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Tang Y, Hu Y, Zhuang J, Feng C, Zhou X. Uncovering individual variations in bystander intervention of injustice through intrinsic brain connectivity patterns. Neuroimage 2024; 285:120468. [PMID: 38042393 DOI: 10.1016/j.neuroimage.2023.120468] [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: 08/03/2023] [Revised: 11/01/2023] [Accepted: 11/20/2023] [Indexed: 12/04/2023] Open
Abstract
When confronted with injustice, individuals often intervene as third parties to restore justice by either punishing the perpetrator or helping the victim, even at their own expense. However, little is known about how individual differences in third-party intervention propensity are related to inter-individual variability in intrinsic brain connectivity patterns and how these associations vary between help and punishment intervention. To address these questions, we employed a novel behavioral paradigm in combination with resting-state fMRI and inter-subject representational similarity analysis (IS-RSA). Participants acted as third-party bystanders and needed to decide whether to maintain the status quo or intervene by either helping the disadvantaged recipient (Help condition) or punishing the proposer (Punish condition) at a specific cost. Our analyses focused on three brain networks proposed in the third-party punishment (TPP) model: the salience (e.g., dorsal anterior cingulate cortex, dACC), central executive (e.g., dorsolateral prefrontal cortex, dlPFC), and default mode (e.g., dorsomedial prefrontal cortex, dmPFC; temporoparietal junction, TPJ) networks. IS-RSA showed that individual differences in resting-state functional connectivity (rs-FC) patterns within these networks were associated with the general third-party intervention propensity. Moreover, rs-FC patterns of the right dlPFC and right TPJ were more strongly associated with individual differences in the helping propensity rather than the punishment propensity, whereas the opposite pattern was observed for the dmPFC. Post-hoc predictive modeling confirmed the predictive power of rs-FC in these regions for intervention propensity across individuals. Collectively, these findings shed light on the shared and distinct roles of key regions in TPP brain networks at rest in accounting for individual variations in justice-restoring intervention behaviors.
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Affiliation(s)
- Yancheng Tang
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China
| | - Yang Hu
- School of Psychology and Cognitive Science, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, East China Normal University, Shanghai, China.
| | - Jie Zhuang
- School of Psychology, Shanghai University of Sport, Shanghai, China
| | - Chunliang Feng
- School of Psychology, Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Center for Studies of Psychological Application, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Xiaolin Zhou
- Key Laboratory of Brain-Machine Intelligence for Information Behavior (Ministry of Education and Shanghai), School of Business and Management, Shanghai International Studies University, Shanghai, China; School of Psychology and Cognitive Science, Shanghai Key Laboratory of Mental Health and Psychological Crisis Intervention, East China Normal University, Shanghai, China.
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2
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Feng C, Tian X, Luo YJ. Neurocomputational Substrates Underlying the Effect of Identifiability on Third-Party Punishment. J Neurosci 2023; 43:8018-8031. [PMID: 37752000 PMCID: PMC10669760 DOI: 10.1523/jneurosci.0460-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 09/08/2023] [Accepted: 09/19/2023] [Indexed: 09/28/2023] Open
Abstract
The identifiable target effect refers to the preference for helping identified victims and punishing identifiable perpetrators compared with equivalent but unidentifiable counterparts. The identifiable target effect is often attributed to the heightened moral emotions evoked by identified targets. However, the specific neurocognitive processes that mediate and/or modulate this effect remain largely unknown. Here, we combined a third-party punishment game with brain imaging and computational modeling to unravel the neurocomputational underpinnings of the identifiable transgressor effect. Human participants (males and females) acted as bystanders and punished identified or anonymous wrongdoers. Participants were more punitive toward identified wrongdoers than anonymous wrongdoers because they took a vicarious perspective of victims and adopted lower reference points of inequity (i.e., more stringent norms) in the identified context than in the unidentified context. Accordingly, there were larger activity of the ventral anterior insula, more distinct multivariate neural patterns in the dorsal anterior insula and dorsal anterior cingulate cortex, and lower strength between ventral anterior insula and dorsolateral PFC and between dorsal anterior insula and ventral striatum connectivity in response to identified transgressors than anonymous transgressors. These findings implicate the interplay of expectancy violations, emotions, and self-interest in the identifiability effect. Last, individual differences in the identifiability effect were associated with empathic concern/social dominance orientation, activity in the precuneus/cuneus and temporo-parietal junction, and intrinsic functional connectivity of the dorsolateral PFC. Together, our work is the first to uncover the neurocomputational processes mediating identifiable transgressor effect and to characterize psychophysiological profiles modulating the effect.SIGNIFICANCE STATEMENT The identifiable target effect, more help to identified victims or stronger punishment to identifiable perpetrators, is common in daily life. We examined the neurocomputational mechanisms mediating/modulating the identifiability effect on third-party punishment by bridging literature from economics and cognitive neuroscience. Our findings reveal that identifiable transgressor effect is mediated by lower reference points of inequity (i.e., more stringent norms), which might be associated with a stronger involvement of the emotion processes and a weaker engagement of the analytic/deliberate processes. Furthermore, personality traits, altered brain activity, and intrinsic functional connectivity contribute to the individual variance in the identifiability effect. Overall, our study advances the understanding of the identifiability effect by shedding light on its component processes and modulating factors.
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Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences, South China Normal University, Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Xia Tian
- Key Laboratory of Brain, Cognition and Education Sciences, South China Normal University, Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China
| | - Yue-Jia Luo
- The State Key Lab of Cognitive and Learning, Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
- Institute for Neuropsychological Rehabilitation, University of Health and Rehabilitation Sciences, Qingdao, 266113, China
- School of Psychology, Chengdu Medical College, Chengdu, 610500, China
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3
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Chen X, Dong D, Zhou F, Gao X, Liu Y, Wang J, Qin J, Tian Y, Xiao M, Xu X, Li W, Qiu J, Feng T, He Q, Lei X, Chen H. Connectome-based prediction of eating disorder-associated symptomatology. Psychol Med 2023; 53:5786-5799. [PMID: 36177890 DOI: 10.1017/s0033291722003026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND Despite increasing knowledge on the neuroimaging patterns of eating disorder (ED) symptoms in non-clinical populations, studies using whole-brain machine learning to identify connectome-based neuromarkers of ED symptomatology are absent. This study examined the association of connectivity within and between large-scale functional networks with specific symptomatic behaviors and cognitions using connectome-based predictive modeling (CPM). METHODS CPM with ten-fold cross-validation was carried out to probe functional networks that were predictive of ED-associated symptomatology, including body image concerns, binge eating, and compensatory behaviors, within the discovery sample of 660 participants. The predictive ability of the identified networks was validated using an independent sample of 821 participants. RESULTS The connectivity predictive of body image concerns was identified within and between networks implicated in cognitive control (frontoparietal and medial frontal), reward sensitivity (subcortical), and visual perception (visual). Crucially, the set of connections in the positive network related to body image concerns identified in one sample was generalized to predict body image concerns in an independent sample, suggesting the replicability of this effect. CONCLUSIONS These findings point to the feasibility of using the functional connectome to predict ED symptomatology in the general population and provide the first evidence that functional interplay among distributed networks predicts body shape/weight concerns.
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Affiliation(s)
- Ximei Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Feng Zhou
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Junjie Wang
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jingmin Qin
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Yun Tian
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Xiaofei Xu
- School of Computing Technologies, RMIT University, Melbourne, Australia
| | - Wei Li
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, Chongqing, China
| | - Xu Lei
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China
- Research Center of Psychology and Social Development, Faculty of Psychology, Southwest University, Chongqing, China
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Peng SJ, Chen YW, Hung A, Wang KW, Tsai JZ. Connectome-based predictive modeling for functional recovery of acute ischemic stroke. Neuroimage Clin 2023; 38:103369. [PMID: 36917922 PMCID: PMC10011051 DOI: 10.1016/j.nicl.2023.103369] [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: 07/19/2022] [Revised: 02/08/2023] [Accepted: 03/03/2023] [Indexed: 03/09/2023]
Abstract
Patients of acute ischemic stroke possess considerable chance of recovery of various levels in the first several weeks after stroke onset. Prognosis of functional recovery is important for decision-making in poststroke patient care and placement. Poststroke functional recovery has conventionally been based on demographic and clinical variables such as age, gender, and severity of stroke impairment. On the other hand, the concept of connectome has become a basis of interpreting the functional impairment and recovery of stroke patients. In this research, the connectome-based predictive modeling was used to provide predictive models for prognosing poststroke functional recovery. Predictive models were developed to use the brain connectivity at stroke onset to predict functional assessment scores at one or three months later, or to use the brain connectivity one-month poststroke to predict functional assessment scores at three months after stroke onset. The brain connectivity was computed from the resting-state fMRI signals. The functional assessment scores used in this research included modified Rankin Scale (mRS) and Barthel Index (BI). This research found significant models that used the brain connectivity at onset to predict the mRS one-month poststroke and to predict the BI three-month poststroke for patients with supratentorial infarction, as well as predictive models that used the brain connectivity one-month poststroke to predict the mRS three-month poststroke for patients with supratentorial infarction in the right hemisphere. The connectome-based predictive modeling could provide clinical value in prognosis of acute ischemic stroke.
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Affiliation(s)
- Syu-Jyun Peng
- Professional Master Program in Artificial Intelligence in Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Yu-Wei Chen
- Department of Neurology, Landseed International Hospital, Taoyuan, Taiwan; Department of Neurology, National Taiwan University Hospital, Taipei, Taiwan.
| | - Andrew Hung
- Department of Electrical Engineering, National Central University, Taoyuan, Taiwan
| | - Kuo-Wei Wang
- Department of General Affairs, Landseed International Hospital, Taoyuan, Taiwan
| | - Jang-Zern Tsai
- Department of Electrical Engineering, National Central University, Taoyuan, Taiwan.
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Anderson AJ, Perone S. Predicting individual differences in behavioral activation and behavioral inhibition from functional networks in the resting EEG. Biol Psychol 2023; 177:108483. [PMID: 36587892 DOI: 10.1016/j.biopsycho.2022.108483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 12/09/2022] [Accepted: 12/28/2022] [Indexed: 12/31/2022]
Abstract
The behavioral activation system (BAS) and behavioral inhibition system (BIS) are thought to underly affective dispositions and self-regulatory processes. The BAS is sensitive to reward and involved in approach behaviors, and the BIS is sensitive to punishment and involved in avoidance behaviors. Trait BAS and BIS relate to distinct behavioral profiles and neural activity, but little is known about how trait BAS and BIS relate to functional networks in EEG. We applied a data-driven method called connectome predictive modeling (CPM) to identify networks relating to trait BAS and BIS and tested whether the strength of those networks predicted trait BAS and BIS in novel subjects using a leave-one-out cross-validation procedure. Adult participants (N = 107) completed a resting state task with eyes closed and eyes open, and trait BAS and BIS were measured via Carver and White's (1994) BIS and BAS scales. We hypothesized distinct positive (more synchronization) and negative (less synchronization) networks would relate to trait BAS and BIS. For eyes closed, we identified two negative networks, one in theta and one in alpha predicted BIS. We identified three positive networks, one in theta and one in beta predicted Fun Seeking and one in theta predicted Drive. For eyes open, negative theta and alpha networks predicted BIS, a positive theta network predicted Fun Seeking, and a negative gamma network predicted mean BAS. Visualization of the networks are presented. Discussion centers on the observed networks and how to advance application of CPM to EEG, including with clinical implications.
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Affiliation(s)
- Alana J Anderson
- Department of Human Development, Washington State University, USA.
| | - Sammy Perone
- Department of Human Development, Washington State University, USA
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Li M, Guo F, Wang X, Chen J, Ham J. Effects of robot gaze and voice human-likeness on users’ subjective perception, visual attention, and cerebral activity in voice conversations. COMPUTERS IN HUMAN BEHAVIOR 2022. [DOI: 10.1016/j.chb.2022.107645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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Zhang Y, Zhai Y, Zhou X, Zhang Z, Gu R, Luo Y, Feng C. Loss context enhances preferences for generosity but reduces preferences for honesty: Evidence from a combined behavioural‐computational approach. EUROPEAN JOURNAL OF SOCIAL PSYCHOLOGY 2022. [DOI: 10.1002/ejsp.2896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Affiliation(s)
- Yijie Zhang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University) Ministry of Education Guangzhou China
- School of Psychology South China Normal University Guangzhou China
- Center for Studies of Psychological Application South China Normal University Guangzhou China
- Guangdong Key Laboratory of Mental Health and Cognitive Science South China Normal University Guangzhou China
| | - Yuzhu Zhai
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University) Ministry of Education Guangzhou China
- School of Psychology South China Normal University Guangzhou China
- Center for Studies of Psychological Application South China Normal University Guangzhou China
- Guangdong Key Laboratory of Mental Health and Cognitive Science South China Normal University Guangzhou China
| | - Xingmei Zhou
- Center of Brain Disorder and Cognitive Sciences College of Psychology and Sociology Shenzhen Key Laboratory of Affective and Social Cognitive Science Shenzhen University Center for Emotion and Brain Shenzhen Institute of Neuroscience Shenzhen China
| | - Zhixin Zhang
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University) Ministry of Education Guangzhou China
- School of Psychology South China Normal University Guangzhou China
- Center for Studies of Psychological Application South China Normal University Guangzhou China
- Guangdong Key Laboratory of Mental Health and Cognitive Science South China Normal University Guangzhou China
| | - Ruolei Gu
- Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Department of Psychology University of Chinese Academy of Sciences Beijing China
| | - Yue‐jia Luo
- The State Key Lab of Cognitive and Learning Faculty of Psychology Beijing Normal University Beijing China
- The Research Center of Brain Science and Visual Cognition Kunming University of Science and Technology Kunming China
- College of Teacher Education Qilu Normal University Jinan China
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University) Ministry of Education Guangzhou China
- School of Psychology South China Normal University Guangzhou China
- Center for Studies of Psychological Application South China Normal University Guangzhou China
- Guangdong Key Laboratory of Mental Health and Cognitive Science South China Normal University Guangzhou China
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Huang J, Choo S, Pugh ZH, Nam CS. Evaluating Effective Connectivity of Trust in Human-Automation Interaction: A Dynamic Causal Modeling (DCM) Study. HUMAN FACTORS 2022; 64:1051-1069. [PMID: 33657902 DOI: 10.1177/0018720820987443] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
OBJECTIVE Using dynamic causal modeling (DCM), we examined how credibility and reliability affected the way brain regions exert causal influence over each other-effective connectivity (EC)-in the context of trust in automation. BACKGROUND Multiple brain regions of the central executive network (CEN) and default mode network (DMN) have been implicated in trust judgment. However, the neural correlates of trust judgment are still relatively unexplored in terms of the directed information flow between brain regions. METHOD Sixteen participants observed the performance of four computer algorithms, which differed in credibility and reliability, of the system monitoring subtask of the Air Force Multi-Attribute Task Battery (AF-MATB). Using six brain regions of the CEN and DMN commonly identified to be activated in human trust, a total of 30 (forward, backward, and lateral) connection models were developed. Bayesian model averaging (BMA) was used to quantify the connectivity strength among the brain regions. RESULTS Relative to the high trust condition, low trust showed unique presence of specific connections, greater connectivity strengths from the prefrontal cortex, and greater network complexity. High trust condition showed no backward connections. CONCLUSION Results indicated that trust and distrust can be two distinctive neural processes in human-automation interaction-distrust being a more complex network than trust, possibly due to the increased cognitive load. APPLICATION The causal architecture of distributed brain regions inferred using DCM can help not only in the design of a balanced human-automation interface design but also in the proper use of automation in real-life situations.
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Affiliation(s)
- Jiali Huang
- 6798 North Carolina State University, Raleigh, USA
| | | | | | - Chang S Nam
- 6798 North Carolina State University, Raleigh, USA
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Li T, Pei Z, Zhu Z, Wu X, Feng C. Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity. Hum Brain Mapp 2022; 43:5616-5629. [PMID: 36054523 PMCID: PMC9704792 DOI: 10.1002/hbm.26038] [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: 03/09/2022] [Revised: 06/06/2022] [Accepted: 07/25/2022] [Indexed: 01/15/2023] Open
Abstract
Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.
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Affiliation(s)
- Ting Li
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University)Ministry of EducationGuangzhouChina,School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhouChina,Institute of Brain and Psychological SciencesSichuan Normal UniversityChengduChina
| | - Zhaodi Pei
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Zhiyuan Zhu
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Xia Wu
- School of Artificial IntelligenceBeijing Normal UniversityBeijingChina,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of EducationBeijing Normal UniversityBeijingChina
| | - Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University)Ministry of EducationGuangzhouChina,School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive ScienceSouth China Normal UniversityGuangzhouChina
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Kim E, Kim S, Kim Y, Cha H, Lee HJ, Lee T, Chang Y. Connectome-based predictive models using resting-state fMRI for studying brain aging. Exp Brain Res 2022; 240:2389-2400. [PMID: 35922524 DOI: 10.1007/s00221-022-06430-7] [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: 02/23/2022] [Accepted: 07/26/2022] [Indexed: 11/25/2022]
Abstract
Changes in the brain with age can provide useful information regarding an individual's chronological age. studies have suggested that functional connectomes identified via resting-state functional magnetic resonance imaging (fMRI) could be a powerful feature for predicting an individual's age. We applied connectome-based predictive modeling (CPM) to investigate individual chronological age predictions via resting-state fMRI using open-source datasets. The significant feature for age prediction was confirmed in 168 subjects from the Southwest University Adult Lifespan Dataset. The higher contributing nodes for age production included a positive connection from the left inferior parietal sulcus and a negative connection from the right middle temporal sulcus. On the network scale, the subcortical-cerebellum network was the dominant network for age prediction. The generalizability of CPM, which was constructed using the identified features, was verified by applying this model to independent datasets that were randomly selected from the Autism Brain Imaging Data Exchange I and the Open Access Series of Imaging Studies 3. CPM via resting-state fMRI is a potential robust predictor for determining an individual's chronological age from changes in the brain.
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Affiliation(s)
- Eunji Kim
- Department of Korea Radioisotope Center for Pharmaceuticals, Korea Institute of Radiological and Medical Sciences, Seoul, Korea
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Seungho Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Yunheung Kim
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hyunsil Cha
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea
| | - Hui Joong Lee
- Department of Radiology, Kyungpook National University School of Medicine, Daegu, Korea
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea
| | - Taekwan Lee
- Korea Brain Research Institute, Chumdanro 61, Dong-gu, Daegu, 41021, Republic of Korea.
| | - Yongmin Chang
- Department of Medical and Biological Engineering, Kyungpook National University, Daegu, Korea.
- Department of Radiology, Kyungpook National University Hospital, Daegu, Korea.
- The Department of Molecular Medicine and Radiology, Kyungpook National University School of Medicine, 200 Dongduk-Ro Jung-Gu, Daegu, Korea.
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Ogihara T, Tanioka K, Hiroyasu T, Hiwa S. Predicting the Degree of Distracted Driving Based on fNIRS Functional Connectivity: A Pilot Study. FRONTIERS IN NEUROERGONOMICS 2022; 3:864938. [PMID: 38235448 PMCID: PMC10790849 DOI: 10.3389/fnrgo.2022.864938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 06/21/2022] [Indexed: 01/19/2024]
Abstract
Distracted driving is one of the main causes of traffic accidents. By predicting the attentional state of drivers, it is possible to prevent distractions and promote safe driving. In this study, we developed a model that could predict the degree of distracted driving based on brain activity. Changes in oxyhemoglobin concentrations were measured in drivers while driving a real car using functional near-infrared spectroscopy (fNIRS). A regression model was constructed for each participant using functional connectivity as an explanatory variable and brake reaction time to random beeps while driving as an objective variable. As a result, we were able to construct a prediction model with the mean absolute error of 5.58 × 102 ms for the BRT of the 12 participants. Furthermore, the regression model with the highest prediction accuracy for each participant was analyzed to gain a better understanding of the neural basis of distracted driving. The 11 of 12 models that showed significant accuracy were classified into five clusters by hierarchical clustering based on their functional connectivity edges used in each cluster. The results showed that the combinations of the dorsal attention network (DAN)-sensory-motor network (SMN) and DAN-ventral attention network (VAN) connections were common in all clusters and that these networks were essential to predict the degree of distraction in complex multitask driving. They also confirmed the existence of multiple types of prediction models with different within- and between-network connectivity patterns. These results indicate that it is possible to predict the degree of distracted driving based on the driver's brain activity during actual driving. These results are expected to contribute to the development of safe driving systems and elucidate the neural basis of distracted driving.
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Affiliation(s)
- Takahiko Ogihara
- Graduate School of Life and Medical Sciences, Doshisha University, Kyoto, Japan
| | - Kensuke Tanioka
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Tomoyuki Hiroyasu
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
| | - Satoru Hiwa
- Department of Biomedical Sciences and Informatics, Doshisha University, Kyoto, Japan
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Li T, Yang Y, Krueger F, Feng C, Wang J. Static and Dynamic Topological Organizations of the Costly Punishment Network Predict Individual Differences in Punishment Propensity. Cereb Cortex 2021; 32:4012-4024. [PMID: 34905766 DOI: 10.1093/cercor/bhab462] [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: 09/15/2021] [Revised: 11/12/2021] [Accepted: 11/13/2021] [Indexed: 12/17/2022] Open
Abstract
Human costly punishment plays a vital role in maintaining social norms. Recently, a brain network model is conceptually proposed indicating that the implement of costly punishment depends on a subset of nodes in three high-level networks. This model, however, has not yet been empirically examined from an integrated perspective of large-scale brain networks. Here, we conducted comprehensive graph-based network analyses of resting-state functional magnetic resonance imaging data to explore system-level characteristics of intrinsic functional connectivity among 18 regions related to costly punishment. Nontrivial organizations (small-worldness, connector hubs, and high flexibility) were found that were qualitatively stable across participants and over time but quantitatively exhibited low test-retest reliability. The organizations were predictive of individual costly punishment propensities, which was reproducible on independent samples and robust against different analytical strategies and parameter settings. Moreover, the prediction was specific to system-level network organizations (rather than interregional functional connectivity) derived from positive (rather than negative or combined) connections among the specific (rather than randomly chosen) subset of regions from the three high-order (rather than primary) networks. Collectively, these findings suggest that human costly punishment emerges from integrative behaviors among specific regions in certain functional networks, lending support to the brain network model for costly punishment.
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Affiliation(s)
- Ting Li
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Yuping Yang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, 22030 VA, USA.,Department of Psychology, George Mason University, Fairfax, 22030 VA, USA
| | - Chunliang Feng
- School of Psychology, South China Normal University, 510631 Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, 510631 Guangzhou, China.,Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, 510631 Guangzhou, China.,Center for Studies of Psychological Application, South China Normal University, 510631 Guangzhou, China.,Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, 510631 Guangzhou, China
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13
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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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14
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Intrinsic functional connectivity of the frontoparietal network predicts inter-individual differences in the propensity for costly third-party punishment. COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE 2021; 21:1222-1232. [PMID: 34331267 DOI: 10.3758/s13415-021-00927-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 06/14/2021] [Indexed: 11/08/2022]
Abstract
Humans are motivated to give norm violators their just deserts through costly punishment even as unaffected third parties (i.e., third-party punishment, TPP). A great deal of individual variability exists in costly punishment; however, how this variability particularly in TPP is represented by the brain's intrinsic network architecture remains elusive. Here, we examined whether inter-individual differences in the propensity for TPP can be predicted based on resting-state functional connectivity (RSFC) combining an economic TPP game with task-free functional neuroimaging and a multivariate prediction framework. Our behavioral results revealed that TPP punishment increased with the severity of unfairness for offers. People with higher TPP propensity punished more harshly across norm-violating scenarios. Our neuroimaging findings showed RSFC within the frontoparietal network predicted individual differences in TPP propensity. Our findings contribute to understanding the neural fingerprint for an individual's propensity to costly punish strangers, and shed some light on how social norm enforcement behaviors are represented by the brain's intrinsic network architecture.
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15
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Feng C, Eickhoff SB, Li T, Wang L, Becker B, Camilleri JA, Hétu S, Luo Y. Common brain networks underlying human social interactions: Evidence from large-scale neuroimaging meta-analysis. Neurosci Biobehav Rev 2021; 126:289-303. [PMID: 33781834 DOI: 10.1016/j.neubiorev.2021.03.025] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Revised: 03/17/2021] [Accepted: 03/19/2021] [Indexed: 01/26/2023]
Abstract
Recent overarching frameworks propose that various human social interactions are commonly supported by a set of fundamental neuropsychological processes, including social cognition, motivation, and cognitive control. However, it remains unclear whether brain networks implicated in these functional constructs are consistently engaged in diverse social interactions. Based on ample evidence from human brain imaging studies (342 contrasts, 7234 participants, 3328 foci), we quantitatively synthesized brain areas involved in broad domains of social interactions, including social interactions versus non-social contexts, positive/negative aspects of social interactions, social learning, and social norms. We then conducted brain network analysis on the ensuing brain regions and characterized the psychological function profiles of identified brain networks. Our findings revealed that brain regions consistently involved in diverse social interactions mapped onto default mode network, salience network, subcortical network and central executive network, which were respectively implicated in social cognition, motivation and cognitive control. These findings implicate a heuristic integrative framework to understand human social life from the perspective of component process and network integration.
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Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China.
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Ting Li
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China; School of Psychology, Institute of Brain Research and Rehabilitation (IBRR), Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Li Wang
- Collaborative Innovation Center of Assessment Toward Basic Education Quality, Beijing Normal University, Beijing, China
| | - Benjamin Becker
- The Clinical Hospital of the Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - Julia A Camilleri
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Sébastien Hétu
- Department of Psychology, Université de Montréal, Montreal, QC, Canada
| | - Yi Luo
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA.
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16
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Wang F, Yin Y, Yang Y, Liang T, Huang T, He C, Hu J, Zhang J, Yang Y, Xing Q, Zhang T, Liu H. Connectome-based prediction of brain age in Rolandic epilepsy: a protocol for a multicenter cross-sectional study. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:511. [PMID: 33850908 PMCID: PMC8039653 DOI: 10.21037/atm-21-574] [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: 11/17/2022]
Abstract
BACKGROUND Rolandic epilepsy (RE) is a common pediatric idiopathic partial epilepsy syndrome. Children with RE display varying degrees of cognitive impairment. In epilepsy, age-related neuroanatomic and cognitive changes differ greatly from those observed in the healthy brain, and may be defined as accelerated brain aging. Connectome-based predictive modeling (CPM) is a recently developed machine learning approach that uses whole-brain connectivity measured with neuroimaging data ("neural fingerprints") to predict brain-behavior relationships. The aim of the study will be to develop and validate a CPM for predicting brain age in patients with RE. METHODS A multicenter, cross-sectional study will be conducted in 5 Chinese hospitals. A total of 100 RE patients (including 50 patients receiving anti-epileptic drugs and 50 drug-naïve patients) and 100 healthy children will be recruited to undergo a neuropsychological test using the Wechsler Intelligence Scale. Magnetic resonance images will also be collected. CPM will be applied to predict the brain age of children with RE based on brain functional connectivity. DISCUSSION The findings of the study will facilitate our understanding of developmental changes in the brain in children with RE and could also be an important milestone in the journey toward developing effective early interventions for this disorder. TRIAL REGISTRATION The study has been registered with Chinese Clinical Trial Registry (ChiCTR2000032984).
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Affiliation(s)
- Fuqin Wang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yu Yin
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yang Yang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Ting Liang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Tingting Huang
- Department of Radiology, the First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
| | - Cheng He
- Department of Radiology, Chongqing University Central Hospital, Chongqing, China
| | - Jie Hu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Jingjing Zhang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Yanli Yang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Qianlu Xing
- Department of Pediatrics, the Second Affiliated Hospital of Zunyi Medical University, Zunyi, China
| | - Tijiang Zhang
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
| | - Heng Liu
- Department of Radiology, the Affiliated Hospital of Zunyi Medical University, Medical Imaging Center of Guizhou Province, Zunyi, China
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Wang Z, Goerlich KS, Ai H, Aleman A, Luo YJ, Xu P. Connectome-Based Predictive Modeling of Individual Anxiety. Cereb Cortex 2021; 31:3006-3020. [DOI: 10.1093/cercor/bhaa407] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 12/05/2020] [Accepted: 12/09/2020] [Indexed: 12/16/2022] Open
Abstract
Abstract
Anxiety-related illnesses are highly prevalent in human society. Being able to identify neurobiological markers signaling high trait anxiety could aid the assessment of individuals with high risk for mental illness. Here, we applied connectome-based predictive modeling (CPM) to whole-brain resting-state functional connectivity (rsFC) data to predict the degree of trait anxiety in 76 healthy participants. Using a computational “lesion” approach in CPM, we then examined the weights of the identified main brain areas as well as their connectivity. Results showed that the CPM successfully predicted individual anxiety based on whole-brain rsFC, especially the rsFC between limbic areas and prefrontal cortex. The prediction power of the model significantly decreased from simulated lesions of limbic areas, lesions of the connectivity within limbic areas, and lesions of the connectivity between limbic areas and prefrontal cortex. Importantly, this neural model generalized to an independent large sample (n = 501). These findings highlight important roles of the limbic system and prefrontal cortex in anxiety prediction. Our work provides evidence for the usefulness of connectome-based modeling in predicting individual personality differences and indicates its potential for identifying personality factors at risk for psychopathology.
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Affiliation(s)
- Zhihao Wang
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of Groningen, Groningen 9713 AW , the Netherlands
| | - Katharina S Goerlich
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of Groningen, Groningen 9713 AW , the Netherlands
| | - Hui Ai
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
| | - André Aleman
- Department of Biomedical Sciences of Cells & Systems, Section Cognitive Neuroscience, University Medical Center Groningen, University of Groningen, Groningen 9713 AW , the Netherlands
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
| | - Yue-jia Luo
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Shenzhen Key Laboratory of Affective and Social Neuroscience, Magnetic Resonance Imaging Center, Center for Brain Disorders and Cognitive Sciences, Shenzhen University, Shenzhen 518060, China
- Department of Psychology, Southern Medical University, Guangzhou 510515, China
- College of Teacher Education, Qilu Normal University, Jining 250200, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen 518106, China
| | - Pengfei Xu
- Beijing Key Laboratory of Applied Experimental Psychology, Faculty of Psychology, Beijing Normal University, Beijing 100875, China
- Center for Neuroimaging, Shenzhen Institute of Neuroscience, Shenzhen 518106, China
- Guangdong-Hong Kong-Macao Greater Bay Area Research Institute for Neuroscience and Neurotechnologies, Kwun Tong, Hong Kong, China
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18
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Liu P, Yang W, Zhuang K, Wei D, Yu R, Huang X, Qiu J. The functional connectome predicts feeling of stress on regular days and during the COVID-19 pandemic. Neurobiol Stress 2020; 14:100285. [PMID: 33385021 PMCID: PMC7772572 DOI: 10.1016/j.ynstr.2020.100285] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2020] [Revised: 11/26/2020] [Accepted: 12/12/2020] [Indexed: 12/05/2022] Open
Abstract
Although many studies have explored the neural mechanism of the feeling of stress, to date, no effort has been made to establish a model capable of predicting the feeling of stress at the individual level using the resting-state functional connectome. Although individuals may be confronted with multidimensional stressors during the coronavirus disease 2019 (COVID-19) pandemic, their appraisal of the impact and severity of these events might vary. In this study, connectome-based predictive modeling (CPM) with leave-one-out cross-validation was conducted to predict individual perceived stress (PS) from whole-brain functional connectivity data from 817 participants. The results showed that the feeling of stress could be predicted by the interaction between the default model network and salience network, which are involved in emotion regulation and salience attribution, respectively. Key nodes that contributed to the prediction model comprised regions mainly located in the limbic systems and temporal lobe. Critically, the CPM model of PS based on regular days can be generalized to predict individual PS levels during the COVID-19 pandemic, which is a multidimensional, uncontrollable stressful situation. The stability of the results was demonstrated by two independent datasets. The present work not only expands existing knowledge regarding the neural mechanism of PS but also may help identify high-risk individuals in healthy populations. Perceived stress (PS) can be predicated by resting-state functional connectome. PS can be predicated by interaction between default model and salience network. Key nodes of the prediction model located in limbic systems and temporal lobe. psCPM of regular days generalized to predict PS level in the COVID-19 pandemic.
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Affiliation(s)
- Peiduo Liu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
- Research Center for Psychology and Social Development, Southwest University, Chongqing, 400715, China
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
- Corresponding author. Faculty of Psychology, Southwest University, No.2 TianSheng Road, Beibei District, Chongqing, 400715, China.
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Rongjun Yu
- Department of Psychology, National University of Singapore, Singapore
| | - Xiting Huang
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing, 400715, China
- Faculty of Psychology, Southwest University (SWU), Chongqing, 400715, China
- Corresponding author. Faculty of Psychology, Southwest University, No.2 TianSheng Road, Beibei District, Chongqing, 400715, China.
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19
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Sui J, Jiang R, Bustillo J, Calhoun V. Neuroimaging-based Individualized Prediction of Cognition and Behavior for Mental Disorders and Health: Methods and Promises. Biol Psychiatry 2020; 88:818-828. [PMID: 32336400 PMCID: PMC7483317 DOI: 10.1016/j.biopsych.2020.02.016] [Citation(s) in RCA: 140] [Impact Index Per Article: 35.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2019] [Revised: 02/13/2020] [Accepted: 02/17/2020] [Indexed: 01/08/2023]
Abstract
The neuroimaging community has witnessed a paradigm shift in biomarker discovery from using traditional univariate brain mapping approaches to multivariate predictive models, allowing the field to move toward a translational neuroscience era. Regression-based multivariate models (hereafter "predictive modeling") provide a powerful and widely used approach to predict human behavior with neuroimaging features. These studies maintain a focus on decoding individual differences in a continuously behavioral phenotype from neuroimaging data, opening up an exciting opportunity to describe the human brain at the single-subject level. In this survey, we provide an overview of recent studies that utilize machine learning approaches to identify neuroimaging predictors over the past decade. We first review regression-based approaches and highlight connectome-based predictive modeling, which has grown in popularity in recent years. Next, we systematically describe recent representative studies using these tools in the context of cognitive function, symptom severity, personality traits, and emotion processing. Finally, we highlight a few challenges related to combining multimodal data, longitudinal prediction, external validations, and the employment of deep learning methods that have emerged from our review of the existing literature, as well as present some promising and challenging future directions.
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Affiliation(s)
- Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China; Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
| | - Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, New Mexico
| | - Vince Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science, Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, Georgia.
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20
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Feng C, Zhu Z, Cui Z, Ushakov V, Dreher JC, Luo W, Gu R, Wu X, Krueger F. Prediction of trust propensity from intrinsic brain morphology and functional connectome. Hum Brain Mapp 2020; 42:175-191. [PMID: 33001541 PMCID: PMC7721234 DOI: 10.1002/hbm.25215] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2020] [Revised: 08/31/2020] [Accepted: 09/09/2020] [Indexed: 01/08/2023] Open
Abstract
Trust forms the basis of virtually all interpersonal relationships. Although significant individual differences characterize trust, the driving neuropsychological signatures behind its heterogeneity remain obscure. Here, we applied a prediction framework in two independent samples of healthy participants to examine the relationship between trust propensity and multimodal brain measures. Our multivariate prediction analyses revealed that trust propensity was predicted by gray matter volume and node strength across multiple regions. The gray matter volume of identified regions further enabled the classification of individuals from an independent sample with the propensity to trust or distrust. Our modular and functional decoding analyses showed that the contributing regions were part of three large‐scale networks implicated in calculus‐based trust strategy, cost–benefit calculation, and trustworthiness inference. These findings do not only deepen our neuropsychological understanding of individual differences in trust propensity, but also provide potential biomarkers in predicting trust impairment in neuropsychiatric disorders.
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Affiliation(s)
- Chunliang Feng
- Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education, Guangzhou, China.,School of Psychology, Center for Studies of Psychological Application, and Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, China
| | - Zhiyuan Zhu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of Education, Beijing Normal University, Beijing, China
| | - Zaixu Cui
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Vadim Ushakov
- National Research Center, Kurchatov Institute, Moscow, Russia.,National Research Nuclear University MEPhI, Moscow Engineering Physics Institute, Moscow, Russia
| | - Jean-Claude Dreher
- Neuroeconomics, Reward and Decision Making Laboratory, Institut des Sciences Cognitives Marc Jeannerod, CNRS, Bron, France
| | - Wenbo Luo
- Research Center of Brain and Cognitive Neuroscience, Liaoning Normal University, Dalian, China
| | - Ruolei Gu
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Xia Wu
- School of Artificial Intelligence, Beijing Normal University, Beijing, China.,Engineering Research Center of Intelligent Technology and Educational Application of Ministry of Education, Beijing Normal University, Beijing, China
| | - Frank Krueger
- School of Systems Biology, George Mason University, Fairfax, Virginia, USA.,Department of Psychology, George Mason University, Fairfax, Virginia, USA
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21
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Lu X, Li T, Xia Z, Zhu R, Wang L, Luo Y, Feng C, Krueger F. Connectome-based model predicts individual differences in propensity to trust. Hum Brain Mapp 2019; 40:1942-1954. [PMID: 30633429 PMCID: PMC6865671 DOI: 10.1002/hbm.24503] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2018] [Revised: 11/15/2018] [Accepted: 12/02/2018] [Indexed: 12/12/2022] Open
Abstract
Trust constitutes a fundamental basis of human society and plays a pivotal role in almost every aspect of human relationships. Although enormous interest exists in determining the neuropsychological underpinnings of a person's propensity to trust utilizing task-based fMRI; however, little progress has been made in predicting its variations by task-free fMRI based on whole-brain resting-state functional connectivity (RSFC). Here, we combined a one-shot trust game with a connectome-based predictive modeling approach to predict propensity to trust from whole-brain RSFC. We demonstrated that individual variations in the propensity to trust were primarily predicted by RSFC rooted in the functional integration of distributed key nodes-caudate, amygdala, lateral prefrontal cortex, temporal-parietal junction, and the temporal pole-which are part of domain-general large-scale networks essential for the motivational, affective, and cognitive aspects of trust. We showed, further, that the identified brain-behavior associations were only evident for trust but not altruistic preferences and that propensity to trust (and its underlying neural underpinnings) were modulated according to the extent to which a person emphasizes general social preferences (i.e., horizontal collectivism) rather than general risk preferences (i.e., trait impulsiveness). In conclusion, the employed data-driven approach enables to predict propensity to trust from RSFC and highlights its potential use as an objective neuromarker of trust impairment in mental disorders.
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Affiliation(s)
- Xiaping Lu
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- Brain, Mind & Markets Laboratory, Department of FinanceThe University of MelbourneMelbourneVictoriaAustralia
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Ting Li
- Collaborative Innovation Center of Assessment toward Basic Education QualityBeijing Normal UniversityBeijingChina
| | - Zhichao Xia
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Ruida Zhu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
| | - Li Wang
- Collaborative Innovation Center of Assessment toward Basic Education QualityBeijing Normal UniversityBeijingChina
| | - Yue‐Jia Luo
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- Center for Emotion and BrainShenzhen Institute of NeuroscienceShenzhenChina
- Medical SchoolKunming University of Science and TechnologyKunmingChina
| | - Chunliang Feng
- Center for Brain Disorders and Cognitive SciencesShenzhen UniveristyShenzhenChina
- College of Information Science and TechnologyBeijing Normal UniversityBeijingChina
| | - Frank Krueger
- School of Systems BiologyGeorge Mason UniversityFairfaxVirginia
- Department of PsychologyUniversity of MannheimMannheimGermany
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