title: Integrated cortical structural marker for Alzheimer's disease.
abstract: In this article, we propose an approach to integrate cortical morphology measures for improving the discrimination of individuals with and without very mild Alzheimer's disease (AD). FreeSurfer was applied to scans collected from 83 participants with very mild AD and 124 cognitively normal individuals. We generated cortex thickness, white matter convexity (aka "sulcal depth"), and white matter surface metric distortion measures on a normalized surface atlas in this first study to integrate high resolution gray matter thickness and white matter surface geometric measures in identifying very mild AD. Principal component analysis was applied to each individual structural measure to generate eigenvectors. Discrimination power based on individual and combined measures are compared, based on stepwise logistic regression and 10-fold cross-validation. Global AD likelihood index and surface-based likelihood maps were also generated. Our results show complementary patterns on the cortical surface between thickness, which reflects gray matter atrophy, convexity, which reflects white matter sulcal depth changes and metric distortion, which reflects white matter surface area changes. The classifier integrating all 3 types of surface measures significantly improved classification performance compared with classification based on single measures. The principal component analysis-based approach provides a framework for achieving high discrimination power by integrating high-dimensional data, and this method could be very powerful in future studies for early diagnosis of diseases that are known to be associated with abnormal gyral and sulcal patterns.
authors.affiliation: Biomedical Engineering, University of Illinois at Chicago, Chicago, IL, USA. Electronic address: leiwang1@northwestern.edu.
authors.forename: Jing
authors.initials: J
authors.lastname: Ming
authors.affiliation: Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: Michael P
authors.initials: MP
authors.lastname: Harms
authors.affiliation: Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO, USA; Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA.
authors.forename: John C
authors.initials: JC
authors.lastname: Morris
authors.affiliation: Biomedical Engineering, Simon Fraser University, British Columbia, Canada.
authors.forename: M Faisal
authors.initials: MF
authors.lastname: Beg
authors.affiliation: Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, USA; Department of Radiology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis / Q000473:pathology*; D001284:Atrophy; D015415:Biomarkers; D002540:Cerebral Cortex / Q000473:pathology*; D005260:Female; D066128:Gray Matter / Q000473:pathology; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D059906:Neuroimaging / Q000379:methods*; D066127:White Matter / Q000473:pathology
chemical_list: D015415:Biomarkers
keywords: Classification; Convexity; Cortical geometry; Cortical thickness; Metric distortion; Neuroimaging