Multisubject independent component analysis of fMRI: a decade of intrinsic networks, default mode, and neurodiagnostic discovery

Abstract

Since the discovery of functional connectivity in fMRI data (i.e., temporal correlations between spatially distinct regions of the brain) there has been a considerable amount of work in this field. One important focus has been on the analysis of brain connectivity using the concept of networks instead of regions. Approximately ten years ago, two important research areas grew out of this concept. First, a network proposed to be "a default mode of brain function" since dubbed the default mode network was proposed by Raichle. Secondly, multisubject or group independent component analysis (ICA) provided a data-driven approach to study properties of brain networks, including the default mode network. In this paper, we provide a focused review of how ICA has contributed to the study of intrinsic networks. We discuss some methodological considerations for group ICA and highlight multiple analytic approaches for studying brain networks. We also show examples of some of the differences observed in the default mode and resting networks in the diseased brain. In summary, we are in exciting times and still just beginning to reap the benefits of the richness of functional brain networks as well as available analytic approaches.

Figure 1. Number of fMRI papers using ICA by year with a few highlighted landmarks relating group ICA, default mode, and brain disease

Figure 2

Comparison of ICA for fMRI and the general linear model: ICA is a linear space/time decomposition similar to the GLM. The difference is the GLM fixed the design matrix and estimates univariate parameter fits whereas ICA estimates the equivalent mixing matrix by maximizing spatial independence among the rows of the component matrix.

Figure 3

Stages of group ICA: The analysis starts with spatially normalized fMRI data and proceeds through a data reduction step using PCA, followed by ICA of the reduced data, then back-reconstruction is used to compute single-subject maps and timecourses for each component which are then analyzed statistically depending on the question of interest.

Figure 4

Forward estimation approaches: ICA of groups of subjects can be approached in different ways. The most flexible (and most challenging) is to use single-subject ICA and attempt to group common components post-hoc. Group ICA with temporal concatenation is the most widely used approach (and arguable has assumptions which are the most compatible with the data such as spatial stationarity). Tensor ICA stacks the data into a cube. And one can also spatially concatenate the data.

Figure 5

Three output measures from group ICA: ICA enable investigation of multiple output measures including 1) spatial maps (left panel) for each component which can be grouped based on the regions involved, 2) functional network connectivity (correlation among ICA timecourses) provides a measure of how temporally correlated the different components are, note the block structure is consistent with the grouping on the left, and 3) spectra of the ICA timecourses (which can help identify artifacts which tend to have much more high frequency power).

Figure 6

Spatial dependencies naturally group functionally related networks: It is intuitive that there are still temporal dependencies in the data, but this figure shows a grouping of 6 components containing spatial dependencies measured via mutual information. These spatial dependencies can be quite informative and tend to group artifacts and sensibly group functional regions together as well.

Figure 7

Hubs of spatial dependence among 35 components for schizophrenia and controls: Using a mutual information-based measure, we can compute graph theoretic measures including the shown graph structure of spatial dependencies, which is complementary to the standard approach of using the temporal information to compute the graph structure and appears to be informative about patient versus control differences.

Figure 8

Comparison of ICA and sbICA in one participant: Results for task-related component for blind ICA (left) and sbICA (right). ICA tends to capture primarily temporal lobe regions and is not highly task-related. The correlation with the novel/target regressor (e.g. the task-relatedness) is significantly increased (0.51 vs. 0.33) for the sbICA analysis and includes expected motor regions (expected since the target stimulus required a button press).

Figure 9

Results from a spatially constrained ICA analysis: (top) spatial templates used in the ICA-R algorithm for the visuomotor (VM) task for right and left stimulation in addition to a default mode template. Results from an unconstrained Infomax algorithm are shown in the middle row. The ICA-R algorithm using all three templates is shown. Left and Right visuomotor results are similar but slightly improved for ICA-R whereas the default mode result (right column) is markedly improved for the ICA-R.

Figure 10

Infomax, EBM, and FBSS results. are shown from left to right: T maps of three algorithms are generated for the AOD task. Each component of interest is entered into a one-sample test and is thresholded at P < 0.001 (FWE corrected). Six slices from each component are shown. The more flexible FBSS and EBM appear to have more included positive regions and less anticorrelated white matter signal.

Figure 11

Dynamic FNC results: (top) low frequency differences in lateral frontal components and (bottom) differences (mainly later in experiment) between temporal lobe and anterior DMN.

Figure 12

Variability of dynamic FNC states (components on x/y axes) varies dramatically in schizophrenia patients (left) and healthy controls (right): This suggests there is important information about the patients in the dynamic changes which is not detectable in the static FNC results.