Home Computer Science Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016
We were able to classify individuals into diagnostic groups (AD vs. NC) with an accuracy of 76.2%. We ran a parallel test by using only one measure (FA-DTI) and the prediction accuracy was 50%. As expected, the resulting maps of significant predictors showed cohesive regional patches of stable coefficients, a property that is favored by the TV regularization term. Figure 1 shows the resulting map for each of the five measures.
FW and MD showed similar predictive properties, with large regions of negative coefficients in the frontal lobes (both hemispheres). FA-DTI and FA-TDF also showed a similar pattern, but FA-TDF showed larger and more cohesive regions in the frontal white matter, especially in areas with fiber crossings. OD showed some similarities with the MD map although the regions with the larger coefficients (both positive and negative) tended to be smaller and more widespread. Many of these observations are in line with what is expected for each measure. The direction of the coefficients is also important to note. It is expected that the anisotropy of the white matter tends to decrease in AD compared to healthy aging controls, but MD, FW and OD on the other hand tend to increase with white matter disruption.
Fig. 1 Regularized maps of useful diagnostic predictors, based on measures computed from diffusion MRI. (a) FA-DTI, (b) FA-TDF, (c) MD-DTI, (d) OD, (e) FW. Color bars show the value of the coefficients, from negative (blue) to positive (red), with zero in green
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