Functional connectivity differences in first episode and chronic schizophrenia patients during an auditory sensorimotor task revealed by independent component analysis of a large multisite study

Juárez, Michelle; White, Thomas P.; Pearlson, G.D.; Bustillo, Juan; Ho, Beng-Choon; Bockholt, H. Jeremy; Clark, VP; Gollub, Randy L.; Magnotta, Vincent; Machado, Gislaine Cristina Lopes; Calhoun, VD

doi:10.1016/s1053-8119(09)71664-7

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2015

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“…However MIGP can be seen to be a slight variation of the familiar “3-step” PCA available in the GIFT toolbox since 2004 10-12 . This is most evident from the second paragraph of the Conclusions section where the parallelization scheme described therein essentially outlines the steps in the original 3-step PCA.…”

Section: Accuracy Of Resultsmentioning

confidence: 99%

Comparison of PCA approaches for very large group ICA

2015

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Large data sets are becoming more common in fMRI and, with the advent of faster pulse sequences, memory efficient strategies for data reduction via principal component analysis (PCA) turn out to be extremely useful, especially for widely used approaches like group independent component analysis (ICA). In this commentary, we discuss results and limitations from a recent paper on the topic and attempt to provide a more complete perspective on available approaches as well as discussing various issues to consider related to large group PCA for group ICA. We also provide an analysis of computation time, memory use, and number of dataloads for a variety of approaches under multiple scenarios of small and extremely large data sets.

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Section: Accuracy Of Resultsmentioning

confidence: 99%