Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1098/rsta.2016.0287 http://scihub22266oqcxt.onion/10.1098/rsta.2016.0287 C5434078!5434078!28507232     free     free     free       Philos+Trans+A+Math+Phys+Eng+Sci 2017 ; 375 (2096): ä Nephropedia Template TP {{tp|p=28507232|t=2017. Energy landscape analysis of neuroimaging data |pdf=|usr=}}{{28507232}} gab.com Text Energy landscape analysis of neuroimaging data JO: Philos Trans A Math Phys Eng Sci http://www.kidney.de/pget.php?&tw=1&pmid=28507232 #FOAvip Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length.This article is part of the themed issue ?Mathematical methods in medicine: neuroscience, cardiology and pathology?. Twit Text FOAVip Energy landscape analysis of neuroimaging data ????Philos Trans A Math Phys Eng Sci http://www.kidney.de/pget.php?&tw=1&pmid=28507232 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28507232 #FOAvip English Wikipedia <ref>{{Cite pmid|28507232}}</ref> Energy landscape analysis of neuroimaging data #MMPMID28507232 Ezaki T; Watanabe T; Ohzeki M; Masuda N Philos Trans A Math Phys Eng Sci 2017[Jun]; 375 (2096): ä PMID28507232show ga Computational neuroscience models have been used for understanding neural dynamics in the brain and how they may be altered when physiological or other conditions change. We review and develop a data-driven approach to neuroimaging data called the energy landscape analysis. The methods are rooted in statistical physics theory, in particular the Ising model, also known as the (pairwise) maximum entropy model and Boltzmann machine. The methods have been applied to fitting electrophysiological data in neuroscience for a decade, but their use in neuroimaging data is still in its infancy. We first review the methods and discuss some algorithms and technical aspects. Then, we apply the methods to functional magnetic resonance imaging data recorded from healthy individuals to inspect the relationship between the accuracy of fitting, the size of the brain system to be analysed and the data length.This article is part of the themed issue ?Mathematical methods in medicine: neuroscience, cardiology and pathology?. äEnergy landscape analysis of neuroimaging data (epmc).pdf DeepDyve Pubget Overpricing