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Zhang N, Chen S, Jiang K, Ge W, Im H, Guan S, Li Z, Wei C, Wang P, Zhu Y, Zhao G, Liu L, Chen C, Chang H, Wang Q. Individualized prediction of anxiety and depressive symptoms using gray matter volume in a non-clinical population. Cereb Cortex 2024; 34:bhae121. [PMID: 38584086 DOI: 10.1093/cercor/bhae121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2024] [Revised: 03/03/2024] [Accepted: 03/05/2024] [Indexed: 04/09/2024] Open
Abstract
Machine learning is an emerging tool in clinical psychology and neuroscience for the individualized prediction of psychiatric symptoms. However, its application in non-clinical populations is still in its infancy. Given the widespread morphological changes observed in psychiatric disorders, our study applies five supervised machine learning regression algorithms-ridge regression, support vector regression, partial least squares regression, least absolute shrinkage and selection operator regression, and Elastic-Net regression-to predict anxiety and depressive symptom scores. We base these predictions on the whole-brain gray matter volume in a large non-clinical sample (n = 425). Our results demonstrate that machine learning algorithms can effectively predict individual variability in anxiety and depressive symptoms, as measured by the Mood and Anxiety Symptoms Questionnaire. The most discriminative features contributing to the prediction models were primarily located in the prefrontal-parietal, temporal, visual, and sub-cortical regions (e.g. amygdala, hippocampus, and putamen). These regions showed distinct patterns for anxious arousal and high positive affect in three of the five models (partial least squares regression, support vector regression, and ridge regression). Importantly, these predictions were consistent across genders and robust to demographic variability (e.g. age, parental education, etc.). Our findings offer critical insights into the distinct brain morphological patterns underlying specific components of anxiety and depressive symptoms, supporting the existing tripartite theory from a neuroimaging perspective.
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Affiliation(s)
- Ning Zhang
- School of Mathematical Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Shuning Chen
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Keying Jiang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Wei Ge
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Hohjin Im
- Independent Researcher, United States
| | - Shunping Guan
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Zixi Li
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Chuqiao Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Pinchun Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Ye Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Guang Zhao
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Liqing Liu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Chunhui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Huibin Chang
- School of Mathematical Sciences, Tianjin Normal University, Tianjin 300387, China
| | - Qiang Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Laboratory of Philosophy and Social Science of Anhui Province on Adolescent Mental Health and Crisis Intelligence Intervention, Hefei Normal University, Hefei, 230061, China
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Martinez CA, Pantazopoulos H, Gisabella B, Stephens ET, Garteiser J, Del Arco A. Choice impulsivity after repeated social stress is associated with increased perineuronal nets in the medial prefrontal cortex. Sci Rep 2024; 14:7093. [PMID: 38528075 DOI: 10.1038/s41598-024-57599-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Accepted: 03/20/2024] [Indexed: 03/27/2024] Open
Abstract
Repeated stress can predispose to substance abuse. However, behavioral and neurobiological adaptations that link stress to substance abuse remain unclear. This study investigates whether intermittent social defeat (ISD), a stress protocol that promotes drug-seeking behavior, alters intertemporal decision-making and cortical inhibitory function in the medial prefrontal cortex (mPFC). Male long evans rats were trained in a delay discounting task (DDT) where rats make a choice between a fast (1 s) small reward (1 sugar pellet) and a large reward (3 sugar pellets) that comes with a time delay (10 s or 20 s). A decreased preference for delayed rewards was used as an index of choice impulsivity. Rats were exposed to ISD and tested in the DDT 24 h after each stress episode, and one- and two-weeks after the last stress episode. Immunohistochemistry was performed in rat's brains to evaluate perineuronal nets (PNNs) and parvalbumin GABA interneurons (PV) labeling as markers of inhibitory function in mPFC. ISD significantly decreased the preference for delayed large rewards in low impulsive, but not high impulsive, animals. ISD also increased the density of PNNs in the mPFC. These results suggest that increased choice impulsivity and cortical inhibition predispose animals to seek out rewards after stress.
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Affiliation(s)
| | - Harry Pantazopoulos
- Department of Psychiatry and Human Behavior, Medical School, University of Mississippi Medical Center, Jackson, MS, USA
| | - Barbara Gisabella
- Department of Psychiatry and Human Behavior, Medical School, University of Mississippi Medical Center, Jackson, MS, USA
| | - Emily T Stephens
- Department of Psychiatry and Human Behavior, Medical School, University of Mississippi Medical Center, Jackson, MS, USA
| | - Jacob Garteiser
- Department of Psychiatry and Human Behavior, Medical School, University of Mississippi Medical Center, Jackson, MS, USA
| | - Alberto Del Arco
- HESRM, School of Applied Sciences, University of Mississippi, Oxford, MS, USA.
- Department of Psychiatry and Human Behavior, Medical School, University of Mississippi Medical Center, Jackson, MS, USA.
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3
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Ji S, Yang F, Li X. Spontaneous neural activity in the three principal networks underlying delay discounting: a resting-state fMRI study. Front Psychiatry 2024; 15:1320830. [PMID: 38370559 PMCID: PMC10869524 DOI: 10.3389/fpsyt.2024.1320830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Accepted: 01/17/2024] [Indexed: 02/20/2024] Open
Abstract
Delay discounting, the decline in the subjective value of future rewards over time, has traditionally been understood through a tripartite neural network model, comprising the valuation, cognitive control, and prospection networks. To investigate the applicability of this model in a resting-state context, we employed a monetary choice questionnaire to quantify delay discounting and utilized resting-state functional magnetic resonance imaging (rs-fMRI) to explore the role of spontaneous brain activity, specifically regional homogeneity (ReHo), in influencing individual differences in delay discounting across a large cohort (N = 257). Preliminary analyses revealed a significant negative correlation between delay discounting tendencies and the ReHo in both the left insula and the right hippocampus, respectively. Subsequent resting-state functional connectivity (RSFC) analyses, using these regions as seed ROIs, disclosed that all implicated brain regions conform to the three principal networks traditionally associated with delay discounting. Our findings offer novel insights into the role of spontaneous neural activity in shaping individual variations in delay discounting at both regional and network levels, providing the first empirical evidence supporting the applicability of the tripartite network model in a resting-state context.
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Affiliation(s)
| | | | - Xueting Li
- Department of Psychology, Renmin University of China, Beijing, China
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4
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Jung WH, Kim E. White matter-based brain network topological properties associated with individual impulsivity. Sci Rep 2023; 13:22173. [PMID: 38092841 PMCID: PMC10719274 DOI: 10.1038/s41598-023-49168-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Accepted: 12/05/2023] [Indexed: 12/17/2023] Open
Abstract
Delay discounting (DD), a parameter derived from the intertemporal choice task, is a representative behavioral indicator of choice impulsivity. Previous research reported not only an association between DD and impulsive control disorders and negative health outcomes but also the neural correlates of DD. However, to date, there are few studies investigating the structural brain network topologies associated with individual differences in DD and whether self-reported measures (BIS-11) of impulsivity associated with DD share the same or distinct neural mechanisms is still unclear. To address these issues, here, we combined graph theoretical analysis with diffusion tensor imaging to investigate the associations between DD and the topological properties of the structural connectivity network and BIS-11 scores. Results revealed that people with a steep DD (greater impatience) had decreased small-worldness (a shift toward weaker small-worldnization) and increased degree centrality in the medial superior prefrontal cortex, associated with subjective value in the task. Though DD was associated with the BIS-11 motor impulsiveness subscale, this subscale was linked to topological properties different from DD; that is, high motor impulsiveness was associated with decreased local efficiency (less segregation) and decreased degree centrality in the precentral gyrus, involved in motor control. These findings provide insights into the systemic brain characteristics underlying individual differences in impulsivity and potential neural markers which could predict susceptibility to impulsive behaviors.
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Affiliation(s)
- Wi Hoon Jung
- Department of Psychology, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, 13120, South Korea.
| | - Euitae Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
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5
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Deng K, Jin W, Jiang K, Li Z, Im H, Chen S, Du H, Guan S, Ge W, Wei C, Zhang B, Wang P, Zhao G, Chen C, Liu L, Wang Q. Reactivity of the ventromedial prefrontal cortex, but not the amygdala, to negative emotion faces predicts greed personality trait. BEHAVIORAL AND BRAIN FUNCTIONS : BBF 2023; 19:21. [PMID: 38041182 PMCID: PMC10690991 DOI: 10.1186/s12993-023-00223-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Accepted: 11/27/2023] [Indexed: 12/03/2023]
Abstract
This study explored whether amygdala reactivity predicted the greed personality trait (GPT) using both task-based and resting-state functional connectivity analyses (ntotal = 452). In Cohort 1 (n = 83), task-based functional magnetic resonance imaging (t-fMRI) results from a region-of-interest (ROI) analysis revealed no direct correlation between amygdala reactivity to fearful and angry faces and GPT. Instead, whole-brain analyses revealed GPT to robustly negatively vary with activations in the right ventromedial prefrontal cortex (vmPFC), supramarginal gyrus, and angular gyrus in the contrast of fearful + angry faces > shapes. Moreover, task-based psychophysiological interaction (PPI) analyses showed that the high GPT group showed weaker functional connectivity of the vmPFC seed with a top-down control network and visual pathways when processing fearful or angry faces compared to their lower GPT counterparts. In Cohort 2, resting-state functional connectivity (rs-FC) analyses indicated stronger connectivity between the vmPFC seed and the top-down control network and visual pathways in individuals with higher GPT. Comparing the two cohorts, bilateral amygdala seeds showed weaker associations with the top-down control network in the high group via PPI analyses in Cohort 1. Yet, they exhibited distinct rs-FC patterns in Cohort 2 (e.g., positive associations of GPT with the left amygdala-top-down network FC but negative associations with the right amygdala-visual pathway FC). The study underscores the role of the vmPFC and its functional connectivity in understanding GPT, rather than amygdala reactivity.
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Affiliation(s)
- Kun Deng
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Weipeng Jin
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, 300060, China
| | - Keying Jiang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Zixi Li
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Hohjin Im
- Department of Psychological Science, University of California, Irvine, CA, 92697-7085, USA
| | - Shuning Chen
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Hanxiao Du
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Shunping Guan
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Wei Ge
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Chuqiao Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Bin Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Pinchun Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Guang Zhao
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China
| | - Chunhui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China.
| | - Liqing Liu
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China.
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China.
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China.
| | - Qiang Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China.
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China.
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China.
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6
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Wang P, Chen S, Deng K, Zhang B, Im H, Feng J, Liu L, Yang Q, Zhao G, He Q, Chen C, Wang H, Wang Q. Distributed attribute representation in the superior parietal lobe during probabilistic decision-making. Hum Brain Mapp 2023; 44:5693-5711. [PMID: 37614216 PMCID: PMC10619403 DOI: 10.1002/hbm.26470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 06/18/2023] [Accepted: 08/10/2023] [Indexed: 08/25/2023] Open
Abstract
Several studies have examined the neural substrates of probabilistic decision-making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies (nStudy1 = 34, nStudy2 = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability-related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision-making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.
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Affiliation(s)
- Pinchun Wang
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
| | - Shuning Chen
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
| | - Kun Deng
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
| | - Bin Zhang
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
| | - Hohjin Im
- Department of Psychological ScienceUniversity of California IrvineIrvineCaliforniaUSA
| | - Junjiao Feng
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and BehaviorTianjin Normal UniversityTianjinChina
- Tianjin Social Science Laboratory of Students' Mental Development and LearningTianjinChina
| | - Liqing Liu
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and BehaviorTianjin Normal UniversityTianjinChina
- Tianjin Social Science Laboratory of Students' Mental Development and LearningTianjinChina
| | - Qinghao Yang
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
| | - Guang Zhao
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and BehaviorTianjin Normal UniversityTianjinChina
- Tianjin Social Science Laboratory of Students' Mental Development and LearningTianjinChina
| | - Qinghua He
- Faculty of Psychology, MOE Key Laboratory of Cognition and PersonalitySouthwest UniversityChongqingChina
| | - Chunhui Chen
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain ResearchBeijing Normal UniversityBeijingChina
| | - He Wang
- Institute of Biomedical EngineeringChinese Academy of Medical Science & Peking Union Medical CollegeTianjinChina
| | - Qiang Wang
- Faculty of PsychologyTianjin Normal UniversityTianjinChina
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and BehaviorTianjin Normal UniversityTianjinChina
- Tianjin Social Science Laboratory of Students' Mental Development and LearningTianjinChina
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7
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Varma MM, Zhen S, Yu R. Not all discounts are created equal: Regional activity and brain networks in temporal and effort discounting. Neuroimage 2023; 280:120363. [PMID: 37673412 DOI: 10.1016/j.neuroimage.2023.120363] [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: 04/14/2023] [Revised: 08/31/2023] [Accepted: 09/03/2023] [Indexed: 09/08/2023] Open
Abstract
Reward outcomes associated with costs like time delay and effort investment are generally discounted in decision-making. Standard economic models predict rewards associated with different types of costs are devalued in a similar manner. However, our review of rodent lesion studies indicated partial dissociations between brain regions supporting temporal- and effort-based decision-making. Another debate is whether options involving low and high costs are processed in different brain substrates (dual-system) or in the same regions (single-system). This research addressed these issues using coordinate-based, connectivity-based, and activation network-based meta-analyses to identify overlapping and separable neural systems supporting temporal (39 studies) and effort (20 studies) discounting. Coordinate-based activation likelihood estimation and resting-state connectivity analyses showed immediate-small reward and delayed-large reward choices engaged distinct regions with unique connectivity profiles, but their activation network mapping was found to engage the default mode network. For effort discounting, salience and sensorimotor networks supported low-effort choices, while the frontoparietal network supported high-effort choices. There was little overlap between the temporal and effort networks. Our findings underscore the importance of differentiating different types of costs in decision-making and understanding discounting at both regional and network levels.
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Affiliation(s)
- Mohith M Varma
- Department of Management, Marketing, and Information Systems, Hong Kong Baptist University, Hong Kong, China
| | - Shanshan Zhen
- Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, China.
| | - Rongjun Yu
- Department of Management, Marketing, and Information Systems, Hong Kong Baptist University, Hong Kong, China.
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8
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Zhu W, Chen X, Wu J, Li Z, Im H, Chen S, Deng K, Zhang B, Wei C, Feng J, Zhang M, Yang S, Wang H, Wang Q. Neuroanatomical and functional substrates of the hypomanic personality trait and its prediction on aggression. Int J Clin Health Psychol 2023; 23:100397. [PMID: 37560478 PMCID: PMC10407439 DOI: 10.1016/j.ijchp.2023.100397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/10/2023] [Indexed: 08/11/2023] Open
Abstract
Hypomanic personality manifests a close link with several psychiatric disorders and its abnormality is a risk indicator for developing bipolar disorders. We systematically investigated the potential neuroanatomical and functional substrates underlying hypomanic personality trait (HPT) and its sub-dimensions (i.e., Social Vitality, Mood Volatility, and Excitement) combined with structural and functional imaging data as well as their corresponding brain networks in a large non-clinical sample across two studies (n = 464). Behaviorally, HPT, specifically Mood Volatility and Excitement, was positively associated with aggressive behaviors in both studies. Structurally, sex-specific morphological characteristics were further observed in the motor and top-down control networks especially for Mood Volatility, although HPT was generally positively associated with grey matter volumes (GMVs) in the prefrontal, temporal, visual, and limbic systems. Functionally, brain activations related to immediate or delayed losses were found to predict individual variability in HPT, specifically Social Vitality and Excitement, on the motor and prefrontal-parietal cortices. Topologically, connectome-based prediction model analysis further revealed the predictive role of individual-level morphological and resting-state functional connectivity on HPT and its sub-dimensions, although it did not reveal any links with general brain topological properties. GMVs in the temporal, limbic (e.g., amygdala), and visual cortices mediated the effects of HPT on behavioral aggression. These findings suggest that the imbalance between motor and control circuits may be critical for HPT and provide novel insights into the neuroanatomical, functional, and topological mechanisms underlying the specific temperament and its impacts on aggression.
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Affiliation(s)
- Wenwei Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing 100088, China
- Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing 100069, China
| | - Jie Wu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students’ Mental Development and Learning, Tianjin 300387, China
| | - Zixi Li
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Hohjin Im
- Department of Psychological Science, University of California, Irvine, CA 92697-7085, USA
| | - Shuning Chen
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Kun Deng
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Bin Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Chuqiao Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Junjiao Feng
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students’ Mental Development and Learning, Tianjin 300387, China
| | - Manman Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students’ Mental Development and Learning, Tianjin 300387, China
| | - Shaofeng Yang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students’ Mental Development and Learning, Tianjin 300387, China
| | - He Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin 300192, China
| | - Qiang Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students’ Mental Development and Learning, Tianjin 300387, China
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9
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Jung WH, Kim E. Different topological patterns in structural covariance networks between high and low delay discounters. Front Psychol 2023; 14:1210652. [PMID: 37711326 PMCID: PMC10498536 DOI: 10.3389/fpsyg.2023.1210652] [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: 04/23/2023] [Accepted: 08/18/2023] [Indexed: 09/16/2023] Open
Abstract
Introduction People prefer immediate over future rewards because they discount the latter's value (a phenomenon termed "delay discounting," used as an index of impulsivity). However, little is known about how the preferences are implemented in brain in terms of the coordinated pattern of large-scale structural brain networks. Methods To examine this question, we classified high discounting group (HDG) and low discounting group (LDG) in young adults by assessing their propensity for intertemporal choice. We compared global and regional topological properties in gray matter volume-based structural covariance networks between two groups using graph theoretical analysis. Results HDG had less clustering coefficient and characteristic path length over the wide sparsity range than LDG, indicating low network segregation and high integration. In addition, the degree of small-worldness was more significant in HDG. Locally, HDG showed less betweenness centrality (BC) in the parahippocampal gyrus and amygdala than LDG. Discussion These findings suggest the involvement of structural covariance network topology on impulsive choice, measured by delay discounting, and extend our understanding of how impulsive choice is associated with brain morphological features.
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Affiliation(s)
- Wi Hoon Jung
- Department of Psychology, Gachon University, Seongnam, Republic of Korea
| | - Euitae Kim
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, Republic of Korea
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10
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Mehta K, Pines A, Adebimpe A, Larsen B, Bassett DS, Calkins ME, Baller EB, Gell M, Patrick LM, Shafiei G, Gur RE, Gur RC, Roalf DR, Romer D, Wolf DH, Kable JW, Satterthwaite TD. Individual differences in delay discounting are associated with dorsal prefrontal cortex connectivity in children, adolescents, and adults. Dev Cogn Neurosci 2023; 62:101265. [PMID: 37327696 PMCID: PMC10285090 DOI: 10.1016/j.dcn.2023.101265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/24/2023] [Accepted: 06/11/2023] [Indexed: 06/18/2023] Open
Abstract
Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including obesity and academic achievement. However, resting-state functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of children, adolescents, and adults. A total of 293 participants (9-23 years) completed a delay discounting task and underwent 3T resting-state fMRI. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a default mode network hub. Greater delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other default mode network regions, but reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest delay discounting in children, adolescents, and adults is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.
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Affiliation(s)
- Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA 19104, USA; Department of Electrical & Systems Engineering, University of Pennsylvania, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA; Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Monica E Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Erica B Baller
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Martin Gell
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany; Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany
| | - Lauren M Patrick
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Golia Shafiei
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel Romer
- Annenberg Public Policy Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph W Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA.
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11
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Wang P, Zhang H, Deng K, Chen S, Im H, Zhu W, Yang S, Wei S, Wang H, Wang Q. Neurobiological substrates of the dread of future losses. Cereb Cortex 2023; 33:5323-5335. [PMID: 36320161 DOI: 10.1093/cercor/bhac420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 05/03/2023] Open
Abstract
When anticipating future losses, people respond by exhibiting 1 of 2 starkly distinct behavioral decision patterns: the dread of future losses (DFL) and the preference of future losses (vs. immediate losses). Yet, how to accurately discriminate between those who exhibit dread vs. preference and uncover the potential neurobiological substrates underlying these 2 groups remain understudied. To address this, we designed a novel experimental task in which the DFL group was defined as selecting immediate-loss options >50% in the trials with approximate subjective value in immediate and delayed options (n = 16), otherwise coding as the preference of future losses (PFL). At the behavioral level, DFL exhibited higher weight for delayed losses than immediate losses via the logistic regression model. At the neural level, DFL manifested hypoactivations on subjective valuations of delayed losses, atypical brain pattern when choosing immediate-loss options, and decreased functional coupling between the valuation and choice-systems when making decisions related to immediate-loss alternatives compared with PFL. Moreover, both these brain activations subserving distinct decision processes and their interactions predicted individual decisions and behavioral preferences. Furthermore, morphological analysis also revealed decreased right precuneus volume in DFL compared with PFL, and brain activations related to valuation and choice process mediated the associations between this region volume and behavioral performances. Taken together, these findings help to clarify potential cognitive and neural mechanisms underlying the DFL and provide a clear discrimination strategy.
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Affiliation(s)
- Pinchun Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Han Zhang
- School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Kun Deng
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Shuning Chen
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Hohjin Im
- Department of Psychological Science, University of California Irvine, Irvine, CA 92697-7085, United States
| | - Wenwei Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Shaofeng Yang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin Normal University, Tianjin 300387, China
| | - Shiyu Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - He Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Science & Peking Union Medical College, Tianjin 300192, China
| | - Qiang Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin Normal University, Tianjin 300387, China
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12
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Wei S, Jin W, Zhu W, Chen S, Feng J, Wang P, Im H, Deng K, Zhang B, Zhang M, Yang S, Peng M, Wang Q. Greed personality trait links to negative psychopathology and underlying neural substrates. Soc Cogn Affect Neurosci 2023; 18:6646951. [PMID: 35856605 PMCID: PMC10036871 DOI: 10.1093/scan/nsac046] [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: 04/20/2022] [Revised: 06/20/2022] [Accepted: 07/20/2022] [Indexed: 11/14/2022] Open
Abstract
Greed personality trait (GPT), characterized by the desire to acquire more and the dissatisfaction of never having enough, has been hypothesized to link with negative emotion/affect characteristics and aggressive behaviors. To describe its emotion-related features, we utilized a series of scales to measure corresponding emotion/affect and aggression (n = 411) and collected their neuroimaging data (n = 330) to explore underlying morphological substrates. Correlational analyses revealed that greedy individuals show more negative symptoms (e.g. depression, loss of interest, negative affect), lower psychological well-being and more aggression. Mediation analyses further demonstrated that negative symptoms and psychological well-being mediated greedy individuals' aggression. Moreover, exploratory factor analysis extracted factor scores across three factors (negative psychopathology, happiness, and motivation) from the measures scales. Negative psychopathology and happiness remained robust mediators. Importantly, these findings were replicated in an independent sample (n = 68). Voxel-based morphometry analysis also revealed that gray matter volumes (GMVs) in the prefrontal-parietal-occipital system were associated with negative psychopathology and happiness, and GMVs in the frontal pole and middle frontal cortex mediated the relationships between GPT and aggressions. These findings provide novel insights into the negative characteristics of dispositional greed, and suggest their mediating roles on greedy individuals' aggression and underlying neuroanatomical substrates.
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Affiliation(s)
- Shiyu Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Weipeng Jin
- Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin 300060, China
| | - Wenwei Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Shuning Chen
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Jie Feng
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Pinchun Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Hohjin Im
- Department of Psychological Science, University of California, Irvine 92697-7085 CA, USA
| | - Kun Deng
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Bin Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
| | - Manman Zhang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin Normal University, Tianjin 300387, China
| | - Shaofeng Yang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin Normal University, Tianjin 300387, China
| | - Maomiao Peng
- Department of Psychology, University of Arizona, Tucson 85721 AZ, USA
| | - Qiang Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin 300387, China
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin 300387, China
- Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin Normal University, Tianjin 300387, China
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13
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Mehta K, Pines A, Adebimpe A, Larsen B, Bassett DS, Calkins ME, Baller E, Gell M, Patrick LM, Gur RE, Gur RC, Roalf DR, Romer D, Wolf DH, Kable JW, Satterthwaite TD. Individual Differences in Delay Discounting are Associated with Dorsal Prefrontal Cortex Connectivity in Youth. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.01.25.525577. [PMID: 36747838 PMCID: PMC9900814 DOI: 10.1101/2023.01.25.525577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Delay discounting is a measure of impulsive choice relevant in adolescence as it predicts many real-life outcomes, including substance use disorders, obesity, and academic achievement. However, the functional networks underlying individual differences in delay discounting during youth remain incompletely described. Here we investigate the association between multivariate patterns of functional connectivity and individual differences in impulsive choice in a large sample of youth. A total of 293 youth (9-23 years) completed a delay discounting task and underwent resting-state fMRI at 3T. A connectome-wide analysis using multivariate distance-based matrix regression was used to examine whole-brain relationships between delay discounting and functional connectivity was then performed. These analyses revealed that individual differences in delay discounting were associated with patterns of connectivity emanating from the left dorsal prefrontal cortex, a hub of the default mode network. Delay discounting was associated with greater functional connectivity between the dorsal prefrontal cortex and other parts of the default mode network, and reduced connectivity with regions in the dorsal and ventral attention networks. These results suggest that delay discounting in youth is associated with individual differences in relationships both within the default mode network and between the default mode and networks involved in attentional and cognitive control.
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Affiliation(s)
- Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Adam Pines
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Azeez Adebimpe
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Dani S. Bassett
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, PA 19104, USA,Department of Electrical & Systems Engineering, University of Pennsylvania, PA 19104, USA,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA,Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, 19104, USA,Santa Fe Institute, Santa Fe, NM, 87051, USA
| | - Monica E. Calkins
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Erica Baller
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Martin Gell
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH Aachen University, Aachen, Germany,Institute of Neuroscience and Medicine (INM-7: Brain & Behaviour), Research Centre Jülich, Jülich, Germany
| | - Lauren M. Patrick
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Ruben C. Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - David R. Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Daniel Romer
- Annenberg Public Policy Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel H. Wolf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Joseph W. Kable
- Department of Psychology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA,Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, USA,Penn/CHOP Lifespan Brain Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
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14
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Ye Y, Wang Y. Multivariate analysis differentiates intertemporal choices in both value and cognitive control network. Front Neurosci 2023; 17:1037294. [PMID: 36925738 PMCID: PMC10011120 DOI: 10.3389/fnins.2023.1037294] [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: 09/05/2022] [Accepted: 02/14/2023] [Indexed: 03/06/2023] Open
Abstract
Choices between immediate smaller reward and long-term larger reward are referred to as intertemporal choice. Numerous functional magnetic resonance imaging (fMRI) studies have investigated the neural substrates of intertemporal choice via conventional univariate analytical approaches, revealing dissociable activations of decisions involving immediately available rewards and decisions involving delayed rewards in value network. With the help of multivariate analyses, which is more sensitive for evaluating information encoded in spatially distributed patterns, we showed that fMRI activity patterns represent viable signatures of intertemporal choice, as well as individual differences while controlling for age. Notably, in addition to value network, regions from cognitive control network play prominent roles in differentiating between different intertemporal choices as well as individuals with distinct discount rates. These findings provide clear evidence that substantiates the important role of value and cognitive control networks in the neural representation of one's intertemporal decisions.
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Affiliation(s)
- Yuting Ye
- Institute of Psychology, School of Public Affairs, Xiamen University, Xiamen, China
| | - Yanqing Wang
- Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
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15
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The medial temporal lobe structure and function support positive affect. Neuropsychologia 2022; 176:108373. [PMID: 36167193 DOI: 10.1016/j.neuropsychologia.2022.108373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 09/20/2022] [Accepted: 09/21/2022] [Indexed: 11/24/2022]
Abstract
Positive affect (PA) is not only associated with individuals' psychological and physical health, but also their cognitive processes. However, whether medial temporal lobe (MTL) and its subfields' volume/functional connectivity can explain individual variability in PA remains understudied. We investigated the morphological (i.e., grey matter volume; GMV) and functional characteristics (i.e., resting-state functional connectivity; rsFC) of PA with a combination of univariate and multivariate pattern analyses (MVPA) using a large sample of participants (n = 321). We simultaneously collected the T1-weighted (n = 321), high-resolution MTL T2-weighted, and resting-state functional imaging data (n = 209). The MTL and its subfields' volumes, including the CA1, CA2+3, DG, and subiculum (SUB), perirhinal cortex (PRC), and parahippocampus (PHC), were extracted using an automatic segmentation of hippocampal subfields (ASHS) software. The morphological results revealed that GMVs in the prefrontal-occipital and limbic (i.e., hippocampus, amygdala, and PHC) systems were associated with variability in PA at the whole-brain level using MVPA but not univariate analysis. Linear regression results further revealed a positive association between the MTL subfields' GMV, especially for the right PRC, and PA after controlling for several covariates. PRC-seed-based rsFC analyses further revealed that its couplings with the fronto-parietal-occipital system predicted PA in both univariate and MVPA. These findings provide novel insights into the neuroanatomical and functional substrates underlying human PA trait. Findings also suggest critical contributions of the MTL and its subfield of the perirhinal cortex, but not hippocampal subfields, as well as its functional coupling with the fronto-parietal control-system on the formation of PA.
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16
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Calabrese JR, Goetschius LG, Murray L, Kaplan MR, Lopez-Duran N, Mitchell C, Hyde LW, Monk CS. Mapping frontostriatal white matter tracts and their association with reward-related ventral striatum activation in adolescence. Brain Res 2022; 1780:147803. [PMID: 35090884 DOI: 10.1016/j.brainres.2022.147803] [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: 07/01/2021] [Revised: 01/17/2022] [Accepted: 01/21/2022] [Indexed: 11/21/2022]
Abstract
The ventral striatum (VS) is implicated in reward processing and motivation. Human and non-human primate studies demonstrate that the VS and prefrontal cortex (PFC), which comprise the frontostriatal circuit, interact to influence motivated behavior. However, there is a lack of research that precisely maps and quantifies VS-PFC white matter tracts. Moreover, no studies have linked frontostriatal white matter to VS activation. Using a multimodal neuroimaging approach with diffusion MRI (dMRI) and functional MRI (fMRI), the present study had two objectives: 1) to chart white matter tracts between the VS and specific PFC structures and 2) assess the association between the degree of VS-PFC white matter tract connectivity and VS activation in 187 adolescents. White matter connectivity was assessed with probabilistic tractography and functional activation was examined with two fMRI tasks (one task with social reward and another task using monetary reward). We found widespread but variable white matter connectivity between the VS and areas of the PFC, with the anterior insula and subgenual cingulate cortex demonstrating the greatest degree of connectivity with the VS. VS-PFC structural connectivity was related to functional activation in the VS though activation depended on the specific PFC region and reward task.
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Affiliation(s)
| | | | - Laura Murray
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Megan R Kaplan
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | | | - Colter Mitchell
- Institute for Social Research, University of Michigan, Ann Arbor, MI, USA; Survey Research Center of the Institute for Social Research, University of Michigan, Ann Arbor, MI, USA; Population Studies Center of the Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Luke W Hyde
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Survey Research Center of the Institute for Social Research, University of Michigan, Ann Arbor, MI, USA
| | - Christopher S Monk
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA; Survey Research Center of the Institute for Social Research, University of Michigan, Ann Arbor, MI, USA; Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA; Department of Psychiatry, University of Michigan, Ann Arbor, MI, USA.
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17
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Wang P, Feng J, Wang Y, Zhu W, Wei S, Im H, Wang Q. Sex-specific static and dynamic functional networks of sub-divisions of striatum linking to the greed personality trait. Neuropsychologia 2021; 163:108066. [PMID: 34678357 DOI: 10.1016/j.neuropsychologia.2021.108066] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 10/15/2021] [Accepted: 10/16/2021] [Indexed: 11/30/2022]
Abstract
The study of greed has been broadly investigated and discussed in the field of social sciences, including economics, political science, and psychology. However, the neural mechanisms underlying greed personality trait (GPT) have received little attention from the cognitive neuroscience field and still remain unclear. In this study, we explored the associations between GPT and static/dynamic reward circuit-specifically its sub-regions' functional networks including caudate, nucleus accumbens (NAcc), and putamen. Behavioral analyses revealed significant associations of GPT with Past-Negative and Present-Fatalistic time attitude as well as attention impulsivity. Imaging analyses revealed a significant interaction effect between sex and GPT on the static reward functional networks. In particular, GPT was positively correlated with static caudate-NAcc, caudate-cerebellum, and NAcc-parahippocampus/medial orbitofrontal cortex (PHG/mOFC) for males but negatively correlated for females. GPT was also marginally and negatively correlated with static putamen-occipital pole functional connectivities among males. Interestingly, sex difference interaction patterns were further observed in the dynamic reward functional networks. Further, dynamic reward functional networks also exhibited some specific characteristics, manifesting in more brain regions involved for greedy behaviors. These findings suggest sex-specific static and dynamic functional networks underlying human dispositional greed, and also implicate the critical contributions of reward circuit, especially for sub-circuits of reward, on greed.
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Affiliation(s)
- Pinchun Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Jie Feng
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Yajie Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Wenwei Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Shiyu Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China
| | - Hohjin Im
- Department of Psychological Science, University of California, Irvine, 92697-7085, CA, USA.
| | - Qiang Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, 300387, China; Key Research Base of Humanities and Social of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, 300387, China; Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, 300387, China.
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18
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Wang Q, Wang Y, Wang P, Peng M, Zhang M, Zhu Y, Wei S, Chen C, Chen X, Luo S, Bai X. Neural representations of the amount and the delay time of reward in intertemporal decision making. Hum Brain Mapp 2021; 42:3450-3469. [PMID: 33934449 PMCID: PMC8249888 DOI: 10.1002/hbm.25445] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/28/2021] [Accepted: 03/30/2021] [Indexed: 11/08/2022] Open
Abstract
Numerous studies have examined the neural substrates of intertemporal decision-making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task-based functional brain networks whose topological properties are related to impulsivity.
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Affiliation(s)
- Qiang Wang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
| | - Yajie Wang
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Pinchun Wang
- Faculty of Education, Tianjin Normal University, Tianjin, China
| | - Maomiao Peng
- Department of Psychology, University of Arizona, Tucson, Arizona, USA
| | - Manman Zhang
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
| | - Yuxuan Zhu
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Shiyu Wei
- Faculty of Psychology, Tianjin Normal University, Tianjin, China
| | - Chuansheng Chen
- Department of Psychological Science, University of California, Irvine, California, USA
| | - Xiongying Chen
- The National Clinical Research Center for Mental Disorders & Beijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Shan Luo
- Department of Internal Medicine, Division of Endocrinology, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA.,Department of Psychology, University of Southern California, Los Angeles, California, USA
| | - Xuejun Bai
- Key Research Base of Humanities and Social Sciences of the Ministry of Education, Academy of Psychology and Behavior, Tianjin Normal University, Tianjin, China.,Faculty of Psychology, Tianjin Normal University, Tianjin, China.,Tianjin Social Science Laboratory of Students' Mental Development and Learning, Tianjin, China
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