LEADER 03651nam 2200565 450 001 9910137529703321 005 20230621141340.0 010 $a9782889194353 (ebook) 035 $a(CKB)3710000000569681 035 $a(SSID)ssj0001680177 035 $a(PQKBManifestationID)16496152 035 $a(PQKBTitleCode)TC0001680177 035 $a(PQKBWorkID)15028289 035 $a(PQKB)10833199 035 $a(WaSeSS)IndRDA00056077 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/52566 035 $a(EXLCZ)993710000000569681 100 $a20160829d2015 uy 0 101 0 $aeng 135 $aur||#|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aMagnetic resonance imaging of healthy and diseased brain networks /$ftopic editors: Yong He and Alan Evans 210 $cFrontiers Media SA$d2015 210 31$aSwitzerland :$cFrontiers Media SA,$d2015 215 $a1 online resource (365 pages) $cillustrations; digital, PDF file(s) 225 0 $aFrontiers Research Topics 300 $aBibliographic Level Mode of Issuance: Monograph 320 $aIncludes bibliographical references. 330 $aAn important aspect of neuroscience is to characterize the underlying connectivity patterns of the human brain. Over the past few years, researchers have demonstrated that by combining a variety of different neuroimaging technologies (e.g., structural MRI, diffusion MRI and functional MRI) with sophisticated analytic strategies such as graph theory, it is possible to non-invasively map the patterns of structural and functional connectivity of human whole-brain networks. With these novel approaches, many studies have shown that human brain networks have non-random properties such as modularity, small-worldness and highly connected hubs. Importantly, these quantifiable network properties change with age, learning and disease. Moreover, there is growing evidence for behavioral and genetic correlates. Network analysis of neuroimaging data is opening up a new avenue of research into the understanding of the organizational principles of the brain that will be of interest for all basic scientists and clinical researchers. Such approaches are powerful but there are a number of challenging issues when extracting reliable brain networks from various imaging modalities and analyzing the topological properties, e.g., definitions of network nodes and edges and reproducibility of network analysis. We welcome contributions related to the state-of-the-art methodologies of brain connectivity and the applications involving development, aging and neuropsychiatric disorders such as Alzheimer?s disease, schizophrenia, attention deficit hyperactivity disorder and mood and anxiety disorders. It is anticipated that the articles in this Research Topic will provide a greater range and depth of provision for the field of imaging brain networks. 606 $aRadiology, MRI, Ultrasonography & Medical Physics$2HILCC 606 $aMedicine$2HILCC 606 $aHealth & Biological Sciences$2HILCC 610 $aconnectomics 610 $aconnectivity 610 $agraph theory 610 $aMRI 610 $aSmall-world 615 7$aRadiology, MRI, Ultrasonography & Medical Physics 615 7$aMedicine 615 7$aHealth & Biological Sciences 700 $aYong He$4auth$01365346 702 $aHe$b Yong 702 $aEvans$b Alan 801 0$bPQKB 801 2$bUkMaJRU 912 $a9910137529703321 996 $aMagnetic resonance imaging of healthy and diseased brain networks$93387155 997 $aUNINA