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Home arrow Computer Science arrow Computational Diffusion MRI: MICCAI Workshop, Athens, Greece, October 2016

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In this article, we evaluated the utility of the TV-L1 prior logistic regression to assess the ability of multiple dMRI reconstruction methods to simultaneously distinguish alterations in WM microstructure between people with AD and matched healthy controls. We computed five dMRI derived microstructural measures from four different reconstruction models that were used together in a regularized classification framework and we were able to successfully classify AD from healthy controls and to derive spatially coherent discrimination patterns across the entire brain for each measure.

AD pathology includes disturbances in the brain’s WM pathways including loss of axons, myelin sheaths, and oligodendroglial cells, which may not all be detected by using DTI based descriptors alone. Machine learning for classification based on dMRI features has been focused mainly on DTI derived measures; although HARDI derived measures have also been explored [19, 20]. Volumetric measures, including hippocampal volume, gray matter volume from voxel-based morphometry, and cortical thickness [14-16, 18], have effectively classified AD patients, but few studies have used dMRI-derived biomarkers for classification purposes. Most of these studies have used DTI based measures: several used voxel-wise features from DTI maps, using methods such as Pearson correlation and ReliefF for feature reduction [8-10], reporting classification accuracies of >90%. In [17], tractography- based connectivity metrics based on fiber count, FA-DTI, and diffusivity were used for SVM classification, reporting an accuracy of 88%. Clearly, these accuracies depend on the problem and dataset used, and are not directly comparable with one another. Spatial and anatomical regularization for classification purposes have also been tested on AD discrimination against controls by Cuingnet et al. [18]. Here they achieved improved classification accuracies by using this type of regularization on cortical features and producing discriminatory parcellated maps of the cortex highlighting the brain regions traditionally compromised in AD.

Here we evaluated 102 subjects and were able to reach a relatively high classification accuracy for a white matter study of AD. Although our approach did not necessarily “beat” prior classification results, our goal was to compare the relative utility of multiple metrics for classification, which leads to some insight on how the disease may affect different fiber properties. Moreover, it was important to see if these measures might complement and add to the information provided by DTI measures—particularly in regions outside the coherent WM. Many dMRI measures are correlated with each other to some extent, but each captures the microstructure slightly differently, and at the various spatial locations, there may be greater sensitivity to detecting subtle changes with one measure versus another.

In conclusion, different reconstruction models and their respective scalar descriptors provide distinct micro-anatomical features, which differ in classification value by brain region. Together these estimates may improve brain-wide classification and may overcome the need to compute localized statistically determined regions of interest, and allow us to observe microstructural changes in the entirety of the brain. We made use of the main functionality of the TV prior, namely its denoising and smoothing capabilities across the image. This is essential in this context since single voxels prove to be very noisy and neighboring anatomy is presumably similar. Future work should compare other classification methods and improve estimates by incorporating tissue volume differences. We will also test if dMRI metrics can contribute to leading classification approaches based on biomarkers such as hippocampal volume, amyloid deposition, and tensor-based morphometry.

Acknowledgment This work is partially supported by an NIH U54 grant to the ENIGMA Center for Worldwide Medicine, Imaging & Genomics.

 
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