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      Gray Matter Atrophy Is Associated With Cognitive Impairment in Patients With Presbycusis: A Comprehensive Morphometric Study

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          Abstract

          Presbycusis (PC) is characterized by bilateral sensorineural hearing loss at high frequencies and speech-perception difficulties in noisy environments and has a strikingly detrimental impact on cognitive function. As the neural consequences of PC may involve the whole brain, we hypothesized that patients with PC would show structural alterations not only in the auditory cortex but also in the cortexes involved in cognitive function. The purpose of this study was to use surface-based morphometry (SBM) analysis to elucidate whole-brain structural differences between patients with PC and age-matched normal hearing controls. Three-dimensional T1-weighted MR images of 26 patients with mild PC and 26 age-, sex- and education-matched healthy controls (HCs) were acquired. All participants underwent a battery of neuropsychological tests. Our results revealed gray matter atrophy in several auditory cortical areas, nodes of the default mode network (DMN), including the bilateral precuneus and inferior parietal lobule, the right posterior cingulate cortex (PCC), and the right insula of patients with PC compared to that in the HCs. Our findings also revealed that hearing loss was associated with reduced gray matter volume in the right primary auditory cortex of patients with PC. Moreover, structural alterations in the nodes of the DMN were associated with cognitive impairments in PC patients. Additionally, this study provides evidence that a thicker right insula is associated with better speech perception in patients with PC. Based on these findings, we argue that the onset of PC seems to trigger its own cascade of conditions, including a need for increased cognitive resources during speech comprehension, which might lead to auditory and cognition-related cortical reorganization.

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          Most cited references26

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          Electrophysiological signatures of resting state networks in the human brain.

          Functional neuroimaging and electrophysiological studies have documented a dynamic baseline of intrinsic (not stimulus- or task-evoked) brain activity during resting wakefulness. This baseline is characterized by slow (<0.1 Hz) fluctuations of functional imaging signals that are topographically organized in discrete brain networks, and by much faster (1-80 Hz) electrical oscillations. To investigate the relationship between hemodynamic and electrical oscillations, we have adopted a completely data-driven approach that combines information from simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Using independent component analysis on the fMRI data, we identified six widely distributed resting state networks. The blood oxygenation level-dependent signal fluctuations associated with each network were correlated with the EEG power variations of delta, theta, alpha, beta, and gamma rhythms. Each functional network was characterized by a specific electrophysiological signature that involved the combination of different brain rhythms. Moreover, the joint EEG/fMRI analysis afforded a finer physiological fractionation of brain networks in the resting human brain. This result supports for the first time in humans the coalescence of several brain rhythms within large-scale brain networks as suggested by biophysical studies.
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            The cognitive control network: Integrated cortical regions with dissociable functions.

            Consensus across hundreds of published studies indicates that the same cortical regions are involved in many forms of cognitive control. Using functional magnetic resonance imaging (fMRI), we found that these coactive regions form a functionally connected cognitive control network (CCN). Network status was identified by convergent methods, including: high inter-regional correlations during rest and task performance, consistently higher correlations within the CCN than the rest of cortex, co-activation in a visual search task, and mutual sensitivity to decision difficulty. Regions within the CCN include anterior cingulate cortex/pre-supplementary motor area (ACC/pSMA), dorsolateral prefrontal cortex (DLPFC), inferior frontal junction (IFJ), anterior insular cortex (AIC), dorsal pre-motor cortex (dPMC), and posterior parietal cortex (PPC). We used a novel visual line search task which included periods when the probe stimuli were occluded but subjects had to maintain and update working memory in preparation for the sudden appearance of a probe stimulus. The six CCN regions operated as a tightly coupled network during the 'non-occluded' portions of this task, with all regions responding to probe events. In contrast, the network was differentiated during occluded search. DLPFC, not ACC/pSMA, was involved in target memory maintenance when probes were absent, while both regions became active in preparation for difficult probes at the end of each occluded period. This approach illustrates one way in which a neuronal network can be identified, its high functional connectivity established, and its components dissociated in order to better understand the interactive and specialized internal mechanisms of that network.
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              Fractionating the default mode network: distinct contributions of the ventral and dorsal posterior cingulate cortex to cognitive control.

              The posterior cingulate cortex (PCC) is a central part of the default mode network (DMN) and part of the structural core of the brain. Although the PCC often shows consistent deactivation when attention is focused on external events, anatomical studies show that the region is not homogeneous, and electrophysiological recordings in nonhuman primates suggest that it is directly involved in some forms of attention. We report a functional magnetic resonance imaging study of an attentionally demanding task (either a zero- or two-back working memory task). Standard subtraction analysis within the PCC shows a relative deactivation as task difficulty increases. In contrast, a dual-regression functional connectivity analysis reveals a clear dissociation between ventral and dorsal parts of the PCC. As task difficulty increases, the ventral PCC shows reduced integration within the DMN and less anticorrelation with the cognitive control network (CCN) activated by the task. The dorsal PCC shows an opposite pattern, with increased DMN integration and more anticorrelation. At rest, the dorsal PCC also shows functional connectivity with both the DMN and attentional networks. As expected, these results provide evidence that the PCC is involved in supporting internally directed thought, as the region is more highly integrated with the DMN at low task demands. In contrast, the task-dependent increases in connectivity between the dorsal PCC and the CCN are consistent with a role for this region in modulating the dynamic interaction between these two networks controlling the efficient allocation of attention.
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                Author and article information

                Contributors
                Journal
                Front Neurosci
                Front Neurosci
                Front. Neurosci.
                Frontiers in Neuroscience
                Frontiers Media S.A.
                1662-4548
                1662-453X
                23 October 2018
                2018
                : 12
                : 744
                Affiliations
                [1] 1Shandong Medical Imaging Research Institute, Shandong University , Jinan, China
                [2] 2Department of Otolaryngology, Jinan Central Hospital, Shandong University , Jinan, China
                [3] 3Vanderbilt University Institute of Imaging Science, Vanderbilt University , Nashville, TN, United States
                [4] 4Huaxi MR Research Center, Department of Radiology, West China Hospital of Sichuan University , Chengdu, China
                [5] 5Central Laboratory, The Second Hospital of Shandong University , Jinan, China
                [6] 6Philips Healthcare , Shanghai, China
                Author notes

                Edited by: Dona M. P. Jayakody, Ear Science Institute Australia, Australia

                Reviewed by: Foteini Christidi, National and Kapodistrian University of Athens Medical School, Greece; Peter Thorne, The University of Auckland, New Zealand

                *Correspondence: Fei Gao, feigao6262@ 123456163.com Bin Zhao, qpqpoo6262@ 123456163.com

                This article was submitted to Neurodegeneration, a section of the journal Frontiers in Neuroscience

                Article
                10.3389/fnins.2018.00744
                6205975
                30405333
                fee3a6ce-774a-40f7-8d4d-8f1877ea3e9f
                Copyright © 2018 Ren, Ma, Li, Sun, Xin, Zong, Chen, Wang, Gao and Zhao.

                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
                : 27 June 2018
                : 27 September 2018
                Page count
                Figures: 5, Tables: 1, Equations: 0, References: 41, Pages: 9, Words: 0
                Categories
                Neuroscience
                Original Research

                Neurosciences
                presbycusis,cognitive impairment,hearing loss,gm atrophy,surface-based morphometry
                Neurosciences
                presbycusis, cognitive impairment, hearing loss, gm atrophy, surface-based morphometry

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