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Measuring Grey Matter Differences with Voxel-based Morphometry

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



Our brains are shaped by experiences, resulting in changes in cortical volume.

For instance, certain proficiencies, like learning and mastering a second language, have been shown to increase the density of gray matter, where cell bodies reside, particularly in structures such as the frontal lobe.

Before modern advances, to measure a particular area’s size, scientists would have to painstakingly trace the region of interest—a very tedious task. Now, more sensitive neuroimaging techniques—known as voxel-based morphometry, VBM—exist to capture small volumetric differences in neuroanatomy.

Based on previous work of Gaser and Shlaug, as well as Bermudez and colleagues, this video demonstrates how to collect structural magnetic resonance images and use VBM to identify the intensity values of voxels in the brains of individuals with different experiences—expert musicians compared to those with very limited training—as well as in other cases of expertise, such as chess playing.

In this experiment, two groups of participants—formally trained musicians and controls with no such training—are asked to lie in an MRI scanner while structural images of their brains are collected.

Particular regions can then be defined using an automated approach, based on the intensity of volumetric pixels, called voxels. For instance, very bright clusters indicate the location of white-matter fiber bundles, while darker voxels correspond to areas with dense gray matter.

Following this segmentation for each brain, the images are transformed—registered to a standard atlas, which is a common space to allow for between-subject comparisons.

Often times, this registration process can stretch out an image, which makes some structures seem like they have more gray matter than they really do.

Therefore, the template must be multiplied by a measure of how much warping has been done, called a Jacobian determinant, to compensate for the repeated stretching, and then all gross differences in anatomy are smoothed out.

After the transformations are applied, the dependent variable is calculated as the differences in gray matter density between musicians’ brains compared to non-musician controls.

Due to the increased use of complex auditory processing in skillful musicians, it is expected that this group will show increased gray matter density in auditory brain regions, such as the superior temporal lobe and Heschl’s gyrus, compared to the control group.

Prior to the experiment, recruit 40 musicians who actively practice any instrument 1 hr a day and have at least 10 years of formal musical training, as well as 40 non-musician controls who have little to no proper training.

On the day of their scan, greet each participant in the laboratory and verify that they meet the safety requirements as they complete the necessary consent forms.

Please refer to another fMRI project in this collection for more details on how to prepare individuals to enter the scanning room and scanner bore.

Now, instruct the participant to lie still in the scanner, and begin scanning the whole brain by collecting a high-resolution, T1-weighted anatomical sequence such as Magnetization Prepared-Rapid Gradient Echo with 1 mm isotropic voxels.

Following the image-collection protocol, dismiss the participant and start the analysis.

To begin preprocessing, isolate the brain from the skull for each scan and check the quality of the stripping.

For this study, create a specific gray matter template by first segmenting each subject’s brain into white and gray matter and cerebral spinal fluid, CSF, based on the intensity of each voxel. Note, the software automatically distinguishes bright voxels as white matter, dark voxels as gray matter, and areas within the ventricles as CSF.

Perform a linear affine transformation with 12° of freedom, to register each subject’s brain to a standard atlas space. Warp each subject’s gray matter image into this space, and average them all together.

Next, mirror this left to right, and once again, average the images together to produce the initial gray matter template.

Then, perform a non-linear transformation to re-register each subject’s brain to the gray matter figure, and average these together. Create a mirrored copy of this new image, and once again average the two together to produce a final, study-specific, gray matter template.

Now register each subject’s brain to the last gray matter figure using a non-linear transformation, and multiply by a Jacobian measure of how much warping has been done to compensate for the amount each brain structure has been stretched to fit the template space.

Subsequently, smooth the data using a Gaussian kernel with a Full-Width Half Maximum of 10 mm to increase the overlap of similar brain voxels across all subjects.

With preprocessing completed, model each group of brains with a separate regressor. Compute a contrast that compares the two groups to generate statistical maps that quantify the likelihood of differences at each voxel.

Finally, perform a multiple comparisons correction technique, such as a False Discovery Rate with a q value of 0.01, to control for the thousands of simultaneous statistical tests performed. This value will estimate the rate of false positives above a threshold of 1%.

Here, the VBM analysis revealed significant bilateral increases in gray matter density in the superior temporal lobe of the musicians’ brains compared to the controls. The greatest difference was shown on the right side, and this included the posterior portion of Heschl’s gyrus, the location of the primary auditory cortex.

Now that you are familiar with how to use VBM to study neuroanatomy, let’s look how researchers use this technique to study structural differences in other populations.

While many tasks involving intense training and experience are associated with increases in gray matter volume, this enlargement is not always the case for all types of learned skill-sets, like in the brain of an experienced chess player.

When compared to controls, gray matter volume was reduced in the occipito-temporal junction, an area important for object recognition. Such findings result in an interesting anomaly that may help scientists further understand how cortical volume relates to performance in demanding tasks.

Individuals who are blind from birth often have smaller gray matter volume in their visual cortex compared to controls. Interestingly, through the use of VBM, researchers have discovered significant enlargement in areas of the brain not responsible for vision, such as the auditory cortex, which was twice the size found in sighted controls.

These structural differences may serve as an anatomical foundation to explain why other senses are heightened in blind individuals.

Furthermore, structural MRI and VBM analysis on medication-naïve patients with major depressive disorder also indicate differences in gray matter volumes compared to controls.

Scientists found that these patients had decreased gray matter volume in the frontal cortex and insula, which may explain why depressed patients have difficulty with cognitive control over negative feelings toward themselves and others.

You’ve just watched JoVE’s video on voxel-based morphometry. Now you should have a good understanding of how to collect anatomical images using MRI as well as how to analyze and interpret differences in gray matter intensity in regions of the auditory cortex. You should have also learned that not all areas of expertise lead to increases in cortical density.

Thanks for watching!

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