Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28829807
&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28829807
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 PLoS+One
2017 ; 12
(8
): e0182797
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
MELD: Mixed effects for large datasets
#MMPMID28829807
Nielson DM
; Sederberg PB
PLoS One
2017[]; 12
(8
): e0182797
PMID28829807
show ga
Mixed effects models provide significant advantages in sensitivity and
flexibility over typical statistical approaches to neural data analysis, but mass
univariate application of mixed effects models to large neural datasets is
computationally intensive. Threshold free cluster enhancement also provides a
significant increase in sensitivity, but requires computationally-intensive
permutation-based significance testing. Not surprisingly, the combination of
mixed effects models with threshold free cluster enhancement and nonparametric
permutation-based significance testing is currently completely impractical. With
mixed effects for large datasets (MELD) we circumvent this impasse by means of a
singular value decomposition to reduce the dimensionality of neural data while
maximizing signal. Singular value decompositions become unstable when there are
large numbers of noise features, so we precede it with a bootstrap-based feature
selection step employing threshold free cluster enhancement to identify stable
features across subjects. By projecting the dependent data into the reduced space
of the singular value decomposition we gain the power of a multivariate approach
and we can greatly reduce the number of mixed effects models that need to be run,
making it feasible to use permutation testing to determine feature level
significance. Due to these innovations, MELD is much faster than an element-wise
mixed effects analysis, and on simulated data MELD was more sensitive than
standard techniques, such as element-wise t-tests combined with threshold-free
cluster enhancement. When evaluated on an EEG dataset, MELD identified more
significant features than the t-tests with threshold free cluster enhancement in
a comparable amount of time.
free
free
free
