Ten Key Observations on the Analysis of Resting-state Functional MR Imaging Data Using Independent Component Analysis

Abstract

For more than 20 years, the powerful, flexible family of independent component analysis (ICA) techniques has been used to examine spatial, temporal, and subject variation in functional magnetic resonance (fMR) imaging data. This article provides an overview of 10 key principles in the basic and advanced application of ICA to resting-state fMR imaging. ICA's core advantages include robustness to artifact; false-positives and autocorrelation; adaptability to variant study designs; agnosticism to the temporal evolution of fMR imaging signals; and ability to extract, identify, and analyze neural networks. ICA remains in the vanguard of fMRI methods development.

Keywords: Brain; Connectivity; Dynamics; Function; Group ICA; Independent component analysis; fMR imaging.

Fig. 1

A spectrum of data-drivenness.

Fig. 2

Examples of graphical output from the GIFT ICA software.

Fig. 3

GIG-ICA for artifact removal. Individual independent components (ICs) and time courses (TCs) for 1 subject obtained from Individual ICA Artifact Removal Plus Group ICA (IRPG), GIG-ICA, and standard group ICA (GICA). The values in parentheses under each estimated IC are the relevant correlation coefficients between the IC and the ground truth (GT) source. The last row shows related TCs. The correlation values under TCs from left to right correspond with IRPG, GIG-ICA, and GICA, respectively. Note that only the nonartifact ICs/TCs are shown. (Modified from Du YH, Allen EA, He H, et al. Artifact removal in the context of group ICA: a comparison of single-subject and group approaches. Hum Brain Mapp 2016;37(3):1005–25.)

Fig. 4

Task-related modulation in blood oxygenation level-dependent (BOLD) signal during the congruent condition of the flanker task. (A) Color on the brain images shows task-related increases and decreases in BOLD signal as revealed by GLM-based analyses. The color bar indicates t values. (B1–B3) Color on the brain images shows regions covered by positive, negative, and neutral ICs, respectively. The color bar indicates the number of overlapping ICs. (Modified from Xu JS, Calhoun VD, Worhunsky PD, et al. Functional network overlap as revealed by fMRI using sICA and its potential relationships with functional heterogeneity, balanced excitation and inhibition, and sparseness of neuron activity. PLoS One 2015;10(2):e0117029.)

Fig. 5

Example ICA component spatial maps from rest fMR imaging data. (Modified from Allen EA, Erhardt EB, Damaraju E, et al. A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci 2011;5(2):2.)

Fig. 6

Example of FNC. The component maps are ordered as shown in Fig. 5. Considerable modularity is observable within the matrix; for example, visual and motor regions tend to be most highly correlated with themselves and the default mode network is showing anticorrelation with multiple other networks. (Modified from Allen EA, Erhardt EB, Damaraju E, et al. A baseline for the multivariate comparison of resting-state networks. Front Syst Neurosci 2011;5(2):2.)

Fig. 7

The top row shows a temporal lobe component from resting-state analysis and the bottom row is a predicted temporal lobe component from a different data set.

Fig. 8

Ratio of false-positives found for group ICA with 28 components on a population of 603 healthy subjects. One million iterations were performed to estimate the false-positive ratio by randomly assigning healthy subjects to 1 of 2 groups.

Fig. 9

Effect of age on the amplitude of low-frequency fluctuations (ALFF). Violin plots show the distributions of ALFF estimates for young (left) and old (right) subjects (40 in each group) based on the first-level (A) and second-level (B) analyses. For the first-level analysis, ALFF values are calculated from the subject-specific TCs; for second-level analysis, ALFF values are the subject loading parameters from the ICA mixing matrix (matrix in Fig. 1B). Horizontal bars indicate the medians for each group. Because data are skewed, group differences are assessed with the nonparametric Wilcoxon rank sum test for equal medians (z-statistics are based on approximate normal distribution). Asterisks denote significantly different medians at P<.001, uncorrected. (From Calhoun VD, Allen E. Extracting intrinsic functional networks with feature-based group independent component analysis. Psychometrika 2013;78(2):243–59.)

Fig. 10

Comparison of components extracted from 75 component ICA performed on resting-state data with 120 time points in 1190 neurotypical young adults. On the left is an intrinsic network or good component. Note the smooth shape to the power spectra (dynamic range) and high low-frequency to high-frequency power ratio. The brain activation pattern is spatially aggregated and clearly in frontal gray matter areas. On the right is a bad component. Here, there is a shallow, ridged appearance to the spectra and low power ratio. Activation is restricted to the ventricles. Likely this component represents artifact from cerebrospinal fluid in the brain ventricles.

Fig. 11

Population average of static windowed functional network connectivity matrix; that is, using all 162 time points (no windowing) for 47 ICA components (networks) obtained from a group ICA decomposition. Grid lines bound 7 functional domains. Rectangular pull-outs are 15 joint functional domain connectivity blocks estimated from the ICA data using the approach described by Miller and colleagues. (Data from Miller RL, Vergara VM, Keator DB, et al. A method for inter-temporal functional domain connectivity analysis: application to schizophrenia reveals distorted directional information flow. IEEE Trans Biomed Eng 2016;63(12):2525–39.)

Fig. 12

Example of dynamic functional network connectivity states estimated from a resting fMR imaging data set for which concurrent EEG data were also collected. Ordering the fMR imaging states according to EEG drowsiness measures reveals a striking pattern, because drowsiness increases the anticorrelated functional connectivity with the default mode network and diminishes that with other networks.

Fig. 13

Nonlinear ICA of sMRI analysis identifies significant nonlinear effects between schizophrenia and healthy controls. On the left is an example of a component captured by the nonlinear ICA approach and on the right is a plot showing the evidence of a nonlinear effect after removing the linear relationship (which only appears in certain components). (Data from Castro E, Hjelm RD, Plis SM, et al. Deep independence network analysis of structural brain imaging: application to schizophrenia. IEEE Trans Med Imaging 2016;35(7):1729–40.)