title: Quantitative EEG and apolipoprotein E-genotype improve classification of patients with suspected Alzheimer's disease.
abstract: OBJECTIVE
  To establish a model for better identification of patients in very early stages of Alzheimer's disease, AD (including patients with amnestic MCI) using high-resolution EEG and genetic data.
  
  
  METHODS
  A total of 26 patients in early stages of probable AD and 12 patients with amnestic MCI were included. Both groups were similar in age and education. All patients had a comprehensive neuropsychological examination and a high resolution EEG. Relative band power characteristics were calculated in source space (LORETA inverse solution for spectral data) and compared between groups. A logistic regression model was calculated including relative band-power at the most significant location, ApoE status, age, education and gender.
  
  
  RESULTS
  Differences in the delta band at 34 temporo-posterior source locations (p<.01) between AD and MCI groups were detected after correction for multiple comparisons. Classification slightly increased when ApoE status was added (p=.06 maximum likelihood test). Adjustment of analyses for the confounding factors age, gender and education did not alter results.
  
  
  CONCLUSIONS
  Quantitative EEG (qEEG) separates between patients with amnestic MCI and patients in early stages of probable AD. Adding information about Apo ε4 allele frequency slightly enhances diagnostic accuracy.
  
  
  CONCLUSIONS
  qEEG may help identifying patients who are candidates for possible benefit from future disease modifying treatments.
authors.affiliation: Department of Neurology, University of Basel, Switzerland.
authors.forename: F
authors.initials: F
authors.lastname: Hatz
authors.forename: N
authors.initials: N
authors.lastname: Benz
authors.forename: M
authors.initials: M
authors.lastname: Hardmeier
authors.forename: R
authors.initials: R
authors.lastname: Zimmermann
authors.forename: S
authors.initials: S
authors.lastname: Rueegg
authors.forename: C
authors.initials: C
authors.lastname: Schindler
authors.forename: A R
authors.initials: AR
authors.lastname: Miserez
authors.forename: U
authors.initials: U
authors.lastname: Gschwandtner
authors.forename: A U
authors.initials: AU
authors.lastname: Monsch
authors.forename: P
authors.initials: P
authors.lastname: Fuhr
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000145:classification* / Q000175:diagnosis / Q000235:genetics*; D001057:Apolipoproteins E / Q000235:genetics*; D001931:Brain Mapping; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000235:genetics*; D003937:Diagnosis, Differential; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D008297:Male; D008959:Models, Neurological
chemical_list: D001057:Apolipoproteins E
keywords: Alzheimer’s disease; Electroencephalography; Frequency analysis; LORETA; Mild cognitive impairment; Surrogate marker; Topographic analysis