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Dear Krista After quick reading your wonderful papers, I have some questions about the meta-ICA approach and want to hear some feedbacks from you (also the expert from the email list). Currently I am focus on relative large database (400 subjects) and want to perform meta-ICA approach to identify the "stable" sub-component of default mode network (dorsal/ventral and anterior part of DMN) for further dual regression analysis. Would you give me some advise of the following questions ? I plan to sample 50 subjects from the entire database randomly for 8-15 times and perform MELODIC ICA with 30 ICs for each resample. Do you think 50 subjects is enough for each individual MELODIC ICA? In your Neuroimage paper, I found you resample/reoder your data 50 times (with 20 ICs for each resample/reorder), but I think this will need a lot of memory for final meta-ICA analysis (the final input of meta-ICA is a single nifti file with 1000 image volumes). I only have a personal computer with 64G memory (I am using centos 5 - 64 bit version), do you think the lower number of resample (only 8-15 times) will cause the instability of identify the DMN subnetwork (or any other network in the brain)? How many ram do you have for your analysis computer? Thanks Best Paul Date: Fri, 10 Jan 2014 10:20:02 -0600 From: [log in to unmask] Subject: Re: [FSL] Different melodic results To: [log in to unmask] Hi Xi,Others have encountered this problem as well, that is somewhat different MELODIC results depending on the random seed of the algorithm and the order of the subjects. For those worried about the impact of these effects on the reliability of the metrics, some approaches are available. One is a meta-MELODIC and another is ICASSO. Together with colleagues in my lab we tested the meta-MELODIC method and how this play out with regard to reliability in two related papers. They are listed below. Wisner KM, Atluri G, Lim KO, Macdonald AW 3rd.Neurometrics of intrinsic connectivity networks at rest using fMRI: retest reliability and cross-validation using a meta-level method. Neuroimage. 2013 Aug 1;76:236-51. Poppe AB, Wisner K, Atluri G, Lim KO, Kumar V, Macdonald AW 3rd.Toward a neurometric foundation for probabilistic independent component analysis of fMRI data. Cogn Affect Behav Neurosci. 2013 Sep;13(3):641-59. We continue to pursue this work and are looking into ways to further improve reliability, as well as examining the reliability of different types of metrics derived from meta-MELODIC + Dual Reg results. Hope that helps.Best,Krista On Thu, Jan 9, 2014 at 3:26 PM, Tan, Xi <[log in to unmask]> wrote: Hi, Mark, Thank you for your reply. I'm now able to post to the list after being manually added as a subscriber by the helpdesk. I ran group melodic 6 times, of which 2 have the same subjects order, and the others have different subject orders from the 2 and from each other. The resulting 6 default mode network components, when thresholded by 3, are pretty consistent in precuneus and posterior cingulate region, but vary a lot especially in medial frontal region and right temporal region. If you would like, I can send you my melodic IC results. Thank you, Xi -- Krista Wisner, M.A. Ph.D. CandidateClinical Science and Psychopathology Research Program University of Minnesota N427 Elliott Hall, 75 E. River Rd. Minneapolis, MN 55455