Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer's Disease and Late Onset Bipolar Disorder

Abstract

It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.

White Matter Tract Integrity in Alzheimer's Disease vs. Late Onset Bipolar Disorder and Its Correlation with Systemic Inflammation and Oxidative Stress Biomarkers

“…For cluster selection, we apply Tract-Based Spatial Statistics (TBSS) (Smith et al, 2006; Bach et al, 2014) to find significant WM tract differences between AD and LOBD patients. We expect TBSS to provide tract specific effects, improving the anatomical finding reported by our previous approaches (Graña et al, 2011; Besga et al, 2012, 2016). TBSS identifies WM tract voxel clusters with significant difference in FA between AD and LOBD on the mean FA skeleton.…”

“…We found positive correlations of the oxido-nitrosative stress biomarker malondialdehyde (MDA) (Besga et al, 2016) with voxel clusters in the superior corona radiata, internal capsule, and superior longitudinal fasciculus. However, confirmation by classification performance was moderate, accuracy was below 80%.…”

“…In this study, demographic and other data gathered from the patients at recruitment, such as psychological tests and MRI data, has been described in Besga et al (2012), Graña et al (2011), and Besga et al (2015, 2016), therefore description of materials can not be duplicated here without breaking imposed journal self-plagiarism rules. Consequently we refer the reader to these publications, while here we provide a summary account of the study and results achieved and reported in previous publications.…”

“…Specifically, Diffusion-Weighted Imaging (DWI) was acquired in order to study significant differences in the white matter (WM) structure. Reasons for eligibility and discarding of patients and full account of the materials are given in Besga et al (2012), Graña et al (2011), and Besga et al (2015, 2016), and we dare not reproduced them here. Previously reported results of this study have been the following ones:

We demonstrate good discrimination between AD and LOBD populations using whole brain fractional anisotropy (FA) coefficients extracted from the DWI data (Graña et al, 2011; Besga et al, 2012), using multivariate machine learning for computer aided diagnosis (CAD) system design (Sigut et al, 2007; Salas-Gonzalez et al, 2009; Ramirez et al, 2010; Savio et al, 2011; Westman et al, 2011; Termenon et al, 2013).

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“…Blood plasma biomarkers alone have little discriminant power but help improve the clinical variables. Besides, we had no anatomical correspondences of the findings because no imaging data was involved.Looking for inferences about the anatomical correlations of blood biomarkers we applied canonical correlation between them and whole brain FA data (Besga et al, 2016) using eigenanatomy tools that decompose the FA volume into eigenvolumes maximally correlated with plasma biomarkers (Avants et al, 2012, 2014) for feature extraction and ensuing AD vs. LOBD classification by machine learning. We found positive correlations of the oxido-nitrosative stress biomarker malondialdehyde (MDA) (Besga et al, 2016) with voxel clusters in the superior corona radiata, internal capsule, and superior longitudinal fasciculus.…”