title: Multimodal magnetic resonance imaging assessment of white matter aging trajectories over the lifespan of healthy individuals.
abstract: BACKGROUND
  Postmortem and volumetric imaging data suggest that brain myelination is a dynamic lifelong process that, in vulnerable late-myelinating regions, peaks in middle age. We examined whether known regional differences in axon size and age at myelination influence the timing and rates of development and degeneration/repair trajectories of white matter (WM) microstructure biomarkers.
  
  
  METHODS
  Healthy subjects (n = 171) 14-93 years of age were examined with transverse relaxation rate (R(2)) and four diffusion tensor imaging measures (fractional anisotropy [FA] and radial, axial, and mean diffusivity [RD, AxD, MD, respectively]) of frontal lobe, genu, and splenium of the corpus callosum WM (FWM, GWM, and SWM, respectively).
  
  
  RESULTS
  Only R(2) reflected known levels of myelin content with high values in late-myelinating FWM and GWM regions and low ones in early-myelinating SWM. In FWM and GWM, all metrics except FA had significant quadratic components that peaked at different ages (R(2) < RD < MD < AxD), with FWM peaking later than GWM. Factor analysis revealed that, although they defined different factors, R(2) and RD were the metrics most closely associated with each other and differed from AxD, which entered into a third factor.
  
  
  CONCLUSIONS
  The R(2) and RD trajectories were most dynamic in late-myelinating regions and reflect age-related differences in myelination, whereas AxD reflects axonal size and extra-axonal space. The FA and MD had limited specificity. The data suggest that the healthy adult brain undergoes continual change driven by development and repair processes devoted to creating and maintaining synchronous function among neural networks on which optimal cognition and behavior depend.
authors.affiliation: Department of Psychiatry, The David Geffen School of Medicine at UCLA, Los Angeles, CA 90095, USA. gbar@ucla.edu
authors.forename: George
authors.initials: G
authors.lastname: Bartzokis
authors.forename: Po H
authors.initials: PH
authors.lastname: Lu
authors.forename: Panthea
authors.initials: P
authors.lastname: Heydari
authors.forename: Alexander
authors.initials: A
authors.lastname: Couvrette
authors.forename: Grace J
authors.initials: GJ
authors.lastname: Lee
authors.forename: Greta
authors.initials: G
authors.lastname: Kalashyan
authors.forename: Frank
authors.initials: F
authors.lastname: Freeman
authors.forename: John W
authors.initials: JW
authors.lastname: Grinstead
authors.forename: Pablo
authors.initials: P
authors.lastname: Villablanca
authors.forename: J Paul
authors.initials: JP
authors.lastname: Finn
authors.forename: Jim
authors.initials: J
authors.lastname: Mintz
authors.forename: Jeffry R
authors.initials: JR
authors.lastname: Alger
authors.forename: Lori L
authors.initials: LL
authors.lastname: Altshuler
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000502:physiology*; D016880:Anisotropy; D001369:Axons / Q000648:ultrastructure; D015415:Biomarkers; D001921:Brain / Q000033:anatomy & histology* / Q000254:growth & development*; D002540:Cerebral Cortex / Q000033:anatomy & histology / Q000254:growth & development; D056324:Diffusion Tensor Imaging / Q000379:methods*; D017352:Echo-Planar Imaging; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008297:Male; D008875:Middle Aged; D009186:Myelin Sheath / Q000502:physiology; D009410:Nerve Degeneration / Q000473:pathology; D009412:Nerve Fibers / Q000648:ultrastructure; D009836:Oligodendroglia / Q000502:physiology; D012016:Reference Values; D012044:Regression Analysis; D012727:Sex Characteristics; D055815:Young Adult
chemical_list: D015415:Biomarkers