Abstract
Functional magnetic resonance imaging (fMRI) during a resting-state condition can reveal the co-activation of specific brain regions in distributed networks, called resting-state networks, which are selected by independent component analysis (ICA) of the fMRI data. One of the major difficulties with component analysis is the automatic selection of the ICA features related to brain activity. In this study we describe a method designed to automatically select networks of potential functional relevance, specifically, those regions known to be involved in motor function, visual processing, executive functioning, auditory processing, memory, and the default-mode network. To do this, image analysis was based on probabilistic ICA as implemented in FSL software. After decomposition, the optimal number of components was selected by applying a novel algorithm which takes into account, for each component, Pearson's median coefficient of skewness of the spatial maps generated by FSL, followed by clustering, segmentation, and spectral analysis. To evaluate the performance of the approach, we investigated the resting-state networks in 25 subjects. For each subject, three resting-state scans were obtained with a Siemens Allegra 3 T scanner (NYU data set). Comparison of the visually and the automatically identified neuronal networks showed that the algorithm had high accuracy (first scan: 95%, second scan: 95%, third scan: 93%) and precision (90%, 90%, 84%). The reproducibility of the networks for visual and automatic selection was very close: it was highly consistent in each subject for the default-mode network (≥92%) and the occipital network, which includes the medial visual cortical areas (≥94%), and consistent for the attention network (≥80%), the right and/or left lateralized frontoparietal attention networks, and the temporal-motor network (≥80%). The automatic selection method may be used to detect neural networks and reduce subjectivity in ICA component assessment.
Highlights
Functional magnetic resonance imaging measures the hemodynamic response induced by neural activity and permits the detection of active brain regions associated with one or more tasks
Tables S1, S2 summarize the performance of our method in 25 subjects: in the first scan 194 out of a total of 577 components decomposed by PICA were true positives, i.e., the number of resting-state networks the method correctly recovered; 22 were false positives; 7 were false negatives; and 354 were true negatives with an accuracy of 95% and a precision of 90%
In the second scan 191 out of a total of 506 components selected by FSL were true positives; 21 were false positives; 2 were false negatives; and 292 were true negatives with an accuracy of 95% and a precision of 90%
Summary
Functional magnetic resonance imaging (fMRI) measures the hemodynamic response induced by neural activity and permits the detection of active brain regions associated with one or more tasks. The most commonly used method to analyze fMRI data is the hypothesisdriven, voxel-based statistical method as a correlation method (Bandettini et al, 1993) and the general linear model (GLM) (Friston et al, 1995). Because GLM and correlation methods are unable to identify spontaneous brain activity, other techniques are required to identify the spatial patterns of coherent BOLD activity. The simplest technique currently being developed for the analysis of resting-state data is to extract the BOLD time course from a region of interest (seed region) and determine the temporal correlation between the extracted signal and the time course from all other brain voxels. In the absence of a standard paradigm design, a multivariate approach/analysis such as independent component analysis (ICA) is the one most often used
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