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Cerebral Cortex Advance Access originally published online on August 5, 2004
Cerebral Cortex 2005 15(4):394-402; doi:10.1093/cercor/bhh142
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Cerebral Cortex V 15 N 4 © Oxford University Press 2004; all rights reserved

Brain Networks Associated with Cognitive Reserve in Healthy Young and Old Adults

Yaakov Stern1,2,3,4,6, Christian Habeck1,2, James Moeller1,3,6, Nikolaos Scarmeas1,2, Karen E. Anderson1,3, H. John Hilton1,2, Joseph Flynn1, Harold Sackeim3,5,6 and Ronald van Heertum5

1 Cognitive Neuroscience Division of the Taub Institute, College of Physicians and Surgeons of Columbia University, New York, 2 Department of Neurology, College of Physicians and Surgeons of Columbia University, New York, 3 Department of Psychiatry, College of Physicians and Surgeons of Columbia University, New York, 4 Department of Psychology, College of Physicians and Surgeons of Columbia University, New York, 5 Department of Radiology, College of Physicians and Surgeons of Columbia University, New York and 6 Department of Biological Psychiatry, New York State Psychiatric Institute, New York, NY, USA

Address correspondence to Dr Stern, 630 W 168th Street, P&S Box 16, New York, NY 10032, USA. Email: ys11{at}columbia.edu.

In order to understand the brain networks that mediate cognitive reserve, we explored the relationship between subjects' network expression during the performance of a memory test and an index of cognitive reserve. Using H215O positron emission tomography, we imaged 17 healthy older subjects and 20 young adults while they performed a serial recognition memory task for nonsense shapes under two conditions: low demand, with a unique shape presented in each study trial; and titrated demand, with a study list size adjusted so that each subject recognized shapes at 75% accuracy. A factor score that summarized years of education, and scores on the NART and the WAIS-R Vocabulary subtest was used as an index of cognitive reserve. The scaled subprofile model was used to identify a set of functionally connected regions (or topography) that changed in expression across the two task conditions and was differentially expressed by the young and elderly subjects. The regions most active in this topography consisted of right hippocampus, posterior insula, thalamus, and right and left operculum; we found concomitant deactivation in right lingual gyrus, inferior parietal lobe and association cortex, left posterior cingulate, and right and left calcarine cortex. Young subjects with higher cognitive reserve showed increased expression of the topography across the two task conditions. Because this topography, which is responsive to increased task demands, was differentially expressed as a function of reserve level, it may represent a neural manifestation of innate or acquired reserve. In contrast, older subjects with higher cognitive reserve showed decreased expression of the topography across tasks. This suggests some functional reorganization of the network used by the young subjects. Thus, for the old subjects this topography may represent an altered, compensatory network that is used to maintain function in the face of age-related physiological changes.

Key Words: compensation • covariance analysis • education • H215O PET • IQ


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