Title:Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer’s Disease and Late Onset Bipolar Disorder
VOLUME: 13 ISSUE: 5
Author(s):Ariadna Besga, Darya Chyzhyk, Itxaso González-Ortega, Alexandre Savio, Borja Ayerdi, Jon Echeveste, Manuel Graña and Ana González-Pinto
Affiliation:Department of Psychiatry, University Hospital of Alava-Santiago, Vitoria, Spain; Centre for Biomedical Research Network on Mental Health (CIBERSAM), Spain and School of Medicine, University of the Basque Country, Vitoria, Spain.
Keywords:Alzheimer's disease, bipolar disorder, eigenanatomy, fractional anisotropy.
Abstract:Background: Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD)
at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from
degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover,
LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant
because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical
features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some
light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select
the best personalized treatment. Objective: We look for white matter features which are correlated with blood plasma
biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD. Materials: A sample of healthy controls
(HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers
have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained
for each subject. Methods: DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to
fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with
plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume
projection coefficients as the classification features, carrying out cross-validation of support vector machine
(SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used
to provide anatomical localizations of the detected feature clusters. Results: Classification results show that one specific
biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score
86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized
in the posterior limb of the internal capsule and superior corona radiata. Conclusion: 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.
