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      Intrinsic Brain Network Abnormalities in Migraines without Aura Revealed in Resting-State fMRI

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          Abstract

          Background

          Previous studies have defined low-frequency, spatially consistent intrinsic connectivity networks (ICN) in resting functional magnetic resonance imaging (fMRI) data which reflect functional interactions among distinct brain areas. We sought to explore whether and how repeated migraine attacks influence intrinsic brain connectivity, as well as how activity in these networks correlates with clinical indicators of migraine.

          Methods/Principal Findings

          Resting-state fMRI data in twenty-three patients with migraines without aura (MwoA) and 23 age- and gender-matched healthy controls (HC) were analyzed using independent component analysis (ICA), in combination with a “dual-regression” technique to identify the group differences of three important pain-related networks [default mode network (DMN), bilateral central executive network (CEN), salience network (SN)] between the MwoA patients and HC. Compared with the HC, MwoA patients showed aberrant intrinsic connectivity within the bilateral CEN and SN, and greater connectivity between both the DMN and right CEN (rCEN) and the insula cortex - a critical region involving in pain processing. Furthermore, greater connectivity between both the DMN and rCEN and the insula correlated with duration of migraine.

          Conclusions

          Our findings may provide new insights into the characterization of migraine as a condition affecting brain activity in intrinsic connectivity networks. Moreover, the abnormalities may be the consequence of a persistent central neural system dysfunction, reflecting cumulative brain insults due to frequent ongoing migraine attacks.

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          Most cited references32

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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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            Resting-state functional connectivity in neuropsychiatric disorders.

            This review considers recent advances in the application of resting-state functional magnetic resonance imaging to the study of neuropsychiatric disorders. Resting-state functional magnetic resonance imaging is a relatively novel technique that has several potential advantages over task-activation functional magnetic resonance imaging in terms of its clinical applicability. A number of research groups have begun to investigate the use of resting-state functional magnetic resonance imaging in a variety of neuropsychiatric disorders including Alzheimer's disease, depression, and schizophrenia. Although preliminary results have been fairly consistent in some disorders (for example, Alzheimer's disease) they have been less reproducible in others (schizophrenia). Resting-state connectivity has been shown to correlate with behavioral performance and emotional measures. It's potential as a biomarker of disease and an early objective marker of treatment response is genuine but still to be realized. Resting-state functional magnetic resonance imaging has made some strides in the clinical realm but significant advances are required before it can be used in a meaningful way at the single-patient level.
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              Selective changes of resting-state networks in individuals at risk for Alzheimer's disease.

              Alzheimer's disease (AD) is a neurodegenerative disorder that prominently affects cerebral connectivity. Assessing the functional connectivity at rest, recent functional MRI (fMRI) studies reported on the existence of resting-state networks (RSNs). RSNs are characterized by spatially coherent, spontaneous fluctuations in the blood oxygen level-dependent signal and are made up of regional patterns commonly involved in functions such as sensory, attention, or default mode processing. In AD, the default mode network (DMN) is affected by reduced functional connectivity and atrophy. In this work, we analyzed functional and structural MRI data from healthy elderly (n = 16) and patients with amnestic mild cognitive impairment (aMCI) (n = 24), a syndrome of high risk for developing AD. Two questions were addressed: (i) Are any RSNs altered in aMCI? (ii) Do changes in functional connectivity relate to possible structural changes? Independent component analysis of resting-state fMRI data identified eight spatially consistent RSNs. Only selected areas of the DMN and the executive attention network demonstrated reduced network-related activity in the patient group. Voxel-based morphometry revealed atrophy in both medial temporal lobes (MTL) of the patients. The functional connectivity between both hippocampi in the MTLs and the posterior cingulate of the DMN was present in healthy controls but absent in patients. We conclude that in individuals at risk for AD, a specific subset of RSNs is altered, likely representing effects of ongoing early neurodegeneration. We interpret our finding as a proof of principle, demonstrating that functional brain disorders can be characterized by functional-disconnectivity profiles of RSNs.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2012
                28 December 2012
                : 7
                : 12
                : e52927
                Affiliations
                [1 ]Life Sciences Research Center, School of Life Sciences and Technology, Xidian University, Xi’an, Shaanxi, China
                [2 ]The 3rd Teaching Hospital, Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, China
                [3 ]Information Processing Laboratory, School of Information Engineering, Inner Mongolia University of Science and Technology, Baotou, Inner Mongolia, China
                [4 ]Intelligent Medical Research Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
                National Research & Technology Council, Argentina
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: TX KY WQ JT. Performed the experiments: TX KY Ling Zhao DY. Analyzed the data: TX KY. Contributed reagents/materials/analysis tools: Limei Zhao TD PC. Wrote the paper: TX KMVD.

                Article
                PONE-D-12-26054
                10.1371/journal.pone.0052927
                3532057
                23285228
                b783731a-d6e0-4760-9308-3dfd2a15185e
                Copyright @ 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 26 August 2012
                : 22 November 2012
                Page count
                Pages: 7
                Funding
                This paper is supported by the Project for the National Key Basic Research and Development Program (973) under Grant Nos. 2011CB707702, 2012CB518501, the National Natural Science Foundation of China under Grant Nos. 30930112, 30970774, 60901064, 30873462, 81000640, 81000641, 81071217, 81101036, 81101108, 31150110171, 30901900, the Fundamental Research Funds for the Central Universities, the Natural Science Foundation of Inner Mongolia under Grant No. 2012MS0908 and the Innovation Fund Project of Inner Mongolia University of Science and Technology Nos. 2010NC030, 2010NC037. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Neuroscience
                Cognitive Neuroscience
                Pain
                Neuroimaging
                Fmri
                Developmental Neuroscience
                Neural Networks
                Neurobiology of Disease and Regeneration
                Medicine
                Neurology
                Headaches
                Migraine
                Neuroimaging
                Neuro-Ophthalmology

                Uncategorized
                Uncategorized

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