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Genetic variability of human brain size and cortical gyral patterns
Abstract
The development of the primate brain is determined by an interaction of genetic programmes and environmental events. We examined quantitatively the contribution of each of these factors to adult human brain hemisphere volume and global cortical gyral patterns by comparing 3-D MRI renderings of brains of 10 pairs of monozygotic (MZ) and nine pairs of same-sex dizygotic (DZ) twins. Brain volume was highly correlated in MZ pairs [unbiased intraclass correlation coefficient, ICC(U) = 0.95, P < 0.00001], but not in DZ pairs [ICC(U) = 0.35, P = 0.09]. Structural equation modelling indicated a 94% heritability of brain volume. Gyral patterns appeared visually more similar in MZ than in DZ pairs. This was confirmed statistically by a cross-correlation analysis of rendered images of lateral and mesial cortical surfaces. MZ twins exhibited significantly greater similarity than did DZ twins in comparisons of gyral patterns; DZ twins were not more alike than unrelated pairings. Ipsilateral hemispheres were significantly more alike than contralateral hemispheres within MZ pairs, but not within DZ pairs. Contralateral hemispheres within an individual were more alike than contralateral hemispheres between twins in the DZ pairs, but not in the MZ pairs. Heritability for gyral-sulcal patterns, as reflected in the cross-correlation data, was low and ill defined. These results indicate that human cerebral size is determined almost entirely by genetic factors and that overall cortical gyral patterns, though significantly affected by genes, are determined primarily by nongenetic factors.
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(1997) · doi:10.1002/(sici)1096-9861(19971006)386:4<661::aid-cne11>3.0.co;2-n
Metadata
- Authors
- Bartley, A.
- Venue
- Brain
- Publisher
- Oxford University Press (OUP)
- Published
- 1997-02-01
- Volume
- 120
- Issue
- 2
- Pages
- 257-269
- ISSN
- 0006-8950 (ISSN-L)
- 1460-2156
- OpenAlex
- W2104682693
- Cited by
- 355
- References
- 0
- FWCI
- 4.56
- Type
- journal article
- Language
- en
- Open access
- closed
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