Robust Data Driven Model Order Estimation for Independent Component Analysis of fMRI Data with Low Contrast to Noise

<div><p>Independent component analysis (ICA) has been successfully utilized for analysis of functional MRI (fMRI) data for task related as well as resting state studies. Although it holds the promise of becoming an unbiased data-driven analysis technique, a few choices have to be made prior to performing ICA, selection of a method for determining the number of independent components (nIC) being one of them. Choice of nIC has been shown to influence the ICA maps, and various approaches (mostly relying on information theoretic criteria) have been proposed and implemented in commonly used ICA analysis packages, such as MELODIC and GIFT. However, there has been no consensus on the optimal method for nIC selection, and many studies utilize arbitrarily chosen values for nIC. Accurate and reliable determination of true nIC is especially important in the setting where the signals of interest contribute only a small fraction of the total variance, i.e. very low contrast-to-noise ratio (CNR), and/or very focal response. In this study, we evaluate the performance of different model order selection criteria and demonstrate that the model order selected based upon bootstrap stability of principal components yields more reliable and accurate estimates of model order. We then demonstrate the utility of this fully data-driven approach to detect weak and focal stimulus-driven responses in real data. Finally, we compare the performance of different multi-run ICA approaches using pseudo-real data.</p></div

Run-specific changes in activation as captured by ICA and hypothesis-driven analysis.

<p>a) Run-specific time-courses for the stimulus-related independent component obtained using ICA_cat capture differences in stimulus-related contribution. “linefit to ICA” represents least squares approximation of the ICA time-course in terms of the reference function. b) These variations are in agreement with the maps obtained using hypothesis-driven (correlation-based) analysis. The images are shown with reversed contrast, so that activated pixels appear hot.</p

High run-to-run reproducibility of activation maps obtained using ICA.

<p>Coincidence maps, representing the percentage of single runs within a session for which ICA identified a given voxel as activated, are shown in the figure. To identify activated voxels, run-specific maps were converted to z-scores and thresholded (|z|>2.5). Negative values indicate that the voxel appears with negative intensity.</p

Strong agreement between model-free data-driven ICA and cross-correlation with stimulus model.

<p>a) A linear relationship is observed between 1) similarity (as measured by correlation) of the temporal profile of the stimulus-related IC and stimulus function (CC_ICA), and 2) average correlation of activated voxels (based on correlation-based activation maps) with stimulus function (CC_act). b) Bland–Altman plots and fitted linear trends of mean vs. difference between CC_ICA and CC_act. The difference shows negligible bias and an insignificant linear trend, suggesting strong agreement between CC_ICA and CC_act.</p

ROC curves (pseudo-real data with 0.4% activation above the baseline.

<p>ICA_avg provides more sensitivity for a given level of specificity, compared with ICA_cat and cross-correlation based hypothesis-driven analysis. This observation is consistent with <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094943#pone-0094943-g006" target="_blank">Figure 6</a> as well as the results obtained for real datasets (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094943#pone-0094943-g002" target="_blank">Figure 2</a>).</p

The 30 most discriminant connections identified–the connections are sorted with respect to the corresponding absolute value in the connectivity difference matrix <i>D</i>.

<p>The positive sign of the <i>D</i> value represents increased connectivity in epilepsy patients while the negative sign represents decreased connectivity in epilepsy patients. Among these 30 connections, 17 are inter-hemispheric (i.e. between left and right hemi-spheres) which are highlighted in italic font. Out of these 17 connections, total 7 connections are between bilaterally homologous brain regions which are highlighted by * in the serial column. Abbreviations: L–left hemi-sphere, R–right hemi-sphere.</p><p>The 30 most discriminant connections identified–the connections are sorted with respect to the corresponding absolute value in the connectivity difference matrix <i>D</i>.</p

Brain region functional network: visualization of the correlation matrix [8] and community matrix obtained using (5).

<p>The difference between healthy and epileptic subjects is not prominent in the correlation matrix while it is prominent in community matrix (highlighted by boxes). This figure is suitable for visualization in color display.</p

Summary of brain lobes functional alterations: (a) inter-group and (b) intra-group.

<p>For this analysis, the brain is considered to be made up of six lobes as suggested by Salvador <i>et al</i>. [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134944#pone.0134944.ref031" target="_blank">31</a>].</p

Relationship between the number of clusters found by AP clustering to the number of closest neighbors <i>n</i> in (4) used for computing the preference value <i>p</i>(<i>i</i>).

<p>(a) normal subject (80 in total), (b) epileptic patients (100 in total).</p

Classification accuracy with consistent neuroimaging marker identification method.

<p>For this experiment, 50% dataset is used for training and rest 50% for testing over 100 trials. It can be seen that the best accuracy is achieved with 450 connections.</p><p>Classification accuracy with consistent neuroimaging marker identification method.</p