Computational protocol: Dissociating the neural correlates of tactile temporal order and simultaneity judgements

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Protocol publication

[…] Imaging and statistical analyses were performed using the statistical parametric mapping package SPM8 (http://www.fil.ion.ucl.ac.uk/spm). Functional images of each run (i.e., session) were realigned using the first scan as a reference to correct for head movements. The T1 anatomical image was preprocessed using the intensity inhomogeneity correction. The T1 anatomical images were coregistered to the first scan of the functional images. Subsequently, the coregistered T1 anatomical images were normalised to a standard T1-template image defined by the Montreal Neurological Institute (MNI), which involved linear and nonlinear three-dimensional transformations. The normalisation parameters were subsequently applied to each of the functional images. Finally, the spatially normalised functional images were resampled to a voxel size of 2 × 2 × 2 mm and smoothed using an isotopic Gaussian kernel of 8-mm full-width at half-maximum to compensate for anatomical variability amongst the participants. The reduction in the voxel size by subsampling and spatial smoothing ensures a good lattice approximation, which is necessary for correcting the statistical inference using the random field theory.We used a random effects model for the statistical analysis. First, we performed a single-participant analysis. The task-related neural activity relative to the baseline resting periods was modelled using a boxcar function and convolved with a canonical haemodynamic response function. We used a high-pass filter with a discrete cosine basis function and a cut-off period of 128 s to eliminate artefactual low-frequency trends. Serial autocorrelation, assuming a first-order autoregressive model, was estimated using the pooled active voxels with a restricted maximum likelihood procedure, and the estimates were used to whiten the data and design matrices.The statistical model included two conditions: TOJ and SJ. T-contrasts were set to perform the following comparisons: TOJ > SJ; SJ > TOJ; TOJ > rest; and SJ > rest. The contrast images were generated for each subject and then entered into a one-sample t-test to create a random effect SPM. The SPM{T} was transformed into normal distribution units SPM{Z}. Significantly activated voxels were identified using a threshold of P < 0.001 uncorrected at the voxel level (Z = 3.09) and P < 0.05 FWE-corrected at the cluster level.In the TOJ > SJ and SJ > TOJ contrasts, we excluded the effect of the σ difference between the tasks, according to the conventions that the behavioural difference between the tasks should be compensated for in the fMRI contrasts. The σ difference for each subject was regressed out as a covariate of no interest in these contrasts. Furthermore, the voxels that were correlated with the σ difference (P < 0.05 uncorrected at the voxel level) were exclusively masked in the present study. In addition, to avoid false activations as a result of deactivations in the contrasting conditions, the TOJ > SJ and SJ > TOJ contrasts were inclusively masked with the TOJ > rest and SJ > rest contrasts (P < 0.05 uncorrected at the voxel level), respectively.We labelled the brain regions and Brodmann areas (BAs) using Talairach Client (ver. 2.4.3; http://www.talairach.org/client.html) after transforming the MNI coordinates of the peak activations to Talairach coordinates using icbm2tal (http://www.brainmap.org/icbm2tal/). We also assessed the labels using the Talairach coordinates transformed by mni2tal (http://medicine.yale.edu/bioimaging/suite/mni2tal/). When discrepancies occurred between the labels using icbm2tal and mni2tal or when there was no output from the Talairach Client, we adjusted or compensated for the labels using the SPM Anatomical Toolbox or MRIcron (www. mricro.com). […]

Pipeline specifications

Software tools SPM, Icbm2tal, MRIcron
Applications Magnetic resonance imaging, Functional magnetic resonance imaging
Organisms Homo sapiens