Single-subject beta maps were generated for each of five stimulus conditions, which were then used to assess between-group differences
in function using analyses of covariance (ANCOVAs). Participant group (i.e., tinnitus patients versus controls) and mean hearing loss (mHL) were entered as a between-subject factor and covariate, respectively. Single-voxel thresholds were chosen (p < 0.001); maps were then corrected for cluster volume at p(corr) < 0.05 using Montecarlo simulations (a means of estimating the rate of false positive voxels; Forman et al., 1995). Single-voxel thresholds were reduced to p(uncorr) < 0.01, k > 108 mm3 in masked analyses (below). Single-voxel GLM analyses assessed anatomical differences between find more tinnitus patients and controls, with compensation for unequal variance between groups in SPM8. t tests were performed across groups, and both age and total gray or white matter Staurosporine manufacturer volume were entered as confound covariates. A single-voxel (i.e., voxel-wise) threshold was chosen of t > 4.65, p < 0.0001; cluster volume was greater than 80 mm3. Single-voxel thresholds were reduced to p < 0.01 in masked analyses. All single-voxel VBM analyses were performed in the same resolution as the tissue probability maps used for segmentation (2 × 2 × 2 mm3). A mask of the auditory system was created for both functional
selleck screening library and anatomical analyses. Auditory cortex was defined by selecting those functional voxels in superior temporal cortex that
survived a sounds > silence contrast with a single-voxel threshold of t > 2.58, p(uncorr) < 0.01, k > 4 (group data). The MGN were defined using the WFU Pick Atlas ( Lancaster et al., 2000 and Maldjian et al., 2003), dilated by 1 mm, and then flipped to create a symmetrical mask in both hemispheres. Additional masks were created using significant clusters from both functional and anatomical analyses. Masks were transferred between programs via image files (ANALYZE format), which were then adjusted to the appropriate format in BrainVoyager or SPM. Coordinate conversions between Talairach and MNI spaces were done using a well-accepted nonlinear transform (http://imaging.mrc-cbu.cam.ac.uk/imaging/MniTalairach). Pairwise correlations between mean fMRI signal or VBM values were performed for ROIs exhibiting significant between-group differences using the statistical tests described above. Cook’s d tests were used to assess the influence of potential outliers on the resulting correlation statistics. Data points from a single participant, Patient #7, had Cook’s d values close to 1.0 (a commonly used benchmark for identifying potential outliers) for four out of six pairwise tests (Table S3). Therefore, we computed correlations both with and without this subject included.