Quantifying the local tissue volume and composition in individual brains with magnetic resonance imaging

Aviv Mezer, Jason D. Yeatman, Nikola Stikov, Kendrick N. Kay, Nam Joon Cho, Robert F. Dougherty, Michael L. Perry, Josef Parvizi, Le H. Hua, Kim Butts-Pauly, Brian A. Wandell

Research output: Contribution to journalArticlepeer-review

197 Scopus citations

Abstract

Here, we describe a quantitative neuroimaging method to estimate the macromolecular tissue volume (MTV), a fundamental measure of brain anatomy. By making measurements over a range of field strengths and scan parameters, we tested the key assumptions and the robustness of the method. The measurements confirm that a consistent quantitative estimate of MTV can be obtained across a range of scanners. MTV estimates are sufficiently precise to enable a comparison between data obtained from an individual subject with control population data. We describe two applications. First, we show that MTV estimates can be combined with T1 and diffusion measurements to augment our understanding of the tissue properties. Second, we show that MTV provides a sensitive measure of disease status in individual patients with multiple sclerosis. The MTV maps are obtained using short clinically appropriate scans that can reveal how tissue changes influence behavior and cognition.

Original languageEnglish (US)
Pages (from-to)1667-1672
Number of pages6
JournalNature Medicine
Volume19
Issue number12
DOIs
StatePublished - Dec 2013
Externally publishedYes

Bibliographical note

Funding Information:
We acknowledge J. Barral, M. Gutman, H. Horiguchi, I. Levesque, A. Sherbondy and A. Takahashi for helpful advice and feedback. We thank S. Phipps, I. Levesque and A. Kerr for help in data analysis and acquisition. This work was supported by the US National Institutes of Health research grants RO1-EY15000 and NSF grant BCS-1228397. A.M. is the recipient of support from the Human Frontier Science Program and a Jewish Community Federation Program Machiah Foundation Fellowship. N.-J.C. is the recipient of support from the Singapore National Research Foundation (NRF-NRFF2011-01).

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