title: Empowering imaging biomarkers of Alzheimer's disease.
abstract: In a previous report, we proposed a method for combining multiple markers of atrophy caused by Alzheimer's disease into a single atrophy score that is more powerful than any one feature. We applied the method to expansion rates of the lateral ventricles, achieving the most powerful ventricular atrophy measure to date. Here, we expand our method's application to tensor-based morphometry measures. We also combine the volumetric tensor-based morphometry measures with previously computed ventricular surface measures into a combined atrophy score. We show that our atrophy scores are longitudinally unbiased with the intercept bias estimated at 2 orders of magnitude below the mean atrophy of control subjects at 1 year. Both approaches yield the most powerful biomarker of atrophy not only for ventricular measures but also for all published unbiased imaging measures to date. A 2-year trial using our measures requires only 31 (22, 43) Alzheimer's disease subjects or 56 (44, 64) subjects with mild cognitive impairment to detect 25% slowing in atrophy with 80% power and 95% confidence.
authors.affiliation: USC Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
authors.forename: Boris A
authors.initials: BA
authors.lastname: Gutman
authors.affiliation: School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
authors.forename: Yalin
authors.initials: Y
authors.lastname: Wang
authors.affiliation: UCLA Joint Institute for Regional Earth System Science and Engineering, Los Angeles, CA, USA.
authors.forename: Igor
authors.initials: I
authors.lastname: Yanovsky
authors.affiliation: USC Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
authors.forename: Xue
authors.initials: X
authors.lastname: Hua
authors.affiliation: Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Clifford R
authors.initials: CR
authors.lastname: Jack
authors.affiliation: Department of Radiology and Biomedical Imaging, UC San Francisco, San Francisco, CA, USA; Department of Medicine, UC San Francisco, San Francisco, CA, USA; Department of Psychiatry, UC San Francisco, San Francisco, CA, USA.
authors.forename: Michael W
authors.initials: MW
authors.lastname: Weiner
authors.affiliation: USC Imaging Genetics Center, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Institute for Neuroimaging and Informatics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA. Electronic address: pthomp@usc.edu.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis* / Q000473:pathology*; D001284:Atrophy; D015415:Biomarkers; D002552:Cerebral Ventricles / Q000473:pathology; D005260:Female; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D059906:Neuroimaging / Q000379:methods*
chemical_list: D015415:Biomarkers
keywords: ADNI; Alzheimer's disease; Biomarker; Drug trial; Lateral ventricles; Linear discriminant analysis; Machine learning; Mild cognitive impairment; Shape analysis; Tensor-based morphometry