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10.1002/hbm.21186 http://scihub22266oqcxt.onion/10.1002/hbm.21186 C4791073!4791073 !21305667     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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\21305667 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       Hum+Brain+Mapp 2012 ; 33 (1 ): 1-13 Nephropedia Template TP {{tp|p=21305667 |t=2012. Minimizing within-experiment and within-group effects in Activation Likelihood Estimation meta-analyses |pdf=|usr=}}{{21305667 }} gab.com Text Minimizing within-experiment and within-group effects in Activation Likelihood Estimation meta-analyses JO: Hum Brain Mapp http://www.kidney.de/pget.php?&tw=1&pmid=21305667 #FOAvip Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. Twit Text FOAVip Minimizing within-experiment and within-group effects in Activation Likelihood Estimation meta-analyses ????Hum Brain Mapp http://www.kidney.de/pget.php?&tw=1&pmid=21305667 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=21305667 #FOAvip English Wikipedia <ref>{{Cite pmid|21305667 }}</ref> Minimizing within-experiment and within-group effects in Activation Likelihood Estimation meta-analyses #MMPMID21305667 Turkeltaub PE ; Eickhoff SB ; Laird AR ; Fox M ; Wiener M ; Fox P Hum Brain Mapp 2012[Jan]; 33 (1 ): 1-13 PMID21305667 show ga Activation Likelihood Estimation (ALE) is an objective, quantitative technique for coordinate-based meta-analysis (CBMA) of neuroimaging results that has been validated for a variety of uses. Stepwise modifications have improved ALE's theoretical and statistical rigor since its introduction. Here, we evaluate two avenues to further optimize ALE. First, we demonstrate that the maximum contribution of an experiment makes to an ALE map is related to the number of foci it reports and their proximity. We present a modified ALE algorithm that eliminates these within-experiment effects. However, we show that these effects only account for 2-3% of cumulative ALE values, and removing them has little impact on thresholded ALE maps. Next, we present an alternate organizational approach to datasets that prevents subject groups with multiple experiments in a dataset from influencing ALE values more than others. This modification decreases cumulative ALE values by 7-9%, changes the relative magnitude of some clusters, and reduces cluster extents. Overall, differences between results of the standard approach and these new methods were small. This finding validates previous ALE reports against concerns that they were driven by within-experiment or within-group effects. We suggest that the modified ALE algorithm is theoretically advantageous compared with the current algorithm, and that the alternate organization of datasets is the most conservative approach for typical ALE analyses and other CBMA methods. Combining the two modifications minimizes both within-experiment and within-group effects, optimizing the degree to which ALE values represent concordance of findings across independent reports. |*Meta-Analysis as Topic [MESH] |Algorithms [MESH] |Brain Mapping/*methods [MESH] |Brain/*physiology [MESH] |Data Interpretation, Statistical [MESH] |Functional Neuroimaging/*methods [MESH] |Humans [MESH] |Image Processing, Computer-Assisted [MESH]Minimizing within-experiment and within-group effects in Activation Li(epmc).pdf DeepDyve Pubget Overpricing