Detection of cortical thickness correlates of cognitive performance: Reliability across MRI scan sessions, scanners, and field strengths
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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.
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Previous studies with this dataset have pointed out that scanner variability is negligible.
“Although two different scanners were used (Siemens model Trio; scanner 1 = 285 subjects, 95.6 %; and scanner 2 = 13 subjects, 4.4 %), previous studies with this dataset have pointed out that this variability is negligible (Dickerson et al., 2008; Fox et al., 2012; Melzer et al., 2020; Noble et al., 2017a; Noble et al., 2017b; Zhou et al., 2018).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Dickerson et al., 2008
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Cortical Thinning in Individuals with Subjective Memory Impairment
Meiberth, D., Scheef, L., Wolfsgruber, S., Boecker, H., Block, W., Träber, F., Erk, S., Heneka, M. T., Jacobi, H., Spottke, A., et al. (2015) · Journal of Alzheimer's Disease · doi:10.3233/jad-142322 · Cited by 84 · see citing works
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Apolipoprotein E ε4-related thickening of the cerebral cortex modulates selective attention
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- Authors
- Dickerson, B. C., Fenstermacher, E., Salat, D. H., Wolk, D. A., Maguire, R. P., Desikan, R., Pacheco, J., Quinn, B. T., Van der Kouwe, A., Greve, D. N., Blacker, D., Albert, M. S., Killiany, R. J., & Fischl, B.
- Affiliations
- Brigham and Women's Hospital (US)
- Harvard University (US)
- University of Pittsburgh (US)
- Pfizer (United States) (US)
- Boston University (US)
- Johns Hopkins University (US)
- Corresponding author
- Dickerson, B.C.
- ORCID
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2008-01
- Volume
- 39
- Issue
- 1
- Pages
- 10-18
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2141650
- OpenAlex
- W2075123043
- Cited by
- 290
- References
- 66
- Citations by year
- 2026: 12 · 2025: 4 · 2024: 13 · 2023: 10 · 2022: 9 · 2021: 30 · 2020: 16 · 2019: 17 · 2018: 19 · 2017: 11 · 2016: 17 · 2015: 20 · 2014: 9 · 2013: 16 · 2012: 16
- FWCI
- 10.75
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Licence
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- U.S. Public Health Service (BIRN002)
- National Institute on Aging (K23AG022509, K23-AG22509)
- National Institute of Neurological Disorders and Stroke (R01NS052585)
- National Institute of Biomedical Imaging and Bioengineering (R01EB001550, U54EB005149)
- National Center for Research Resources (P41-RR-14075, U24RR021382, R01 RR1659401A1, P41RR014075, R01RR016594)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Cognitive Abilities and Testing
- Keywords
- cortical thickness, cognitive performance, reliability statistics, field strength, verbal memory, medial temporal cortex, visuomotor speed, set-shifting, parietal cortex, brain-behavior relationship, imaging biomarkers, older adults
- MeSH terms
- Aged, Aged, 80 and over, Cerebral Cortex, Cognition, Equipment Design, Female, Humans, Magnetic Resonance Imaging, Male, Organ Size, Radiation Dosage, Sensitivity and Specificity, Statistics as Topic, Task Performance and Analysis, Reproducibility of Results, Equipment Failure Analysis, Imaging, Three-Dimensional
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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