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  Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies

Tardif, C. L., Steele, C., Lampe, L., Bazin, P.-L., Ragert, P., Villringer, A., et al. (2017). Investigation of the confounding effects of vasculature and metabolism on computational anatomy studies. NeuroImage, 149, 233-243. doi:10.1016/j.neuroimage.2017.01.025.

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Tardif, C. L.1, Author
Steele, Christopher1, 2, Author              
Lampe, Leonie2, Author              
Bazin, Pierre-Louis2, Author              
Ragert, Patrick3, Author
Villringer, Arno2, 4, 5, Author              
Gauthier, C. J.6, Author
1Douglas Mental Health University Institute, McGill University, Montréal, QC, Canada, ou_persistent22              
2Department Neurology, MPI for Human Cognitive and Brain Sciences, Max Planck Society, ou_634549              
3Department of Sport Science, University of Leipzig, Germany, ou_persistent22              
4Clinic for Cognitive Neurology, University of Leipzig, Germany, ou_persistent22              
5Berlin School of Mind and Brain, Humboldt University Berlin, Germany, ou_persistent22              
6Department of Physics, PERFORM Centre, Concordia University, Montreal, QC, Canada, ou_persistent22              


Free keywords: Computational anatomy; Vascular bias; Metabolic bias; Blood volume; Cortical thickness; Grey matter volume
 Abstract: Computational anatomy studies typically use T1-weighted magnetic resonance imaging contrast to look at local differences in cortical thickness or grey matter volume across time or subjects. This type of analysis is a powerful and non-invasive tool to probe anatomical changes associated with neurodevelopment, aging, disease or experience-induced plasticity. However, these comparisons could suffer from biases arising from vascular and metabolic subject- or time-dependent differences. Differences in blood flow and volume could be caused by vasodilation or differences in vascular density, and result in a larger signal contribution of the blood compartment within grey matter voxels. Metabolic changes could lead to differences in dissolved oxygen in brain tissue, leading to T1 shortening. Here, we analyze T1 maps and T1-weighted images acquired during different breathing conditions (ambient air, hypercapnia (increased CO2) and hyperoxia (increased O2)) to evaluate the effect size that can be expected from changes in blood flow, volume and dissolved O2 concentration in computational anatomy studies. Results show that increased blood volume from vasodilation during hypercapnia is associated with an overestimation of cortical thickness (1.85%) and grey matter volume (3.32%), and that both changes in O2 concentration and blood volume lead to changes in the T1 value of tissue. These results should be taken into consideration when interpreting existing morphometry studies and in future study design. Furthermore, this study highlights the overlap in structural and physiological MRI, which are conventionally interpreted as two independent modalities.


Language(s): eng - English
 Dates: 2016-09-262017-01-112017-02-012017-04-01
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/j.neuroimage.2017.01.025
PMID: 28159689
Other: Epub 2017
 Degree: -



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Funding organization : Alexander von Humboldt Foundation
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Funding organization : Fonts de la Reserche du Quebec – Sante (FRQS)
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Funding organization : Heart and Stroke Foundation

Source 1

Title: NeuroImage
Source Genre: Journal
Publ. Info: Orlando, FL : Academic Press
Pages: - Volume / Issue: 149 Sequence Number: - Start / End Page: 233 - 243 Identifier: ISSN: 1053-8119
CoNE: https://pure.mpg.de/cone/journals/resource/954922650166