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Cited by 24 publications
(20 citation statements)
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References 7 publications
(7 reference statements)
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“…Using magnetic resonance images (MRI), chronological age can be predicted with mean absolute deviation of 5 years in healthy participants (Franke et al, 2010). Various diseases, including Alzheimer's disease, schizophrenia, epilepsy, traumatic brain injury, bipolar disorder, major depression, cognitive impairment, diabetes mellitus, and HIV, are associated with older brain age than chronological age Cole, 2017;Cole and Franke, 2017;Cole et al, 2016;Franke et al, 2012;Franke et al, 2010). Alongside these biomarkers, EEG-based brain age is a sensible potential complement, which has several advantages: (1) EEG-based brain age could reflect functional changes rather than anatomical changes; (2) EEG is more economical, participant friendly, and in principle could be measured by home-based devices; (3) EEGbased brain age could facilitate within-participant repeated measures to assess the effectiveness of interventions, such as medications (Roehrs and Roth, 2010) or brain stimulation (Tasali et al, 2008) that aim to preserve or improve brain function.…”
Section: Introductionmentioning
confidence: 99%
“…Using magnetic resonance images (MRI), chronological age can be predicted with mean absolute deviation of 5 years in healthy participants (Franke et al, 2010). Various diseases, including Alzheimer's disease, schizophrenia, epilepsy, traumatic brain injury, bipolar disorder, major depression, cognitive impairment, diabetes mellitus, and HIV, are associated with older brain age than chronological age Cole, 2017;Cole and Franke, 2017;Cole et al, 2016;Franke et al, 2012;Franke et al, 2010). Alongside these biomarkers, EEG-based brain age is a sensible potential complement, which has several advantages: (1) EEG-based brain age could reflect functional changes rather than anatomical changes; (2) EEG is more economical, participant friendly, and in principle could be measured by home-based devices; (3) EEGbased brain age could facilitate within-participant repeated measures to assess the effectiveness of interventions, such as medications (Roehrs and Roth, 2010) or brain stimulation (Tasali et al, 2008) that aim to preserve or improve brain function.…”
Section: Introductionmentioning
confidence: 99%
“…Despite recent progress in the automated assessment of AD, the development of an automatic approach that addresses the uncertainty of diagnosing AD is still challenging and requires further data. Previous studies have shown that neuroimaging data along with advanced pattern recognition techniques can be used for predicting clinical scores (Moradi, Hallikainen, Hänninen, Tohka, & Neuroimaging, 2017; Shen et al., 2011; Stonnington et al., 2010) as well as chronological age (Cole, 2017). …”
Section: Introductionmentioning
confidence: 99%
“…Similar to polygenic risk scores (Dudbridge, 2013), the prediction R 2 of grey-matter BLUP scores increases with the training sample size and is capped by the association R 2 with all vertices (Figure 3). Future application of the grey-matter scores include studying correlates of brain age (Cole, 2017; Cole et al, 2017; Liem et al, 2017), body size and substance use, especially in samples where this information was not collected.…”
Section: Discussionmentioning
confidence: 99%