title: Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.
abstract: BACKGROUND
  The objective of this study was to assess the utility of novel verbal fluency scores for predicting conversion from mild cognitive impairment (MCI) to clinical Alzheimer's disease (AD).
  
  
  METHODS
  Verbal fluency lists (animals, vegetables, F, A, and S) from 107 MCI patients and 51 cognitively normal controls were transcribed into electronic text files and automatically scored with traditional raw scores and five types of novel scores computed using methods from machine learning and natural language processing. Additional scores were derived from structural MRI scans: region of interest measures of hippocampal and ventricular volumes and gray matter scores derived from performing ICA on measures of cortical thickness. Over 4 years of follow-up, 24 MCI patients converted to AD. Using conversion as the outcome variable, ensemble classifiers were constructed by training classifiers on the individual groups of scores and then entering predictions from the primary classifiers into regularized logistic regression models. Receiver operating characteristic curves were plotted, and the area under the curve (AUC) was measured for classifiers trained with five groups of available variables.
  
  
  RESULTS
  Classifiers trained with novel scores outperformed those trained with raw scores (AUC 0.872 vs 0.735; P < .05 by DeLong test). Addition of structural brain measurements did not improve performance based on novel scores alone.
  
  
  CONCLUSIONS
  The brevity and cost profile of verbal fluency tasks recommends their use for clinical decision making. The word lists generated are a rich source of information for predicting outcomes in MCI. Further work is needed to assess the utility of verbal fluency for early AD.
authors.affiliation: Department of Neurology, Medical University of South Carolina, Charleston, SC, USA; Department of Neurology, Ralph H. Johnson VA Medical Center, Charleston, SC, USA.
authors.forename: David Glenn
authors.initials: DG
authors.lastname: Clark
authors.affiliation: Ontario Neurodegenerative Disease Research Initiative, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
authors.forename: Paula M
authors.initials: PM
authors.lastname: McLaughlin
authors.affiliation: Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
authors.forename: Ellen
authors.initials: E
authors.lastname: Woo
authors.affiliation: Oakland University William Beaumont School of Medicine, Rochester, MI, USA.
authors.forename: Kristy
authors.initials: K
authors.lastname: Hwang
authors.affiliation: Drexel University College of Medicine, Philadelphia, PA, USA.
authors.forename: Sona
authors.initials: S
authors.lastname: Hurtz
authors.affiliation: Drexel University College of Medicine, Philadelphia, PA, USA.
authors.forename: Leslie
authors.initials: L
authors.lastname: Ramirez
authors.affiliation: Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
authors.forename: Jennifer
authors.initials: J
authors.lastname: Eastman
authors.affiliation: Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
authors.forename: Reshil-Marie
authors.initials: RM
authors.lastname: Dukes
authors.affiliation: Department of Neurology, SUNY Upstate Medical University, Syracuse, NY, USA.
authors.forename: Puneet
authors.initials: P
authors.lastname: Kapur
authors.affiliation: Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.
authors.forename: Thomas P
authors.initials: TP
authors.lastname: DeRamus
authors.affiliation: Department of Neurology, Indiana University, Indianapolis, IN, USA.
authors.forename: Liana G
authors.initials: LG
authors.lastname: Apostolova
keywords: Alzheimer's disease; Cognitive neuropsychology; Dementia; MCI (mild cognitive impairment); MRI (magnetic resonance imaging); Machine learning; Natural language processing