title: Age-related differences in regional brain volumes: a comparison of optimized voxel-based morphometry to manual volumetry.
abstract: Regional manual volumetry is the gold standard of in vivo neuroanatomy, but is labor-intensive, can be imperfectly reliable, and allows for measuring limited number of regions. Voxel-based morphometry (VBM) has perfect repeatability and assesses local structure across the whole brain. However, its anatomic validity is unclear, and with its increasing popularity, a systematic comparison of VBM to manual volumetry is necessary. The few existing comparison studies are limited by small samples, qualitative comparisons, and limited selection and modest reliability of manual measures. Our goal was to overcome those limitations by quantitatively comparing optimized VBM findings with highly reliable multiple regional measures in a large sample (N=200) across a wide agespan (18-81). We report a complex pattern of similarities and differences. Peak values of VBM volume estimates (modulated density) produced stronger age differences and a different spatial distribution from manual measures. However, when we aggregated VBM-derived information across voxels contained in specific anatomically defined regions (masks), the patterns of age differences became more similar, although important discrepancies emerged. Notably, VBM revealed stronger age differences in the regions bordering CSF and white matter areas prone to leukoaraiosis, and VBM was more likely to report nonlinearities in age-volume relationships. In the white matter regions, manual measures showed stronger negative associations with age than the corresponding VBM-based masks. We conclude that VBM provides realistic estimates of age differences in the regional gray matter only when applied to anatomically defined regions, but overestimates effects when individual peaks are interpreted. It may be beneficial to use VBM as a first-pass strategy, followed by manual measurement of anatomically defined regions.
authors.affiliation: Institute of Gerontology, Department of Psychology, Wayne State University, Detroit, MI, USA.
authors.forename: Kristen M
authors.initials: KM
authors.lastname: Kennedy
authors.forename: Kirk I
authors.initials: KI
authors.lastname: Erickson
authors.forename: Karen M
authors.initials: KM
authors.lastname: Rodrigue
authors.forename: Michelle W
authors.initials: MW
authors.lastname: Voss
authors.forename: Stan J
authors.initials: SJ
authors.lastname: Colcombe
authors.forename: Arthur F
authors.initials: AF
authors.lastname: Kramer
authors.forename: James D
authors.initials: JD
authors.lastname: Acker
authors.forename: Naftali
authors.initials: N
authors.lastname: Raz
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology*; D001331:Automation / Q000379:methods; D001921:Brain / Q000473:pathology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000473:pathology; D009929:Organ Size; D012044:Regression Analysis; D012727:Sex Characteristics; D055815:Young Adult