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Chen Y, Xu J, Wu J, Chen H, Kang Y, Yang Y, Gong Z, Huang Y, Wang H, Wang B, Zhan S, Tan W. Aberrant concordance among dynamics of spontaneous brain activity in patients with migraine without aura: A multivariate pattern analysis study. Heliyon 2024; 10:e30008. [PMID: 38737279 PMCID: PMC11088259 DOI: 10.1016/j.heliyon.2024.e30008] [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: 10/05/2023] [Revised: 04/08/2024] [Accepted: 04/18/2024] [Indexed: 05/14/2024] Open
Abstract
Background Alterations in the static and dynamic characteristics of spontaneous brain activity have been extensively studied to investigate functional brain changes in migraine without aura (MwoA). However, alterations in concordance among the dynamics of spontaneous brain activity in MwoA remain largely unknown. This study aimed to determine the possibilities of diagnosis based on the concordance indices. Methods Resting-state functional MRI scans were performed on 32 patients with MwoA and 33 matched healthy controls (HCs) in the first cohort, as well as 36 patients with MwoA and 32 HCs in the validation cohort. The dynamic indices including fractional amplitude of low-frequency fluctuation, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity were analyzed. We calculated the concordance of grey matter volume-wise (across voxels) and voxel-wise (across time windows) to quantify the degree of integration among different functional levels represented by these dynamic indices. Subsequently, the voxel-wise concordance alterations were analyzed as features for multi-voxel pattern analysis (MVPA) utilizing the support vector machine. Results Compared with that of HCs, patients with MwoA had lower whole-grey matter volume-wise concordance, and the mean value of volume-wise concordance was negatively correlated with the frequency of migraine attacks. The MVPA results revealed that the most discriminative brain regions were the right thalamus, right cerebellar Crus II, left insula, left precentral gyrus, right cuneus, and left inferior occipital gyrus. Conclusions Concordance alterations in the dynamics of spontaneous brain activity in brain regions could be an important feature in the identification of patients with MwoA.
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Affiliation(s)
- Yilei Chen
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jun Xu
- Pharmacy Department, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jiazhen Wu
- Department of Radiology, Shanghai Municipal Hospital of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hui Chen
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yingjie Kang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yuchan Yang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Zhigang Gong
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Yanwen Huang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Hui Wang
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Bo Wang
- Department of Acupuncture and Moxibustion, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Songhua Zhan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wenli Tan
- Department of Radiology, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
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Shi Y, Wang Z, Chen P, Cheng P, Zhao K, Zhang H, Shu H, Gu L, Gao L, Wang Q, Zhang H, Xie C, Liu Y, Zhang Z. Episodic Memory-Related Imaging Features as Valuable Biomarkers for the Diagnosis of Alzheimer's Disease: A Multicenter Study Based on Machine Learning. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2023; 8:171-180. [PMID: 33712376 DOI: 10.1016/j.bpsc.2020.12.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 12/16/2020] [Accepted: 12/16/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Individualized and reliable biomarkers are crucial for diagnosing Alzheimer's disease (AD). However, lack of accessibility and neurobiological correlation are the main obstacles to their clinical application. Machine learning algorithms can effectively identify personalized biomarkers based on the prominent symptoms of AD. METHODS Episodic memory-related magnetic resonance imaging (MRI) features of 143 patients with amnesic mild cognitive impairment (MCI) were identified using a multivariate relevance vector regression algorithm. The support vector machine classification model was constructed using these MRI features and verified in 2 independent datasets (N = 994). The neurobiological basis was also investigated based on cognitive assessments, neuropathologic biomarkers of cerebrospinal fluid, and positron emission tomography images of amyloid-β plaques. RESULTS The combination of gray matter volume and amplitude of low-frequency fluctuation MRI features accurately predicted episodic memory impairment in individual patients with amnesic MCI (r = 0.638) when measured using an episodic memory assessment panel. The MRI features that contributed to episodic memory prediction were primarily distributed across the default mode network and limbic network. The classification model based on these features distinguished patients with AD from normal control subjects with more than 86% accuracy. Furthermore, most identified episodic memory-related regions showed significantly different amyloid-β positron emission tomography measurements among the AD, MCI, and normal control groups. Moreover, the classification outputs significantly correlated with cognitive assessment scores and cerebrospinal fluid pathological biomarkers' levels in the MCI and AD groups. CONCLUSIONS Neuroimaging features can reflect individual episodic memory function and serve as potential diagnostic biomarkers of AD.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Zan Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Pindong Chen
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Piaoyue Cheng
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Kun Zhao
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Hongxing Zhang
- Department of Psychology, Xinxiang Medical University, Xinxiang, China; Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Hao Shu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Lihua Gu
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Lijuan Gao
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Qing Wang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Haisan Zhang
- Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China
| | - Yong Liu
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, China; School of Life Science and Technology, The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, China; Department of Psychology, Xinxiang Medical University, Xinxiang, China; Second Affiliated Hospital of Xinxiang Medical University, Xinxiang, China.
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Zhang D, Yu L, Chen Y, Shen J, Du L, Lin L, Wu J. Connectome-based predictive modeling predicts paranoid ideation in young men with paranoid personality disorder: a resting-state functional magnetic resonance imaging study. Cereb Cortex 2023:6992943. [PMID: 36657794 DOI: 10.1093/cercor/bhac531] [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: 10/31/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 01/21/2023] Open
Abstract
Paranoid personality disorder (PPD), a mental disorder that affects interpersonal relationships and work, is frequently neglected during diagnosis and evaluation at the individual-level. This preliminary study aimed to investigate whether connectome-based predictive modeling (CPM) can predict paranoia scores of young men with PPD using whole-brain resting-state functional connectivity (rs-FC). College students with paranoid tendencies were screened using paranoia scores ≥60 derived from the Minnesota Multiphasic Personality Inventory; 18 participants were ultimately diagnosed with PPD according to the Diagnostic and Statistical Manual of Mental Disorders and subsequently underwent resting-state functional magnetic resonance imaging. Whole-brain rs-FC was constructed, and the ability of this rs-FC to predict paranoia scores was evaluated using CPM. The significance of the models was assessed using permutation tests. The model constructed based on the negative prediction network involving the limbic system-temporal lobe was observed to have significant predictive ability for paranoia scores, whereas the model constructed using the positive and combined prediction network had no significant predictive ability. In conclusion, using CPM, whole-brain rs-FC predicted the paranoia score of patients with PPD. The limbic system-temporal lobe FC pattern is expected to become an important neurological marker for evaluating paranoid ideation.
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Affiliation(s)
- Die Zhang
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China.,Department of Radiology, Shenzhen Third People's Hospital, Shenzhen 518000, China
| | - Lan Yu
- Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou 211166,China
| | - Yingying Chen
- Department of Radiology, National Cancer Center, National Clinical Research Center for Cancer, Cancer Hospital & Shenzhen Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Shenzhen 518172, China
| | - Jing Shen
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lina Du
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Lin Lin
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
| | - Jianlin Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, 6 Jiefang Street, Dalian 116001, China
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Fan L, Zhuang K, Wang X, Zhang J, Liu C, Gu J, Qiu J. Exploring the behavioral and neural correlates of semantic distance in creative writing. Psychophysiology 2022; 60:e14239. [PMID: 36537015 DOI: 10.1111/psyp.14239] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 10/26/2022] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Creativity is critical to economic growth and societal progress. However, assessing creativity using objective approaches remains a challenge. To address this, we employ three objective indicators based on semantic distance to quantify the originality and appropriateness of creativity by analyzing long texts in a story-writing experiment. Global and local distances were generated separately by computing the mean distance of the whole text and the distance between adjacent sentences, and they were positively correlated with story originality in writing. Global cohesion was positively correlated with story rationality in writing, as generated by computing the semantic coherence between the text and story context. At the behavioral level, three semantic indicators were used to measure originality and appropriateness of creativity and reflected individual differences, including creative achievement and creative personality. At the neural level, global distance was best predicted by the features of the salience and default networks, whereas global cohesion corresponded to the control and salience networks. These findings point to a stable neural basis for semantic indicators and verify the idea of separating different dimensions of creativity. Taken together, our results demonstrate the significance of semantic indicators in assessing creativity and provide insights into analyzing long texts in natural paradigm.
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Affiliation(s)
- Li Fan
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Kaixiang Zhuang
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Xueyang Wang
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jingyi Zhang
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Cheng Liu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jing Gu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- Faculty of Psychology Southwest University Chongqing China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU) Ministry of Education Chongqing China
- 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
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Shi Y, Mao H, Gao Q, Xi G, Zeng S, Ma L, Zhang X, Li L, Wang Z, Ji W, He P, You Y, Chen K, Shao J, Mao X, Fang X, Wang F. Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients. Front Aging Neurosci 2022; 14:973054. [PMID: 36118707 PMCID: PMC9475066 DOI: 10.3389/fnagi.2022.973054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 08/16/2022] [Indexed: 12/04/2022] Open
Abstract
Background Reliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD. Materials and methods A prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers. Results The gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients. Conclusion Brain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- *Correspondence: Yachen Shi,
| | - Haixia Mao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Qianqian Gao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Guangjun Xi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Siyuan Zeng
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Lin Ma
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xiuping Zhang
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Lei Li
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Zhuoyi Wang
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Wei Ji
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Ping He
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Yiping You
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Kefei Chen
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Junfei Shao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xuqiang Mao
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xiangming Fang
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Xiangming Fang,
| | - Feng Wang
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Feng Wang,
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Scherer KR, Costa M, Ricci-Bitti P, Ryser VA. Appraisal Bias and Emotion Dispositions Are Risk Factors for Depression and Generalized Anxiety: Empirical Evidence. Front Psychol 2022; 13:857419. [PMID: 35859849 PMCID: PMC9289678 DOI: 10.3389/fpsyg.2022.857419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 05/27/2022] [Indexed: 11/13/2022] Open
Abstract
Appraisal theory of emotion predicts that appraisal biases may generate stable emotion dispositions, which can ultimately lead to affective disorders. One example is the habitual underestimation of one’s potential to cope with adverse events, which favors frequent experiences of sadness and worry and therefore increases the risk for development of depression and generalized anxiety disorders. To examine the relationships between these variables as potential risk factors, in Study 1, we used appraisal and emotion questions in the Swiss Household Panel (SHP), a nationwide representative sample, and analyzed data for N = 4,859 participants in one annual survey wave (Wave 14, SHP 2012) via theory-based hierarchical regressions. Path analysis of the nomological network linking frequent experiences of depression and anxiety to the emotion dispositions of sadness and worry, and measures of perceived coping potential (appraisal bias) supports the theoretical predictions and further identifies the effects of important background variables such as personality, motivation, and life events. Discriminant analysis shows that these predictors allow correct classification of close to 70% of the participants with elevated risk. In Study 2, we used established validated instruments to assess the risk for depression and anxiety disorders, as well as a recently validated scenario method to assess appraisal bias and emotion disposition in a survey with N = 152 students. The results correspond to the theoretical predictions and largely confirm the findings with the household survey. The results of both studies demonstrate the utility of using current emotion theory to provide new vistas for research on risk factors for affective disorders and to inform the development of appropriate interventions to reduce the level of risk.
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Affiliation(s)
- Klaus R. Scherer
- Department of Psychology and Swiss Center for Affective Sciences, University of Geneva, Geneva, Switzerland
- *Correspondence: Klaus R. Scherer, , orcid.org/0000-0001-9526-0144
| | - Marco Costa
- Department of Psychology, University of Bologna, Bologna, Italy
| | - Pio Ricci-Bitti
- Department of Psychology, University of Bologna, Bologna, Italy
| | - Valérie-Anne Ryser
- FORS – The Swiss Centre of Expertise in the Social Sciences, University of Lausanne, Lausanne, Switzerland
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Jiang R, Woo CW, Qi S, Wu J, Sui J. Interpreting Brain Biomarkers: Challenges and solutions in interpreting machine learning-based predictive neuroimaging. IEEE SIGNAL PROCESSING MAGAZINE 2022; 39:107-118. [PMID: 36712588 PMCID: PMC9880880 DOI: 10.1109/msp.2022.3155951] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Predictive modeling of neuroimaging data (predictive neuroimaging) for evaluating individual differences in various behavioral phenotypes and clinical outcomes is of growing interest. However, the field is experiencing challenges regarding the interpretability of the results. Approaches to defining the specific contribution of functional connections, regions, or networks in prediction models are urgently needed, which may help explore the underlying mechanisms. In this article, we systematically review the methods and applications for interpreting brain signatures derived from predictive neuroimaging based on a survey of 326 research articles. Strengths, limitations, and the suitable conditions for major interpretation strategies are also deliberated. In-depth discussion of common issues in existing literature and the corresponding recommendations to address these pitfalls are provided. We highly recommend exhaustive validation on the reliability and interpretability of the biomarkers across multiple datasets and contexts, which thereby could translate technical advances in neuroimaging into concrete improvements in precision medicine.
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Affiliation(s)
- Rongtao Jiang
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA, 06520
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, South Korea, 16419
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea, 16419
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Suwon, Korea, 16419
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China, 211106
| | - Jing Wu
- Department of Medical Oncology, Beijing You-An Hospital, Capital Medical University, Beijing, China, 100069
| | - Jing Sui
- State Key Laboratory of Brain Cognition and Learning, Beijing Normal University, Beijing, China, 100875
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Co-occurrence of schizo-obsessive traits and its correlation with altered executive control network functional connectivity. Eur Arch Psychiatry Clin Neurosci 2022; 272:301-312. [PMID: 33389057 DOI: 10.1007/s00406-020-01222-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Accepted: 12/12/2020] [Indexed: 10/22/2022]
Abstract
The prevalence of obsessive-compulsive symptoms (OCS) in schizophrenia patients is as around 30%. Evidence suggested that mild OCS could reduce symptoms of schizophrenia, supporting the presence of compensatory functions. However, severe OCS could aggravate various impairments in schizophrenia patients, supporting the "double jeopardy hypothesis". Patients with schizo-obsessive comorbidity, schizophrenia patients and obsessive-compulsive disorder patients have been found to have similarities in executive dysfunctions and altered resting-state functional connectivity within the executive control network (ECN). Executive functions could be associated with the ECN. However, little is known as to whether such overlap exists in the subclinical populations of individuals with schizo-obsessive traits (SOT), schizotypal individuals and individuals with high levels of obsessive-compulsive symptoms (OCS). In this study, we recruited 30 schizotypal individuals, 25 individuals with OCS, 29 individuals with SOT and 29 controls for a resting-state ECN-related functional connectivity (rsFC) and a go/shift/no-go task. We found that individuals with SOT exhibited increased rsFC within the ECN compared with controls, while schizotypal individuals exhibited the opposite. Individuals with OCS exhibited decreased rsFC within the ECN and between the ECN and the default mode network (DMN), relative to controls. No significant correlational results between altered rsFC related to the ECN with executive function performance were found after corrections for multiple comparisons in three subclinical groups. Our findings showed that individuals with SOT had increased rsFC within the ECN, while schizotypal individuals and individuals with OCS showed the opposite. Our findings provide evidence for possible neural substrates of subclinical comorbidity of OCS and schizotypy.
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Shi Y, Zhang L, He C, Yin Y, Song R, Chen S, Fan D, Zhou D, Yuan Y, Xie C, Zhang Z. Sleep disturbance-related neuroimaging features as potential biomarkers for the diagnosis of major depressive disorder: A multicenter study based on machine learning. J Affect Disord 2021; 295:148-155. [PMID: 34461370 DOI: 10.1016/j.jad.2021.08.027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 08/05/2021] [Accepted: 08/18/2021] [Indexed: 01/21/2023]
Abstract
BACKGROUND Objective biomarkers are crucial for overcoming the clinical dilemma in major depressive disorder (MDD), and the individualized diagnosis is essential to facilitate the precise medicine for MDD. METHODS Sleep disturbance-related magnetic resonance imaging (MRI) features was identified in the internal dataset (92 MDD patients) using the relevance vector regression algorithm, which was further verified in 460 MDD patients of an independent, multicenter dataset. Subsequently, using these MRI features, the eXtreme Gradient Boosting classification model was constructed in the current multicenter dataset (460 MDD patients and 470 normal controls). Meanwhile, the association between classification outputs and the severity of depressive symptoms was also investigated. RESULTS In MDD patients, the combination of gray matter density and fractional amplitude of low-frequency fluctuation can accurately predict individual sleep disturbance score that was calculated by the sum of item 4 score, item 5 score, and item 6 score of the 17-Item Hamilton Rating Scale for Depression (HAMD-17) (R2 = 0.158 in the internal dataset; R2 = 0.110 in multicenter dataset). Furthermore, the classification model based on these MRI features distinguished MDD patients from normal controls with 86.3% accuracy (area under the curve = 0.937). Importantly, the classification outputs significantly correlated with HAMD-17 scores in MDD patients. LIMITATION Lacking some specialized tools to assess the personal sleep quality, e.g. Pittsburgh Sleep Quality Index. CONCLUSION Neuroimaging features can reflect accurately individual sleep disturbance manifestation and serve as potential diagnostic biomarkers of MDD.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Linhai Zhang
- School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, Jiangsu 211189, China
| | - Cancan He
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Yingying Yin
- Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Ruize Song
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Suzhen Chen
- Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China
| | - Dandan Fan
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China
| | - Deyu Zhou
- School of Computer Science and Engineering, Key Laboratory of Computer Network and Information Integration, Ministry of Education, Southeast University, Nanjing, Jiangsu 211189, China
| | - Yonggui Yuan
- Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China.
| | - Chunming Xie
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China.
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Institution of Neuropsychiatry, Southeast University, Nanjing, Jiangsu 210009, China; The Key Laboratory of Developmental Genes and Human Disease, Southeast University, Nanjing, Jiangsu 210009, China; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; Research Center for Brain Health, Pazhou Lab, Guangzhou, Guangdong 510330, China.
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10
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Multivariate morphological brain signatures enable individualized prediction of dispositional need for closure. Brain Imaging Behav 2021; 16:1049-1064. [PMID: 34724163 PMCID: PMC8558548 DOI: 10.1007/s11682-021-00574-w] [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: 03/22/2021] [Accepted: 09/28/2021] [Indexed: 12/03/2022]
Abstract
Need for closure (NFC) reflects stable individual differences in the desire for a quick, definite, and stable answer to a question. A large body of research has documented the association between NFC and various cognitive, emotional and social processes. Despite considerable interest in psychology, little effort has been made to uncover the neural substrates of individual variations in NFC. Herein, we took a data-driven approach to predict NFC trait combining machine learning framework and the whole-brain grey matter volume (GMV) features, which represent a reliable brain imaging measure and have been commonly employed to explore neural basis underlying individual differences of cognition and behaviors. Brain regions contributing to the prediction were then subjected to functional connectivity and decoding analyses for a quantitative inference on their psychophysiological functions. Our results indicated that multivariate patterns of GMV derived from multiple regions across distributed brain systems predicted NFC at individual level. The contributing regions are distributed across the emotional processing network (e.g., striatum), cognitive control network (e.g., lateral prefrontal cortex), social cognition network (e.g., temporoparietal junction) and perceptual processing network (e.g., occipital cortex). The current study provided the first evidence that dispositional NFC is embodied in multiple large-scale brain networks, helping to delineate a more complete picture about the neuropsychological processes that support individual differences in NFC. Beyond these findings, the current interdisciplinary approach to constructing and interpreting neuroimaging-based prediction model of personality traits would be informative to a wide range of future studies on personality.
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11
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Cortico-striatal-thalamic loop as a neural correlate of neuroticism in the mind-body interface. J Psychosom Res 2021; 149:110590. [PMID: 34385032 DOI: 10.1016/j.jpsychores.2021.110590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 07/26/2021] [Accepted: 07/28/2021] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Although brain structural studies have demonstrated the neural correlates of neuroticism, the outcomes are not easily identified because of the various possible brain regions involved, low statistical power (low number of subjects), and brain structural measures available, such as mean diffusivity (MD), which are more suitable than standard regional measures of grey and white-matter volume (rGMV, rWMV) and fractional anisotropy (FA). We hypothesized that neuroticism neural correlates could be detected by MD and differentially identified using other measures. We aimed to visualize the neural correlates of neuroticism. METHODS A voxel-by-voxel regression analysis was performed using the MD, rGMV, rWMV, or FA value as the dependent variable and with neuroticism scores based on the NEO-FFI and its confounding factors as independent variables in 1207 (693 men and 514 women; age, 20.7 ± 1.8, 18-27 years), non-clinical students in a cross-sectional study. RESULTS MD in the cortico- (orbitofrontal cortex, anterior cingulate cortex, and posterior insula) striatal- (caudate and putamen) thalamic loop regions, including the right posterior limb of the internal capsule, were positively associated with neuroticism using the threshold-free cluster enhancement method with a family-wise error-corrected threshold of P < 0.0125 (0.05/4, Bonferroni correction for four types of MRI data [MD, rGMV, rWMV, and FA]) at the whole-brain level. CONCLUSIONS An increased MD has generally been associated with reduced neural tissues and possibly area function. Accordingly, this finding helps elucidate the mechanism of somatization in neuroticism because the regions related to neuroticism are considered neural correlates of somatoform disorders.
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12
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Wang Z, Yuan Y, Jiang Y, You J, Zhang Z. Identification of specific neural circuit underlying the key cognitive deficit of remitted late-onset depression: A multi-modal MRI and machine learning study. Prog Neuropsychopharmacol Biol Psychiatry 2021; 108:110192. [PMID: 33285264 DOI: 10.1016/j.pnpbp.2020.110192] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2020] [Revised: 11/23/2020] [Accepted: 11/30/2020] [Indexed: 01/21/2023]
Abstract
Neuropsychological impairment is a key feature of late-onset depression (LOD), with deficits observed across multiple cognitive domains. And this neuropsychological impairment can persist even after the remission of depressive symptoms. However, none of previous studies have explored the pattern of cognitive deficit in remitted LOD (rLOD), and investigated the specific neural circuit underlying the key cognitive deficit of LOD. 40 rLOD patients and 36 controls underwent comprehensive neuropsychological assessments and magnetic resonance imaging (MRI) scans. The influence of executive function or information processing speed deficit on other cognitive domains was first investigated. We then applied a multivariate machine learning technique known as relevance vector regression to evaluate the potential of multiple-modal MRI (i.e., integrating whole-brain grey-matter [GM] volume and white-matter [WM] tract features) for making accurate predictions about the key cognitive deficit for individual rLOD patient. We revealed that the information processing speed appears to represent a key cognitive deficit in rLOD. Further the machine learning model identified a wide range of GM regions and WM tracts that significantly contributed to the prediction of individual performance on information processing speed (r = 0.50, P < 0.001). The GM regions mainly located in the frontal-subcortical and limbic systems; and the WM tracts mainly located in the frontal-limbic pathway, including the anterior corona radiata, fornix, posterior cingulate bundle, and uncinate fasciculus. This present study provide strongly evidence supporting the concept of rLOD that the core aspect of the cognitive deficits (i.e., information processing speed) is associated with disruption of the frontal-subcortical-limbic pathway.
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Affiliation(s)
- Zan Wang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
| | - Yonggui Yuan
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Psychosomatics and Psychiatry, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China
| | - Ying Jiang
- Department of Neurology, the 962nd Hospital of the PLA Joint Logistic Support Force, Harbin 150080, China
| | - Jiayong You
- Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing 210029, China
| | - Zhijun Zhang
- School of Medicine, Southeast University, Nanjing 210009, China; Department of Neurology, Affiliated ZhongDa Hospital of Southeast University, Nanjing 210009, China.
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13
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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: 136] [Impact Index Per Article: 34.0] [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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14
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Segato A, Marzullo A, Calimeri F, De Momi E. Artificial intelligence for brain diseases: A systematic review. APL Bioeng 2020; 4:041503. [PMID: 33094213 PMCID: PMC7556883 DOI: 10.1063/5.0011697] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2020] [Accepted: 09/09/2020] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is a major branch of computer science that is fruitfully used for analyzing complex medical data and extracting meaningful relationships in datasets, for several clinical aims. Specifically, in the brain care domain, several innovative approaches have achieved remarkable results and open new perspectives in terms of diagnosis, planning, and outcome prediction. In this work, we present an overview of different artificial intelligent techniques used in the brain care domain, along with a review of important clinical applications. A systematic and careful literature search in major databases such as Pubmed, Scopus, and Web of Science was carried out using "artificial intelligence" and "brain" as main keywords. Further references were integrated by cross-referencing from key articles. 155 studies out of 2696 were identified, which actually made use of AI algorithms for different purposes (diagnosis, surgical treatment, intra-operative assistance, and postoperative assessment). Artificial neural networks have risen to prominent positions among the most widely used analytical tools. Classic machine learning approaches such as support vector machine and random forest are still widely used. Task-specific algorithms are designed for solving specific problems. Brain images are one of the most used data types. AI has the possibility to improve clinicians' decision-making ability in neuroscience applications. However, major issues still need to be addressed for a better practical use of AI in the brain. To this aim, it is important to both gather comprehensive data and build explainable AI algorithms.
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Affiliation(s)
- Alice Segato
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
| | - Aldo Marzullo
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Francesco Calimeri
- Department of Mathematics and Computer Science, University of Calabria, Rende 87036, Italy
| | - Elena De Momi
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan 20133, Italy
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15
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Jiang R, Zuo N, Ford JM, Qi S, Zhi D, Zhuo C, Xu Y, Fu Z, Bustillo J, Turner JA, Calhoun VD, Sui J. Task-induced brain connectivity promotes the detection of individual differences in brain-behavior relationships. Neuroimage 2019; 207:116370. [PMID: 31751666 PMCID: PMC7345498 DOI: 10.1016/j.neuroimage.2019.116370] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2019] [Revised: 11/12/2019] [Accepted: 11/15/2019] [Indexed: 02/05/2023] Open
Abstract
Although both resting and task-induced functional connectivity (FC) have been used to characterize the human brain and cognitive abilities, the potential of task-induced FCs in individualized prediction for out-of-scanner cognitive traits remains largely unexplored. A recent study Greene et al. (2018) predicted the fluid intelligence scores using FCs derived from rest and multiple task conditions, suggesting that task-induced brain state manipulation improved prediction of individual traits. Here, using a large dataset incorporating fMRI data from rest and 7 distinct task conditions, we replicated the original study by employing a different machine learning approach, and applying the method to predict two reading comprehension-related cognitive measures. Consistent with their findings, we found that task-based machine learning models often outperformed rest-based models. We also observed that combining multi-task fMRI improved prediction performance, yet, integrating the more fMRI conditions can not necessarily ensure better predictions. Compared with rest, the predictive FCs derived from language and working memory tasks were highlighted with more predictive power in predominantly default mode and frontoparietal networks. Moreover, prediction models demonstrated high stability to be generalizable across distinct cognitive states. Together, this replication study highlights the benefit of using task-based FCs to reveal brain-behavior relationships, which may confer more predictive power and promote the detection of individual differences of connectivity patterns underlying relevant cognitive traits, providing strong evidence for the validity and robustness of the original findings.
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Affiliation(s)
- Rongtao Jiang
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Nianming Zuo
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Judith M Ford
- Department of Psychiatry, University of California, San Francisco, CA, 94143, USA; San Francisco VA Medical Center, San Francisco, CA, 94143, USA
| | - Shile Qi
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303
| | - Dongmei Zhi
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Chuanjun Zhuo
- Department of Psychiatric-Neuroimaging-Genetics and Morbidity Laboratory (PNGC-Lab), Nankai University Affiliated Anding Hospital, Tianjin Mental Health Center, Tianjin, 300222, China
| | - Yong Xu
- Department of Psychiatry, First Hospital of Shanxi Medical University, Taiyuan, 030001, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303
| | - Juan Bustillo
- Department of Psychiatry, University of New Mexico, Albuquerque, NM, 87131, USA
| | - Jessica A Turner
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303; Department of Psychology and Neuroscience, Georgia State University, Atlanta, GA, 30302, USA
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303.
| | - Jing Sui
- Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA, 30303; Chinese Academy of Sciences Center for Excellence in Brain Science, Institute of Automation, Beijing, China.
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16
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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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