Computational protocol: Neuroimaging paradigms for tonotopic mapping (I): The influence of sound stimulus type

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Protocol publication

[…] Data were preprocessed using the SPM12 software package (Wellcome Department of Imaging Neuroscience, http://www.fil.ion.ucl.ac.uk/spm/) (). Functional imaging volumes were corrected for motion effects using rigid body transformations and co-registered to the subject's anatomical image. Voxel signals were converted to fMRI units of percentage signal change using the LogTransform toolbox (http://www.fil.ion.ucl.ac.uk/spm/ext/#LogTransform), and images were moderately smoothed by convolution with a 5-mm full-width at half-maximum (FWHM) Gaussian kernel. The two images of each acquired pair were averaged to form a single image volume. The anatomical images were segmented and all images were normalised and resampled at 1-mm resolution inside a bounding box of x = − 75… + 75, y = − 60… + 40, z = − 20… + 30 in Montreal Neurological Institute (MNI) stereotaxic space. Cortical surface meshes were generated from the anatomical images using the standard processing pipeline of the FreeSurfer v5.1.0 software package (Martinos Center for Biomedical Imaging, http://surfer.nmr.mgh.harvard.edu/) ().Linear regression models were formulated for each of the three stimulus protocols. Models included a constant baseline term, plus nk stimulus regressors consisting of a vector of zeros and ones (where nnarrowband = 12, nbroadband = 7, and nsweep = 12). In the narrowband and broadband models, these regressors encoded the stimulus condition that was presented preceding each acquired image pair, except for the silent condition that served as baseline. In the sweep model, the regressors encoded the distinct phases of the sweep at an instant 4.4 s before the middle of each acquired image pair to account for the hemodynamic response delay. The image pair that followed the silent gap between sweeps (with assigned phase equal to 0°) served as baseline. The phases for the downward sweeps were conjugated (i.e. reversed) compared to the upward sweeps to account for the opposite direction; thus, low and high phases always corresponded with low and high sound frequencies, respectively.Individual subject models as well as fixed-effects group models were evaluated. To determine brain regions with significant sound-evoked activity, omnibus F-tests were carried out on the nk stimulus-related regression coefficients, averaged across runs. To define a region of interest (ROI), the group-level results were thresholded at a confidence level p < 0.001 and a cluster size kE > 1.0 cm3. The activation clusters according to the narrowband, broadband, and sweep models were merged to obtain a single ROI comprising 39,072 voxels in bilateral auditory cortex (see ). The activation levels of all ROI voxels in response to the various conditions were aggregated into 39,072 × nk data matrices Yk. These matrices were subsequently analysed separately by means of PCA to summarise the variances within datasets, as well as analysed together by means of generalised CCA to summarise the covariances between datasets (; ; ). In all analyses, second-order moments relative to the baseline signal were used instead of variances relative to the mean; in other words, data were not centred.PCA (, ) produced an ordered set of principal components (indexed i = 1…nk) for each dataset. Each component was described by a 39,072-element map xi,k characterising the component's strength across voxels, and an nk-element response profile vi,k characterising the component's strength across conditions. The scaling of xi,k and vi,k was fixed by constraining the response profiles to have unit root-mean-square amplitude, thus allowing the maps to be interpreted in units of percentage signal change. Principal component maps are orthogonal, as are the corresponding response profiles (i.e.xi,kTxj,k=vi,kTvj,k=0iffi≠j). The products xi,kvi,kT cumulate to form optimal approximations of the original dataset Yk in least-squares sense.Generalised CCA () produced an ordered set of canonical variates (indexed i = 1…min{nk}) for each dataset. Each variate consisted of a 39,072-element map zi,k that described the strength of the variate across voxels. It was derived from the original dataset Yk by means of canonical coefficients ci,k according to zi,k = Ykci,k (in fMRI, ci,k can be identified with a contrast vector). The scaling of the zi,k and ci,k was fixed by constraining the maps to have unit root-mean-square amplitude. Canonical variates are constructed such that the average pairwise correlation across corresponding variates of the three datasets is maximal (equivalent to optimising ∑k,lzi,kTzi,l), whilst variates are constrained to remain orthogonal within each dataset individually at the same time (i.e.zi,kTzj,k=0iffi≠j). […]

Pipeline specifications

Software tools SPM, FreeSurfer
Application Functional magnetic resonance imaging
Organisms Homo sapiens