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10.1007/s10548-014-0406-2 http://scihub22266oqcxt.onion/10.1007/s10548-014-0406-2 C4405424!4405424!25331991     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Brain+Topogr 2015 ; 28 (5): 666-79 Nephropedia Template TP {{tp|p=25331991|t=2015. Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models |pdf=|usr=}}{{25331991}} gab.com Text Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models JO: Brain Topogr http://www.kidney.de/pget.php?&tw=1&pmid=25331991 #FOAvip Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain?s functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain?s functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84 % of PTSD patients and 86 % of NC subjects are successfully classified via multiple HMMs using majority voting. Twit Text FOAVip Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models ????Brain Topogr http://www.kidney.de/pget.php?&tw=1&pmid=25331991 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25331991 #FOAvip English Wikipedia <ref>{{Cite pmid|25331991}}</ref> Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models #MMPMID25331991 Ou J; Xie L; Jin C; Li X; Zhu D; Jiang R; Chen Y; Zhang J; Li L; Liu T Brain Topogr 2015[Sep]; 28 (5): 666-79 PMID25331991show ga Functional connectivity measured from resting state fMRI (R-fMRI) data has been widely used to examine the brain?s functional activities and has been recently used to characterize and differentiate brain conditions. However, the dynamical transition patterns of the brain?s functional states have been less explored. In this work, we propose a novel computational framework to quantitatively characterize the brain state dynamics via hidden Markov models (HMMs) learned from the observations of temporally dynamic functional connectomics, denoted as functional connectome states. The framework has been applied to the R-fMRI dataset including 44 post-traumatic stress disorder (PTSD) patients and 51 normal control (NC) subjects. Experimental results show that both PTSD and NC brains were undergoing remarkable changes in resting state and mainly transiting amongst a few brain states. Interestingly, further prediction with the best-matched HMM demonstrates that PTSD would enter into, but could not disengage from, a negative mood state. Importantly, 84 % of PTSD patients and 86 % of NC subjects are successfully classified via multiple HMMs using majority voting. äCharacterizing and Differentiating Brain State Dynamics via Hidden Mar(epmc).pdf DeepDyve Pubget Overpricing