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

Andrew B. Poppe, Krista Wisner, Gowtham Atluri, Kelvin O. Lim, Vipin Kumar, Angus W. MacDonald

Research output: Contribution to journalArticlepeer-review

26 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)641-659
Number of pages19
JournalCognitive, Affective and Behavioral Neuroscience
Issue number3
StatePublished - Sep 2013

Bibliographical note

Funding Information:
We are grateful to the Center for Magnetic Resonance Research for providing facilities and support to collect the neuroimaging data and for the computing resources and technical support from the University of Minnesota Supercomputing Institute. This project was supported by grants 5R01MH084861-03S1 (A.W.M.) and R01MH060662 (K.O.L.). K.M.W. was supported by the NIMH Training Grant 5T32MH017069-29. In addition, the work reported here was supported by the following ARRA-supported NIMH grants to the following investigators without whom this work would not have been possible: 5R01MH084840-03S1 (Deanna M. Barch), 5R01MH084826-03S1 (Cameron S. Carter), 5R01MH084828-0S13 (Steve M. Silverstein), and 5R01MH084821-03S1 (James M. Gold). We are also grateful to the staff and subjects for their time and efforts.


  • Cognitive control
  • Functional connectivity
  • Independent component analysis
  • Resting state
  • Retest reliability


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