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Clark DG, McLaughlin PM, Woo E, et al. Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment. Alzheimers Dement (Amst). 2016;2:113-22doi: 10.1016/j.dadm.2016.02.001.
Clark, D. G., McLaughlin, P. M., Woo, E., Hwang, K., Hurtz, S., Ramirez, L., Eastman, J., Dukes, R. M., Kapur, P., DeRamus, T. P., & Apostolova, L. G. (2016). Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment. Alzheimer's & dementia (Amsterdam, Netherlands), 2113-22. https://doi.org/10.1016/j.dadm.2016.02.001
Clark, David Glenn, et al. "Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment." Alzheimer's & dementia (Amsterdam, Netherlands) vol. 2 (2016): 113-22. doi: https://doi.org/10.1016/j.dadm.2016.02.001
Clark DG, McLaughlin PM, Woo E, Hwang K, Hurtz S, Ramirez L, Eastman J, Dukes RM, Kapur P, DeRamus TP, Apostolova LG. Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment. Alzheimers Dement (Amst). 2016 Feb 15;2:113-22. doi: 10.1016/j.dadm.2016.02.001. eCollection 2016. PMID: 27239542; PMCID: PMC4879664.
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Alzheimers Dement (Amst). 2016 Feb 15;2:113-22. doi: 10.1016/j.dadm.2016.02.001. eCollection 2016.

Novel verbal fluency scores and structural brain imaging for prediction of cognitive outcome in mild cognitive impairment.

Alzheimer's & dementia (Amsterdam, Netherlands)

David Glenn Clark, Paula M McLaughlin, Ellen Woo, Kristy Hwang, Sona Hurtz, Leslie Ramirez, Jennifer Eastman, Reshil-Marie Dukes, Puneet Kapur, Thomas P DeRamus, Liana G Apostolova

Affiliations

  1. Department of Neurology, Medical University of South Carolina, Charleston, SC, USA; Department of Neurology, Ralph H. Johnson VA Medical Center, Charleston, SC, USA.
  2. Ontario Neurodegenerative Disease Research Initiative, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.
  3. Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
  4. Oakland University William Beaumont School of Medicine, Rochester, MI, USA.
  5. Drexel University College of Medicine, Philadelphia, PA, USA.
  6. Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
  7. Department of Neurology, Medical University of South Carolina, Charleston, SC, USA.
  8. Department of Neurology, SUNY Upstate Medical University, Syracuse, NY, USA.
  9. Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.
  10. Department of Neurology, Indiana University, Indianapolis, IN, USA.

PMID: 27239542 PMCID: PMC4879664 DOI: 10.1016/j.dadm.2016.02.001

Abstract

INTRODUCTION: 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).

METHOD: 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.

CONCLUSION: 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.

Keywords: Alzheimer's disease; Cognitive neuropsychology; Dementia; MCI (mild cognitive impairment); MRI (magnetic resonance imaging); Machine learning; Natural language processing

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