Morphometricity as a measure of the neuroanatomical signature of a trait

Media type: E-Article
Title: Morphometricity as a measure of the neuroanatomical signature of a trait
Contributor: Sabuncu, Mert R.; Ge, Tian; Holmes, Avram J.; Smoller, Jordan W.; Buckner, Randy L.; Fischl, Bruce; Weiner, Michael W.; Aisen, Paul; Weiner, Michael; Aisen, Paul; Petersen, Ronald; Jack, Clifford R.; Jagust, William; Trojanowki, John Q.; Toga, Arthur W.; Beckett, Laurel; Green, Robert C.; Saykin, Andrew J.; Morris, John; Shaw, Leslie M.; Khachaturian, Zaven; Sorensen, Greg; Carrillo, Maria; Kuller, Lew; [...]
imprint: Proceedings of the National Academy of Sciences, 2016
Published in: Proceedings of the National Academy of Sciences
Language: English
DOI: 10.1073/pnas.1604378113
ISSN: 0027-8424; 1091-6490
Keywords: Multidisciplinary
Origination:
Footnote:
Description: <jats:title>Significance</jats:title> <jats:p>Neuroimaging has largely focused on two goals: mapping associations between neuroanatomical features and phenotypes and building individual-level prediction models. This paper presents a complementary analytic strategy called morphometricity that aims to measure the neuroanatomical signatures of different phenotypes. Inspired by prior work on heritability, we define morphometricity as the proportion of phenotypic variation that can be explained by brain morphology (e.g., as captured by structural brain MRI). In the dawning era of large-scale datasets comprising traits across a broad phenotypic spectrum, morphometricity will be critical in prioritizing and characterizing behavioral, cognitive, and clinical phenotypes based on their neuroanatomical signatures. Furthermore, the proposed framework will be significant in dissecting the functional, morphological, and molecular underpinnings of different traits.</jats:p>
Access State: Open Access

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