Diffusion Tensor Magnetic Resonance Imaging in de analyse van neurodegeneratieve ziekten

The overall goal of the following experiment is to use diffusion tensor imaging analysis to define a distinct white matter pathoanatomy of different brain diseases by the combination of whole brain-based and tract based fractional anes atropy statistics. This is achieved by appropriate diffusion tensor imaging or DTI data pre-processing, including quality control and stereotactic normalization. As a second step, whole brain-based spatial statistics or WBSS is performed, which allows for a foel wise comparison of fractional an atropy or FA maps of different subject groups in order to detect the pathological differences.

Next, TrackWise fractional. An ATROPY statistics or TFAS is performed in order to compliment the results of voxel wise comparison by comparing the brain structures that were defined by a fiber tracking procedure. Results are obtained that show the differences between diseased groups and control groups based on DTI based analysis.

The main advantage of this technique over other methods is that fiber tracking on group average data sets becomes feasible. This method can help answer key questions in the neuroimaging field, such as the identification of brain structures that are affected by neurodegenerative diseases. The implications of this technique extend toward the use as a neuroimaging based surrogate marker because it might potentially demonstrate longitudinal effects both at the individual and at the group level.

We shall Demonstration of this method is critical as the data processing steps are difficult to learn because data analysis was performed on a custom made software package that is T, and several steps in data processing and analysis are very time consuming To perform an artifact correction. A custom made software developed by our lab is used to detect GD with at least one slice showing decreased intensity IE motion artifacts caused by spontaneous subject movement. The software used is tensor imaging and fiber tracking, and is custom made by our lab for any diffusion weighted volume.

Compute the mean intensity for each slice, then compare its intensity with the same slice in all other volumes by using a weighted average approach. The weighting factor is the dot product of vectors of two gd. If Q is under a certain threshold, a threshold of 0.8 in this case as an example, then eliminate that whole volume or gd.

A threshold of 0.8 is considered a stable solution. Shown here are the motion artifacts visible in sagittal reconstructions and detected by the QC algorithm. In this example, out of the total number of GD 17, were below the red line, which corresponds to Q equals 0.8 and should be eliminated.

An example of a volume elimination statistics for the whole study is shown here in this example DTI data of 29 presymptomatic HD subjects were compared to DTI data of 30 controls for the stereotaxic normalization create a study specific B equals zero template and an FA template. A complete nonlinear stereotaxic normalization consists of three deformation components. Consequently, the resulting diffusion tensor of each voxel eye has to be rotated according to all the rotations considered previously shown.

Here is a rigid brain transformation to align the basic coordinate frames. This figure shows a linear deformation according to the landmarks. The components of the iGen vectors have to be adapted according to the six normalization parameters of S of the linear deformation, and here is a nonlinear normalization, equalizing nonlinear brain shape differences.

The 3D vector shifts are different for each voxel leading to a separate transformation for each vle of the 3D voxel array. After this individual normalization procedure, use all individual DTI data sets for creating a study specific B equals zero template and an FA template. As the non PHE registration to an FA template has the advantage that it provides more contrast in comparison to B equals zero images define an FA template by averaging all individually derived FA maps of the patients and the controls.

In a second step, perform a nonlinear MNI normalization of the DTI data sets by minimizing the mismatch between regional intensities of the FA map to be fitted, and if the FA template according to the square differences based on these data new templates T two are derived. Repeat this iterative process until the correlation between individual FA maps and the FA template is larger than 0.7. Usually this is reached after two iterations.

Now whole brain-based spatial statistics can be performed by calculating fractional anes atropic maps from normalized DTI data smoothing the fractional an atropic maps and statistical evaluation, including correction for multiple comparisons in the following. The differences in fractional antrop maps of amyotrophic lateral sclerosis patients versus controls are calculated by whole brain-based spatial statistics. Calculate FA maps from normalized DTI data in order to preserve directional information as a pre-processing step before voxel wise comparison, apply a smoothing filter to the individual normalized FA maps for smoothing.

The fact that the filter size influences the results of DTI data analysis requires application of the matched filter theorem, which states that the width of the filter used to process the data should be tailored to the size of the expected difference. Compare the patient groups and the corresponding control group foxwell Y using the student's T-test. This is done by comparing the FA values of the patient's FA maps with the FA values of the controls FA maps for each foxhole separately, then correct the statistical results for multiple comparisons by using the false discovery rate algorithm at P less than 0.05.

Further reduce the alpha error using a spatial correlation algorithm that eliminates isolated voxels or small isolated groups of voxels in the size range of the smoothing kernel leading to a minimum threshold cluster size of 512 foxholes in the following track twice. Fractional anes atropic statistics are calculated for amyotrophic lateral sclerosis patients versus controls. In order to apply group-based fiber tracking algorithms generate average DTI data sets from the patient's data and from the controls data together, then perform tractography and average DTI data sets of subject groups by application of a streamlined tracking technique.

Identify manually defined seed points adjacent to the local maxima by the whole brain-based FA analysis, which are the basis for the consecutive fiber tracking analysis after identification of the seeds, perform t tractography and define the voxels of the delineated fibers as a group specific mask for the following TFAS. In order to quantify the T tractography results apply TFAS by using the fiber tracks that were created on the average DTI data sets of all subjects of each group for the selection of the voxels that contribute to a comparison between the patients and the controls FA maps to obtain comprehensive information by WBSS and TFAS consider all resulting voxels with an FA value above 0.2 for statistical analysis by students T-test. This animation shows the group differences in FA maps detected by WBSS between a sample of a LS patients and matched controls in slice wise visualization.

This video shows fiber tracking with starting points in the corticospinal tract used as the basis for TFAS Once mastered. This technique can be done almost automatically within a few hours if it is performed properly. After watching this video, you should have a good understanding of how to perform DTI analysis at the group level using whole brain based spatial statistics and TrackWise FA statistics.