title: Discrimination between Alzheimer's Disease and Late Onset Bipolar Disorder Using Multivariate Analysis.
abstract: BACKGROUND
  Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment.
  
  
  OBJECTIVE
  The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables.
  
  
  METHODS
  A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time.
  
  
  METHODS
  We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch's t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance.
  
  
  RESULTS
  Welch's t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%.
  
  
  CONCLUSIONS
  It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.
authors.affiliation: Department of Psychiatry, University Hospital of Alava-Santiago , Vitoria , Spain ; Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; School of Medicine, University of the Basque Country , Vitoria , Spain.
authors.forename: Ariadna
authors.initials: A
authors.lastname: Besga
authors.affiliation: Department of Psychiatry, University Hospital of Alava-Santiago , Vitoria , Spain ; Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; School of Psychology, University of the Basque Country , San Sebastian , Spain.
authors.forename: Itxaso
authors.initials: I
authors.lastname: Gonzalez
authors.affiliation: Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; School of Psychology, University of the Basque Country , San Sebastian , Spain.
authors.forename: Enrique
authors.initials: E
authors.lastname: Echeburua
authors.affiliation: Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain ; ENGINE Centre, Wrocław University of Technology , Wrocław , Poland.
authors.forename: Alexandre
authors.initials: A
authors.lastname: Savio
authors.affiliation: Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain.
authors.forename: Borja
authors.initials: B
authors.lastname: Ayerdi
authors.affiliation: Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain ; Department of Computer and Information Science and Engineering, University of Florida , Gainesville, FL , USA.
authors.forename: Darya
authors.initials: D
authors.lastname: Chyzhyk
authors.affiliation: Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; Department of Pharmacology, Faculty of Medicine, University Complutense and IIS Hospital 12 de Octubre , Madrid , Spain.
authors.forename: Jose L M
authors.initials: JL
authors.lastname: Madrigal
authors.affiliation: Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain ; Department of Pharmacology, Faculty of Medicine, University Complutense and IIS Hospital 12 de Octubre , Madrid , Spain.
authors.forename: Juan C
authors.initials: JC
authors.lastname: Leza
authors.affiliation: Computational Intelligence Group (GIC), University of the Basque Country , San Sebastian , Spain ; ENGINE Centre, Wrocław University of Technology , Wrocław , Poland ; Asociacion de Ciencias de la Programacion Python San Sebastian (ACPySS) , San Sebastian , Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: Graña
authors.affiliation: Department of Psychiatry, University Hospital of Alava-Santiago , Vitoria , Spain ; Centre for Biomedical Research Network on Mental Health (CIBERSAM) , Madrid , Spain.
authors.forename: Ana Maria
authors.initials: AM
authors.lastname: Gonzalez-Pinto
keywords: Alzheimer’s disease; and blood; biomarkers; clinical; computer-aided diagnosis; late onset bipolar disorder; neuropsychological; pharmacological