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      Large‐scale brain networks are distinctly affected in right and left mesial temporal lobe epilepsy

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          Abstract

          Mesial temporal lobe epilepsy (MTLE) with hippocampus sclerosis (HS) is associated with functional and structural alterations extending beyond the temporal regions and abnormal pattern of brain resting state networks (RSNs) connectivity. We hypothesized that the interaction of large‐scale RSNs is differently affected in patients with right‐ and left‐MTLE with HS compared to controls. We aimed to determine and characterize these alterations through the analysis of 12 RSNs, functionally parceled in 70 regions of interest (ROIs), from resting‐state functional‐MRIs of 99 subjects (52 controls, 26 right‐ and 21 left‐MTLE patients with HS). Image preprocessing and statistical analysis were performed using UF 2C‐toolbox, which provided ROI‐wise results for intranetwork and internetwork connectivity. Intranetwork abnormalities were observed in the dorsal default mode network (DMN) in both groups of patients and in the posterior salience network in right‐MTLE. Both groups showed abnormal correlation between the dorsal‐DMN and the posterior salience, as well as between the dorsal‐DMN and the executive‐control network. Patients with left‐MTLE also showed reduced correlation between the dorsal‐DMN and visuospatial network and increased correlation between bilateral thalamus and the posterior salience network. The ipsilateral hippocampus stood out as a central area of abnormalities. Alterations on left‐MTLE expressed a low cluster coefficient, whereas the altered connections on right‐MTLE showed low cluster coefficient in the DMN but high in the posterior salience regions. Both right‐ and left‐MTLE patients with HS have widespread abnormal interactions of large‐scale brain networks; however, all parameters evaluated indicate that left‐MTLE has a more intricate bihemispheric dysfunction compared to right‐MTLE. Hum Brain Mapp 37:3137–3152, 2016. © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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          Scale-free brain functional networks.

          Functional magnetic resonance imaging is used to extract functional networks connecting correlated human brain sites. Analysis of the resulting networks in different tasks shows that (a) the distribution of functional connections, and the probability of finding a link versus distance are both scale-free, (b) the characteristic path length is small and comparable with those of equivalent random networks, and (c) the clustering coefficient is orders of magnitude larger than those of equivalent random networks. All these properties, typical of scale-free small-world networks, reflect important functional information about brain states.
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            Parcellation-dependent small-world brain functional networks: a resting-state fMRI study.

            Recent studies have demonstrated small-world properties in both functional and structural brain networks that are constructed based on different parcellation approaches. However, one fundamental but vital issue of the impact of different brain parcellation schemes on the network topological architecture remains unclear. Here, we used resting-state functional MRI (fMRI) to investigate the influences of different brain parcellation atlases on the topological organization of brain functional networks. Whole-brain fMRI data were divided into ninety and seventy regions of interest according to two predefined anatomical atlases, respectively. Brain functional networks were constructed by thresholding the correlation matrices among the parcellated regions and further analyzed using graph theoretical approaches. Both atlas-based brain functional networks were found to show robust small-world properties and truncated power-law connectivity degree distributions, which are consistent with previous brain functional and structural networks studies. However, more importantly, we found that there were significant differences in multiple topological parameters (e.g., small-worldness and degree distribution) between the two groups of brain functional networks derived from the two atlases. This study provides quantitative evidence on how the topological organization of brain networks is affected by the different parcellation strategies applied. (c) 2008 Wiley-Liss, Inc.
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              Default mode network abnormalities in mesial temporal lobe epilepsy: a study combining fMRI and DTI.

              Studies of in mesial temporal lobe epilepsy (mTLE) patients with hippocampal sclerosis (HS) have reported reductions in both functional and structural connectivity between hippocampal structures and adjacent brain regions. However, little is known about the connectivity among the default mode network (DMN) in mTLE. Here, we hypothesized that both functional and structural connectivity within the DMN were disturbed in mTLE. To test this hypothesis, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) were applied to examine the DMN connectivity of 20 mTLE patients, and 20 gender- and age-matched healthy controls. Combining these two techniques, we explored the changes in functional (temporal correlation coefficient derived from fMRI) and structural (path length and connection density derived from DTI tractography) connectivity of the DMN. Compared to the controls, we found that both functional and structural connectivity were significantly decreased between the posterior cingulate cortex (PCC)/precuneus (PCUN) and bilateral mesial temporal lobes (mTLs) in patients. No significant between-group difference was found between the PCC/PCUN and medial prefrontal cortex (mPFC). In addition, functional connectivity was found to be correlated with structural connectivity in two pairwise regions, namely between the PCC/PCUN and bilateral mTLs, respectively. Our results suggest that the decreased functional connectivity within the DMN in mTLE may be a consequence of the decreased connection density underpinning the degeneration of structural connectivity. Copyright © 2010 Wiley-Liss, Inc.
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                Author and article information

                Journal
                Hum Brain Mapp
                Hum Brain Mapp
                10.1002/(ISSN)1097-0193
                HBM
                Human Brain Mapping
                John Wiley and Sons Inc. (Hoboken )
                1065-9471
                1097-0193
                02 May 2016
                September 2016
                : 37
                : 9 ( doiID: 10.1002/hbm.v37.9 )
                : 3137-3152
                Affiliations
                [ 1 ] Neuroimaging Laboratory Department of NeurologyUniversity of Campinas Campinas São PauloBrazil
                Author notes
                [*] [* ]Correspondence to: Fernando Cendes, Departamento de Neurologia, Faculdade de Ciências Médicas – UNICAMP, Cidade Universitária Zeferino Vaz, Campinas SP, Brazil, CEP 13083‐970. E‐mail: fcendes@ 123456unicamp.br
                Author information
                http://orcid.org/0000-0003-1261-8257
                Article
                HBM23231
                10.1002/hbm.23231
                5074272
                27133613
                70c211db-f2d5-48f2-bcee-cba48bac147e
                © 2016 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

                This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

                History
                : 14 January 2016
                : 04 April 2016
                : 15 April 2016
                Page count
                Pages: 16
                Funding
                Funded by: FAPESP (São Paulo Research Foundation)
                Award ID: 2013/00099‐7
                Award ID: 2013/07559‐3
                Award ID: 2014/15918‐6
                Categories
                Research Article
                Research Articles
                Custom metadata
                2.0
                hbm23231
                September 2016
                Converter:WILEY_ML3GV2_TO_NLMPMC version:4.9.5 mode:remove_FC converted:21.10.2016

                Neurology
                functional connectivity,functional magnetic resonance imaging,default mode network,salience network,hippocampus,visuospatial network

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