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      Abnormal Functional Connectivity in Cognitive Control Network, Default Mode Network, and Visual Attention Network in Internet Addiction: A Resting-State fMRI Study

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          Abstract

          Internet addiction (IA) has become a global mental and social problem, which may lead to a series of psychiatric symptoms including uncontrolled use of internet, and lack of concentration. However, the exact pathophysiology of IA remains unclear. Most of functional connectivity studies were based on pre-selected regions of interest (ROI), which could not provide a comprehensive picture of the communication abnormalities in IA, and might lead to limited or bias observations. Using local functional connectivity density (lFCD), this study aimed to explore the whole-brain abnormalities of functional connectivity in IA. We evaluated the whole-brain lFCD resulting from resting-state fMRI data in 28 IA individuals and 30 demographically matched healthy control subjects (HCs). The correlations between clinical characteristics and aberrant lFCD were also assessed. Compared with HCs, subjects with IA exhibited heightened lFCD values in the right dorsolateral prefrontal cortex (DLPFC), left parahippocampal gyrus (PHG), and cerebellum, and the bilateral middle cingulate cortex (MCC) and superior temporal pole (STP), as well as decreased lFCD values in the right inferior parietal lobe (IPL), and bilateral calcarine and lingual gyrus. Voxel-based correlation analysis revealed the significant correlations between the Young's Internet Addiction Test (IAT) score and altered lFCD values in the left PHG and bilateral STP. These findings revealed the hyper-connectivity in cognitive control network and default mode network as well as the hypo-connectivity in visual attention network, verifying the common mechanism in IA and substance addiction, and the underlying association between IA, and attention deficit/hyperactivity disorder in terms of neurobiology.

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          Self-control in decision-making involves modulation of the vmPFC valuation system.

          Every day, individuals make dozens of choices between an alternative with higher overall value and a more tempting but ultimately inferior option. Optimal decision-making requires self-control. We propose two hypotheses about the neurobiology of self-control: (i) Goal-directed decisions have their basis in a common value signal encoded in ventromedial prefrontal cortex (vmPFC), and (ii) exercising self-control involves the modulation of this value signal by dorsolateral prefrontal cortex (DLPFC). We used functional magnetic resonance imaging to monitor brain activity while dieters engaged in real decisions about food consumption. Activity in vmPFC was correlated with goal values regardless of the amount of self-control. It incorporated both taste and health in self-controllers but only taste in non-self-controllers. Activity in DLPFC increased when subjects exercised self-control and correlated with activity in vmPFC.
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            Remembering the past to imagine the future: the prospective brain.

            A rapidly growing number of recent studies show that imagining the future depends on much of the same neural machinery that is needed for remembering the past. These findings have led to the concept of the prospective brain; an idea that a crucial function of the brain is to use stored information to imagine, simulate and predict possible future events. We suggest that processes such as memory can be productively re-conceptualized in light of this idea.
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              Frequencies contributing to functional connectivity in the cerebral cortex in "resting-state" data.

              In subjects performing no specific cognitive task ("resting state"), time courses of voxels within functionally connected regions of the brain have high cross-correlation coefficients ("functional connectivity"). The purpose of this study was to measure the contributions of low frequencies and physiological noise to cross-correlation maps. In four healthy volunteers, task-activation functional MR imaging and resting-state data were acquired. We obtained four contiguous slice locations in the "resting state" with a high sampling rate. Regions of interest consisting of four contiguous voxels were selected. The correlation coefficient for the averaged time course and every other voxel in the four slices was calculated and separated into its component frequency contributions. We calculated the relative amounts of the spectrum that were in the low-frequency (0 to 0.1 Hz), the respiratory-frequency (0.1 to 0.5 Hz), and cardiac-frequency range (0.6 to 1.2 Hz). For each volunteer, resting-state maps that resembled task-activation maps were obtained. For the auditory and visual cortices, the correlation coefficient depended almost exclusively on low frequencies (<0.1 Hz). For all cortical regions studied, low-frequency fluctuations contributed more than 90% of the correlation coefficient. Physiological (respiratory and cardiac) noise sources contributed less than 10% to any functional connectivity MR imaging map. In blood vessels and cerebrospinal fluid, physiological noise contributed more to the correlation coefficient. Functional connectivity in the auditory, visual, and sensorimotor cortices is characterized predominantly by frequencies slower than those in the cardiac and respiratory cycles. In functionally connected regions, these low frequencies are characterized by a high degree of temporal coherence.
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                Author and article information

                Contributors
                Journal
                Front Neurol
                Front Neurol
                Front. Neurol.
                Frontiers in Neurology
                Frontiers Media S.A.
                1664-2295
                18 September 2019
                2019
                : 10
                : 1006
                Affiliations
                [1] 1School of Acupuncture and Tuina, Chengdu University of Traditional Chinese Medicine , Chengdu, China
                [2] 2Department of Rehabilitation, Shuangliu Maternal and Child Health Care Hospital , Chengdu, China
                [3] 3Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China , Chengdu, China
                [4] 4School of Medicine, Chengdu University , Chengdu, China
                [5] 5School of Rehabilitation and Health Preservation, Chengdu University of Traditional Chinese Medicine , Chengdu, China
                [6] 6Department of Rehabilitation, Zigong Fifth People's Hospital , Zigong, China
                Author notes

                Edited by: Peter Sörös, University of Oldenburg, Germany

                Reviewed by: Luke Norman, University of Michigan, United States; Junjie Wu, Emory University School of Medicine, United States

                *Correspondence: Tianmin Zhu tianminzhu@ 123456cdutcm.edu.cn

                This article was submitted to Applied Neuroimaging, a section of the journal Frontiers in Neurology

                Article
                10.3389/fneur.2019.01006
                6759465
                31620077
                4dbd7d65-66e4-4f0a-aa6b-e74d19e51c6f
                Copyright © 2019 Wang, Qin, Li, Yao, Sun, Li, Li, Dai, Wen, Zhang, Zhang, Zhu and Luo.

                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
                : 28 April 2019
                : 03 September 2019
                Page count
                Figures: 2, Tables: 2, Equations: 0, References: 99, Pages: 9, Words: 7328
                Funding
                Funded by: National Natural Science Foundation of China 10.13039/501100001809
                Award ID: 81072852
                Award ID: 81574047
                Categories
                Neurology
                Original Research

                Neurology
                internet addiction,cognitive control network,default mode network,visual attention network,functional connectivity density,substance addiction,attention deficit/hyperactivity disorder

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