Morphometricity as a measure of the neuroanatomical signature of a trait
Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is define...
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| Format: | Article |
| Language: | en_US |
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National Academy of Sciences (U.S.)
2017
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| Online Access: | http://hdl.handle.net/1721.1/108820 https://orcid.org/0000-0002-5002-1227 |
| _version_ | 1826194230582706176 |
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| author | Ge, Tian Holmes, Avram J. Smoller, Jordan W. Buckner, Randy L. Alzheimer's Disease Neuroimaging Initiative Sabuncu, Mert R Fischl, Bruce |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Ge, Tian Holmes, Avram J. Smoller, Jordan W. Buckner, Randy L. Alzheimer's Disease Neuroimaging Initiative Sabuncu, Mert R Fischl, Bruce |
| author_sort | Ge, Tian |
| collection | MIT |
| description | Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques. |
| first_indexed | 2024-09-23T09:52:37Z |
| format | Article |
| id | mit-1721.1/108820 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T09:52:37Z |
| publishDate | 2017 |
| publisher | National Academy of Sciences (U.S.) |
| record_format | dspace |
| spelling | mit-1721.1/1088202022-09-26T14:17:14Z Morphometricity as a measure of the neuroanatomical signature of a trait Ge, Tian Holmes, Avram J. Smoller, Jordan W. Buckner, Randy L. Alzheimer's Disease Neuroimaging Initiative Sabuncu, Mert R Fischl, Bruce Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Sabuncu, Mert R Fischl, Bruce Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer’s disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques. National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB006758) National Institute for Biomedical Imaging and Bioengineering (U.S.) (P41EB015896) National Institute for Biomedical Imaging and Bioengineering (U.S.) (R21EB018907) National Institute for Biomedical Imaging and Bioengineering (U.S.) (R01EB019956) National Institute on Aging (5R01AG008122) National Institute on Aging (R01AG016495) National Institute of Neurological Diseases and Stroke (U.S.) (R01NS0525851) National Institute of Neurological Diseases and Stroke (U.S.) (R21NS072652) National Institute of Neurological Diseases and Stroke (U.S.) (R01NS070963) National Institute of Neurological Diseases and Stroke (U.S.) (R01NS083534) National Institute of Neurological Diseases and Stroke (U.S.) (5U01NS086625) United States. National Institutes of Health (5U01-MH093765) United States. National Institutes of Health (R01NS083534) United States. National Institutes of Health (R01NS070963) United States. National Institutes of Health (R41AG052246) United States. National Institutes of Health (1K25EB013649-01) 2017-05-11T17:49:49Z 2017-05-11T17:49:49Z 2016-09 2016-03 Article http://purl.org/eprint/type/JournalArticle 0027-8424 1091-6490 http://hdl.handle.net/1721.1/108820 Sabuncu, Mert R.; Ge, Tian; Holmes, Avram J.; Smoller, Jordan W.; Buckner, Randy L. and Fischl, Bruce. “Morphometricity as a Measure of the Neuroanatomical Signature of a Trait.” Proceedings of the National Academy of Sciences 113, no. 39 (September 2016): E5749–E5756. © 2016 National Academy of Sciences https://orcid.org/0000-0002-5002-1227 en_US http://dx.doi.org/10.1073/pnas.1604378113 Proceedings of the National Academy of Sciences Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf National Academy of Sciences (U.S.) PNAS |
| spellingShingle | Ge, Tian Holmes, Avram J. Smoller, Jordan W. Buckner, Randy L. Alzheimer's Disease Neuroimaging Initiative Sabuncu, Mert R Fischl, Bruce Morphometricity as a measure of the neuroanatomical signature of a trait |
| title | Morphometricity as a measure of the neuroanatomical signature of a trait |
| title_full | Morphometricity as a measure of the neuroanatomical signature of a trait |
| title_fullStr | Morphometricity as a measure of the neuroanatomical signature of a trait |
| title_full_unstemmed | Morphometricity as a measure of the neuroanatomical signature of a trait |
| title_short | Morphometricity as a measure of the neuroanatomical signature of a trait |
| title_sort | morphometricity as a measure of the neuroanatomical signature of a trait |
| url | http://hdl.handle.net/1721.1/108820 https://orcid.org/0000-0002-5002-1227 |
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