title: Prediction of cognitive decline based on hemispheric cortical surface maps of FDDNP PET.
abstract: OBJECTIVE
  A cross-sectional study to establish whether a subject's cognitive state can be predicted based on regional values obtained from brain cortical maps of FDDNP Distribution Volume Ratio (DVR), which shows the pattern of beta amyloid and neurofibrillary binding, along with those of early summed FDDNP PET images (reflecting the pattern of perfusion) was performed.
  
  
  METHODS
  Dynamic FDDNP PET studies were performed in a group of 23 subjects (8 control (NL), 8 Mild Cognitive Impairment (MCI) and 7 Alzheimer's Disease (AD) subjects). FDDNP DVR images were mapped to the MR derived hemispheric cortical surface map warped into a common space. A set of Regions of Interest (ROI) values of FDDNP DVR and early summed FDDNP PET (0-6 min post tracer injection), were thus calculated for each subject which along with the MMSE score were used to construct a linear mathematical model relating ROI values to MMSE. After the MMSE prediction models were developed, the models' predictive ability was tested in a non-overlapping set of 8 additional individuals, whose cognitive status was unknown to the investigators who constructed the predictive models.
  
  
  RESULTS
  Among all possible subsets of ROIs, we found that the standard deviation of the predicted MMSE was 1.8 by using only DVR values from medial and lateral temporal and prefrontal regions plus the early summed FDDNP value in the posterior cingulate gyrus. The root mean square prediction error for the eight new subjects was 1.6.
  
  
  CONCLUSIONS
  FDDNP scans reflect progressive neuropathology accumulation and can potentially be used to predict the cognitive state of an individual.
authors.affiliation: Department of Biomathematics, David Geffen School of Medicine at UCLA, CA 90095, USA. hillary.protas@bannerhealth.com
authors.forename: Hillary D
authors.initials: HD
authors.lastname: Protas
authors.forename: Vladimir
authors.initials: V
authors.lastname: Kepe
authors.forename: Kiralee M
authors.initials: KM
authors.lastname: Hayashi
authors.forename: Andrea D
authors.initials: AD
authors.lastname: Klunder
authors.forename: Meredith N
authors.initials: MN
authors.lastname: Braskie
authors.forename: Linda
authors.initials: L
authors.lastname: Ercoli
authors.forename: Prabha
authors.initials: P
authors.lastname: Siddarth
authors.forename: Susan Y
authors.initials: SY
authors.lastname: Bookheimer
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
authors.forename: Gary W
authors.initials: GW
authors.lastname: Small
authors.forename: Jorge R
authors.initials: JR
authors.lastname: Barrio
authors.forename: Sung-Cheng
authors.initials: SC
authors.lastname: Huang
mesh_terms: D000368:Aged; D000544:Alzheimer Disease / Q000000981:diagnostic imaging*; D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping / Q000379:methods*; D060825:Cognitive Dysfunction / Q000000981:diagnostic imaging*; D003430:Cross-Sectional Studies; D005260:Female; D005462:Fluorine Radioisotopes; D006801:Humans; D008297:Male; D049268:Positron-Emission Tomography / Q000379:methods*; D011868:Radioisotopes
chemical_list: D005462:Fluorine Radioisotopes; D011868:Radioisotopes