Abstract
The high gray‐white matter contrast and spatial resolution provided by T1‐weighted magnetic resonance imaging (MRI) has made it a widely used imaging protocol for computational anatomy studies of the brain. While the image intensity in T1‐weighted images is predominantly driven by T1, other MRI parameters affect the image contrast, and hence brain morphological measures derived from the data. Because MRI parameters are correlates of different histological properties of brain tissue, this mixed contribution hampers the neurobiological interpretation of morphometry findings, an issue which remains largely ignored in the community. We acquired quantitative maps of the MRI parameters that determine signal intensities in T1‐weighted images (R 1 (=1/T1), R 2*, and PD) in a large cohort of healthy subjects (n = 120, aged 18–87 years). Synthetic T1‐weighted images were calculated from these quantitative maps and used to extract morphometry features—gray matter volume and cortical thickness. We observed significant variations in morphometry measures obtained from synthetic images derived from different subsets of MRI parameters. We also detected a modulation of these variations by age. Our findings highlight the impact of microstructural properties of brain tissue—myelination, iron, and water content—on automated measures of brain morphology and show that microstructural tissue changes might lead to the detection of spurious morphological changes in computational anatomy studies. They motivate a review of previous morphological results obtained from standard anatomical MRI images and highlight the value of quantitative MRI data for the inference of microscopic tissue changes in the healthy and diseased brain. Hum Brain Mapp 37:1801–1815, 2016. © 2016 The Authors. Human Brain Mapping Published by Wiley Periodicals, Inc.
Highlights
In the past two decades computational anatomy emerged as a useful tool for studying non-invasively the healthy and diseased brain [Ashburner, 2009]
We use magnetic resonance imaging (MRI) biomarkers of brain tissue microstructure to investigate the origin of morphological brain changes commonly reported in neuroscience research
Iron and water content yield regionally specific contributions to gray matter r 1811 r volume and cortical thickness estimates obtained from T1weighted MRI images
Summary
In the past two decades computational anatomy emerged as a useful tool for studying non-invasively the healthy and diseased brain [Ashburner, 2009]. In conjunction with image segmentation, image registration and intra-cortical surface extraction techniques that draw on the microstructural information provided by qMRI [Bazin et al, 2014; Tardif et al, 2015b; Waehnert et al, 2014, 2016], the combination of high-resolution quantitative and functional MRI data opens new perspectives for the study of brain structure [Helbling et al, 2015; Olman et al, 2012; Polimeni et al, 2010; Turner and Geyer, 2014]
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