Format

Send to

Choose Destination
See comment in PubMed Commons below
Cogn Affect Behav Neurosci. 2013 Sep;13(3):641-59. doi: 10.3758/s13415-013-0180-8.

Toward a neurometric foundation for probabilistic independent component analysis of fMRI data.

Author information

1
Department of Psychology, University of Minnesota, N426 Elliott Hall , 75 East River Road, Minneapolis, MN, 55455, USA, poppe076@umn.edu.

Abstract

Improved fMRI data analysis methods hold promise for breakthroughs in cognitive and affective neuroscience. Group probabilistic independent component analysis (pICA), such as that implemented by MELODIC (Beckmann & Smith IEEE Transactions on Medical Imaging 23:137-152, 2004), is one popular technique that typifies this development. Recently pICA has been proposed to be a reliable method for studying connectivity networks (Zuo et al. NeuroImage 49:2163-2177, 2010); however, there is no "standard" way to complete a pICA, and the full impact of the options on neurometric properties of resulting components is unknown. In the present study, we sought to assess the robustness, reproducibility, and within-subject test-retest reliability of ICA in two data sets: The first included 30 subjects imaged 3 weeks apart while completing a cognitive control task, and the second included 27 subjects imaged 9 months apart during rest. In addition to examining the impact of analytic parameters on the neurometrics, this study was the first to simultaneously investigate within-subject reliability of ICA-derived components from rest and task fMRI data. Results suggested that for both task and rest, meta-level analyses using 25 subject orders optimized robustness of the components. The impact of dimensionality and voxel threshold for components was subsequently examined regarding properties of reproducibility and within-subject retest reliability. Component thresholds between 0.2 and 0.6 of the maximum value optimized reproducibility across multiple dimensionalities and produced generally fair to moderate reliability estimates (Cicchetti & Sparrow American Journal of Mental Deficiency 86:127-137, 1981). These guidelines strengthen the foundation for this data-driven approach to fMRI analysis by providing prescriptive findings and a descriptive set of neurometrics for resting-state and task fMRI.

PMID:
23836423
DOI:
10.3758/s13415-013-0180-8
[Indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Springer
    Loading ...
    Support Center