title: Eigenanatomy on Fractional Anisotropy Imaging Provides White Matter Anatomical Features Discriminating Between Alzheimer's Disease and Late Onset Bipolar Disorder.
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.
  
  
  METHODS
  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.
  
  
  CONCLUSIONS
  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.
authors.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. ariadna.besgabasterra@osakidetza.net.
authors.forename: Ariadna
authors.initials: A
authors.lastname: Besga
authors.forename: Darya
authors.initials: D
authors.lastname: Chyzhyk
authors.forename: Itxaso
authors.initials: I
authors.lastname: González-Ortega
authors.forename: Alexandre
authors.initials: A
authors.lastname: Savio
authors.forename: Borja
authors.initials: B
authors.lastname: Ayerdi
authors.forename: Jon
authors.initials: J
authors.lastname: Echeveste
authors.forename: Manuel
authors.initials: M
authors.lastname: Graña
authors.forename: Ana
authors.initials: A
authors.lastname: González-Pinto
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000000981:diagnostic imaging* / Q000473:pathology*; D016880:Anisotropy; D001714:Bipolar Disorder / Q000000981:diagnostic imaging* / Q000473:pathology*; D056324:Diffusion Tensor Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D011569:Psychiatric Status Rating Scales; D012680:Sensitivity and Specificity; D013223:Statistics as Topic; D060388:Support Vector Machine; D066127:White Matter / Q000000981:diagnostic imaging*