Elsevier

Magnetic Resonance Imaging

Volume 22, Issue 10, December 2004, Pages 1493-1504
Magnetic Resonance Imaging

Research article
Cortex-based independent component analysis of fMRI time series

https://doi.org/10.1016/j.mri.2004.10.020Get rights and content

Abstract

The cerebral cortex is the main target of analysis in many functional magnetic resonance imaging (fMRI) studies. Since only about 20% of the voxels of a typical fMRI data set lie within the cortex, statistical analysis can be restricted to the subset of the voxels obtained after cortex segmentation. While such restriction does not influence conventional univariate statistical tests, it may have a substantial effect on the performance of multivariate methods.

Here, we describe a novel approach for data-driven analysis of single-subject fMRI time series that combines techniques for the segmentation and reconstruction of the cortical surface of the brain and the spatial independent component analysis (sICA) of the functional time courses (TCs). We use the mesh of the white matter/gray matter boundary, automatically reconstructed from high-spatial-resolution anatomical MR images, to limit the sICA decomposition of a coregistered functional time series to those voxels which are within a specified region with respect to the cortical sheet (cortex-based ICA, or cbICA). We illustrate our analysis method in the context of fMRI blocked and event-related experimental designs and in an fMRI experiment with perceptually ambiguous stimulation, in which an a priori specification of the stimulation protocol is not possible.

A comparison between cbICA and conventional hypothesis-driven statistical methods shows that cortical surface maps and component TCs blindly obtained with cbICA reliably reflect task-related spatiotemporal activation patterns. Furthermore, the advantages of using cbICA when the specification of a temporal model of the expected hemodynamic response is not straightforward are illustrated and discussed. A comparison between cbICA and anatomically unconstrained ICA reveals that — beside reducing computational demand — the cortex-based approach improves the fitting of the ICA model in the gray matter voxels, the separation of cortical components and the estimation of their TCs, particularly in the case of fMRI data sets with a complex spatiotemporal statistical structure.

Introduction

One of the major advantages of functional magnetic resonance imaging (fMRI) over the other brain mapping techniques is the potentiality to look at the relationships between brain anatomy and function noninvasively and on an individual basis. This unique feature of fMRI is fully exploited when the functional topography of the brain areas is analyzed in relation to an explicit anatomical representation of the subject's cortex. In such cases, maps of brain activation as obtained from the statistical analysis of the functional time series are visualized on a folded or morphed (inflated, flattened) computerized reconstruction of the subject's cortical surface (see, e.g., Refs. [1], [2], [3], [4], [5], [6]).

In this type of approach, anatomical and functional information is merged together only at the final stage of the data analysis stream and mainly for the purpose of visualization. Recently, individual anatomical constraints have been used at an earlier stage as an additional constraint for the statistical analysis of functional imaging data [7], [8], [9]. Goebel and Singer [7] used the reconstruction of the cortical surface to restrict the detection of functional activation only to those voxels of a functional data set that lie within the cortex. This subset of voxels represents about 20% of the more than 100,000 voxel time courses (TCs) that are usually recorded. When the cortex is the target of the investigation, this subset also contains all the relevant functional information. This simple approach has been shown to enhance the sensitivity and the specificity of conventional statistical methods by reducing the severity of the multiple-comparison problem in whole-brain fMRI experiments. Kiebel et al. [8] used more directly individual anatomical constraints in the analysis of fMRI time series by introducing the framework of anatomically informed basis functions (AIBF). AIBFs allow incorporating anatomical prior knowledge based on reconstructed gray matter surfaces into spatiotemporal models of the fMRI time series. Andrade et al. [9] showed that cortical surface-based smoothing of functional time series increases the sensitivity of conventional voxel-based approaches that rely on general linear model (GLM) parameter estimation.

In all these cases, methods employed for the functional analysis were univariate [7], [9] or multivariate [8] hypothesis-driven statistical methods that require an a priori specification of a temporal model of the effects of interest. Exploratory data-driven approaches that do not make assumptions about the time profile of the effects of interest offer a complementary perspective to the conventional analysis of fMRI time series [10]. This might be especially useful in those cases in which the event of interest is not predictable (hallucinations, epileptic seizures) and when the hemodynamic response is difficult to model, such as in event-related designs with complex cognitive tasks or in experiments with perceptually ambiguous stimuli (see below).

Among data-driven methods, independent component analysis (ICA, [11]) appears to be particularly promising for the analysis of fMRI data (see Ref. [12] for a recent review). In the first applications of ICA to fMRI data analysis [13], [14], the ICA variant used is spatial in that the observed 4D fMRI signals are modeled as linear “mixtures” of unknown spatially independent processes (e.g., BOLD signal changes related to the cognitive task, physiological pulsations, head movements, artifacts, etc.), each contributing to the data set with an unknown time profile. An adaptive ICA algorithm [15] was adopted to decompose the time series into spatial components (ICs), each having a unique TC. The decomposition process maximizes the spatial statistical independence of the components, the idea being that the new representation of the data (ICs/TCs) reflects the “unmixed” configuration of the original spatial processes. Less frequently, a temporal ICA variant has also been adopted [16], [17], [18].

In the spatial ICA (sICA), as proposed in McKeown et al. [14], the entire matrix of the fMRI time series is blindly decomposed. This matrix includes not only signals from the cerebral cortex, but also from other parts of the brain, including subcortical structures, white matter and ventricles. The resulting decomposition, thus, also models the dynamics of the signal in these other structures.

Here, we combine sICA with methods of cortex segmentation and reconstruction and restrict the sICA decomposition to the “cortical” subregion of the matrix. We use the mesh of the white matter/gray matter boundary, automatically reconstructed from T1-weighted MR images [19], to limit the spatial ICA only to those voxels of the T2*-weighted functional time series which are within a specified region with respect to the cortical sheet (cortex-based ICA, or cbICA). We expect this cortex-based approach to improve the separation and anatomical accuracy of the ICs that represent cortical cognitive activations for two reasons. First, the number of voxels included in the data matrix does not affect the maximal number of spatial components which can be obtained (it equals the number of time samples, i.e., functional scans). Exclusion of extracortical contributions to the signal data set, thus, allows using the same number of components, otherwise used to separate noninteresting processes (e.g., signal changes in the ventricles, near the eyes, imaging artifacts), only for the processes occurring on the cortical surface. Second, improvements are also expected because of consideration on how the observed spatial mixtures are influenced by the “nature” of the included signals. Signals from, for example, the ventricles or near the eyes are “uninformative” with respect to the signals on the cortex. Thus, their inclusion in the data matrix leads to an increase of the complexity of the mixtures (in terms of number of sources) but does not improve the estimation of the cortical sources. Conversely, the restricted (yet statistically acceptable) sample of spatial observations considered in the cortex-based approach is highly “informative” with respect to the “interesting” sources and may lead to a better estimation of their spatial distribution.

We illustrate the cbICA framework in the context of whole-brain fMRI block and event-related experimental designs and in an experiment with perceptually ambiguous stimuli, in which the subject's perceptual state is not known a priori. Functional MRI time series is decomposed blindly into cortical surface components (CSCs) that can be directly visualized on the folded or morphed representation of the cortex. Results are compared to the results of conventional methods of linear multiple regression and principal component analysis (PCA), as well as to anatomically unconstrained spatial ICA [14].

Section snippets

General description of the cbICA approach

The steps of the cbICA are schematically illustrated in Fig. 1. Input data sets consist of a high-spatial-resolution 3D anatomical volume and a functional time series of the same subject, which have been previously coregistered (see below).

The initial steps of the cbICA approach are the generation of a cortical mask of each subject and the selection of a restricted functional data set consisting of the TCs corresponding to the cortical voxels in the functional time series. First, a polygonal

Experiment 1 (blocked design)

Fig. 2A shows the components (referred to as CSC1 and CSC2) corresponding to the cortical response to the visual stimulation (objects presented in the right and left visual hemifield alternately). CSC1 and CSC2 are the two components whose TCs were most highly correlated (R=.76 and R=.73) with hemodynamic predictor functions computed on the basis of the stimulation protocol by a linear model. The component maps were projected onto the folded and flattened representation of the subject's cortex.

Discussion

Cortex-based ICA is an approach for the analysis of fMRI data that combines techniques for the reconstruction and morphing (inflation, flattening) of the cortex from anatomical MR images with sICA of functional time series. We have demonstrated the validity of this approach by analyzing various fMRI data sets collected with different experimental designs (blocked and event-related designs, ambiguous stimulation) and by comparing the results we obtained with those of conventional

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