ONLINE SUPPLEMENTARY MATERIAL - Bengtsson Et Al

ONLINE SUPPLEMENTARY METHODS - Bengtsson et al.

Participants

Sixteen right-handed1 males participated in the study: eight concert pianists (age 25-

41 yrs; mean age 32.6 ± 5.7 yrs) and a control group of eight non-musicians (age 24-

55 yrs; mean age 33.1 ± 9.8 yrs). All pianists had a piano soloist diploma (M.A.) from the Royal Academy of Music, Stockholm, Sweden and are professionally active as piano soloists. The experimental procedures were undertaken with the understanding and written consent of each participant, conformed to The Code of Ethics of the

World Medical Association (Declaration of Helsinki), and were ethically approved by the Karolinska Hospital Ethical Committee (Dn 02-194).

Acquisition of biographical data on practicing

Each participant filled out a detailed questionnaire on self-estimated amount of practicing in different age periods. The participants were asked to retrospectively identify key events in their pianistic training, such as when they started practicing the piano, and when they started studying with a particular teacher or at a particular school. They thereafter estimated the mean hours of piano practiced per week in the time periods between these key events. From this biographical information we calculated, for each participant, the total number of hours practiced in three different age periods: childhood, i.e. the time from the start of piano studies to age 11; adolescence, i.e. age 12-16; and adulthood, i.e. the time from age 17 to the time of the experiment.

To evaluate the reliability of the practicing data, the participants were given the same questionnaire 12 months later. Test-retest reliability coefficients, i.e. the correlations (Pearson Product-Moment Correlation) between the first and the second measures, were calculated separately for the three different age periods.

MRI data acquisition

A single scanning session was performed on each participant using a 1.5-Tesla

General Electric Signa Echospeed scanner. The DTI scheme included the collection of

20 images with non-collinear diffusion gradients (b = 1000 s/mm2) and four non- diffusion-weighted images (b = 0 s/mm2), employing a single shot, echo planar imaging sequence which was triggered with pulse gating. From each participant 36 slices were collected. The field of view was 23 cm, the acquisition matrix was 128 x

128, and the slice thickness was 3 mm, resulting in voxel-dimensions of 1.8 mm × 1.8 mm × 3 mm. The echo time and repetition time were 107 ms and 18 s, respectively.

From each participant a coronal, T1-weighted 3D image was also collected. The imaging parameters for this were a field of view of 22 cm, echo time of 6 ms, repetition time of 24 ms, flip angle of 35° and voxel-dimensions of 0.86 mm × 0.86 mm × 2 mm.

Fractional anisotropy images

The DTI signal reflects the mean water diffusion rates in several unique directions.

Such information is of particular interest for inferences about the microstructural properties of white matter, since diffusion is faster along axons than in the perpendicular direction. The main barrier is presumably the axonal membrane itself, with the myelin sheath giving rise to additional hindrance. Diffusion in white matter is thus anisotropic, i.e. the diffusion rates in different directions are unequal; isotropic diffusion, in contrast, is equally fast in all directions. Fractional anisotropy (FA) in each voxel was used as a measure of the degree of diffusion anisotropy. FA varies between 0, representing isotropic diffusion, and 1, in the case of the diffusion taking place entirely in one direction.

The DTI data series was movement corrected and eddy currents were removed prior to the voxel-wise estimation of the diffusion tensors2. From the diffusion tensors, fractional anisotropy (FA) was calculated3. The SPM-99 software package

(http://www.fil.ion.ucl.ac.uk/spm/) was used to spatially register the images into a standard space. First, the corresponding anatomical images and the mean of the four non-diffusion-weighted images were co-registered. Subsequently, the anatomical image of each participant was normalized to the SPM stereotactic space, using the template brain of the Montréal Neurological Institute. The normalization involved both linear transformations (translation, rotation, scaling and shearing) and nonlinear deformations are based on a cosine basis set4. Finally, the deformation parameters of this normalization were applied to the FA images.

Statistical analysis

The normalized FA images formed the basis for the regression analysis and group comparisons in this study. We confined the search to voxels with FA > 0.15, in each single participant. This cut-off was used to reliably isolate white matter from the rest of the brain5, and resulted in a mean search volume of 59,519 voxels (771 cm3). We used a linear regression analysis to look for areas where the FA was related to the amount of practicing in an age period. In this analysis, we used the SPM-2 simple regression model with practicing as the covariate, and FA as the dependent variable, to localize areas where the slope of the regression was significantly greater than zero.

The practicing data was mean-corrected to have a zero mean. Regions with a significant regression were inferred using cluster-level statistics6.

In this procedure, corrections for multiple comparisons are based on the spatial extent of the clusters, using the theory of Gaussian random fields. A threshold t value of 3.14 and a spatial extent threshold of 20 voxels were used in the statistical analyses. Only clusters with a P < 0.05, after correction for multiple comparisons, were considered statistically significant. The peak coordinate, corrected P-value, extent and mean FA value of each cluster are reported in Table 1; mean FA values are also given for the corresponding regions in the non-musician group. Regions with significant differences in white matter structure between the pianists and the non-musician control group were determined using a two-sample t-test. The same cluster-extent based statistics were used to determine significance as in the regression analysis. To validate the FA data, a white matter voxel based morphometry (VBM) analysis was performed on the pianist group (see below).

Single subject analyses using Voxel Based Morphometry

In the isthmus and splenium of the corpus callosum, a significant regression was found for childhood practicing versus FA, and adolescence practicing versus FA, respectively. Both these clusters are located relatively close to the white matter boundary. It was therefore important to confirm that the clusters were located within the white matter in all subjects. The outlines of these clusters are superimposed on normalized FA-images from individual subjects in Figs. S1 and S2, showing that the clusters are indeed located within white matter in each individual. To confirm this visual impression quantitatively, we performed a standard voxel-based morphometrical (VBM) analysis7. The histograms in Figs. S1 and S2 show the distribution of white matter densities in all voxels of the cluster for each participant. The great majority of voxels had high values, indicating that they were within the white matter proper in all subjects8.

Secondly, we performed a linear regression analysis on the white matter VBM images, to investigate whether there were significant correlations between the size of white matter regions and practicing, that could confound the FA findings. No significant regression was found between any white matter region and childhood, adolescence or adult practicing.

VBM analysis

We performed a VBM analysis according to the standard protocol7 using the SPM-99 software package. First, an anatomical template was created from the control group.

The T1-weighted images from each control subject were normalized to the template brain of the Montréal Neurological Institute, with a voxel size of 1.5 × 1.5 × 1.5 mm 3.

These normalized images were smoothed with an 8-mm full-width at half-maximum

(FWHM) isotropic Gaussian kernel. A mean image of these smoothed images was used as template.

The T1-weighted images of the pianists were normalized to this template, using the same voxel size. These normalized scans were segmented into grey matter, white matter and CSF/non-brain partitions, using an image intensity non-uniformity correction9. The segmented images were smoothed with a 12-mm FWHM isotropic

Gaussian kernel, after which the intensity in each voxel is a locally weighted average of tissue density from a region of surrounding voxels7. The histograms in Figs. S1 and

S2 show the distribution of white matter density values for the voxels within the illustrated cluster, in each participant. Finally, we also performed a linear regression analysis to localize any white matter areas where the tissue density was related to the amount of practicing in an age period. The SPM-2 simple regression model was used with mean-corrected childhood, adolescence or adult practicing as covariate. In none of these three regression analyses were any white matter regions found when using voxel-based statistics and a threshold of P < 0.05 after correction for multiple comparison, nor when using cluster-based statistics as described for the FA analysis above. We thus did not find any evidence for practicing-related size differences in white matter regions within the pianist group, that could confound the findings on FA. REFERENCES

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