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Tsigeman E, Zemliak V, Likhanov M, Papageorgiou KA, Kovas Y. AI can see you: Machiavellianism and extraversion are reflected in eye-movements. PLoS One 2024; 19:e0308631. [PMID: 39196880 PMCID: PMC11355565 DOI: 10.1371/journal.pone.0308631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 07/27/2024] [Indexed: 08/30/2024] Open
Abstract
INTRODUCTION Recent studies showed an association between personality traits and individual patterns of visual behaviour in laboratory and other settings. The current study extends previous research by measuring multiple personality traits in natural settings; and by comparing accuracy of prediction of multiple machine learning algorithms. METHODS Adolescent participants (N = 35) completed personality questionnaires (Big Five Inventory and Short Dark Triad Questionnaire) and visited an interactive museum while their eye movements were recorded with head-mounted eye tracking. To predict personality traits the eye-movement data was analysed using eight machine-learning methods: Random Forest, Adaboost, Naive Bayes, Support Vector Machine, Logistic Regression, k Nearest Neighbours, Decision Tree and a three-layer Perceptron. RESULTS AND DISCUSSION Extracted eye movement features introduced to machine learning algorithms predicted personality traits with above 33% chance accuracy (34%-48%). This result is comparable to previous ecologically valid studies, but lower than in laboratory-based research. Better prediction was achieved for Machiavellianism and Extraversion compared to other traits (10 and 9 predictions above the chance level by different algorithms from different parts of the recording). Conscientiousness, Narcissism and Psychopathy were not reliably predicted from eye movements. These differences in predictability across traits might be explained by differential activation of different traits in different situations, such as new vs. familiar, exciting vs. boring, and complex vs. simple settings. In turn, different machine learning approaches seem to be better at capturing specific gaze patterns (e.g. saccades), associated with specific traits evoked by the situation. Further research is needed to gain better insights into trait-situation-algorithm interactions.
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Affiliation(s)
- Elina Tsigeman
- Laboratory for Social & Cognitive Informatics, HSE University, Saint-Petersburg, Russia
| | | | - Maxim Likhanov
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | | | - Yulia Kovas
- Department of Psychology, Goldsmiths University of London, London, United Kingdom
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2
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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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3
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Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic fMRI-derived functional connectomes represent largely similar information. Netw Neurosci 2023; 7:1266-1301. [PMID: 38144686 PMCID: PMC10631791 DOI: 10.1162/netn_a_00325] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/06/2023] [Indexed: 12/26/2023] Open
Abstract
Functional connectivity (FC) of blood oxygen level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate), and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
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Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale University, New Haven, CT, USA
- Department of Psychiatry, Yale University, New Haven, CT, USA
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana, Ljubljana, Slovenia
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van der Horn HJ, de Koning ME, Visser K, Kok MGJ, Spikman JM, Scheenen ME, Renken RJ, Calhoun VD, Vergara VM, Cabral J, Mayer AR, van der Naalt J. Dynamic phase-locking states and personality in sub-acute mild traumatic brain injury: An exploratory study. PLoS One 2023; 18:e0295984. [PMID: 38100479 PMCID: PMC10723684 DOI: 10.1371/journal.pone.0295984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Research has shown that maladaptive personality characteristics, such as Neuroticism, are associated with poor outcome after mild traumatic brain injury (mTBI). The current exploratory study investigated the neural underpinnings of this process using dynamic functional network connectivity (dFNC) analyses of resting-state (rs) fMRI, and diffusion MRI (dMRI). Twenty-seven mTBI patients and 21 healthy controls (HC) were included. After measuring the Big Five personality dimensions, principal component analysis (PCA) was used to obtain a superordinate factor representing emotional instability, consisting of high Neuroticism, moderate Openness, and low Extraversion, Agreeableness, and Conscientiousness. Persistent symptoms were measured using the head injury symptom checklist at six months post-injury; symptom severity (i.e., sum of all items) was used for further analyses. For patients, brain MRI was performed in the sub-acute phase (~1 month) post-injury. Following parcellation of rs-fMRI using independent component analysis, leading eigenvector dynamic analysis (LEiDA) was performed to compute dynamic phase-locking brain states. Main patterns of brain diffusion were computed using tract-based spatial statistics followed by PCA. No differences in phase-locking state measures were found between patients and HC. Regarding dMRI, a trend significant decrease in fractional anisotropy was found in patients relative to HC, particularly in the fornix, genu of the corpus callosum, anterior and posterior corona radiata. Visiting one specific phase-locking state was associated with lower symptom severity after mTBI. This state was characterized by two clearly delineated communities (each community consisting of areas with synchronized phases): one representing an executive/saliency system, with a strong contribution of the insulae and basal ganglia; the other representing the canonical default mode network. In patients who scored high on emotional instability, this relationship was even more pronounced. Dynamic phase-locking states were not related to findings on dMRI. Altogether, our results provide preliminary evidence for the coupling between personality and dFNC in the development of long-term symptoms after mTBI.
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Affiliation(s)
- Harm J. van der Horn
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
- The Mind Research Network/Lovelace Biomedical Research Institute, Pete & Nancy Domenici Hall, Albuquerque, NM, United States of America
| | | | - Koen Visser
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Marius G. J. Kok
- Department of Radiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jacoba M. Spikman
- Department of Neuropsychology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Myrthe E. Scheenen
- Department of Neuropsychology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Remco J. Renken
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Vince D. Calhoun
- Tri-institutional Center for Translational Research (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States of America
| | - Victor M. Vergara
- Tri-institutional Center for Translational Research (TReNDS), Georgia State, Georgia Tech, Emory, Atlanta, GA, United States of America
| | - Joana Cabral
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Andrew R. Mayer
- The Mind Research Network/Lovelace Biomedical Research Institute, Pete & Nancy Domenici Hall, Albuquerque, NM, United States of America
- Department of Neurology, University of New Mexico School of Medicine, Albuquerque, NM, United States of America
- Department of Psychiatry, University of New Mexico School of Medicine, Albuquerque, NM, United States of America
- Department of Psychology, University of New Mexico School of Medicine, Albuquerque, NM, United States of America
| | - Joukje van der Naalt
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
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5
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Han S, Gao J, Xing W, Zhou X, Luo Y. Facial attractiveness in the eyes of men with high arousal. Brain Behav 2023; 13:e3132. [PMID: 37367435 PMCID: PMC10498057 DOI: 10.1002/brb3.3132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 05/04/2023] [Accepted: 06/08/2023] [Indexed: 06/28/2023] Open
Abstract
INTRODUCTION Individuals differ in how they judge facial attractiveness. However, little is known about the role of arousal level and gender differences in individuals' facial attractiveness judgments. METHODS We used resting-state electroencephalogram (EEG) to investigate this issue. A total of 48 men (aged 22.5 ± 3.03 years [mean ± SD], range: 18-30 years) and 27 women (aged 20.3 ± 2.03 years [mean ± SD], range: 18-25 years) participated in the experiment. After the EEG was collected, participants were instructed to complete a facial attractiveness judgment task. Connectome-based predictive modeling was used to predict individual judgment of facial attractiveness. RESULTS Men with high arousal judged female faces as more attractive (M = 3.85, SE = 0.81) than did men with low arousal (M = 3.33, SE = 0.81) and women (M = 3.24, SE = 1.02). Functional connectivity of the alpha band predicted judgment of female facial attractiveness in men but not in women. After controlling for the age and variability, the prediction effect was still significant. CONCLUSION Our results provide neural evidence for the enhancement of the judgment of facial attractiveness in men with high arousal levels, which supports the hypothesis that individuals' spontaneous arousal contributes to variations in facial attractiveness preferences.
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Affiliation(s)
- Shangfeng Han
- Department of Psychology and Center for Brain and Cognitive Sciences, School of EducationGuangzhou UniversityGuangzhouChina
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Center for Brain Disorders and Cognitive Sciences, School of PsychologyShenzhen UniversityShenzhenChina
| | - Jie Gao
- School of Psychology, Sichuan Center of Applied PsychologyChengdu Medical CollegeChengduChina
| | - Wenjuan Xing
- College of Economics and ManagementQilu Normal UniversityJiningChina
| | - Xinyi Zhou
- Institute for Neuropsychological RehabilitationUniversity of Health and Rehabilitation SciencesQingdaoChina
| | - Yuejia Luo
- School of Psychology, Sichuan Center of Applied PsychologyChengdu Medical CollegeChengduChina
- Institute for Neuropsychological RehabilitationUniversity of Health and Rehabilitation SciencesQingdaoChina
- The State Key Lab of Cognitive and Learning, State Key Laboratory of Cognitive Neuroscience and Learning, Faculty of PsychologyBeijing Normal UniversityBeijingChina
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6
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Matkovič A, Anticevic A, Murray JD, Repovš G. Static and dynamic functional connectomes represent largely similar information. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.24.525348. [PMID: 36747845 PMCID: PMC9900764 DOI: 10.1101/2023.01.24.525348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Functional connectivity (FC) of blood-oxygen-level-dependent (BOLD) fMRI time series can be estimated using methods that differ in sensitivity to the temporal order of time points (static vs. dynamic) and the number of regions considered in estimating a single edge (bivariate vs. multivariate). Previous research suggests that dynamic FC explains variability in FC fluctuations and behavior beyond static FC. Our aim was to systematically compare methods on both dimensions. We compared five FC methods: Pearson's/full correlation (static, bivariate), lagged correlation (dynamic, bivariate), partial correlation (static, multivariate) and multivariate AR model with and without self-connections (dynamic, multivariate). We compared these methods by (i) assessing similarities between FC matrices, (ii) by comparing node centrality measures, and (iii) by comparing the patterns of brain-behavior associations. Although FC estimates did not differ as a function of sensitivity to temporal order, we observed differences between the multivariate and bivariate FC methods. The dynamic FC estimates were highly correlated with the static FC estimates, especially when comparing group-level FC matrices. Similarly, there were high correlations between the patterns of brain-behavior associations obtained using the dynamic and static FC methods. We conclude that the dynamic FC estimates represent information largely similar to that of the static FC.
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Affiliation(s)
- Andraž Matkovič
- Department of Psychology, Faculty of Arts, University of Ljubljana
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, United States
- Interdepartmental Neuroscience Program, Yale University, New Haven, United States
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, United States
- Interdepartmental Neuroscience Program, Yale University, New Haven, United States
- Department of Psychiatry, Yale University, New Haven, United States
| | - Grega Repovš
- Department of Psychology, Faculty of Arts, University of Ljubljana
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7
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Sankar A, Shen X, Colic L, Goldman DA, Villa LM, Kim JA, Pittman B, Scheinost D, Constable RT, Blumberg HP. Predicting depressed and elevated mood symptomatology in bipolar disorder using brain functional connectomes. Psychol Med 2023; 53:1-10. [PMID: 36891769 PMCID: PMC10491744 DOI: 10.1017/s003329172300003x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 12/07/2022] [Accepted: 01/03/2023] [Indexed: 03/10/2023]
Abstract
BACKGROUND The study is aimed to identify brain functional connectomes predictive of depressed and elevated mood symptomatology in individuals with bipolar disorder (BD) using the machine learning approach Connectome-based Predictive Modeling (CPM). METHODS Functional magnetic resonance imaging data were obtained from 81 adults with BD while they performed an emotion processing task. CPM with 5000 permutations of leave-one-out cross-validation was applied to identify functional connectomes predictive of depressed and elevated mood symptom scores on the Hamilton Depression and Young Mania rating scales. The predictive ability of the identified connectomes was tested in an independent sample of 43 adults with BD. RESULTS CPM predicted the severity of depressed [concordance between actual and predicted values (r = 0.23, pperm (permutation test) = 0.031) and elevated (r = 0.27, pperm = 0.01) mood. Functional connectivity of left dorsolateral prefrontal cortex and supplementary motor area nodes, with inter- and intra-hemispheric connections to other anterior and posterior cortical, limbic, motor, and cerebellar regions, predicted depressed mood severity. Connectivity of left fusiform and right visual association area nodes with inter- and intra-hemispheric connections to the motor, insular, limbic, and posterior cortices predicted elevated mood severity. These networks were predictive of mood symptomatology in the independent sample (r ⩾ 0.45, p = 0.002). CONCLUSIONS This study identified distributed functional connectomes predictive of depressed and elevated mood severity in BD. Connectomes subserving emotional, cognitive, and psychomotor control predicted depressed mood severity, while those subserving emotional and social perceptual functions predicted elevated mood severity. Identification of these connectome networks may help inform the development of targeted treatments for mood symptoms.
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Affiliation(s)
- Anjali Sankar
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Neurobiology Research Unit, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark
| | - Xilin Shen
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lejla Colic
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
- German Center for Mental Health, Halle-Jena-Magdeburg, Magdeburg, Germany
| | - Danielle A. Goldman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Interdepartmental Neuroscience Program, Yale School of Medicine, New Haven, CT, USA
| | - Luca M. Villa
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Jihoon A. Kim
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Brian Pittman
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
| | - Dustin Scheinost
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - R. Todd Constable
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Hilary P. Blumberg
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
- Child Study Center, Yale School of Medicine, New Haven, CT, USA
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8
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He H, Lin W, Yang J, Chen Y, Tan S, Guan Q. Age-related intrinsic functional connectivity underlying emotion utilization. Cereb Cortex 2023:7033308. [PMID: 36758953 DOI: 10.1093/cercor/bhad023] [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: 11/02/2022] [Revised: 01/15/2023] [Accepted: 01/16/2023] [Indexed: 02/11/2023] Open
Abstract
Previous studies investigated the age-related positivity effect in terms of emotion perception and management, whereas little is known about whether the positivity effect is shown in emotion utilization (EU). If yes, the EU-related intrinsic functional connectivity and its age-associated alterations remain to be elucidated. In this study, we collected resting-state functional magnetic resonance imaging data from 62 healthy older adults and 72 undergraduates as well as their self-ratings of EU. By using the connectome-based predictive modeling (CPM) method, we constructed a predictive model of the positive relationship between EU self-ratings and resting-state functional connectivity. Lesion simulation analyses revealed that the medial-frontal network, default mode network, frontoparietal network, and subcortical regions played key roles in the EU-related CPM. Older subjects showed significantly higher EU self-ratings than undergraduates, which was associated with strengthened connectivity between the left dorsolateral prefrontal cortex and bilateral frontal poles, and between the left frontal pole and thalamus. A mediation analysis indicated that the age-related EU network mediated the age effect on EU self-ratings. Our findings extend previous research on the age-related "positivity effect" to the EU domain, suggesting that the positivity effect on the self-evaluation of EU is probably associated with emotion knowledge which accumulates with age.
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Affiliation(s)
- Hao He
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Wenyi Lin
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Jiawang Yang
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China
| | - Yiqi Chen
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Department of Psychology, University of Mannheim, Mannheim, Germany
| | - Siping Tan
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Qing Guan
- Center for Brain Disorders and Cognitive Sciences, School of Psychology, Magnetic Resonance Imaging Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
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9
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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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10
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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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11
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Chamberlain TA, Rosenberg MD. Propofol selectively modulates functional connectivity signatures of sustained attention during rest and narrative listening. Cereb Cortex 2022; 32:5362-5375. [PMID: 35285485 DOI: 10.1093/cercor/bhac020] [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: 11/15/2021] [Revised: 01/06/2022] [Accepted: 01/08/2022] [Indexed: 12/27/2022] Open
Abstract
Sustained attention is a critical cognitive function reflected in an individual's whole-brain pattern of functional magnetic resonance imaging functional connectivity. However, sustained attention is not a purely static trait. Rather, attention waxes and wanes over time. Do functional brain networks that underlie individual differences in sustained attention also underlie changes in attentional state? To investigate, we replicate the finding that a validated connectome-based model of individual differences in sustained attention tracks pharmacologically induced changes in attentional state. Specifically, preregistered analyses revealed that participants exhibited functional connectivity signatures of stronger attention when awake than when under deep sedation with the anesthetic agent propofol. Furthermore, this effect was relatively selective to the predefined sustained attention networks: propofol administration modulated strength of the sustained attention networks more than it modulated strength of canonical resting-state networks and a network defined to predict fluid intelligence, and the functional connections most affected by propofol sedation overlapped with the sustained attention networks. Thus, propofol modulates functional connectivity signatures of sustained attention within individuals. More broadly, these findings underscore the utility of pharmacological intervention in testing both the generalizability and specificity of network-based models of cognitive function.
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Affiliation(s)
- Taylor A Chamberlain
- Department of Psychology, The University of Chicago, 5848 S University Ave, IL 60637, Chicago
| | - Monica D Rosenberg
- Department of Psychology, The University of Chicago, 5848 S University Ave, IL 60637, Chicago.,Neuroscience Institute, The University of Chicago, 5812 South Ellis Ave., MC 0912, Suite P-400, IL 60637, Chicago
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12
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Ooi LQR, Chen J, Zhang S, Kong R, Tam A, Li J, Dhamala E, Zhou JH, Holmes AJ, Yeo BTT. Comparison of individualized behavioral predictions across anatomical, diffusion and functional connectivity MRI. Neuroimage 2022; 263:119636. [PMID: 36116616 DOI: 10.1016/j.neuroimage.2022.119636] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/24/2022] [Accepted: 09/15/2022] [Indexed: 10/31/2022] Open
Abstract
A fundamental goal across the neurosciences is the characterization of relationships linking brain anatomy, functioning, and behavior. Although various MRI modalities have been developed to probe these relationships, direct comparisons of their ability to predict behavior have been lacking. Here, we compared the ability of anatomical T1, diffusion and functional MRI (fMRI) to predict behavior at an individual level. Cortical thickness, area and volume were extracted from anatomical T1 images. Diffusion Tensor Imaging (DTI) and approximate Neurite Orientation Dispersion and Density Imaging (NODDI) models were fitted to the diffusion images. The resulting metrics were projected to the Tract-Based Spatial Statistics (TBSS) skeleton. We also ran probabilistic tractography for the diffusion images, from which we extracted the stream count, average stream length, and the average of each DTI and NODDI metric across tracts connecting each pair of brain regions. Functional connectivity (FC) was extracted from both task and resting-state fMRI. Individualized prediction of a wide range of behavioral measures were performed using kernel ridge regression, linear ridge regression and elastic net regression. Consistency of the results were investigated with the Human Connectome Project (HCP) and Adolescent Brain Cognitive Development (ABCD) datasets. In both datasets, FC-based models gave the best prediction performance, regardless of regression model or behavioral measure. This was especially true for the cognitive component. Furthermore, all modalities were able to predict cognition better than other behavioral components. Combining all modalities improved prediction of cognition, but not other behavioral components. Finally, across all behaviors, combining resting and task FC yielded prediction performance similar to combining all modalities. Overall, our study suggests that in the case of healthy children and young adults, behaviorally-relevant information in T1 and diffusion features might reflect a subset of the variance captured by FC.
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Affiliation(s)
- Leon Qi Rong Ooi
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Shaoshi Zhang
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Ru Kong
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Angela Tam
- Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
| | - Jingwei Li
- Institute of Neuroscience and Medicine, Brain & Behavior (INM-7), Research Center Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany
| | - Elvisha Dhamala
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Kavli Institute for Neuroscience, Yale University, New Haven, CT, United States
| | - Juan Helen Zhou
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, United States; Wu Tsai Institute, Yale University, New Haven, CT, United States
| | - B T Thomas Yeo
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore; Centre for Sleep & Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore; N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore.
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13
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Xie Y, Yuan C, Sun M, Sun J, Zhang N, Qin W, Liu F, Xue H, Ding H, Wang S, He J, Hu L, Li X, Yu C. Personality and brain contribute to academic achievements of medical students. Front Neurosci 2022; 16:964904. [PMID: 36148147 PMCID: PMC9489117 DOI: 10.3389/fnins.2022.964904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 08/04/2022] [Indexed: 11/13/2022] Open
Abstract
There are many factors that influence the academic achievements of medical students, but how personality and brain modulate the academic achievements of medical students remains unclear. The study collected the personality, brain imaging, and academic data from 448 medical students at Tianjin Medical University with admission time between 2008 and 2017. Four types of academic achievements, including behavioral and social sciences, clinical sciences and skills, basic biomedical sciences, and scientific methods, were assessed by the academic records of 58 courses. Personality was evaluated by Tridimensional Personality Questionnaire and Neuroticism Extraversion Openness Personality Inventory. Brain structural and functional properties, including gray matter volume, spontaneous brain activity and functional connectivity, were computed based on magnetic resonance imaging (MRI). Linear regression was used to evaluate the associations between personality and academic achievements. A voxel-wise correlation was used to identify areas of the brain where structural and functional properties were associated with academic achievements. Mediation analysis was used to test whether brain properties and personality independently contribute to academic achievements. Our results showed that novelty seeking (NS) was negatively correlated, and conscientiousness was positively correlated with all types of academic achievements. Brain functional properties showed negatively correlated with academic achievement in basic biomedical sciences. However, we did not find any mediation effect of the brain functional properties on the association between personality (NS and conscientiousness) and academic achievement in basic biomedical sciences, nor mediation effect of the personality (NS and conscientiousness) on the association between brain functional properties and academic achievement in basic biomedical sciences. These findings suggest that specific personality (NS and conscientiousness) and brain functional properties independently contribute to academic achievements in basic biomedical sciences, and that modulation of these properties may benefit academic achievements among medical students.
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Affiliation(s)
- Yingying Xie
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Congcong Yuan
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Mengru Sun
- Tianjin Medical University, Tianjin, China
| | - Jie Sun
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Ningnannan Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hui Xue
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Hao Ding
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Sijia Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Jinyan He
- Tianjin Medical University, Tianjin, China
| | - Lizhi Hu
- Tianjin Medical University, Tianjin, China
| | - Xiaoxia Li
- Tianjin Medical University, Tianjin, China
- *Correspondence: Xiaoxia Li,
| | - Chunshui Yu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
- Chunshui Yu,
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14
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Personality similarity predicts synchronous neural responses in fMRI and EEG data. Sci Rep 2022; 12:14325. [PMID: 35995958 PMCID: PMC9395427 DOI: 10.1038/s41598-022-18237-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 08/08/2022] [Indexed: 11/27/2022] Open
Abstract
Successful communication and cooperation among different members of society depends, in part, on a consistent understanding of the physical and social world. What drives this alignment in perspectives? We present evidence from two neuroimaging studies using functional magnetic resonance imaging (fMRI; N = 66 with 2145 dyadic comparisons) and electroencephalography (EEG; N = 225 with 25,200 dyadic comparisons) to show that: (1) the extent to which people’s neural responses are synchronized when viewing naturalistic stimuli is related to their personality profiles, and (2) that this effect is stronger than that of similarity in gender, ethnicity and political affiliation. The localization of the fMRI results in combination with the additional eye tracking analyses suggest that the relationship between personality similarity and neural synchrony likely reflects alignment in the interpretation of stimuli and not alignment in overt visual attention. Together, the findings suggest that similarity in psychological dispositions aligns people’s reality via shared interpretations of the external world.
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15
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Speer SPH, Smidts A, Boksem MAS. Cognitive control and dishonesty. Trends Cogn Sci 2022; 26:796-808. [PMID: 35840475 DOI: 10.1016/j.tics.2022.06.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 06/07/2022] [Accepted: 06/13/2022] [Indexed: 01/10/2023]
Abstract
Dishonesty is ubiquitous and imposes substantial financial and social burdens on society. Intuitively, dishonesty results from a failure of willpower to control selfish behavior. However, recent research suggests that the role of cognitive control in dishonesty is more complex. We review evidence that cognitive control is not needed to be honest or dishonest per se, but that it depends on individual differences in what we call one's 'moral default': for those who are prone to dishonesty, cognitive control indeed aids in being honest, but for those who are already generally honest, cognitive control may help them cheat to occasionally profit from small acts of dishonesty. Thus, the role of cognitive control in (dis)honesty is to override the moral default.
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Affiliation(s)
- Sebastian P H Speer
- Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands; Social Brain Lab, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
| | - Ale Smidts
- Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands
| | - Maarten A S Boksem
- Rotterdam School of Management, Erasmus University Rotterdam, Rotterdam, The Netherlands.
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16
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Reward enhances connectivity between the ventral striatum and the default mode network. Neuroimage 2022; 258:119398. [PMID: 35724856 PMCID: PMC9343171 DOI: 10.1016/j.neuroimage.2022.119398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 05/23/2022] [Accepted: 06/16/2022] [Indexed: 11/24/2022] Open
Abstract
The default mode network (DMN) has been theorized to participate in a range of social, cognitive, and affective functions. Yet, previous accounts do not consider how the DMN contributes to other brain regions depending on psychological context, thus rendering our understanding of DMN function incomplete. We addressed this gap by applying a novel network-based psychophysiological interaction (nPPI) analysis to the reward task within the Human Connectome Project. We first focused on the task-evoked responses of the DMN and other networks involving the prefrontal cortex, including the executive control network (salience network) and the left and right frontoparietal networks. Consistent with a host of prior studies, the DMN exhibited a relative decrease in activation during the task, while the other networks exhibited a relative increase during the task. Next, we used nPPI analyses to assess whether these networks exhibit task-dependent changes in connectivity with other brain regions. Strikingly, we found that the experience of reward enhances task-dependent connectivity between the DMN and the ventral striatum, an effect that was specific to the DMN. Surprisingly, the strength of DMN-VS connectivity was correlated with personality characteristics relating to openness. Taken together, these results advance models of DMN by demonstrating how it contributes to other brain systems during task performance and how those contributions relate to individual differences.
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17
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Meng D, Wang S, Wong PCM, Feng G. Generalizable predictive modeling of semantic processing ability from functional brain connectivity. Hum Brain Mapp 2022; 43:4274-4292. [PMID: 35611721 PMCID: PMC9435002 DOI: 10.1002/hbm.25953] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 03/11/2022] [Accepted: 05/06/2022] [Indexed: 11/08/2022] Open
Abstract
Semantic processing (SP) is one of the critical abilities of humans for representing and manipulating conceptual and meaningful information. Neuroimaging studies of SP typically collapse data from many subjects, but its neural organization and behavioral performance vary between individuals. It is not yet understood whether and how the individual variabilities in neural network organizations contribute to the individual differences in SP behaviors. We aim to identify the neural signatures underlying SP variabilities by analyzing functional connectivity (FC) patterns based on a large‐sample Human Connectome Project (HCP) dataset and rigorous predictive modeling. We used a two‐stage predictive modeling approach to build an internally cross‐validated model and to test the model's generalizability with unseen data from different HCP samples and other out‐of‐sample datasets. FC patterns within a putative semantic brain network were significantly predictive of individual SP scores summarized from five SP‐related behavioral tests. This cross‐validated model can be used to predict unseen HCP data. The model generalizability was enhanced in the language task compared with other tasks used during scanning and was better for females than males. The model constructed from the HCP dataset can be partially generalized to two independent cohorts that participated in different semantic tasks. FCs connecting to the Perisylvian language network show the most reliable contributions to predictive modeling and the out‐of‐sample generalization. These findings contribute to our understanding of the neural sources of individual differences in SP, which potentially lay the foundation for personalized education for healthy individuals and intervention for SP and language deficits patients.
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Affiliation(s)
- Danting Meng
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Suiping Wang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, China
| | - Patrick C M Wong
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong SAR, China.,Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Gangyi Feng
- Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Hong Kong SAR, China.,Brain and Mind Institute, The Chinese University of Hong Kong, Hong Kong SAR, China
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18
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Using resting thalamic connectivity to identify the relationship between Eysenck personality traits and intelligence in healthy adults. Brain Res 2022; 1787:147922. [PMID: 35460643 DOI: 10.1016/j.brainres.2022.147922] [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: 01/07/2022] [Revised: 04/05/2022] [Accepted: 04/15/2022] [Indexed: 11/23/2022]
Abstract
Personality refers to a set of relatively stable psychological characteristics of individuals and has been associated with intelligence. It is well known that the thalamus plays an important role in cognitive processes and personality traits, but the relationship between personality traits, thalamic function, and intelligence has rarely been directly explored. Hence, we investigated the relationship between Eysenck personality traits, resting-state functional connectivity (rsFC) of the thalamus, and intelligence in a large sample of healthy adults (N = 176). We found that the trait of psychoticism was negatively associated with intelligence. The high intelligence group showed significantly lower psychoticism and demonstrated enhanced thalamic connectivity to the amygdala, inferior parietal lobules, pallidum, medial superior/middle frontal gyrus, and precuneus. Furthermore, a mediation analysis indicated that the FC between the left thalamus and left amygdala significantly mediated the correlation between psychoticism and full IQ (FIQ). These findings suggest that intelligent people may be less prone to psychoticism. Meanwhile, thalamic rsFC may reflect individual differences in intelligence and play a key role in the relationship between personality traits and intellectual abilities.
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19
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Massullo C, Panno A, Carbone GA, Della Marca G, Farina B, Imperatori C. Need for cognitive closure is associated with different intra-network functional connectivity patterns: A resting state EEG study. Soc Neurosci 2022; 17:143-153. [PMID: 35167428 DOI: 10.1080/17470919.2022.2043432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
Need for Cognitive Closure (NCC) is a construct referring to the desire for predictability, unambiguity and firm answers to issues. Neuroscientific literature about NCC processes has mainly focused on task-related brain activity. According to the Triple Network model (TN), the main aim of the current study was to investigate resting state (RS) electroencephalographic (EEG) intra-network dynamics associated with NCC. Fifty-two young adults (39 females) were enrolled and underwent EEG recordings during RS. Functional connectivity analysis was computed through exact Low-Resolution Electromagnetic Tomography (eLORETA) software. Our results showed that higher levels of NCC were associated with both i) decreased alpha EEG connectivity within the Central Executive Network (CEN), and ii) increased delta connectivity within the Default Mode Network (DMN). No significant correlations were observed between NCC and functional connectivity in the Salience Network (SN). Our data would seem to suggest that high levels of NCC are characterized by a specific communication pattern within the CEN and the DMN during RS. These neurophysiological patterns might reflect several typical NCC-related cognitive characteristics (e.g., lower flexibility and preference for habitual and rigid response schemas).
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Affiliation(s)
| | - Angelo Panno
- Cognitive and Clinical Psychology Laboratory, Department of Human Sciences, European University of Rome, Rome, Italy
| | - Giuseppe Alessio Carbone
- Cognitive and Clinical Psychology Laboratory, Department of Human Sciences, European University of Rome, Rome, Italy
| | - Giacomo Della Marca
- Department of Neurosciences, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Benedetto Farina
- Cognitive and Clinical Psychology Laboratory, Department of Human Sciences, European University of Rome, Rome, Italy
| | - Claudio Imperatori
- Cognitive and Clinical Psychology Laboratory, Department of Human Sciences, European University of Rome, Rome, Italy
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20
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Yoon L, Carranza AF, Swartz JR. Resting-State Functional Connectivity Associated With Extraversion and Agreeableness in Adolescence. Front Behav Neurosci 2022; 15:644790. [PMID: 35046781 PMCID: PMC8762207 DOI: 10.3389/fnbeh.2021.644790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 12/08/2021] [Indexed: 11/23/2022] Open
Abstract
Although adolescence is a period in which developmental changes occur in brain connectivity, personality formation, and peer interaction, few studies have examined the neural correlates of personality dimensions related to social behavior within adolescent samples. The current study aims to investigate whether adolescents’ brain functional connectivity is associated with extraversion and agreeableness, personality dimensions linked to peer acceptance, social network size, and friendship quality. Considering sex-variant neural maturation in adolescence, we also examined sex-specific associations between personality and functional connectivity. Using resting-state functional magnetic resonance imaging (fMRI) data from a community sample of 70 adolescents aged 12–15, we examined associations between self-reported extraversion and agreeableness and seed-to-whole brain connectivity with the amygdala as a seed region of interest. Then, using 415 brain regions that correspond to 8 major brain networks and subcortex, we explored neural connectivity within brain networks and across the whole-brain. We conducted group-level multiple regression analyses with the regressors of extraversion, agreeableness, and their interactions with sex. Results demonstrated that amygdala connectivity with the postcentral gyrus, middle temporal gyrus, and the temporal pole is positively associated with extraversion in girls and negatively associated with extraversion in boys. Agreeableness was positively associated with amygdala connectivity with the middle occipital cortex and superior parietal cortex, in the same direction for boys and girls. Results of the whole-brain connectivity analysis revealed that the connectivity of the postcentral gyrus, located in the dorsal attention network, with regions in default mode network (DMN), salience/ventral attention network, and control network (CON) was associated with extraversion, with most connections showing positive associations in girls and negative associations in boys. For agreeableness, results of the within-network connectivity analysis showed that connections within the limbic network were positively associated with agreeableness in boys while negatively associated with or not associated with agreeableness in girls. Results suggest that intrinsic functional connectivity may contribute to adolescents’ individual differences in extraversion and agreeableness and highlights sex-specific neural connectivity patterns associated with the two personality dimensions. This study deepens our understanding of the neurobiological correlates of adolescent personality that may lead to different developmental trajectories of social experience.
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21
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McCulloch DEW, Madsen MK, Stenbæk DS, Kristiansen S, Ozenne B, Jensen PS, Knudsen GM, Fisher PM. Lasting effects of a single psilocybin dose on resting-state functional connectivity in healthy individuals. J Psychopharmacol 2022; 36:74-84. [PMID: 34189985 PMCID: PMC8801642 DOI: 10.1177/02698811211026454] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/04/2022]
Abstract
BACKGROUND Psilocybin is a psychedelic drug that has shown lasting positive effects on clinical symptoms and self-reported well-being following a single dose. There has been little research into the long-term effects of psilocybin on brain connectivity in humans. AIM Evaluate changes in resting-state functional connectivity (RSFC) at 1 week and 3 months after one psilocybin dose in 10 healthy psychedelic-naïve volunteers and explore associations between change in RSFC and related measures. METHODS Participants received 0.2-0.3 mg/kg psilocybin in a controlled setting. Participants completed resting-state functional magnetic resonance imaging (fMRI) scans at baseline, 1-week and 3-month post-administration and [11C]Cimbi-36 PET scans at baseline and 1 week. We examined changes in within-network, between-network and region-to-region RSFC. We explored associations between changes in RSFC and psilocybin-induced phenomenology as well as changes in psychological measures and neocortex serotonin 2A receptor binding. RESULTS Psilocybin was well tolerated and produced positive changes in well-being. At 1 week only, executive control network (ECN) RSFC was significantly decreased (Cohen's d = -1.73, pFWE = 0.010). We observed no other significant changes in RSFC at 1 week or 3 months, nor changes in region-to-region RSFC. Exploratory analyses indicated that decreased ECN RSFC at 1 week predicted increased mindfulness at 3 months (r = -0.65). CONCLUSIONS These findings in a small cohort indicate that psilocybin affects ECN function within the psychedelic 'afterglow' period. Our findings implicate ECN modulation as mediating psilocybin-induced, long-lasting increases in mindfulness. Although our findings implicate a neural pathway mediating lasting psilocybin effects, it is notable that changes in neuroimaging measures at 3 months, when personality changes are observed, remain to be identified.
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Affiliation(s)
| | - Martin Korsbak Madsen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Dea Siggaard Stenbæk
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Sara Kristiansen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark
| | - Brice Ozenne
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Section of Biostatistics, Department of Public Health, University of Copenhagen, Copenhagen, Denmark
| | - Peter Steen Jensen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark
| | - Gitte Moos Knudsen
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Patrick MacDonald Fisher
- Neurobiology Research Unit and NeuroPharm, Rigshospitalet, Copenhagen, Denmark,Patrick MacDonald Fisher, Neurobiology Research Unit, Rigshospitalet Building 8057, 8 Inge Lehmanns Vej, Copenhagen 2100, Denmark.
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22
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Niu H, Li W, Wang G, Hu Q, Hao R, Li T, Zhang F, Cheng T. Performances of whole-brain dynamic and static functional connectivity fingerprinting in machine learning-based classification of major depressive disorder. Front Psychiatry 2022; 13:973921. [PMID: 35958666 PMCID: PMC9360427 DOI: 10.3389/fpsyt.2022.973921] [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: 06/20/2022] [Accepted: 07/08/2022] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND Alterations in static and dynamic functional connectivity during resting state have been widely reported in major depressive disorder (MDD). The objective of this study was to compare the performances of whole-brain dynamic and static functional connectivity combined with machine learning approach in differentiating MDD patients from healthy controls at the individual subject level. Given the dynamic nature of brain activity, we hypothesized that dynamic connectivity would outperform static connectivity in the classification. METHODS Seventy-one MDD patients and seventy-one well-matched healthy controls underwent resting-state functional magnetic resonance imaging scans. Whole-brain dynamic and static functional connectivity patterns were calculated and utilized as classification features. Linear kernel support vector machine was employed to design the classifier and a leave-one-out cross-validation strategy was used to assess classifier performance. RESULTS Experimental results of dynamic functional connectivity-based classification showed that MDD patients could be discriminated from healthy controls with an excellent accuracy of 100% irrespective of whether or not global signal regression (GSR) was performed (permutation test with P < 0.0002). Brain regions with the most discriminating dynamic connectivity were mainly and reliably located within the default mode network, cerebellum, and subcortical network. In contrast, the static functional connectivity-based classifiers exhibited unstable classification performances, i.e., a low accuracy of 38.0% without GSR (P = 0.9926) while a high accuracy of 96.5% with GSR (P < 0.0002); moreover, there was a considerable variability in the distribution of brain regions with static connectivity most informative for classification. CONCLUSION These findings suggest the superiority of dynamic functional connectivity in machine learning-based classification of depression, which may be helpful for a better understanding of the neural basis of MDD as well as for the development of effective computer-aided diagnosis tools in clinical settings.
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Affiliation(s)
- Heng Niu
- Department of MRI, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Weirong Li
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Guiquan Wang
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Qiong Hu
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Rui Hao
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Tianliang Li
- Department of Ultrasound, Shanxi Cardiovascular Hospital, Taiyuan, China
| | - Fan Zhang
- Department of Medical Imaging, Shanxi Traditional Chinese Medical Hospital, Taiyuan, China
| | - Tao Cheng
- Department of Neurology, Shanxi Cardiovascular Hospital, Taiyuan, China
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23
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Wang X, Zhuang K, Li Z, Qiu J. The functional connectivity basis of creative achievement linked with openness to experience and divergent thinking. Biol Psychol 2021; 168:108260. [PMID: 34979153 DOI: 10.1016/j.biopsycho.2021.108260] [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: 04/20/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 01/21/2023]
Abstract
Openness to experience and divergent thinking are considered to be critical in real-life creative achievement. However, there is still a lack of neural evidence to explain how creative achievement is related to openness to experience and divergent thinking. Here, a structural equation model and resting-state functional connectivity were used to investigate their relationships in college students. The structural equation model results repeatedly showed that openness to experience and divergent thinking are positively associated with creative achievement, and the resting-state functional connectivity results showed that openness to experience and divergent thinking were both correlated with the attention network and default mode network. However, openness to experience was also correlated with the primary sensorimotor network and frontoparietal control network. Mediation models further corroborated this result. Collectively, these findings support previous works and further indicate that different neural bases may underlie the associations of creative achievement with openness to experience and divergent thinking.
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Affiliation(s)
- Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Zhenyu Li
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China; School of Psychology, Southwest University (SWU), Chongqing 400715, China; Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, China.
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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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Predicting multilingual effects on executive function and individual connectomes in children: An ABCD study. Proc Natl Acad Sci U S A 2021; 118:2110811118. [PMID: 34845019 DOI: 10.1073/pnas.2110811118] [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] [Accepted: 10/13/2021] [Indexed: 11/18/2022] Open
Abstract
While there is a substantial amount of work studying multilingualism's effect on cognitive functions, little is known about how the multilingual experience modulates the brain as a whole. In this study, we analyzed data of over 1,000 children from the Adolescent Brain Cognitive Development (ABCD) Study to examine whether monolinguals and multilinguals differ in executive function, functional brain connectivity, and brain-behavior associations. We observed significantly better performance from multilingual children than monolinguals in working-memory tasks. In one finding, we were able to classify multilinguals from monolinguals using only their whole-brain functional connectome at rest and during an emotional n-back task. Compared to monolinguals, the multilingual group had different functional connectivity mainly in the occipital lobe and subcortical areas during the emotional n-back task and in the occipital lobe and prefrontal cortex at rest. In contrast, we did not find any differences in behavioral performance and functional connectivity when performing a stop-signal task. As a second finding, we investigated the degree to which behavior is reflected in the brain by implementing a connectome-based behavior prediction approach. The multilingual group showed a significant correlation between observed and connectome-predicted individual working-memory performance scores, while the monolingual group did not show any correlations. Overall, our observations suggest that multilingualism enhances executive function and reliably modulates the corresponding brain functional connectome, distinguishing multilinguals from monolinguals even at the developmental stage.
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Ye S, Zhu B, Zhao L, Tian X, Yang Q, Krueger F. Connectome-based model predicts individual psychopathic traits in college students. Neurosci Lett 2021; 769:136387. [PMID: 34883220 DOI: 10.1016/j.neulet.2021.136387] [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: 06/22/2021] [Revised: 11/26/2021] [Accepted: 12/02/2021] [Indexed: 10/19/2022]
Abstract
BACKGROUND Psychopathic traits have been suggested to increase the risk of violations of socio-moral norms. Previous studies revealed that abnormal neural signatures are associated with elevated psychopathic traits; however, whether the intrinsic network architecture can predict psychopathic traits at the individual level remains unclear. METHODS The present study utilized connectome-based predictive modeling (CPM) to investigate whether whole-brain resting-state functional connectivity (RSFC) can predict psychopathic traits in the general population. Resting-state fMRI data were collected from 84 college students with varying psychopathic traits measured by the Levenson Self-Report Psychopathy Scale (LSRP). RESULTS Functional connections that were negatively correlated with psychopathic traits predicted individual differences in total LSRP and secondary psychopathy score but not primary score. Particularly, nodes with the most connections in the predictive connectome anchored in the prefrontal cortex (e.g., anterior prefrontal cortex and orbitofrontal cortex) and limbic system (e.g., anterior cingulate cortex and insula). In addition, the connections between the occipital network (OCCN) and cingulo-opercular network (CON) served as a significant predictive connectome for total LSRP and secondary psychopathy score. CONCLUSION CPM constituted by whole-brain RSFC significantly predicted psychopathic traits individually in the general population. The brain areas including the prefrontal cortex and limbic system and large-scale networks including the CON and OCCN play special roles in the predictive model-possibly reflecting atypical cognitive control and affective processing for individuals with elevated psychopathic traits. These findings may facilitate detection and potential intervention of individuals with maladaptive psychopathic tendency.
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Affiliation(s)
- Shuer Ye
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Bing Zhu
- School of Marxism, Zhejiang Yuexiu University, Shaoxing, China
| | - Lei Zhao
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China; Center for Cognition and Brain Disorders, the Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Xuehong Tian
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China
| | - Qun Yang
- Department of Psychology, Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, VA, USA; Department of Psychology, University of Mannheim, Mannheim, Germany
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Individual differences in (dis)honesty are represented in the brain's functional connectivity at rest. Neuroimage 2021; 246:118761. [PMID: 34861396 DOI: 10.1016/j.neuroimage.2021.118761] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 11/23/2021] [Accepted: 11/23/2021] [Indexed: 01/22/2023] Open
Abstract
Measurement of the determinants of socially undesirable behaviors, such as dishonesty, are complicated and obscured by social desirability biases. To circumvent these biases, we used connectome-based predictive modeling (CPM) on resting state functional connectivity patterns in combination with a novel task which inconspicuously measures voluntary cheating to gain access to the neurocognitive determinants of (dis)honesty. Specifically, we investigated whether task-independent neural patterns within the brain at rest could be used to predict a propensity for (dis)honest behavior. Our analyses revealed that functional connectivity, especially between brain networks linked to self-referential thinking (vmPFC, temporal poles, and PCC) and reward processing (caudate nucleus), reliably correlates, in an independent sample, with participants' propensity to cheat. Participants who cheated the most also scored highest on several self-report measures of impulsivity which underscores the generalizability of our results. Notably, when comparing neural and self-report measures, the neural measures were found to be more important in predicting cheating propensity.
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Wang XH, Li L. A Unified Framework for Inattention Estimation From Resting State Phase Synchrony Using Machine Learning. Front Genet 2021; 12:728913. [PMID: 34630522 PMCID: PMC8495194 DOI: 10.3389/fgene.2021.728913] [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: 06/22/2021] [Accepted: 08/23/2021] [Indexed: 11/13/2022] Open
Abstract
Inattention is one of the most significant clinical symptoms for evaluating attention deficit hyperactivity disorder (ADHD). Previous inattention estimations were performed using clinical scales. Recently, predictive models for inattention have been established for brain-behavior estimation using neuroimaging features. However, the performance of inattention estimation could be improved for conventional brain-behavior models with additional feature selection, machine learning algorithms, and validation procedures. This paper aimed to propose a unified framework for inattention estimation from resting state fMRI to improve the classical brain-behavior models. Phase synchrony was derived as raw features, which were selected with minimum-redundancy maximum-relevancy (mRMR) method. Six machine learning algorithms were applied as regression methods. 100 runs of 10-fold cross-validations were performed on the ADHD-200 datasets. The relevance vector machines (RVMs) based on the mRMR features for the brain-behavior models significantly improve the performance of inattention estimation. The mRMR-RVM models could achieve a total accuracy of 0.53. Furthermore, predictive patterns for inattention were discovered by the mRMR technique. We found that the bilateral subcortical-cerebellum networks exhibited the most predictive phase synchrony patterns for inattention. Together, an optimized strategy named mRMR-RVM for brain-behavior models was found for inattention estimation. The predictive patterns might help better understand the phase synchrony mechanisms for inattention.
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Affiliation(s)
- Xun-Heng Wang
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
| | - Lihua Li
- Institute of Biomedical Engineering and Instrumentation, Hangzhou Dianzi University, Hangzhou, China
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29
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Brief segments of neurophysiological activity enable individual differentiation. Nat Commun 2021; 12:5713. [PMID: 34588439 PMCID: PMC8481307 DOI: 10.1038/s41467-021-25895-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Accepted: 09/07/2021] [Indexed: 11/08/2022] Open
Abstract
Large, openly available datasets and current analytic tools promise the emergence of population neuroscience. The considerable diversity in personality traits and behaviour between individuals is reflected in the statistical variability of neural data collected in such repositories. Recent studies with functional magnetic resonance imaging (fMRI) have concluded that patterns of resting-state functional connectivity can both successfully distinguish individual participants within a cohort and predict some individual traits, yielding the notion of an individual's neural fingerprint. Here, we aim to clarify the neurophysiological foundations of individual differentiation from features of the rich and complex dynamics of resting-state brain activity using magnetoencephalography (MEG) in 158 participants. We show that akin to fMRI approaches, neurophysiological functional connectomes enable the differentiation of individuals, with rates similar to those seen with fMRI. We also show that individual differentiation is equally successful from simpler measures of the spatial distribution of neurophysiological spectral signal power. Our data further indicate that differentiation can be achieved from brain recordings as short as 30 seconds, and that it is robust over time: the neural fingerprint is present in recordings performed weeks after their baseline reference data was collected. This work, thus, extends the notion of a neural or brain fingerprint to fast and large-scale resting-state electrophysiological dynamics.
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30
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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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31
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Naqvi S, Sleyp Y, Hoskens H, Indencleef K, Spence JP, Bruffaerts R, Radwan A, Eller RJ, Richmond S, Shriver MD, Shaffer JR, Weinberg SM, Walsh S, Thompson J, Pritchard JK, Sunaert S, Peeters H, Wysocka J, Claes P. Shared heritability of human face and brain shape. Nat Genet 2021; 53:830-839. [PMID: 33821002 PMCID: PMC8232039 DOI: 10.1038/s41588-021-00827-w] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2020] [Accepted: 02/16/2021] [Indexed: 02/08/2023]
Abstract
Evidence from model organisms and clinical genetics suggests coordination between the developing brain and face, but the role of this link in common genetic variation remains unknown. We performed a multivariate genome-wide association study of cortical surface morphology in 19,644 individuals of European ancestry, identifying 472 genomic loci influencing brain shape, of which 76 are also linked to face shape. Shared loci include transcription factors involved in craniofacial development, as well as members of signaling pathways implicated in brain-face cross-talk. Brain shape heritability is equivalently enriched near regulatory regions active in either forebrain organoids or facial progenitors. However, we do not detect significant overlap between shared brain-face genome-wide association study signals and variants affecting behavioral-cognitive traits. These results suggest that early in embryogenesis, the face and brain mutually shape each other through both structural effects and paracrine signaling, but this interplay may not impact later brain development associated with cognitive function.
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Affiliation(s)
- Sahin Naqvi
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA.
| | - Yoeri Sleyp
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Hanne Hoskens
- Department of Human Genetics, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
| | - Karlijne Indencleef
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Jeffrey P Spence
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Rose Bruffaerts
- Department of Neurosciences, KU Leuven, Leuven, Belgium, Hasselt University, Hasselt, Belgium
- Neurology Department, University Hospitals Leuven, Leuven, Belgium, Hasselt University, Hasselt, Belgium
- Biomedical Research Institute Hasselt University Hasselt Belgium, Hasselt University, Hasselt, Belgium
| | - Ahmed Radwan
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Imaging and Pathology, Translational MRI, KU Leuven, Leuven, Belgium
| | - Ryan J Eller
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - Stephen Richmond
- Applied Clinical Research and Public Health, School of Dentistry, Cardiff University, Cardiff, UK
| | - Mark D Shriver
- Department of Anthropology, Pennsylvania State University, State College, PA, USA
| | - John R Shaffer
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Seth M Weinberg
- Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Oral and Craniofacial Sciences, Center for Craniofacial and Dental Genetics, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Anthropology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Susan Walsh
- Department of Biology, Indiana University Purdue University Indianapolis, Indianapolis, IN, USA
| | - James Thompson
- Department of Psychology, George Mason University, Fairfax, VA, USA
| | - Jonathan K Pritchard
- Departments of Genetics and Biology, Stanford University School of Medicine, Stanford, CA, USA
| | - Stefan Sunaert
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium
- Department of Imaging and Pathology, Translational MRI, KU Leuven, Leuven, Belgium
| | - Hilde Peeters
- Department of Human Genetics, KU Leuven, Leuven, Belgium
| | - Joanna Wysocka
- Department of Chemical and Systems Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University School of Medicine, Stanford, CA, USA.
| | - Peter Claes
- Department of Human Genetics, KU Leuven, Leuven, Belgium.
- Medical Imaging Research Center, University Hospitals Leuven, Leuven, Belgium.
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
- Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
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32
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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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33
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Arbula S, Pisanu E, Rumiati RI. Representation of social content in dorsomedial prefrontal cortex underlies individual differences in agreeableness trait. Neuroimage 2021; 235:118049. [PMID: 33848626 DOI: 10.1016/j.neuroimage.2021.118049] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Revised: 04/01/2021] [Accepted: 04/02/2021] [Indexed: 01/31/2023] Open
Abstract
Personality traits reflect key aspects of individual variability in different psychological domains. Understanding the mechanisms that give rise to these differences requires an exhaustive investigation of the behaviors associated with such traits, and their underlying neural sources. Here we investigated the mechanisms underlying agreeableness, one of the five major dimensions of personality, which has been linked mainly to socio-cognitive functions. In particular, we examined whether individual differences in the neural representations of social information are related to differences in agreeableness of individuals. To this end, we adopted a multivariate representational similarity approach that captured within single individuals the activation pattern similarity of social and non-social content, and tested its relation to the agreeableness trait in a hypothesis-driven manner. The main result confirmed our prediction: processing social and non-social content led to similar patterns of activation in individuals with low agreeableness, while in more agreeable individuals these patterns were more dissimilar. Critically, this association between agreeableness and encoding similarity of social and random content was significant only in the dorsomedial prefrontal cortex, a brain region consistently involved during attributions of mental states. The present finding reveals the link between neural mechanisms underlying social information processing and agreeableness, a personality trait highly related to socio-cognitive abilities, thereby providing a step forward in characterizing its neural determinants. Furthermore, it emphasizes the advantage of multivariate pattern analysis approaches in capturing and understanding the neural sources of individual variations.
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Affiliation(s)
- Sandra Arbula
- Neuroscience Area, International School for Advanced Studies (SISSA), via Bonomea 265, Trieste 34136, Italy.
| | - Elisabetta Pisanu
- Neuroscience Area, International School for Advanced Studies (SISSA), via Bonomea 265, Trieste 34136, Italy
| | - Raffaella I Rumiati
- Neuroscience Area, International School for Advanced Studies (SISSA), via Bonomea 265, Trieste 34136, Italy; Scuola superiore di studi avanzati Sapienza (SSAS), Rome, Italy
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34
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Zhu J, Li Y, Fang Q, Shen Y, Qian Y, Cai H, Yu Y. Dynamic functional connectome predicts individual working memory performance across diagnostic categories. NEUROIMAGE-CLINICAL 2021; 30:102593. [PMID: 33647810 PMCID: PMC7930367 DOI: 10.1016/j.nicl.2021.102593] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 02/02/2021] [Accepted: 02/05/2021] [Indexed: 11/23/2022]
Abstract
We created transdiagnostic predictive working memory models using connectome-based predictive modeling (CPM). Dynamic functional connectivity-based CPM models successfully predicted working memory. Static functional connectivity-based CPM models fell short in prediction. Frontoparietal, somato-motor, default mode and visual networks contributed most to prediction.
Working memory impairment is a common feature of psychiatric disorders. Although its neural mechanisms have been extensively examined in healthy subjects or individuals with a certain clinical condition, studies investigating neural predictors of working memory in a transdiagnostic sample are scarce. The objective of this study was to create a transdiagnostic predictive working memory model from whole-brain functional connectivity using connectome-based predictive modeling (CPM), a recently developed machine learning approach. Resting-state functional MRI data from 242 subjects across 4 diagnostic categories (healthy controls and individuals with schizophrenia, bipolar disorder, and attention deficit/hyperactivity) were used to construct dynamic and static functional connectomes. Spatial working memory was assessed by the spatial capacity task. CPM was conducted to predict individual working memory from dynamic and static functional connectivity patterns. Results showed that dynamic connectivity-based CPM models successfully predicted overall working memory capacity and accuracy as well as mean reaction time, yet their static counterparts fell short in the prediction. At the neural level, we found that dynamic connectivity of the frontoparietal and somato-motor networks were negatively correlated with working memory capacity and accuracy, and those of the default mode and visual networks were positively associated with mean reaction time. Moreover, different feature selection thresholds, parcellation strategies and model validation methods as well as diagnostic categories did not significantly influence the prediction results. Our findings not only are coherent with prior reports that dynamic functional connectivity encodes more behavioral information than static connectivity, but also help advance the translation of cognitive “connectome fingerprinting” into real-world application.
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Affiliation(s)
- Jiajia Zhu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yating Li
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Qian Fang
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yuhao Shen
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yinfeng Qian
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Huanhuan Cai
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China
| | - Yongqiang Yu
- Department of Radiology, The First Affiliated Hospital of Anhui Medical University, Hefei 230022, China.
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