Abstract: Many studies of monozygotic (MZ) twins have revealed evidence of genetic influences on intellectual functions and their derangement in certain neurologic and psychiatric diseases afflicting the forebrain. Relatively little is known about genetic influences on the size and shape of the human forebrain and its gross morphologic subdivisions. Using MRI and quantitative image analysis techniques, we examined neuroanatomic similarities in MZ twins and their relationship to head size and intelligence quotient (IQ).

ANOVA were carried out using each measure as  the dependent variable and genotype, birth order, and sex, separately, as between-subject factors. Painvise correlations between measures were also computed. We found significant effects of genotype but not birth order for the following neuro anatomic measures: forebrain volume (raw, p 5 0.0001; normalized by body weight, p = 0.0003); cortical surface area (raw, p = 0.002; normalized, p = 0.001); and callosal area (raw, p 5 0.0001; normalized by forebrain volume, p = 0.02). We also found significant effects of genotype but not birth order for head circumference (raw, p = 0.0002; normalized, p 5 0.0001) and full-scale I& (p = 0.001). There were no significant sex effects except for raw head circumference (p = 0.03). Significant correlations were observed among forebrain volume, cortical surface area, and callosal area and between each brain measure and head circumference. There was no significant correlation between I& and any brain measure or head circumference.

These results indicate that: 1) forebrain volume, cortical surface area, and callosal area are similar in MZ twins; and 2) these brain measures are tightly correlated with one another and with head circumference but not with I& in young, healthy adults.

Keywords: Monozygotic twins (MZ), Intelligence quotient (IQ), Brain size analysis, Algorithmic analysis, Neuroimaging, MRI scans, Computational neuroscience, Brain structure, Automated analysis, Medical research, Cognitive conditions, Neurological disorders, Brain health, Data processing, Computational methods, Research applications, Brain development, Neuroinformatics, Image processing, Neurological insights, Precision measurements, Advanced technology.

PDF | DOI: 10.17148/IJARCCE.2023.12821

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