title: Predicting the development of mild cognitive impairment: a new use of pattern recognition.
abstract: While the conversion from mild cognitive impairment to Alzheimer's disease has received much recent attention, the transition from normal cognition to mild cognitive impairment is largely unexplored. The present pattern recognition study addressed this by using neuropsychological test scores and neuroimaging morphological measures to predict the later development of mild cognitive impairment in cognitively normal community-dwelling individuals aged 70-90years. A feature selection algorithm chose a subset of neuropsychological and FreeSurfer-derived morphometric features that optimally differentiated between individuals who developed mild cognitive impairment and individuals who remained cognitively normal. Support vector machines were used to train classifiers and test prediction performance, which was evaluated via 10-fold cross-validation to reduce variability. Prediction performance was greater when using a combination of neuropsychological scores and morphological measures than when using either of these alone. Results for the combined method were: accuracy 78.51%, sensitivity 73.33%, specificity 79.75%, and an area under the receiver operating characteristic curve of 0.841. Of all the features investigated, memory performance and measures of the prefrontal cortex and parietal lobe were the most discriminative. Our prediction method offers the potential to detect elderly individuals with apparently normal cognition at risk of imminent cognitive decline. Identification at this stage will facilitate the early start of interventions designed to prevent or slow the development of Alzheimer's disease and other dementias.
authors.affiliation: School of Design, Communication & Information Technology, University of Newcastle, Callaghan, NSW, Australia.
authors.forename: Yue
authors.initials: Y
authors.lastname: Cui
authors.forename: Perminder S
authors.initials: PS
authors.lastname: Sachdev
authors.forename: Darren M
authors.initials: DM
authors.lastname: Lipnicki
authors.forename: Jesse S
authors.initials: JS
authors.lastname: Jin
authors.forename: Suhuai
authors.initials: S
authors.lastname: Luo
authors.forename: Wanlin
authors.initials: W
authors.lastname: Zhu
authors.forename: Nicole A
authors.initials: NA
authors.lastname: Kochan
authors.forename: Simone
authors.initials: S
authors.lastname: Reppermund
authors.forename: Tao
authors.initials: T
authors.lastname: Liu
authors.forename: Julian N
authors.initials: JN
authors.lastname: Trollor
authors.forename: Henry
authors.initials: H
authors.lastname: Brodaty
authors.forename: Wei
authors.initials: W
authors.lastname: Wen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D060825:Cognitive Dysfunction / Q000175:diagnosis* / Q000503:physiopathology*; D018450:Disease Progression; D042241:Early Diagnosis; D005260:Female; D006801:Humans; D008297:Male; D046709:Pattern Recognition, Physiological