We describe a system of surface-assisted parcellation (SAP) of the human cerebellar cortex derived from neural systems functional and behavioral anatomy. This system is based on MRI and preserves the unique morphologic and topographic features of the individual cerebellum. All major fissures of the cerebellum were identified and traced in the flattened representation of the cerebellar cortex using the program “Free Surfer.” Parcellation of the cerebellar cortex followed using the fissure information in conjunction with landmarks using the program “Cardviews” to create 64 gyral-based cerebellar parcellation units. Computer-assisted algorithms enable the execution of the cerebellar parcellation procedure as well as volumetric measurements and topographic localization. The SAP technique makes it possible to represent multimodal structural and functional imaging data on the flattened surface of the cerebellar cortex as illustrated in one functional MRI experiment.

Although it is well known that there is considerable variation among individuals in the size of the human brain, the etiology of less extreme individual differences in brain size is largely unknown. We present here data from the first large twin sample ( N =132 individuals) in which the size of brain structures has been measured. As part of an ongoing project examining the brain correlates of reading disability (RD), whole brain morphometric analyses of structural magnetic response image (MRI) scans were performed on a sample of adolescent twins. Specifically, there were 25 monozygotic (MZ) and 23 dizygotic (DZ) pairs in which at least one member of each pair had RD and 9 MZ and 9 DZ pairs in which neither member had RD. We first factor-analyzed volume data for 13 individual brain structures, comprising all of the neocortex and most of the subcortex. This analysis yielded two factors (“cortical” and “subcortical”) that accounted for 64% of the variance. We next tested whether genetic and environmental influences on brain size variations varied for these two factors or by hemisphere. We computed intraclass correlations within MZ and DZ pairs in each sample for the cortical and subcortical factor scores, for left and right neocortex, and for the total cerebral volume. All five MZ correlations were substantial ( r's =.78 to .98) and significant in both samples, as well as being larger than the corresponding DZ correlations, ( r's =0.32 to 0.65) in both samples. The MZ-DZ difference was significant for 3 variables in the RD sample and for one variable in the smaller control sample. These results indicate significant genetic influences on these variables. The magnitude of genetic influence did not vary markedly either for the 2 factors or the 2 hemispheres. There was also a positive correlation between brain size and full-scale IQ, consistent with the results of earlier studies. The total cerebral volume was moderately correlated ( r =.42, p <.01, two-tailed) with full-scale IQ in the RD sample; there was a similar trend in the smaller control sample ( r =.31, p <.07, two-tailed). Corrections of similar magnitude were found between the subcortical factor and full-scale IQ, whereas the results for the cortical factor ( r =.16 and .13) were smaller and not significant. In sum, these results provide evidence for the heritability of individual differences in brain size which do not vary markedly by hemisphere or for neocortex relative to subcortex. Since there are also correlations between brain size and full-scale IQ in this sample, it is possible that genetic influences on brain size partly contribute to individual differences in IQ.

We describe a system of parcellation of the human neocortex, based upon magnetic resonance images, that conserves the topographic uniqueness of the individual brain. Subdivision of the neocortex, according to this system, is based entirely upon the configuration of a specified set of cerebral landmarks, principally neocortical fissures. These are present but unique in the details of their configurations in each individual brain. We introduce here a computer-assisted algorithm that ensures that a skilled investigator can execute the parcellation routine in a manageable period of time. Secondly, we outline a comprehensive set of conventions that specify how the boundaries of parcellation units are defined by anatomic landmarks. The average interobserver agreement in voxel assignment to parcellation units within the overall neocortex was 80.2%.