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      Resting-State Functional Connectivity of the Punishment Network Associated With Conformity

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          Abstract

          Fear of punishment prompts individuals to conform. However, why some people are more inclined than others to conform despite being unaware of any obvious punishment remains unclear, which means the dispositional determinants of individual differences in conformity propensity are poorly understood. Here, we explored whether such individual differences might be explained by individuals’ stable neural markers to potential punishment. To do this, we first defined the punishment network (PN) by combining all potential brain regions involved in punishment processing. We subsequently used a voxel-based global brain connectivity (GBC) method based on resting-state functional connectivity (FC) to characterize the hubs in the PN, which reflected an ongoing readiness state (i.e., sensitivity) for potential punishment. Then, we used the within-network connectivity (WNC) of each voxel in the PN of 264 participants to explain their tendency to conform by using a conformity scale. We found that a stronger WNC in the right thalamus, left insula, postcentral gyrus, and dACC was associated with a stronger tendency to conform. Furthermore, the FC among the four hubs seemed to form a three-phase ascending pathway, contributing to conformity propensity at every phase. Thus, our results suggest that task-independent spontaneous connectivity in the PN could predispose individuals to conform.

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          Most cited references102

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          The human brain is intrinsically organized into dynamic, anticorrelated functional networks.

          During performance of attention-demanding cognitive tasks, certain regions of the brain routinely increase activity, whereas others routinely decrease activity. In this study, we investigate the extent to which this task-related dichotomy is represented intrinsically in the resting human brain through examination of spontaneous fluctuations in the functional MRI blood oxygen level-dependent signal. We identify two diametrically opposed, widely distributed brain networks on the basis of both spontaneous correlations within each network and anticorrelations between networks. One network consists of regions routinely exhibiting task-related activations and the other of regions routinely exhibiting task-related deactivations. This intrinsic organization, featuring the presence of anticorrelated networks in the absence of overt task performance, provides a critical context in which to understand brain function. We suggest that both task-driven neuronal responses and behavior are reflections of this dynamic, ongoing, functional organization of the brain.
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            Improved optimization for the robust and accurate linear registration and motion correction of brain images.

            Linear registration and motion correction are important components of structural and functional brain image analysis. Most modern methods optimize some intensity-based cost function to determine the best registration. To date, little attention has been focused on the optimization method itself, even though the success of most registration methods hinges on the quality of this optimization. This paper examines the optimization process in detail and demonstrates that the commonly used multiresolution local optimization methods can, and do, get trapped in local minima. To address this problem, two approaches are taken: (1) to apodize the cost function and (2) to employ a novel hybrid global-local optimization method. This new optimization method is specifically designed for registering whole brain images. It substantially reduces the likelihood of producing misregistrations due to being trapped by local minima. The increased robustness of the method, compared to other commonly used methods, is demonstrated by a consistency test. In addition, the accuracy of the registration is demonstrated by a series of experiments with motion correction. These motion correction experiments also investigate how the results are affected by different cost functions and interpolation methods.
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              Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging.

              The majority of functional neuroscience studies have focused on the brain's response to a task or stimulus. However, the brain is very active even in the absence of explicit input or output. In this Article we review recent studies examining spontaneous fluctuations in the blood oxygen level dependent (BOLD) signal of functional magnetic resonance imaging as a potentially important and revealing manifestation of spontaneous neuronal activity. Although several challenges remain, these studies have provided insight into the intrinsic functional architecture of the brain, variability in behaviour and potential physiological correlates of neurological and psychiatric disease.
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                Author and article information

                Contributors
                Journal
                Front Behav Neurosci
                Front Behav Neurosci
                Front. Behav. Neurosci.
                Frontiers in Behavioral Neuroscience
                Frontiers Media S.A.
                1662-5153
                16 December 2020
                2020
                : 14
                : 617402
                Affiliations
                [1] 1Faculty of Psychology, Beijing Normal University , Beijing, China
                [2] 2Department of Psychology, Tsinghua Laboratory of Brain and Intelligence, Tsinghua University , Beijing, China
                Author notes

                Edited by: Gennady Knyazev, State Scientific Research Institute of Physiology and Basic Medicine, Russia

                Reviewed by: Tingyong Feng, Southwest University, China; Wenbin Guo, Central South University, China

                Specialty section: This article was submitted to Individual and Social Behaviors, a section of the journal Frontiers in Behavioral Neuroscience

                Article
                10.3389/fnbeh.2020.617402
                7772235
                5ac1ac93-239d-4133-ba16-dbc75dac2978
                Copyright © 2020 Du, Wang, Yu, Tian and Liu.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 14 October 2020
                : 26 November 2020
                Page count
                Figures: 4, Tables: 2, Equations: 0, References: 102, Pages: 13, Words: 9774
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 31700978, 31861143039, 31872786
                Funded by: National University’s Basic Research Foundation of China 10.13039/501100013804
                Award ID: 2018YFC0810602
                Funded by: Changjiang Scholar Program of Chinese Ministry of Education 10.13039/501100005240
                Categories
                Behavioral Neuroscience
                Original Research

                Neurosciences
                punishment network,functional connectivity,conformity propensity,thalamus,insula,postcentral gyrus,dacc

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