Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths

B. C. Dickerson, E. Fenstermacher, D. H. Salat, D. A. Wolk, R. P. Maguire, R. Desikan, J. Pacheco, B. T. Quinn, A. Van der Kouwe, D. N. Greve, D. Blacker, M. S. Albert, R. J. Killiany, B. Fischl

Research output: Contribution to journalArticlepeer-review

204 Scopus citations


In normal humans, relationships between cognitive test performance and cortical structure have received little study, in part, because of the paucity of tools for measuring cortical structure. Computational morphometric methods have recently been developed that enable the measurement of cortical thickness from MRI data, but little data exist on their reliability. We undertook this study to evaluate the reliability of an automated cortical thickness measurement method to detect correlates of interest between thickness and cognitive task performance. Fifteen healthy older participants were scanned four times at 2-week intervals on three different scanner platforms. The four MRI data sets were initially treated independently to investigate the reliability of the spatial localization of findings from exploratory whole-cortex analyses of cortical thickness-cognitive performance correlates. Next, the first data set was used to define cortical ROIs based on the exploratory results that were then applied to the remaining three data sets to determine whether the relationships between cognitive performance and regional cortical thickness were comparable across different scanner platforms and field strengths. Verbal memory performance was associated with medial temporal cortical thickness, while visuomotor speed/set shifting was associated with lateral parietal cortical thickness. These effects were highly reliable - in terms of both spatial localization and magnitude of absolute cortical thickness measurements - across the four scan sessions. Brain-behavior relationships between regional cortical thickness and cognitive task performance can be reliably identified using an automated data analysis system, suggesting that these measures may be useful as imaging biomarkers of disease or performance ability in multicenter studies in which MRI data are pooled.

Original languageEnglish (US)
Pages (from-to)10-18
Number of pages9
Issue number1
StatePublished - Jan 1 2008

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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