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10.3389/fncom.2017.00014 http://scihub22266oqcxt.onion/10.3389/fncom.2017.00014 C5357637!5357637!28373838     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Front+Comput+Neurosci 2017 ; 11 (ä): ä Nephropedia Template TP {{tp|p=28373838|t=2017. Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods |pdf=|usr=}}{{28373838}} gab.com Text Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods JO: Front Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=28373838 #FOAvip An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project. Twit Text FOAVip Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods ????Front Comput Neurosci http://www.kidney.de/pget.php?&tw=1&pmid=28373838 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28373838 #FOAvip English Wikipedia <ref>{{Cite pmid|28373838}}</ref> Decoding Time-Varying Functional Connectivity Networks via Linear Graph Embedding Methods #MMPMID28373838 Monti RP; Lorenz R; Hellyer P; Leech R; Anagnostopoulos C; Montana G Front Comput Neurosci 2017[]; 11 (ä): ä PMID28373838show ga An exciting avenue of neuroscientific research involves quantifying the time-varying properties of functional connectivity networks. As a result, many methods have been proposed to estimate the dynamic properties of such networks. However, one of the challenges associated with such methods involves the interpretation and visualization of high-dimensional, dynamic networks. In this work, we employ graph embedding algorithms to provide low-dimensional vector representations of networks, thus facilitating traditional objectives such as visualization, interpretation and classification. We focus on linear graph embedding methods based on principal component analysis and regularized linear discriminant analysis. The proposed graph embedding methods are validated through a series of simulations and applied to fMRI data from the Human Connectome Project. äDecoding Time-Varying Functional Connectivity Networks via Linear Grap(epmc).pdf DeepDyve Pubget Overpricing