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

Ariadna Besga,Itxaso Gonzalez-Ortega,Darya Chyzhyk,Manuel Graña,Jon Echeveste,Ana Gonzalez-Pinto,Marina Graña-Lecuona

doi:10.3389/fnagi.2017.00179

Abstract

Background: Late Onset Bipolar Disorder (LOBD) is the development of Bipolar Disorder (BD) at an age above 50 years old. It is often difficult to differentiate from other aging dementias, such as Alzheimer's Disease (AD), because they share cognitive and behavioral impairment symptoms.Objectives: We look for WM tract voxel clusters showing significant differences when comparing of AD vs. LOBD, and its correlations with systemic blood plasma biomarkers (inflammatory, neurotrophic factors, and oxidative stress).Materials: A sample of healthy controls (HC) (n = 19), AD patients (n = 35), and LOBD patients (n = 24) was recruited at the Alava University Hospital. Blood plasma samples were obtained at recruitment time and analyzed to extract the inflammatory, oxidative stress, and neurotrophic factors. Several modalities of MRI were acquired for each subject,Methods: Fractional anisotropy (FA) coefficients are obtained from diffusion weighted imaging (DWI). Tract based spatial statistics (TBSS) finds FA skeleton clusters of WM tract voxels showing significant differences for all possible contrasts between HC, AD, and LOBD. An ANOVA F-test over all contrasts is carried out. Results of F-test are used to mask TBSS detected clusters for the AD > LOBD and LOBD > AD contrast to select the image clusters used for correlation analysis. Finally, Pearson's correlation coefficients between FA values at cluster sites and systemic blood plasma biomarker values are computed.Results: The TBSS contrasts with by ANOVA F-test has identified strongly significant clusters in the forceps minor, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus, and cingulum gyrus. The correlation analysis of these tract clusters found strong negative correlation of AD with the nerve growth factor (NGF) and brain derived neurotrophic factor (BDNF) blood biomarkers. Negative correlation of AD and positive correlation of LOBD with inflammation biomarker IL6 was also found.Conclusion: TBSS voxel clusters tract atlas localizations are consistent with greater behavioral impairment and mood disorders in LOBD than in AD. Correlation analysis confirms that neurotrophic factors (i.e., NGF, BDNF) play a great role in AD while are absent in LOBD pathophysiology. Also, correlation results of IL1 and IL6 suggest stronger inflammatory effects in LOBD than in AD.

Highlights

Bipolar disorder (BD) is a chronic mood disorder characterized by maniac and depressive alternating episodes, interspersed by euthymic periods
Reported results of this study have been the following ones: 1. We demonstrate good discrimination between Alzheimer’s disease (AD) and late onset BD (LOBD) populations using whole brain fractional anisotropy (FA) coefficients extracted from the Diffusion-Weighted Imaging (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)
We found no differential effect of NO2, because both populations showed the same sizes of negative correlation clusters, but MDA shows positive correlation with LOBD hinting to an added pathogenesis factor

Summary

Introduction

Bipolar disorder (BD) is a chronic mood disorder characterized by maniac and depressive alternating episodes, interspersed by euthymic periods. When the onset age is above 50 years, it is considered a late onset BD (LOBD) (Depp and Jeste, 2004; Zanetti et al, 2007; Prabhakar and Balon, 2010; Besga et al, 2011; Carlino et al, 2013; Chou et al, 2015), which may be difficult to differentiate from Alzheimer’s disease (AD), because of overlapping symptoms (Zahodne et al, 2015) Another example of the fuzzy boundaries between brain pathologies is the discovery of an AD biomarker signature that identifies Parkinson’s Disease patients with dementia (PDD) (Berlyand et al, 2016) opening the door for crossover treatment of PDD with AD therapies. It is often difficult to differentiate from other aging dementias, such as Alzheimer’s Disease (AD), because they share cognitive and behavioral impairment symptoms

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