title: Neural mechanisms underlying probabilistic category learning in normal aging.
abstract: Probabilistic category learning engages neural circuitry that includes the prefrontal cortex and caudate nucleus, two regions that show prominent changes with normal aging. However, the specific contributions of these brain regions are uncertain, and the effects of normal aging have not been examined previously in probabilistic category learning. In the present study, using a blood oxygenation level-dependent functional magnetic resonance imaging block design, 18 healthy young adults (mean age, 25.5 +/- 2.6 years) and 15 older adults (mean age, 67.1 +/- 5.3 years) were assessed on the probabilistic category learning "weather prediction" test. Whole-brain functional images acquired using a 1.5T scanner (General Electric, Milwaukee, WI) with gradient echo, echo planar imaging (3/1 mm; repetition time, 3000 ms; echo time, 50 ms) were analyzed using second-level random-effects procedures [SPM99 (Statistical Parametric Mapping)]. Young and older adults displayed equivalent probabilistic category learning curves, used similar strategies, and activated analogous neural networks, including the prefrontal and parietal cortices and the caudate nucleus. However, the extent of caudate and prefrontal activation was less and parietal activation was greater in older participants. The percentage correct and reaction time were mainly positively correlated with caudate and prefrontal activation in young individuals but positively correlated with prefrontal and parietal cortices in older individuals. Differential activation within a circumscribed neural network in the context of equivalent learning suggests that some brain regions, such as the parietal cortices, may provide a compensatory mechanism for healthy older adults in the context of deficient prefrontal cortex and caudate nuclei responses.
authors.affiliation: Clinical Brain Disorders Branch, National Institute of Mental Health, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland 20892, USA.
authors.forename: Francesco
authors.initials: F
authors.lastname: Fera
authors.forename: Thomas W
authors.initials: TW
authors.lastname: Weickert
authors.forename: Terry E
authors.initials: TE
authors.lastname: Goldberg
authors.forename: Alessandro
authors.initials: A
authors.lastname: Tessitore
authors.forename: Ahmad
authors.initials: A
authors.lastname: Hariri
authors.forename: Sumitra
authors.initials: S
authors.lastname: Das
authors.forename: Sam
authors.initials: S
authors.lastname: Lee
authors.forename: Brad
authors.initials: B
authors.lastname: Zoltick
authors.forename: Martijn
authors.initials: M
authors.lastname: Meeter
authors.forename: Catherine E
authors.initials: CE
authors.lastname: Myers
authors.forename: Mark A
authors.initials: MA
authors.lastname: Gluck
authors.forename: Daniel R
authors.initials: DR
authors.lastname: Weinberger
authors.forename: Venkata S
authors.initials: VS
authors.lastname: Mattay
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging / Q000502:physiology*; D005260:Female; D006801:Humans; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000379:methods / Q000706:statistics & numerical data; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D015233:Models, Statistical; D009415:Nerve Net / Q000502:physiology*; D011340:Problem Solving / Q000502:physiology*; D011930:Reaction Time / Q000502:physiology