fMRI Data Analysis

Stage 1: ROI analysis of sensory cortices

To investigate top-down modulatory effect of multisensory attention on respective sensory regions, we conducted a ROI-based analysis (Poldrack, 2007) using retinotopically defined visual ROIs and functionally defined primary somatosensory ROIs. We used all EPI data from all runs, and calculated mean activity in each ROI individually for each subject. The EPI volumes were initially realigned to correct for head movements artefacts, and then smoothed using 8mm isotropic Gaussian kernel. Afterwards, a high-pass filter at 128s was applied to remove slow drifts in the time series. Using SPM8, we first estimated two beta-contrasts for two attentional conditions, attention to vision and attention to touch, collapsed across attended sides. Mean activation for each ROI and condition was then calculated using a custom MATLAB script, by averaging beta-contrast value of that condition for all voxels in that ROI, producing single value for each ROI and subject. The result was then summarized on a group- level by averaging the mean activity of each ROI across all subjects, yielding a single activation value for each ROI and attentional condition.

Stage 2: univariate conjunction analysis

We conducted a GLM conjunction analysis to examine commonly activated voxels in the visual and tactile attention conditions. Neuroimaging data were analysed using SPM8 (Friston et al., 1995b, 2006). Before the analysis, all EPI runs were first realigned to correct for head movements. In this analysis, we used EPI data from all odd numbered runs (the first data set), and reserved all even numbered runs (the second data set) for subsequent ROI-based MVPA

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analysis. Independent datasets were used to avoid circularity that could bias the analysis results (Kriegeskorte et al., 2009).

The first dataset was spatially smoothed with an 8mm isotropic full-width half-maximum Gaussian kernel. A high-pass filter at 128s was then applied to the dataset together with pre- whitening by AR(1) autoregressive model. Analysis was performed in individual native space (first level analysis) to find whole-brain activations correlated with each attentional condition. Four regressors corresponding to each attention condition (visual-left, visual-right, tactile-left, and tactile-right) were modelled by convolving a canonical hemodynamic response function with box-car models that represent the onset and duration of individual sustained attention blocks. The contrast of interest in this analysis was conjunction between the visual attention condition and tactile attention condition in the respective location: (visual-left AND tactile- left) and (visual-right AND tactile-right). GLM conjunction analysis with the global-null hypothesis (Friston et al., 2005) was computed on the whole-brain volume to produce the contrasts (p < 0.001, uncorrected). These conjunction maps were then converted into a binary mask to produce ROIs for ROI-based MVPA analysis described in the 3rd analysis.

Whole-brain statistical maps shown in Figure 4.4a and 4.4c were produced using similar steps of analysis described above. Individual structural images were co-registered with the EPI volumes and normalized into SPM-MNI coordinates. The normalization was then applied to all EPI volumes in the first dataset. After smoothing, high-pass filtering and modelling of the four regressors, first level GLM analysis was performed to produce four contrasts ([visual- left], [tactile-left], [visual-right], [tactile-right]) for each subject (p <0.001, uncorrected). The resulting contrasts of all participants were entered into a second-level random effects analysis for computing conjunction between [visual-left AND tactile-left] contrast and [visual-right and tactile-right] contrast (threshold set at p < 0.05, FDR).

Stage 3: ROI-based MVPA analysis

The second dataset, consisting of EPI data from all even numbered runs, was subjected to multivoxel pattern analysis (MVPA) to test whether groups of commonly activated voxels found in the previous analysis contained distributed information that could distinguish currently attended modality. For each participant, whole-brain conjunction map ROI obtained

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from the previous analysis were parcellated into different sections according to their individual brain‟s anatomical structures, resulting in several ROIs corresponding to different brain regions. We used Mindboggle software (Klein et al., 2005) for parcellating and labelling brain structural volumes into commonly recognized regions.

Multivariate pattern classification of the second dataset was undertaken using the Princeton MVPA Toolbox for MATLAB (Detre et al., 2006) and a linear support vector machine (SVM) as the learning algorithm (Cortes and Vapnik, 1995). Pattern analysis was performed in individual native space using raw, unsmoothed EPI data. MVPA for each participant and each ROI was conducted in several steps: First, raw EPI data of the second dataset were loaded together with the ROI mask (voxels outside the mask were discarded). Second, the BOLD time series was normalized using a z-score transform. Third, a binary regressor matrix specifying task condition blocks (visual or tactile) was loaded as category labels for each sample volume. The regressor was shifted by 6 seconds (2 TRs) to account for haemodynamic lag in the BOLD signal. Fourth, cross-validation indices were prepared for a leave-one-run- out training procedure. In this procedure, N-1 runs were used to train the classifier and the last remaining independent run was used for the testing set. Each different run was used in turn as a testing set, so with N runs the classifier was tested and trained exactly N times. Fifth, classification accuracy was computed as the average of N times of training and testing in the cross-validation procedure. Sixth, group-level classification accuracy was then computed by averaging classification result of all participants for each ROI. Finally, to assess statistical significance of the classification result, we undertook nonparametric permutation testing to estimate the null distribution (Golland and Fischl, 2003; Pereira and Botvinick, 2011). The category labels within each training set were randomly permuted 1000 times, and for each random permutation cross-validation classification was conducted, resulting in 1000 samples of accuracy under the null-hypothesis (null-samples) for each subject. On a group-level, mean classification accuracy under the null-hypothesis was computed by randomly selecting one sample from the null-samples of each subject, and then averaged the accuracy values across all subjects. This was repeated 100,000 times and the final group-level p-value for each ROI was estimated by computing the proportion of these random mean classification accuracies that were greater than or equal to the actual mean classification accuracy (i.e. p = [# of random mean classification accuracies >= actual mean classification accuracy] / 100,000).

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Stage 4: whole-brain MVPA searchlight analysis

Searchlight analysis was conducted using Princeton MVPA Toolbox for MATLAB (Detre et al., 2006) with linear-SVM (Cortes and Vapnik, 1995) as the learning algorithm. Before analysis, the EPI images were motion corrected and normalized using a z-score transformation. Raw, unsmoothed EPI volumes from all experimental runs were used in this analysis. We next prepared a binary regressors matrix specifying which experimental condition was represented by each sample volume, and the regressors was then shifted by 2 TRs (6s) to account for haemodynamic lag in the BOLD signals. Sample volumes from 8 trials within each experimental block were then averaged in order to increase statistical power for classification (Preston and Eckstein, 2010; Etzel et al., 2011).

To extract local activity pattern, we moved a 3D spherical searchlight of 8mm radius through the whole brain volume, centred on each voxel in turn. Classification accuracy at each voxel was then computed by running a statistical classifier using signals from all voxels within the searchlight volume. Eightfold cross-validation with leave-one-run-out procedure was performed for each searchlight sphere, resulting in a map representing how well local multivariate signals on each voxel can discriminate the experimental conditions. The searchlight map was initially obtained for each subject in their individual brain space and then combined on a group-level by normalizing each map into standard MNI template and averaging the classification accuracy values at each voxel across all subjects. Statistical significance map was then produced for the group-level map using random-effect analysis by computing an independent one-sample t-test against chance (Haynes et al., 2007; Soon et al., 2008; Stokes et al., 2009a). Accuracy maps of voxels that survived correction for multiple comparisons were superimposed on a normal brain template and visualized as the final result of the analysis.

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4.3 Results