1 Department of Psychology, Concordia University, Montreal, Québec, Canada.
2 Montreal Institute for Learning Algorithms, Université de Montréal, Montreal, Québec, Canada.
3 Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
4 Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.
5 Department of Physics, Concordia University, Montreal, Québec, Canada.
6 School of Health, Concordia University, Montreal, Québec, Canada.
7 Montreal Heart Institute, Montreal, Québec, Canada.
8 Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
9 Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany.
10 Faculty of Social and Behavioral Sciences, University of Amsterdam, Amsterdam, Netherlands.
11 Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.

The study of brain structure and change in neuroscience is commonly conducted using macroscopic morphological measures of the brain such as regional volume or cortical thickness, providing little insight into the microstructure and physiology of the brain. In contrast, quantitative Magnetic Resonance Imaging (MRI) allows the monitoring of microscopic brain change non-invasively in-vivo, and provides directly comparable values between tissues, regions, and individuals. To support the development and common use of qMRI for cognitive neuroscience, we analysed a set of qMRI and dMRI metrics (R1, R2*, Magnetization Transfer saturation, Proton Density saturation, Fractional Anisotropy, Mean Diffusivity) in 101 healthy young adults. Here we provide a comprehensive descriptive analysis of these metrics and their linear relationships to each other in grey and white matter to develop a more complete understanding of the relationship to tissue microstructure. Furthermore, we provide evidence that combinations of metrics may uncover informative gradients across the brain by showing that lower variance components of PCA may be used to identify cortical gradients otherwise hidden within individual metrics. We discuss these results within the context of microstructural and physiological neuroscience research.