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Yu Y, Oh Y, Kounios J, Beeman M. Electroencephalography Spectral-power Volatility Predicts Problem-solving Outcomes. J Cogn Neurosci 2024; 36:901-915. [PMID: 38437171 DOI: 10.1162/jocn_a_02136] [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] [Indexed: 03/06/2024]
Abstract
Temporal variability is a fundamental property of brain processes and is functionally important to human cognition. This study examined how fluctuations in neural oscillatory activity are related to problem-solving performance as one example of how temporal variability affects high-level cognition. We used volatility to assess step-by-step fluctuations of EEG spectral power while individuals attempted to solve word-association puzzles. Inspired by recent results with hidden-state modeling, we tested the hypothesis that spectral-power volatility is directly associated with problem-solving outcomes. As predicted, volatility was lower during trials solved with insight compared with those solved analytically. Moreover, volatility during prestimulus preparation for problem-solving predicted solving outcomes, including solving success and solving time. These novel findings were replicated in a separate data set from an anagram-solving task, suggesting that less-rapid transitions between neural oscillatory synchronization and desynchronization predict better solving performance and are conducive to solving with insight for these types of problems. Thus, volatility can be a valuable index of cognition-related brain dynamics.
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Affiliation(s)
- Yuhua Yu
- Northwestern University, Evanston, IL
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Yi H, Xiao M, Chen X, Yan Q, Yang Y, Liu Y, Song S, Gao X, Chen H. Resting-state functional network connectivity underlying conscientiousness in school-aged children. Child Neuropsychol 2024; 30:486-502. [PMID: 37278282 DOI: 10.1080/09297049.2023.2221757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
Conscientiousness is a personality trait that matures from early childhood to late adolescence, yet little is known about its underlying brain mechanisms during this period. To investigate this, our study examined the resting-state functional network connectivity (rsFNC) of 69 school-aged children (mean age = 10.12 years, range = 9-12) using a whole-brain region-of-interest (ROI) based analysis, based on functional magnetic resonance imaging (fMRI). The results indicated a positive association between conscientiousness and the rsFNC between the fronto-parietal network (FPN) and two brain networks: the somatosensory motor-hand network (SMHN) and the auditory network (AN). However, conscientiousness was negatively associated with the rsFNC between FPN and two other networks: the salience network (SN); the default mode network (DMN). Moreover, our results suggest that the FPN may play a hub role in the neural performance of children's conscientiousness. Intrinsic brain networks, particularly those involved in higher-order cognitive functions, impact children's conscientiousness. Therefore, FPN plays an important role in the development of children's personality, providing insight into the neural mechanisms underlying children's personality.
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Affiliation(s)
- Haijing Yi
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Ximei Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Qiaoling Yan
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yue Yang
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shiqing Song
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
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Wang G, Zeng M, Li J, Liu Y, Wei D, Long Z, Chen H, Zang X, Yang J. Neural Representation of Collective Self-esteem in Resting-state Functional Connectivity and its Validation in Task-dependent Modality. Neuroscience 2023; 530:66-78. [PMID: 37619767 DOI: 10.1016/j.neuroscience.2023.08.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/01/2023] [Accepted: 08/09/2023] [Indexed: 08/26/2023]
Abstract
INTRODUCTION Collective self-esteem (CSE) is an important personality variable, defined as self-worth derived from membership in social groups. A study explored the neural basis of CSE using a task-based functional magnetic resonance imaging (fMRI) paradigm; however, task-independent neural basis of CSE remains to be explored, and whether the CSE neural basis of resting-state fMRI is consistent with that of task-based fMRI is unclear. METHODS We built support vector regression (SVR) models to predict CSE scores using topological metrics measured in the resting-state functional connectivity network (RSFC) as features. Then, to test the reliability of the SVR analysis, the activation pattern of the identified brain regions from SVR analysis was used as features to distinguish collective self-worth from other conditions by multivariate pattern classification in task-based fMRI dataset. RESULTS SVR analysis results showed that leverage centrality successfully decoded the individual differences in CSE. The ventromedial prefrontal cortex, anterior cingulate cortex, posterior cingulate gyrus, precuneus, orbitofrontal cortex, posterior insula, postcentral gyrus, inferior parietal lobule, temporoparietal junction, and inferior frontal gyrus, which are involved in self-referential processing, affective processing, and social cognition networks, participated in this prediction. Multivariate pattern classification analysis found that the activation pattern of the identified regions from the SVR analysis successfully distinguished collective self-worth from relational self-worth, personal self-worth and semantic control. CONCLUSION Our findings revealed CSE neural basis in the whole-brain RSFC network, and established the concordance between leverage centrality and the activation pattern (evoked during collective self-worth task) of the identified regions in terms of representing CSE.
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Affiliation(s)
- Guangtong Wang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Mei Zeng
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Jiwen Li
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Yadong Liu
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Dongtao Wei
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Zhiliang Long
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Haopeng Chen
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Xinlei Zang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China
| | - Juan Yang
- Faculty of Psychology, Southwest University, Chongqing 400715, China; Key Laboratory of Cognition and Personality, Ministry of Education, Southwest University, Chongqing 400715, China.
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Personality Associations With Amyloid and Tau: Results From the Baltimore Longitudinal Study of Aging and Meta-analysis. Biol Psychiatry 2022; 91:359-369. [PMID: 34663503 PMCID: PMC8792161 DOI: 10.1016/j.biopsych.2021.08.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 08/12/2021] [Accepted: 08/20/2021] [Indexed: 12/17/2022]
Abstract
BACKGROUND Higher neuroticism and lower conscientiousness are risk factors for Alzheimer's disease and related dementias, but the underlying neuropathological correlates remain unclear. Our aim was to examine whether personality traits are associated with amyloid and tau neuropathology in a new sample and meta-analyses. METHODS Participants from the BLSA (Baltimore Longitudinal Study of Aging) completed the Revised NEO Personality Inventory and underwent amyloid (11C-labeled Pittsburgh compound B) and tau (18F-flortaucipir) positron emission tomography. RESULTS Among cognitively normal BLSA participants, neuroticism was associated with higher cortical amyloid burden (odds ratio 1.68, 95% CI 1.20-2.34), and conscientiousness was associated with lower cortical amyloid burden (odds ratio 0.61, 95% CI 0.44-0.86). These associations remained significant after accounting for age, sex, education, depressive symptoms, hippocampal volume, and APOE ε4. Similar associations were found with tau in the entorhinal cortex. Random-effects meta-analyses of 12 studies found that higher neuroticism (N = 3015, r = 0.07, p = .008) and lower conscientiousness (N = 2990, r = -0.11, p < .001) were associated with more amyloid deposition. Meta-analyses of 8 studies found that higher neuroticism (N = 2231, r = 0.15, p < .001) and lower conscientiousness (N = 2206, r = -0.14, p < .001) were associated with more tau pathology. The associations were moderated by cognitive status, with stronger effects in cognitively normal compared with heterogeneous samples, suggesting that the associations between personality and proteopathies are not phenomena that emerge with neuropsychiatric clinical symptoms. CONCLUSIONS By aggregating results across samples, this study advances knowledge on the association between personality and neuropathology. Neuroticism and conscientiousness may contribute to resistance against amyloid and tau neuropathology.
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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: 11] [Impact Index Per Article: 3.7] [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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Azevedo T, Passamonti L, Lio P, Toschi N. A deep spatiotemporal graph learning architecture for brain connectivity analysis. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1120-1123. [PMID: 33018183 DOI: 10.1109/embc44109.2020.9175360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
In recent years, the conceptualisation of the brain as a "connectome" as summary measures derived from graph theory analyses, has become increasingly popular. Still, such approaches are inherently limited by the need to condense and simplify temporal fMRI dynamics and architecture into a purely spatial representation. We formulate a novel architecture based on Geometric Deep Learning which is specifically tailored to the one-step integration of spatial relationship between nodes and single-node temporal dynamics. We compare different spatiotemporal modelling mechanisms and demonstrate the effectiveness of our architecture in a binary prediction task based on a large homogeneous fMRI dataset made publicly available by the Human Connectome Project (HCP). As the idea of e.g. a dynamical network connectivity is beginning to make its way into the more mainstream toolset which neuroscientists commonly employ with neuroimaging data, our model can contribute to laying the groundwork for explicitly incorporating spatiotemporal information into every association and prediction problem in neuroscience.
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Levine SM, Alahäivälä ALI, Wechsler TF, Wackerle A, Rupprecht R, Schwarzbach JV. Linking Personality Traits to Individual Differences in Affective Spaces. Front Psychol 2020; 11:448. [PMID: 32231631 PMCID: PMC7082752 DOI: 10.3389/fpsyg.2020.00448] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Accepted: 02/26/2020] [Indexed: 11/13/2022] Open
Abstract
Different individuals respond differently to emotional stimuli in their environment. Therefore, to understand how emotions are represented mentally will ultimately require investigations into individual-level information. Here we tasked participants with freely arranging emotionally charged images on a computer screen according to their subjective emotional similarity (yielding a unique affective space for each participant) and subsequently sought external validity of the layout of the individuals’ affective spaces through the five-factor personality model (Neuroticism, Extraversion, Openness to Experience, Agreeableness, Conscientiousness) assessed via the NEO Five-Factor Inventory. Applying agglomerative hierarchical clustering to the group-level affective space revealed a set of underlying affective clusters whose within-cluster dissimilarity, per individual, was then correlated with individuals’ personality scores. These cluster-based analyses predominantly revealed that the dispersion of the negative cluster showed a positive relationship with Neuroticism and a negative relationship with Conscientiousness, a finding that would be predicted by prior work. Such results demonstrate the non-spurious structure of individualized emotion information revealed by data-driven analyses of a behavioral task (and validated by incorporating psychological measures of personality) and corroborate prior knowledge of the interaction between affect and personality. Future investigations can similarly combine hypothesis- and data-driven methods to extend such findings, potentially yielding new perspectives on underlying cognitive processes, disease susceptibility, or even diagnostic/prognostic markers for mental disorders involving emotion dysregulation.
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Affiliation(s)
- Seth M Levine
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Aino L I Alahäivälä
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Theresa F Wechsler
- Department of Clinical Psychology and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Anja Wackerle
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Rainer Rupprecht
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
| | - Jens V Schwarzbach
- Department of Psychiatry and Psychotherapy, University of Regensburg, Regensburg, Germany
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Simon SS, Varangis E, Stern Y. Associations between personality and whole-brain functional connectivity at rest: Evidence across the adult lifespan. Brain Behav 2020; 10:e01515. [PMID: 31903706 PMCID: PMC7249003 DOI: 10.1002/brb3.1515] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 12/01/2019] [Indexed: 12/23/2022] Open
Abstract
INTRODUCTION Personality is associated with cognitive, emotional, and social functioning, and can play a role in age-related cognitive decline and dementia risk; however, little is known about the brain dynamics underlying personality characteristics, and whether they are moderated by age. METHODS We investigated the associations between personality and resting-state functional MRI data from 365 individuals across the adult lifespan (20-80 years). Participants completed the 50-item International Personality Item Pool and a resting-state imaging protocol on a 3T MRI scanner. Within-network connectivity values were computed based on predefined networks. Regression analyzes were conducted in order to investigate personality-connectivity associations, as well as moderation by age. All models controlled for potential confounders (such as age, sex, education, IQ, and the other personality traits). RESULTS We found that openness was positively associated with connectivity in the default-mode network, neuroticism was negatively associated with both the ventral and dorsal attention networks, and agreeableness was negatively associated with the dorsal attention network. In addition, age moderated the association between conscientiousness and the frontoparietal network, indicating that this association become stronger in older age. CONCLUSIONS Our findings demonstrate that personality is associated with brain connectivity, which may contribute to identifying personality profiles that play a role in protection against or risk for age-related brain changes and dementia.
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Affiliation(s)
- Sharon S Simon
- Cognitive Neuroscience Division, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Eleanna Varangis
- Cognitive Neuroscience Division, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
| | - Yaakov Stern
- Cognitive Neuroscience Division, Department of Neurology, College of Physicians and Surgeons, Columbia University, New York, NY, USA
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