental Health Services Capital Region of Denmark
| | - Gitte M Knudsen
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark.,Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Patrick M Fisher
- Neurobiology Research Unit, Copenhagen University Hospital Rigshospitalet, Copenhagen 2100, Denmark
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17
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Liu X, Lai H, Li J, Becker B, Zhao Y, Cheng B, Wang S. Gray matter structures associated with neuroticism: A meta-analysis of whole-brain voxel-based morphometry studies. Hum Brain Mapp 2021; 42:2706-2721. [PMID: 33704850 PMCID: PMC8127153 DOI: 10.1002/hbm.25395] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2020] [Revised: 01/28/2021] [Accepted: 02/22/2021] [Indexed: 02/05/2023] Open
Abstract
Neuroticism is major higher-order personality trait and has been robustly associated with mental and physical health outcomes. Although a growing body of studies have identified neurostructural markers of neuroticism, the results remained highly inconsistent. To characterize robust associations between neuroticism and variations in gray matter (GM) structures, the present meta-analysis investigated the concurrence across voxel-based morphometry (VBM) studies using the anisotropic effect size signed differential mapping (AES-SDM). A total of 13 studies comprising 2,278 healthy subjects (1,275 females, 29.20 ± 14.17 years old) were included. Our analysis revealed that neuroticism was consistently associated with the GM structure of a cluster spanning the bilateral dorsal anterior cingulate cortex and extending to the adjacent medial prefrontal cortex (dACC/mPFC). Meta-regression analyses indicated that the neuroticism-GM associations were not confounded by age and gender. Overall, our study is the first whole-brain meta-analysis exploring the brain structural correlates of neuroticism, and the findings may have implications for the intervention of high-neuroticism individuals, who are at risk of mental disorders, by targeting the dACC/mPFC.
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Affiliation(s)
- Xiqin Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Han Lai
- Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
| | - Jingguang Li
- College of Teacher Education, Dali University, Dali, China
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Yajun Zhao
- School of Education and Psychology, Southwest Minzu University, Chengdu, China
| | - Bochao Cheng
- Department of Radiology, West China Second University Hospital, Sichuan University, Chengdu, China
| | - Song Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China.,Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China
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18
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Teeuw J, Hulshoff Pol HE, Boomsma DI, Brouwer RM. Reliability modelling of resting-state functional connectivity. Neuroimage 2021; 231:117842. [PMID: 33581291 DOI: 10.1016/j.neuroimage.2021.117842] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Revised: 02/02/2021] [Accepted: 02/04/2021] [Indexed: 12/12/2022] Open
Abstract
Resting-state functional magnetic resonance imaging (rs-fMRI) has an inherently low signal-to-noise ratio largely due to thermal and physiological noise that attenuates the functional connectivity (FC) estimates. Such attenuation limits the reliability of FC and may bias its association with other traits. Low reliability also limits heritability estimates. Classical test theory can be used to obtain a true correlation estimate free of random measurement error from parallel tests, such as split-half sessions of a rs-fMRI scan. We applied a measurement model to split-half FC estimates from the resting-state fMRI data of 1003 participants from the Human Connectome Project (HCP) to examine the benefit of reliability modelling of FC in association with traits from various domains. We evaluated the efficiency of the measurement model on extracting a stable and reliable component of FC and its association with several traits for various sample sizes and scan durations. In addition, we aimed to replicate our previous findings of increased heritability estimates when using a measurement model in a longitudinal adolescent twin cohort. The split-half measurement model improved test-retest reliability of FC on average with +0.33 points (from +0.49 to +0.82), improved strength of associations between FC and various traits on average 1.2-fold (range 1.09-1.35), and increased heritability estimates on average with +20% points (from 39% to 59%) for the full HCP dataset. On average, about half of the variance in split-session FC estimates was attributed to the stable and reliable component of FC. Shorter scan durations showed greater benefit of reliability modelling (up to 1.6-fold improvement), with an additional gain for smaller sample sizes (up to 1.8-fold improvement). Reliability modelling of FC based on a split-half using a measurement model can benefit genetic and behavioral studies by extracting a stable and reliable component of FC that is free from random measurement error and improves genetic and behavioral associations.
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Affiliation(s)
- Jalmar Teeuw
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands.
| | - Hilleke E Hulshoff Pol
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands
| | - Dorret I Boomsma
- Department of Biological Psychology, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Rachel M Brouwer
- Brain Center Rudolf Magnus and Department of Psychiatry, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX Utrecht, Netherlands
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19
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The Multilayer Network Approach in the Study of Personality Neuroscience. Brain Sci 2020; 10:brainsci10120915. [PMID: 33260895 PMCID: PMC7761383 DOI: 10.3390/brainsci10120915] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 11/24/2020] [Accepted: 11/25/2020] [Indexed: 02/06/2023] Open
Abstract
It has long been understood that a multitude of biological systems, from genetics, to brain networks, to psychological factors, all play a role in personality. Understanding how these systems interact with each other to form both relatively stable patterns of behaviour, cognition and emotion, but also vast individual differences and psychiatric disorders, however, requires new methodological insight. This article explores a way in which to integrate multiple levels of personality simultaneously, with particular focus on its neural and psychological constituents. It does so first by reviewing the current methodology of studies used to relate the two levels, where psychological traits, often defined with a latent variable model are used as higher-level concepts to identify the neural correlates of personality (NCPs). This is known as a top-down approach, which though useful in revealing correlations, is not able to include the fine-grained interactions that occur at both levels. As an alternative, we discuss the use of a novel complex system approach known as a multilayer network, a technique that has recently proved successful in revealing veracious interactions between networks at more than one level. The benefits of the multilayer approach to the study of personality neuroscience follow from its well-founded theoretical basis in network science. Its predictive and descriptive power may surpass that of statistical top-down and latent variable models alone, potentially allowing the discernment of more complete descriptions of individual differences, and psychiatric and neurological changes that accompany disease. Though in its infancy, and subject to a number of methodological unknowns, we argue that the multilayer network approach may contribute to an understanding of personality as a complex system comprised of interrelated psychological and neural features.
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20
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Neumann CS. Structural equation modeling of the associations between amygdala activation, personality, and internalizing, externalizing symptoms of psychopathology. PERSONALITY NEUROSCIENCE 2020; 3:e8. [PMID: 32743337 PMCID: PMC7372165 DOI: 10.1017/pen.2020.8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 05/10/2020] [Accepted: 05/13/2020] [Indexed: 12/19/2022]
Abstract
There is an expanding literature on the theoretical and empirical connections between personality and psychopathology, and their shared neurobiological correlates. Recent cybernetic theories of personality and psychopathology, as well as affective neuroscience theory, provide grounding for understanding neurobiological-personality-psychopathology (NPP) associations. With the emergence of large sample datasets (e.g., Human Connectome Project) advanced quantitative modeling can be used to rigorously test dynamic statistical representations of NPP connections. Also, research suggests that these connections are influenced by sex, and large samples provide the opportunity to examine how NPP associations might be moderated by sex. The current study used a large sample from the Duke Neurogenetics Study (DNS) to examine how amygdala activation to facial expressions was linked with self-report of personality traits and clinical interviews of internalizing and externalizing symptoms of psychopathology. Structural equation modeling results revealed direct associations of amygdala activation with personality trait expression, as well as indirect associations (though personality) with symptoms of psychopathology. Moreover, the NPP links were moderated by sex. The current results are in line with research that identifies a broader role played by the amygdala in personality and provide potential insights for continued research in personality neuroscience and recent theories on the neurobiology of personality.
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Affiliation(s)
- Craig S. Neumann
- Department of Psychology, University of North Texas, Denton, TX, USA
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21
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Vatansever D, Karapanagiotidis T, Margulies DS, Jefferies E, Smallwood J. Distinct patterns of thought mediate the link between brain functional connectomes and well-being. Netw Neurosci 2020; 4:637-657. [PMID: 32885119 PMCID: PMC7462429 DOI: 10.1162/netn_a_00137] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2019] [Accepted: 03/04/2020] [Indexed: 12/29/2022] Open
Abstract
Ongoing thought patterns constitute important aspects of both healthy and abnormal human cognition. However, the neural mechanisms behind these daily experiences and their contribution to well-being remain a matter of debate. Here, using resting-state fMRI and retrospective thought sampling in a large neurotypical cohort (n = 211), we identified two distinct patterns of thought, broadly describing the participants' current concerns and future plans, that significantly explained variability in the individual functional connectomes. Consistent with the view that ongoing thoughts are an emergent property of multiple neural systems, network-based analysis highlighted the central importance of both unimodal and transmodal cortices in the generation of these experiences. Importantly, while state-dependent current concerns predicted better psychological health, mediating the effect of functional connectomes, trait-level future plans were related to better social health, yet with no mediatory influence. Collectively, we show that ongoing thoughts can influence the link between brain physiology and well-being.
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Affiliation(s)
- Deniz Vatansever
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | | | - Daniel S Margulies
- Brain and Spine Institute, French National Centre for Scientific Research, Paris, France
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22
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Automatic Recognition of Personality Profiles Using EEG Functional Connectivity During Emotional Processing. Brain Sci 2020; 10:brainsci10050278. [PMID: 32375222 PMCID: PMC7288068 DOI: 10.3390/brainsci10050278] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2020] [Revised: 04/27/2020] [Accepted: 04/27/2020] [Indexed: 12/03/2022] Open
Abstract
Personality is the characteristic set of an individual’s behavioral and emotional patterns that evolve from biological and environmental factors. The recognition of personality profiles is crucial in making human–computer interaction (HCI) applications realistic, more focused, and user friendly. The ability to recognize personality using neuroscientific data underpins the neurobiological basis of personality. This paper aims to automatically recognize personality, combining scalp electroencephalogram (EEG) and machine learning techniques. As the resting state EEG has not so far been proven efficient for predicting personality, we used EEG recordings elicited during emotion processing. This study was based on data from the AMIGOS dataset reflecting the response of 37 healthy participants. Brain networks and graph theoretical parameters were extracted from cleaned EEG signals, while each trait score was dichotomized into low- and high-level using the k-means algorithm. A feature selection algorithm was used afterwards to reduce the feature-set size to the best 10 features to describe each trait separately. Support vector machines (SVM) were finally employed to classify each instance. Our method achieved a classification accuracy of 83.8% for extraversion, 86.5% for agreeableness, 83.8% for conscientiousness, 83.8% for neuroticism, and 73% for openness.
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Levine SM, Alahäivälä ALI, Wechsler TF, Wackerle A, Rupprecht R, Schwarzbach JV. Linking Personality Traits to Individual Differences in Affective Spaces. Front Psychol 2020; 11:448. [PMID: 32231631 PMCID: PMC7082752 DOI: 10.3389/fpsyg.2020.00448] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/26/2020] [Indexed: 11/13/2022] Open
Abstract
Different individuals respond differently to emotional stimuli in their environment. Therefore, to understand how emotions are represented mentally will ultimately require investigations into individual-level information. Here we tasked participants with freely arranging emotionally charged images on a computer screen according to their subjective emotional similarity (yielding a unique affective space for each participant) and subsequently sought external validity of the layout of the individuals’ affective spaces through the five-factor personality model (Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) assessed via the NEO Five-Factor Inventory. Applying agglomerative hierarchical clustering to the group-level affective space revealed a set of underlying affective clusters whose within-cluster dissimilarity, per individual, was then correlated with individuals’ personality scores. These cluster-based analyses predominantly revealed that the dispersion of the negative cluster showed a positive relationship with Neuroticism and a negative relationship with Conscientiousness, a finding that would be predicted by prior work. Such results demonstrate the non-spurious structure of individualized emotion information revealed by data-driven analyses of a behavioral task (and validated by incorporating psychological measures of personality) and corroborate prior knowledge of the interaction between affect and personality. Future investigations can similarly combine hypothesis- and data-driven methods to extend such findings, potentially yielding new perspectives on underlying cognitive processes, disease susceptibility, or even diagnostic/prognostic markers for mental disorders involving emotion dysregulation.
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Affiliation(s)
- Seth M Levine
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Aino L I Alahäivälä
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Theresa F Wechsler
- Department of Clinical Psychology and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Anja Wackerle
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Rainer Rupprecht
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Jens V Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
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Kabbara A, Paban V, Weill A, Modolo J, Hassan M. Brain Network Dynamics Correlate with Personality Traits. Brain Connect 2020; 10:108-120. [DOI: 10.1089/brain.2019.0723] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Affiliation(s)
| | | | - Arnaud Weill
- LNSC, Aix Marseille University, CNRS, Marseille, France
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25
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Simon SS, Varangis E, Stern Y. Associations between personality and whole-brain functional connectivity at rest: Evidence across the adult lifespan. Brain Behav 2020; 10:e01515. [PMID: 31903706 PMCID: PMC7249003 DOI: 10.1002/brb3.1515] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 12/01/2019] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Personality is associated with cognitive, emotional, and social functioning, and can play a role in age-related cognitive decline and dementia risk; however, little is known about the brain dynamics underlying personality characteristics, and whether they are moderated by age. METHODS We investigated the associations between personality and resting-state functional MRI data from 365 individuals across the adult lifespan (20-80 years). Participants completed the 50-item International Personality Item Pool and a resting-state imaging protocol on a 3T MRI scanner. Within-network connectivity values were computed based on predefined networks. Regression analyzes were conducted in order to investigate personality-connectivity associations, as well as moderation by age. All models controlled for potential confounders (such as age, sex, education, IQ, and the other personality traits). RESULTS We found that openness was positively associated with connectivity in the default-mode network, neuroticism was negatively associated with both the ventral and dorsal attention networks, and agreeableness was negatively associated with the dorsal attention network. In addition, age moderated the association between conscientiousness and the frontoparietal network, indicating that this association become stronger in older age. CONCLUSIONS Our findings demonstrate that personality is associated with brain connectivity, which may contribute to identifying personality profiles that play a role in protection against or risk for age-related brain changes and dementia.
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Affiliation(s)
- Sharon S Simon
- Cognitive Neuroscience Division, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Eleanna Varangis
- Cognitive Neuroscience Division, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
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26
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Passamonti L, Riccelli R, Indovina I, Duggento A, Terracciano A, Toschi N. Time-resolved connectome of the five-factor model of personality. Sci Rep 2019; 9:15066. [PMID: 31636295 PMCID: PMC6803687 DOI: 10.1038/s41598-019-51469-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2019] [Accepted: 10/02/2019] [Indexed: 12/13/2022] Open
Abstract
The human brain is characterized by highly dynamic patterns of functional connectivity. However, it is unknown whether this time-variant 'connectome' is related to the individual differences in the behavioural and cognitive traits described in the five-factor model of personality. To answer this question, inter-network time-variant connectivity was computed in n = 818 healthy people via a dynamical conditional correlation model. Next, network dynamicity was quantified throughout an ad-hoc measure (T-index) and the generalizability of the multi-variate associations between personality traits and network dynamicity was assessed using a train/test split approach. Conscientiousness, reflecting enhanced cognitive and emotional control, was the sole trait linked to stationary connectivity across several circuits such as the default mode and prefronto-parietal network. The stationarity in the 'communication' across large-scale networks offers a mechanistic description of the capacity of conscientious people to 'protect' non-immediate goals against interference over-time. This study informs future research aiming at developing more realistic models of the brain dynamics mediating personality differences.
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Affiliation(s)
- L Passamonti
- Institute of Bioimaging & Molecular Physiology, National Research Council, Milano, Italy.
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.
| | - R Riccelli
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179, Rome, Italy
| | - I Indovina
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179, Rome, Italy
- Saint Camillus International University of Health and Medical Sciences, 00131, Rome, Italy
| | - A Duggento
- Department of Biomedicine & Prevention, University "Tor Vergata", Rome, Italy
| | - A Terracciano
- Department of Geriatrics, Florida State University College of Medicine, Tallahassee, USA
| | - N Toschi
- Department of Biomedicine & Prevention, University "Tor Vergata", Rome, Italy
- Department of Radiology, Martinos Center for Biomedical Imaging, Boston & Harvard medical School, Boston, USA
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27
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Zhang Z, Allen GI, Zhu H, Dunson D. Tensor network factorizations: Relationships between brain structural connectomes and traits. Neuroimage 2019; 197:330-343. [PMID: 31029870 PMCID: PMC6613218 DOI: 10.1016/j.neuroimage.2019.04.027] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2019] [Revised: 04/02/2019] [Accepted: 04/07/2019] [Indexed: 12/30/2022] Open
Abstract
Advanced brain imaging techniques make it possible to measure individuals' structural connectomes in large cohort studies non-invasively. Given the availability of large scale data sets, it is extremely interesting and important to build a set of advanced tools for structural connectome extraction and statistical analysis that emphasize both interpretability and predictive power. In this paper, we developed and integrated a set of toolboxes, including an advanced structural connectome extraction pipeline and a novel tensor network principal components analysis (TN-PCA) method, to study relationships between structural connectomes and various human traits such as alcohol and drug use, cognition and motion abilities. The structural connectome extraction pipeline produces a set of connectome features for each subject that can be organized as a tensor network, and TN-PCA maps the high-dimensional tensor network data to a lower-dimensional Euclidean space. Combined with classical hypothesis testing, canonical correlation analysis and linear discriminant analysis techniques, we analyzed over 1100 scans of 1076 subjects from the Human Connectome Project (HCP) and the Sherbrooke test-retest data set, as well as 175 human traits measuring different domains including cognition, substance use, motor, sensory and emotion. The test-retest data validated the developed algorithms. With the HCP data, we found that structural connectomes are associated with a wide range of traits, e.g., fluid intelligence, language comprehension, and motor skills are associated with increased cortical-cortical brain structural connectivity, while the use of alcohol, tobacco, and marijuana are associated with decreased cortical-cortical connectivity. We also demonstrated that our extracted structural connectomes and analysis method can give superior prediction accuracies compared with alternative connectome constructions and other tensor and network regression methods.
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Affiliation(s)
- Zhengwu Zhang
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.
| | - Genevera I Allen
- Departments of Statistics, Computer Science, Electrical and Computer Engineering, Rice University, Houston, TX, USA; Neurological Research Institute, Baylor College of Medicine, Houston, TX, USA
| | - Hongtu Zhu
- Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - David Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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28
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Toschi N, Passamonti L. Intra-cortical myelin mediates personality differences. J Pers 2019; 87:889-902. [PMID: 30317636 PMCID: PMC6767500 DOI: 10.1111/jopy.12442] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Revised: 09/26/2018] [Accepted: 10/06/2018] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Differences in myelination in the cortical mantle are important neurobiological mediators of variability in cognitive, emotional, and behavioral functioning. Past studies have found that personality traits reflecting such variability are linked to neuroanatomical and functional changes in prefrontal and temporo-parietal cortices. Whether these effects are partially mediated by the differences in intra-cortical myelin remains to be established. METHOD To test this hypothesis, we employed vertex-wise intra-cortical myelin maps in n = 1,003 people from the Human Connectome Project. Multivariate regression analyses were used to test for the relationship between intra-cortical myelin and each of the five-factor model's personality traits, while accounting for age, sex, intelligence quotient, total intracranial volume, and the remaining personality traits. RESULTS Neuroticism negatively related to frontal-pole myelin and positively to occipital cortex myelin. Extraversion positively related to superior parietal myelin. Openness negatively related to anterior cingulate myelin, while Agreeableness positively related to orbitofrontal myelin. Conscientiousness positively related to frontal-pole myelin and negatively to myelin content in the dorsal anterior cingulate cortex. CONCLUSIONS Intra-cortical myelin levels in brain regions with prolonged myelination are positively associated with personality traits linked to favorable outcome measures. These findings improve our understanding of the neurobiological underpinnings of variability in common behavioral dispositions.
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Affiliation(s)
- Nicola Toschi
- Department of Biomedicine & PreventionUniversity “Tor Vergata”RomeItaly
- Department of RadiologyMartinos Center for Biomedical ImagingBostonMassachusetts
| | - Luca Passamonti
- Institute of Bioimaging & Molecular PhysiologyNational Research CouncilMilanoItaly
- Department of Clinical NeurosciencesUniversity of CambridgeCambridgeUK
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29
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Conti A, Duggento A, Guerrisi M, Passamonti L, Indovina I, Toschi N. Variability and Reproducibility of Directed and Undirected Functional MRI Connectomes in the Human Brain. ENTROPY (BASEL, SWITZERLAND) 2019; 21:E661. [PMID: 33267375 PMCID: PMC7515158 DOI: 10.3390/e21070661] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/01/2019] [Revised: 06/23/2019] [Accepted: 07/04/2019] [Indexed: 12/16/2022]
Abstract
A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.
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Affiliation(s)
- Allegra Conti
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
| | - Andrea Duggento
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Maria Guerrisi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Luca Passamonti
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, UK
- Institute of Bioimaging and Molecular Physiology, National Research Council, 20090 Milano, Italy
| | - Iole Indovina
- Laboratory of Neuromotor Physiology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy
- Saint Camillus International University of Health and Medical Sciences, 00131 Rome, Italy
| | - Nicola Toschi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, 00133 Rome, Italy
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Boston, MA 02129, USA
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30
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Jiang R, Calhoun VD, Zuo N, Lin D, Li J, Fan L, Qi S, Sun H, Fu Z, Song M, Jiang T, Sui J. Connectome-based individualized prediction of temperament trait scores. Neuroimage 2018; 183:366-374. [PMID: 30125712 DOI: 10.1016/j.neuroimage.2018.08.038] [Citation(s) in RCA: 57] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2018] [Revised: 08/13/2018] [Accepted: 08/16/2018] [Indexed: 12/16/2022] Open
Abstract
Temperament consists of multi-dimensional traits that affect various domains of human life. Evidence has shown functional connectome-based predictive models are powerful predictors of cognitive abilities. Putatively, individuals' innate temperament traits may be predictable by unique patterns of brain functional connectivity (FC) as well. However, quantitative prediction for multiple temperament traits at the individual level has not yet been studied. Therefore, we were motivated to realize the individualized prediction of four temperament traits (novelty seeking [NS], harm avoidance [HA], reward dependence [RD] and persistence [PS]) using whole-brain FC. Specifically, a multivariate prediction framework integrating feature selection and sparse regression was applied to resting-state fMRI data from 360 college students, resulting in 4 connectome-based predictive models that enabled prediction of temperament scores for unseen subjects in cross-validation. More importantly, predictive models for HA and NS could be successfully generalized to two relevant personality traits for unseen individuals, i.e., neuroticism and extraversion, in an independent dataset. In four temperament trait predictions, brain connectivities that show top contributing power commonly concentrated on the hippocampus, prefrontal cortex, basal ganglia, amygdala, and cingulate gyrus. Finally, across independent datasets and multiple traits, we show person's temperament traits can be reliably predicted using functional connectivity strength within frontal-subcortical circuits, indicating that human social and behavioral performance can be characterized by specific brain connectivity profile.
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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
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA; Dept. of Psychiatry and Neurosciences, University of New Mexico, Albuquerque, NM, 87131, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Dongdong Lin
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Jin Li
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Lingzhong Fan
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Shile Qi
- 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; The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Hailun Sun
- 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
| | - Zening Fu
- The Mind Research Network and Lovelace Biomedical and Environmental Research Institute, Albuquerque, NM, 87106, USA
| | - Ming Song
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - 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, 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, China.
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31
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Network Neuroscience and Personality. PERSONALITY NEUROSCIENCE 2018; 1:e14. [PMID: 32435733 PMCID: PMC7219685 DOI: 10.1017/pen.2018.12] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2018] [Revised: 03/28/2018] [Accepted: 04/14/2018] [Indexed: 12/11/2022]
Abstract
Personality and individual differences originate from the brain. Despite major advances in the affective and cognitive neurosciences, however, it is still not well understood how personality and single personality traits are represented within the brain. Most research on brain-personality correlates has focused either on morphological aspects of the brain such as increases or decreases in local gray matter volume, or has investigated how personality traits can account for individual differences in activation differences in various tasks. Here, we propose that personality neuroscience can be advanced by adding a network perspective on brain structure and function, an endeavor that we label personality network neuroscience. With the rise of resting-state functional magnetic resonance imaging (MRI), the establishment of connectomics as a theoretical framework for structural and functional connectivity modeling, and recent advancements in the application of mathematical graph theory to brain connectivity data, several new tools and techniques are readily available to be applied in personality neuroscience. The present contribution introduces these concepts, reviews recent progress in their application to the study of individual differences, and explores their potential to advance our understanding of the neural implementation of personality. Trait theorists have long argued that personality traits are biophysical entities that are not mere abstractions of and metaphors for human behavior. Traits are thought to actually exist in the brain, presumably in the form of conceptual nervous systems. A conceptual nervous system refers to the attempt to describe parts of the central nervous system in functional terms with relevance to psychology and behavior. We contend that personality network neuroscience can characterize these conceptual nervous systems on a functional and anatomical level and has the potential do link dispositional neural correlates to actual behavior.
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