Mapping the Regional Influence of Genetics on Brain Structure Variability - A Tensor-Based Morphometry Study
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Auteurs : Caroline Brun [États-Unis] ; Natasha Leporé [États-Unis] ; Xavier Pennec [France] ; Agatha D. Lee [États-Unis] ; Marina Barysheva [États-Unis] ; Sarah K. Madsen [États-Unis] ; Christina Avedissian [États-Unis] ; Yi-Yu Chou [États-Unis] ; Greig I. De Zubicaray [Australie] ; Katie Mcmahon [Australie] ; Margaret Wright [Australie] ; Arthur W. Toga [États-Unis] ; Paul M. Thompson [États-Unis]Source :
- NeuroImage [ 1053-8119 ] ; 2009.
Abstract
Genetic and environmental factors influence brain structure and function profoundly The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3
Url:
DOI: 10.1016/j.neuroimage.2009.05.022
PubMed: 19446645
PubMed Central: 2859973
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<front><div type="abstract" xml:lang="en"><p id="P1">Genetic and environmental factors influence brain structure and function profoundly The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3<italic>D</italic>
maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8 ± 1.8 SD years). All 92 twins’ 3<italic>D</italic>
brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject’s anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions which have a more protracted maturational time-course.</p>
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<front><journal-meta><journal-id journal-id-type="nlm-journal-id">9215515</journal-id>
<journal-id journal-id-type="pubmed-jr-id">20498</journal-id>
<journal-id journal-id-type="nlm-ta">Neuroimage</journal-id>
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<title-group><article-title>Mapping the Regional Influence of Genetics on Brain Structure Variability - A Tensor-Based Morphometry Study</article-title>
</title-group>
<contrib-group><contrib contrib-type="author"><name><surname>Brun</surname>
<given-names>Caroline</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Leporé</surname>
<given-names>Natasha</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Pennec</surname>
<given-names>Xavier</given-names>
</name>
<xref rid="A2" ref-type="aff">b</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Lee</surname>
<given-names>Agatha D.</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Barysheva</surname>
<given-names>Marina</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Madsen</surname>
<given-names>Sarah K.</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Avedissian</surname>
<given-names>Christina</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Chou</surname>
<given-names>Yi-Yu</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>de Zubicaray</surname>
<given-names>Greig I.</given-names>
</name>
<xref rid="A3" ref-type="aff">c</xref>
</contrib>
<contrib contrib-type="author"><name><surname>McMahon</surname>
<given-names>Katie</given-names>
</name>
<xref rid="A3" ref-type="aff">c</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Wright</surname>
<given-names>Margaret</given-names>
</name>
<xref rid="A4" ref-type="aff">d</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Toga</surname>
<given-names>Arthur W.</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
<contrib contrib-type="author"><name><surname>Thompson</surname>
<given-names>Paul M.</given-names>
</name>
<xref rid="A1" ref-type="aff">a</xref>
</contrib>
</contrib-group>
<aff id="A1"><label>a</label>
Laboratory of Neuro Imaging, Department of Neurology, UCLA School of Medicine, 635 Charles Young Drive South Suite 225, Los Angeles, CA 90095-7334, USA</aff>
<aff id="A2"><label>b</label>
Asclepios Research Project, INRIA, 2004 route des Lucioles - BP 93, 06902 Sophia-Antipolis Cedex, France</aff>
<aff id="A3"><label>c</label>
Centre for Magnetic Resonance, University of Queensland, Brisbane, Queensland, 4072, Australia</aff>
<aff id="A4"><label>d</label>
Genetic Epidemiology Lab, Queensland Institute of Medical Research, PO Royal Brisbane Hospital, Queensland 4029, Australia</aff>
<pub-date pub-type="nihms-submitted"><day>12</day>
<month>4</month>
<year>2010</year>
</pub-date>
<pub-date pub-type="epub"><day>14</day>
<month>5</month>
<year>2009</year>
</pub-date>
<pub-date pub-type="ppub"><day>15</day>
<month>10</month>
<year>2009</year>
</pub-date>
<pub-date pub-type="pmc-release"><day>15</day>
<month>10</month>
<year>2010</year>
</pub-date>
<volume>48</volume>
<issue>1</issue>
<fpage>37</fpage>
<lpage>49</lpage>
<permissions><copyright-statement>© 2009 Elsevier Inc. All rights reserved.</copyright-statement>
<copyright-year>2009</copyright-year>
</permissions>
<abstract><p id="P1">Genetic and environmental factors influence brain structure and function profoundly The search for heritable anatomical features and their influencing genes would be accelerated with detailed 3<italic>D</italic>
maps showing the degree to which brain morphometry is genetically determined. As part of an MRI study that will scan 1150 twins, we applied Tensor-Based Morphometry to compute morphometric differences in 23 pairs of identical twins and 23 pairs of same-sex fraternal twins (mean age: 23.8 ± 1.8 SD years). All 92 twins’ 3<italic>D</italic>
brain MRI scans were nonlinearly registered to a common space using a Riemannian fluid-based warping approach to compute volumetric differences across subjects. A multi-template method was used to improve volume quantification. Vector fields driving each subject’s anatomy onto the common template were analyzed to create maps of local volumetric excesses and deficits relative to the standard template. Using a new structural equation modeling method, we computed the voxelwise proportion of variance in volumes attributable to additive (A) or dominant (D) genetic factors versus shared environmental (C) or unique environmental factors (E). The method was also applied to various anatomical regions of interest (ROIs). As hypothesized, the overall volumes of the brain, basal ganglia, thalamus, and each lobe were under strong genetic control; local white matter volumes were mostly controlled by common environment. After adjusting for individual differences in overall brain scale, genetic influences were still relatively high in the corpus callosum and in early-maturing brain regions such as the occipital lobes, while environmental influences were greater in frontal brain regions which have a more protracted maturational time-course.</p>
</abstract>
<contract-num rid="HD1">R01 HD050735-02
||HD</contract-num>
<contract-sponsor id="HD1">National Institute of Child Health & Human Development : NICHD</contract-sponsor>
</article-meta>
</front>
</pmc>
</record>
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