2017
DOI: 10.1093/brain/awx050
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Selective impairment of hippocampus and posterior hub areas in Alzheimer’s disease: an MEG-based multiplex network study

Abstract: Although frequency-specific network analyses have shown that functional brain networks are altered in patients with Alzheimer's disease, the relationships between these frequency-specific network alterations remain largely unknown. Multiplex network analysis is a novel network approach to study complex systems consisting of subsystems with different types of connectivity patterns. In this study, we used magnetoencephalography to integrate five frequency-band specific brain networks in a multiplex framework. Pr… Show more

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Cited by 141 publications
(150 citation statements)
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References 123 publications
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“…Finally, in this project we focused on the ability of ComBat harmonization to preserve age-related associations with several network connectivity and efficiency measures. However, previous studies (Bullmore and Sporns, 2012; Stam, 2014; Fornito et al, 2015; Yu et al, 2016, 2017) have shown that functional brain network organizations are highly correlated with other demographic (e.g. gender, educational level), clinical phenotypes (e.g.…”
Section: | Discussionmentioning
confidence: 94%
See 1 more Smart Citation
“…Finally, in this project we focused on the ability of ComBat harmonization to preserve age-related associations with several network connectivity and efficiency measures. However, previous studies (Bullmore and Sporns, 2012; Stam, 2014; Fornito et al, 2015; Yu et al, 2016, 2017) have shown that functional brain network organizations are highly correlated with other demographic (e.g. gender, educational level), clinical phenotypes (e.g.…”
Section: | Discussionmentioning
confidence: 94%
“…Finally, in this project we focused on the ability of ComBat harmonization to preserve age-related associations with several network connectivity and efficiency measures. However, previous studies (Bullmore & Sporns, 2012;Fornito, Zalesky, & Breakspear, 2015;Stam, 2014;Yu et al, 2016Yu et al, , 2017 have shown that functional brain network organizations are highly correlated with other demographic (e.g., gender, educational level), clinical phenotypes (e.g., disease severity for neurological disorders), and pathological biomarkers (e.g., amyloid-b 42 and tau proteins in Alzheimer's disease). In particular, the EMBARC functional dataset was originally designed to study the potential differences on fMRI measurements between MDD patients and healthy controls (Greenberg et al, 2015;Trivedi et al, 2016;Webb et al, 2016).…”
Section: Power Atlas Outperforms Aal and Gordon Atlasesmentioning
confidence: 97%
“…We, however, found an increased dwPLI-based global clustering coefficient in the alpha frequency band, showing an increased functional segregation during tonic pain. Such significant changes of electrophysiological global network measures have been observed in different neurological and psychiatric disorders such as Alzheimer's disease (Stam, Jones, Nolte, Breakspear, & Scheltens, 2007;Yu et al, 2017), multiple sclerosis, and epilepsy (Stam, 2014). Although such changes have not directly been shown in chronic pain so far, fMRI studies reporting a global reorganization of the brain in chronic pain (Mano et al, 2018;Mansour et al, 2016) [Bradley et al, 2017]).…”
Section: Discussionmentioning
confidence: 97%
“…Anatomical and functional information has been integrated to gain insights about the macro-scale topology of the Macaque monkey [26] and the human brain [27]. Temporal [28,29] and multi-frequency [30,31,32,33] decompositions of human brain activity, followed by their successive integration into multilayer networks, have been used to improve our understanding of brain function in cognitive tasks and brain diseases, such as Alzheimer's, Parkinson's or Schizophrenia.…”
Section: Connectomicsmentioning
confidence: 99%