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      The connectome‐based prediction of trust propensity in older adults: A resting‐state functional magnetic resonance imaging study

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          Abstract

          A recent neuropsychoeconomic model of trust propensity argues that an individual uses economic (executive functions) and social (social cognition) rationality strategies to transform the risk of treachery (affect) into positive expectations of reciprocity, promoting trust in another person. Previous studies have shown that the trust of older adults is associated with affect and social cognition. However, little is known about the intrinsic functional connectivity correlated with trust propensity or whether trust propensity is associated with executive functions in older adults. In this study, we examined the association between trust propensity (measured by a one‐shot trust game [TG]), social preference (measured by a one‐shot dictator game), and executive functions (measured by a battery of neuropsychological tests). We also performed connectome‐based predictive modeling (CPM) and computational lesion analysis to identify the key large‐scale resting‐state functional connectivity (RSFC) underlying the prediction of trust propensity. Our behavioral results showed a lower trust propensity in older adults in our study than in younger adults in a previous meta‐analysis. Furthermore, trust propensity was associated with social preference, but there was no significant relationship between trust propensity and executive functions. The neuroimaging results showed that the cingulo‐opercular network (CON) and the default mode network (DMN), rather than the frontoparietal network (FPN), significantly contributed to the prediction of trust propensity in older adults. Our findings suggest that older adults rely less on economic rationality (executive functions, associated with FPN) in trust games. Rather, they are likely to depend more on social rationality (social cognition, associated with social preference and DMN) to resolve the risk of treachery (affect, associated with CON) in trust dilemmas. This study contributes to a better understanding of the neural underpinnings of older adults' trust propensity.

          Abstract

          We investigated the association between trust propensity, social preference, and executive functions in older adults. We used connectome‐based predictive modeling (CPM) and computational lesion analysis to identify the key large‐scale resting‐state functional connectivity underlying the prediction of trust propensity in older adults. Older adults rely less on economic rationality in trust games, and are likely to depend more on social rationality to resolve the risk of treachery in trust dilemmas.

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          Most cited references101

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          Executive Functions

          Executive functions (EFs) make possible mentally playing with ideas; taking the time to think before acting; meeting novel, unanticipated challenges; resisting temptations; and staying focused. Core EFs are inhibition [response inhibition (self-control—resisting temptations and resisting acting impulsively) and interference control (selective attention and cognitive inhibition)], working memory, and cognitive flexibility (including creatively thinking “outside the box,” seeing anything from different perspectives, and quickly and flexibly adapting to changed circumstances). The developmental progression and representative measures of each are discussed. Controversies are addressed (e.g., the relation between EFs and fluid intelligence, self-regulation, executive attention, and effortful control, and the relation between working memory and inhibition and attention). The importance of social, emotional, and physical health for cognitive health is discussed because stress, lack of sleep, loneliness, or lack of exercise each impair EFs. That EFs are trainable and can be improved with practice is addressed, including diverse methods tried thus far.
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            DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging.

            Brain imaging efforts are being increasingly devoted to decode the functioning of the human brain. Among neuroimaging techniques, resting-state fMRI (R-fMRI) is currently expanding exponentially. Beyond the general neuroimaging analysis packages (e.g., SPM, AFNI and FSL), REST and DPARSF were developed to meet the increasing need of user-friendly toolboxes for R-fMRI data processing. To address recently identified methodological challenges of R-fMRI, we introduce the newly developed toolbox, DPABI, which was evolved from REST and DPARSF. DPABI incorporates recent research advances on head motion control and measurement standardization, thus allowing users to evaluate results using stringent control strategies. DPABI also emphasizes test-retest reliability and quality control of data processing. Furthermore, DPABI provides a user-friendly pipeline analysis toolkit for rat/monkey R-fMRI data analysis to reflect the rapid advances in animal imaging. In addition, DPABI includes preprocessing modules for task-based fMRI, voxel-based morphometry analysis, statistical analysis and results viewing. DPABI is designed to make data analysis require fewer manual operations, be less time-consuming, have a lower skill requirement, a smaller risk of inadvertent mistakes, and be more comparable across studies. We anticipate this open-source toolbox will assist novices and expert users alike and continue to support advancing R-fMRI methodology and its application to clinical translational studies.
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              A component based noise correction method (CompCor) for BOLD and perfusion based fMRI.

              A component based method (CompCor) for the reduction of noise in both blood oxygenation level-dependent (BOLD) and perfusion-based functional magnetic resonance imaging (fMRI) data is presented. In the proposed method, significant principal components are derived from noise regions-of-interest (ROI) in which the time series data are unlikely to be modulated by neural activity. These components are then included as nuisance parameters within general linear models for BOLD and perfusion-based fMRI time series data. Two approaches for the determination of the noise ROI are considered. The first method uses high-resolution anatomical data to define a region of interest composed primarily of white matter and cerebrospinal fluid, while the second method defines a region based upon the temporal standard deviation of the time series data. With the application of CompCor, the temporal standard deviation of resting-state perfusion and BOLD data in gray matter regions was significantly reduced as compared to either no correction or the application of a previously described retrospective image based correction scheme (RETROICOR). For both functional perfusion and BOLD data, the application of CompCor significantly increased the number of activated voxels as compared to no correction. In addition, for functional BOLD data, there were significantly more activated voxels detected with CompCor as compared to RETROICOR. In comparison to RETROICOR, CompCor has the advantage of not requiring external monitoring of physiological fluctuations.
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                Author and article information

                Contributors
                taowh@szu.edu.cn
                guanqing@szu.edu.cn
                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley & Sons, Inc. (Hoboken, USA )
                1065-9471
                1097-0193
                06 June 2023
                1 August 2023
                : 44
                : 11 ( doiID: 10.1002/hbm.v44.11 )
                : 4337-4351
                Affiliations
                [ 1 ] Center for Brain Disorders and Cognitive Sciences, School of Psychology Shenzhen University Shenzhen China
                [ 2 ] Department of Psychology University of Mannheim Mannheim Germany
                [ 3 ] Shenzhen‐Hong Kong Institute of Brain Science‐Shenzhen Fundamental Research Institutions Shenzhen China
                [ 4 ] Department of Radiology Huazhong University of Science and Technology Union Shenzhen Hospital Shenzhen China
                [ 5 ] School of Systems Biology George Mason University Fairfax Virginia USA
                Author notes
                [*] [* ] Correspondence

                Wuhai Tao and Qing Guan, Center for Brain Disorders and Cognitive Sciences, Shenzhen University Nanhai Avenue 3688, Nanshan District, Shenzhen City, Guangdong Province, Shenzhen, China.

                Email: taowh@ 123456szu.edu.cn and guanqing@ 123456szu.edu.cn

                Author information
                https://orcid.org/0000-0002-1281-3290
                Article
                HBM26385
                10.1002/hbm.26385
                10318203
                37278571
                4d0f1bdb-5d10-44c7-b886-a6f32fdcdbe7
                © 2023 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 14 April 2023
                : 26 December 2022
                : 11 May 2023
                Page count
                Figures: 4, Tables: 4, Pages: 15, Words: 12995
                Funding
                Funded by: Shenzhen Science and Technology Innovation Commission , doi 10.13039/501100010877;
                Award ID: JCYJ20190808121415365
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 32071100
                Award ID: 31920103009
                Award ID: 32000793
                Funded by: Shenzhen‐Hong Kong Institute of Brain Science‐Shenzhen Fundamental Research Institutions
                Award ID: 2023SHIBS0003
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                August 1, 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.3.0 mode:remove_FC converted:04.07.2023

                Neurology
                cognitive functions,elderly,intrinsic neural connectivity,neuroeconomics,rs‐fmri,social dilemma

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