This video demonstrates and compares multivariate and univa analysis techniques for neuroimaging data with applications to Alzheimer's diagnosis. First brain activity from 20 early Alzheimer's patients in 20 age matched healthy controls is contrasted for the uni variate analysis. SPM is used to identify the brain region that shows the largest relative deficit in FDG PET signal in the Alzheimer's patients relative to control subjects.
For the multi-variate analysis, principal components analysis with linear discriminant analysis is used to derive the marker results indicate that the multi-variate marker provides better diagnostic performance than the uni variate marker and gives superior generalization to independent data. Hello, I'm Christian Halbeck from the TA Institute in the Department of Neurology at Columbia University Medical Center. Today we will show you an example of multivariate data analysis of neuro imaging data.
We use this procedure in our lab to study the optimal way of detecting early Alzheimer's disease from neuro imaging data. So let's get started. Let's begin with a conceptual overview of multivariate analysis of neuroimaging data.
Multivariate approaches evaluate the correlation of activity across brain regions rather than proceeding on a voxel by voxel basis as is done with univa analysis. For example, we can picture a simple hypothetical dataset for 50 human participants where only three regions denoted as voxels in the brain were measured. The general aim of multi-variate analysis is to identify the major sources of variance in the data and then to describe the main effects of interests in the data.
In terms of these sources of variants, uni variate analysis does not pay attention to the correlation between voxels. Both panels seen here would be treated in an identical manner by Univar analysis whether there is correlation between the voxels or not. In this video, the analysis procedure for both univar and multivariate analysis will be demonstrated on a clinical dataset containing FTG PET resting scans for 95 early Alzheimer's patients or AD and 102 H matched controls of all the brain images, 20 were randomly picked of both patients in controls to be the derivation sample.
The remaining 75 and 82 scans respectively constitute the replication sample. Now let's see how to apply both univar and multivariate analyses to this dataset. Beginning with the univa approach, obtain the Univar Alzheimer's disease marker by using SPM software To contrast the 20 ad scans with the 20 control scans in the derivation sample, select the brain location that shows the largest decrease in pet signal in the AD patients as compared to controls.
Using a T-test to test the diagnostic efficacy of this region, check the data in the replication sample for this same brain region, and plot its pet signal as a function of disease status. Next, obtain the multivariate marker. A custom MATLAB toolbox available online is used for the multivariate analysis using the custom MATLAB toolbox.
Perform principle components analysis or PCA on the combined 40 scans in the derivation sample. Then construct a Covance pattern from the first six principle components whose subject score shows a maximal mean difference between 80 patients and healthy controls. Once the diagnostic Covance pattern is obtained from the derivation sample, apply it to the replication sample, plot the resulting subject scores as a function of disease status and compare the performance in the two samples.
To provide a more general comparison of univa and multivariate approaches, perform a split sample simulation and repeat the prior steps 1000 times on resampled data, each time forming a 20 to 20 derivation sample and a 75 to 82 replication of 80 patients and healthy controls afresh. Split sample simulations are common in methodological research since they provide an easy test of the markers derived in the derivation sample by recording their prediction errors in the replication sample, compute univariate and multivariate disease markers from the derivation sample and set the decision threshold such that at most one healthy control is misclassified as ad, which corresponds to a specificity of 95%Finally, apply the disease markers with their specific decision thresholds to the replication samples and record the classification error rates in the replication sample for all resampling iterations. Now we'll take a look at the results of the univa and multivariate analyses and the split sample simulations emulations.
For the univ area analysis. The area of the largest ad related FDG deficit was found in the Precuneus Broadman area 31. The area under the rock curve achieved was 0.90.
The generalization of this contrast to the replication sample was quite good with an area under the rock curve of 0.84 for the multivariate analysis areas with positive loadings hinting at a relative preservation of signal in the face of disease were found in the cerebellum while associated signal loss was found in the parot temporal and frontal areas and the posterior cingulate gyrus, the areas under rock curves in both derivation and replication samples were slightly better than the univariate marker at 0.96 and 0.88 respectively. From the total classification error rates recorded in the 1000 split sample simulations, we can appreciate that the multivariate marker gives better replication of diagnostic performance than the univariate marker. We've just shown you how to apply multivariate analysis to the Alzheimer's diagnostic problem in Ft.G PET scans.
All data is publicly available from the website of the Alzheimer's Disease Neuroimaging Initiative. When performing this procedure on real data, it is important to remember that the usual steps of quality control have been applied prior. No analysis can perform magic on poor data.
So that's it. Thank you for watching and good luck with your analysis.
