Structural Correlates of Cognitive Performance: Intracortical Myelin and Cortical Thickness
Poster No:
754
Submission Type:
Abstract Submission
Authors:
Mazen Elkhayat1, Stella Heo1, Maya Kovacheff1, Christopher Rowley1, Niousha Gazor1, Rodrigo Mansur2, Roger McIntyre3, Roumen Milev4, Luciano Minuzzi1, Valerie Taylor5, Rudolf Uher6, Gustavo Vazquez4, Lakshmi Yatham7, Benicio Frey1, Nicholas Bock1
Institutions:
1McMaster University, Hamilton, Ontario, 2University Health Network, Toronto, Ontario, 3University of Toronto, Toronto, Ontario, 4Queen's University, Kingston, Ontario, 5University of Calgary, Calgary, Alberta, 6Dalhousie University, Halifax, Nova Scotia, 7University of British Columbia, Vancouver, British Columbia
First Author:
Co-Author(s):
Introduction:
Numerous studies have explored the link between brain anatomy and cognition, focusing on the cortex due to its involvement in higher-order processes. This relationship has been investigated primarily through broad measures such as cortical thickness (CTh), with fewer studies on cortical composition (Grydeland et al., 2013). A feature that may contribute to cognition is intracortical myelin (ICM), which optimizes brain network synchrony (Haroutunian et al., 2014). Although indirect evidence suggests a positive relationship between ICM and cognition, empirical studies remain limited. The longitudinal relaxation rate (R1), a quantitative magnetic resonance imaging (qMRI) metric spatially correlates with ICM (Shams et al., 2019). This study examines the relationship between R1, a surrogate marker of ICM, and cognitive performance, in comparison to that of CTh.
Methods:
Participants
Data from 78 healthy individuals (43 females), aged 16-43 years (mean: 27±7), were collected from 5 sites in Canada as part of a longitudinal study of bipolar disorder. Follow-up data collected 1 and 2 years later were available for 37 participants.
Neurocognitive Data
Raw scores from the following tests were analyzed: Brief Assessment of Cognition in Schizophrenia Symbol Coding, Stroop Adult Color and Word Test, Trail-Making Test Part A and B, Weschler Memory Scale-III: Spatial Span, Letter-Number Span, Category Fluency – Animal Naming, the Vocabulary and Matrix Reasoning subtests of the Weschler Abbreviated Scale of Intelligence-II, and the Weschler Test of Adult Reading (WTAR).
Image Acquisition & Processing
MRI data were collected using 3T General Electric and Siemens scanners. Anatomical T1-weighted images (1 mm isotropic) were collected for registration and segmentation. R1 maps were calculated from the ratio between a 3D inversion-recovery gradient echo image, optimized to maximize intracortical contrast and a 3D gradient echo image without an inversion pulse, optimized to minimize intracortical contrast. B1+ field maps were used to correct for flip angle inaccuracies. R1 maps were corrected for inter-site variation using scaling factors based on 2 subjects imaged at all sites. R1 maps were segmented into 360 cortical regions-of-interest (ROIs) based on the Human Connectome Project's MMP atlas in Connectome Workbench (Glasser et al., 2016). CTh was computed in FreeSurfer as the distance between the pial and white matter surfaces (Fischl & Dale, 2000).
Statistical Analysis
Pearson's correlations between raw cognitive scores and each of R1 and CTh were computed for each ROI, and the correlation coefficients were displayed on the cortical surface.
Data from 78 healthy individuals (43 females), aged 16-43 years (mean: 27±7), were collected from 5 sites in Canada as part of a longitudinal study of bipolar disorder. Follow-up data collected 1 and 2 years later were available for 37 participants.
Neurocognitive Data
Raw scores from the following tests were analyzed: Brief Assessment of Cognition in Schizophrenia Symbol Coding, Stroop Adult Color and Word Test, Trail-Making Test Part A and B, Weschler Memory Scale-III: Spatial Span, Letter-Number Span, Category Fluency – Animal Naming, the Vocabulary and Matrix Reasoning subtests of the Weschler Abbreviated Scale of Intelligence-II, and the Weschler Test of Adult Reading (WTAR).
Image Acquisition & Processing
MRI data were collected using 3T General Electric and Siemens scanners. Anatomical T1-weighted images (1 mm isotropic) were collected for registration and segmentation. R1 maps were calculated from the ratio between a 3D inversion-recovery gradient echo image, optimized to maximize intracortical contrast and a 3D gradient echo image without an inversion pulse, optimized to minimize intracortical contrast. B1+ field maps were used to correct for flip angle inaccuracies. R1 maps were corrected for inter-site variation using scaling factors based on 2 subjects imaged at all sites. R1 maps were segmented into 360 cortical regions-of-interest (ROIs) based on the Human Connectome Project's MMP atlas in Connectome Workbench (Glasser et al., 2016). CTh was computed in FreeSurfer as the distance between the pial and white matter surfaces (Fischl & Dale, 2000).
Statistical Analysis
Pearson's correlations between raw cognitive scores and each of R1 and CTh were computed for each ROI, and the correlation coefficients were displayed on the cortical surface.
Results:
Despite regional heterogeneity, R1 displayed more positive correlations on average with cognitive scores (mean: 0.047, range: -0.269 to 0.41), while CTh generally displayed more negative correlations (mean: -0.035, range: -0.36 to 0.35). One-sided Wilcoxon signed-rank tests indicated that the median of R1 correlation coefficients was significantly greater than 0 (p < 0.001, effect size: 0.389), whereas the median of the correlation coefficients of CTh was significantly less than 0 (p < 0.001, effect size: 0.312). Correlational patterns were consistent across visits for the 37 participants with follow-up data.
Conclusions:
In conclusion, while relationships vary across regions and tests, R1 generally shows more positive correlations with cognitive performance, whereas CTh shows more negative correlations. These findings align with prior research. Grydeland et al. (2013) reported a positive association between a myelin-related metric (T1W/T2W ratio) in the cortex and performance stability on a speeded task, while Cheng et al. (2018) and Naumczyk et al. (2018) found negative relationships between CTh and cognition in younger and mixed age samples.
Higher Cognitive Functions:
Higher Cognitive Functions Other 1
Language:
Language Other
Learning and Memory:
Working Memory
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping 2
Perception, Attention and Motor Behavior:
Perception and Attention Other
Keywords:
Cognition
Cortex
Language
Myelin
NORMAL HUMAN
STRUCTURAL MRI
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Fischl, B., & Dale, A. M. (2000). Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proceedings of the National Academy of Sciences of the United States of America, 97(20), 11050–11055. https://doi.org/10.1073/pnas.200033797
Glasser, M. F., Coalson, T. S., Robinson, E. C., Hacker, C. D., Harwell, J., Yacoub, E., Ugurbil, K., Andersson, J., Beckmann, C. F., Jenkinson, M., Smith, S. M., & Van Essen, D. C. (2016). A multi-modal parcellation of human cerebral cortex. Nature, 536(7615), 171–178. https://doi.org/10.1038/nature18933
Grydeland, H., Walhovd, K. B., Tamnes, C. K., Westlye, L. T., & Fjell, A. M. (2013). Intracortical Myelin Links with Performance Variability across the Human Lifespan: Results from T1- and T2-Weighted MRI Myelin Mapping and Diffusion Tensor Imaging. The Journal of Neuroscience, 33(47), 18618–18630. https://doi.org/10.1523/JNEUROSCI.2811-13.2013
Haroutunian, V., Katsel, P., Roussos, P., Davis, K. L., Altshuler, L. L., & Bartzokis, G. (2014). Myelination, oligodendrocytes, and serious mental illness. Glia, 62(11), 1856–1877. https://doi.org/10.1002/glia.22716
Naumczyk, P., Sawicka, A. K., Brzeska, B., Sabisz, A., Jodzio, K., Radkowski, M., Czachowska, K., Winklewski, P. J., Finc, K., Szurowska, E., Demkow, U., & Szarmach, A. (2018). Cognitive Predictors of Cortical Thickness in Healthy Aging. In M. Pokorski (Ed.), Clinical Medicine Research (pp. 51–62). Springer International Publishing. https://doi.org/10.1007/5584_2018_265
Shams, Z., Norris, D. G., & Marques, J. P. (2019). A comparison of in vivo MRI based cortical myelin mapping using T1w/T2w and R1 mapping at 3T. PLOS ONE, 14(7), e0218089. https://doi.org/10.1371/journal.pone.0218089