Measuring Grey Matter Differences with Voxel-based Morphometry: The Musical Brain

1. Recruit 40 musicians and 40 non-musicians.

1. Musicians should have at least 10 years of formal musical training. Training with any musical instrument is acceptable. Musicians should also be actively practicing their instrument for at least one hr/day.
2. Control subjects should have little to formal training in playing a musical instrument.
3. All participants should be right-handed.
4. All participants should have no history of neurological, psychiatric, or cardiac disorders.
5. All participants should have no metal in their bodies that they cannot remove to ensure that they are MRI-safe.

2. Pre-scan procedures

1. Fill out pre-scan paperwork.
2. When participants come in for their fMRI scan, have them first fill out a metal screen form to make sure they have no counter-indications for MRI, an incidental findings form giving consent for their scan to be looked at by a radiologist, and a consent form detailing the risks and benefits of the study.
3. Prepare participants to go in the scanner by removing all metal from their body, including belts, wallets, phones, hair clips, coins, and all jewelry.

3. Put the participant in the scanner.

1. Give the participant ear plugs to protect their ears from the noise of the scanner and ear phones to wear so they can hear the experimenter during the scan, and have them lie down on the bed with their head in the coil.
2. Give the participant the emergency squeeze ball and instruct them to squeeze it in case of emergency during the scan.
3. Use foam pads to secure the participants head in the coil to avoid excess movement during the scan, and remind the participant that it is very important to stay as still as possible during the scan, as even the smallest movements blur the images.

4. Data collection

1. Collect a high-resolution anatomical scan of the whole brain. This scan should be a T1-weighted sequence such as a Magnetization Prepared Rapid Gradient Echo (MP-RAGE) with isotropic 1 mm voxels.

5. Data analysis

1. Remove the skull from each anatomical brain scan using automated software. Check the skull stripping for quality.
2. Create a study-specific gray matter template using an iterative process of linear and non-linear registration (Figure 1).
   1. Use automated software to segment each subject's brain into white matter, gray matter, and CSF based on the intensity at each voxel.
   2. Perform a linear affine transformation with 12 degrees of freedom to register each subject's brain to a standard atlas space, such as the MNI152 atlas.
   3. Warp each subject's gray matter image into this space, and average them all together.
   4. Mirror this image left to right and then average the mirror images together to produce the gray matter template brain.
   5. Re-register each subject's brain to this template using a nonlinear transformation.
   6. Average all of the newly transformed brains together.
   7. Make a mirror image of this new template, and average the two mirror images together to produce a final gray matter template for this study.

Figure 1: Creation of study-specific gray matter template. Using iterative linear and nonlinear transformations, each brain is registered to a common space and averaged together to create a study-specific gray matter template brain.

3. Register each subject's gray matter image to the template and preprocess.
   1. Use a nonlinear transformation to register each subject's brain to the study-specific template.
   2. To compensate for the amount each brain structure has been stretched to fit the template space, multiply by a measure of how much warping has been done. This measure is called the Jacobian of the warp field. This step is to account for the fact that structures may appear to have more gray matter simply because they have been stretched more by the nonlinear registration process.
   3. Smooth the data using a Gaussian kernel with a Full Width Half Maximum (FWHM) of 10 mm.
   4. These aligned, smoothed brains will serve as the final data for voxel-based analysis.
4. Use the General Linear Model to analyze the difference between the groups at every voxel.
   1. Model each group of brains with a separate regressor, and compute a contrast that compares the two groups, generating statistical maps that quantify the likelihood of differences at each voxel.
   2. Threshold the statistical maps to identify statistically significant clusters.
      1. Employ a multiple comparisons correction technique such as False Discovery Rate (FDR) to control for the fact that were are doing thousands of simultaneous statistical tests. With FDR, a q value of 0.01 will estimate the rate of false positives above threshold to be 1%.