Evaluation of spatio-temporal decomposition techniques for group analysis of fMRI resting state data sets

Adaptive independent vector analysis for multi-subject complex-valued fMRI data

“…Compared to tensor decomposition, ICAbased analysis extracts subject-specific TCs and/or SMs for emphasizing inter-subject variability. Two such approaches are group ICA (Calhoun et al, 2001(Calhoun et al, , 2008Guo and Pagnonib, 2008;Erhardt et al, 2011;Calhoun and Adali, 2012b;Eloyan et al, 2013;Afshin-Pour et al, 2014) and independent vector analysis (IVA, a kind of joint ICA) (Lee et al, 2008a;Dea et al, 2011;Michael et al, 2014;Ma et al, 2014;Laney et al, 2015aLaney et al, , 2015bGopal et al, 2015Adali et al, 2015). While group ICA provides individual TCs or SMs via ICA of temporally or spatially concatenated multi-subject fMRI datasets, IVA generates individual TCs and SMs via joint ICA of multi-subject fMRI datasets where similar SMs among different subjects were concatenated as source component vectors (SCVs).…”

Local low frequency fMRI synchrony is a proxy for the spatial extent of functional connectivity

“…The LASY maps do not depict functional connectivity that is smoothly varying or approximately locally homogenous. On the contrary, they point to a much more fine scale and irregular structure than the one represented by graph nodes and parcellations commonly defined in fcMRI studies 17,18,30,31 . The spatial pattern of functional connectivity presented here seem to indicate that the functional architecture of the cortex may be better characterized by a collectivist model of many interacting structural elements than by a connectionist model of a relatively small number of spatio-temporally static nodes 32 .…”

A hierarchy of cognitive brain networks revealed by multivariate performance metrics

“…In all tasks, the inter-trial interval was 2000 ms. An fMRI run was acquired for each subject using a block design with eight alternating taskfixation conditions (FIX) per run (20-25 scans/task-period alternating with 10 scans/fixation-period, TR=2s) for four tasks (ATT, DMS, PMT, RT) with two repetitions of each task. Preprocessing of the fMRI time series data included the following steps for each subject [16]: (1) slice-timing correction, (2) rigid body within-subject realignment (3) spatial smoothing with a 7 mm FWHM Gaussian kernel, (4) artifact-carrying independent components were qualitatively identified and removed using the MELODIC package [17], (5) between-subject alignment of fMRI scans based on spatial normalization of the structural scan to a study specific template (details are described in [15]), (5) using standard white matter and CSF masks, mean within-mask signals were obtained and regressed from the time course of each voxel, (6) temporal linear trends were regressed out per voxel, (7) the scans were masked with an approximate whole-brain mask retaining 21401 voxels. For the classification analysis we discarded two transition scans at the start of each block, which gave 18-23 scans per task block for an average of 83.3 scans total, per subject for a data matrix of 1584 x 21401 with J=19 subjects.…”

“…The validity of these two approaches has been significantly increased by recent work linking ICA spatial components from both resting state fMRI experiments and meta-analytic spatial activation summaries from BrainMap [3,4]. In addition, we have recently shown that in resting state experiments essentially the same spatial subspace is spanned by ICA, and subsampled techniques based on an initial bivariate, spatio-temporal principal component analysis (PCA) followed by agnostic canonical variates analysis (aCVA, a form of multi-class linear discriminant) or generalized canonical correlation analysis (gCCA) [5]. Therefore, the initial bivariate decomposition is likely to be unimportant (i.e., ICA or PCA) when followed by LD or gCCA.…”