We investigated the scale relationship between size and cortical structure of human brains in a large sample of magnetic resonance imaging data. Cortical structure was estimated with several measures (cortical volume, surface area, and thickness, sulcal depth, and absolute mean curvature in sulcal regions and sulcal walls) using three-dimensional surface-based methods in 148 normal subjects (n [men/women]: 83/65, age [mean +/- standard deviation]: 25.0 +/- 4.9 years). We found significantly larger scaling exponents than geometrically predicted for cortical surface area, absolute mean curvature in sulcal regions and in sulcal walls, and smaller ones for cortical volume and thickness. As brain size increases, the cortex thickens only slightly, but the degree of sulcal convolution increases dramatically, indicating that human cortices are not simply scaled versions of one another. Our results are consistent with previous hypotheses that greater local clustering of interneuronal connections would be required in a larger brain, and fiber tension between local cortical areas would induce cortical folds. We suggest that sex effects are explained by brain size effects in cortical structure at a macroscopic and lobar regional level, and that it is necessary to consider true relationships between cortical measures and brain size due to the limitations of linear stereotaxic normalization.

Noninvasive parcellation of the human cerebral cortex is an important goal for understanding and examining brain functions. Recently, the patterns of anatomical connections using diffusion tensor imaging (DTI) have been used to parcellate brain regions. Here, we present a noninvasive parcellation approach that uses “functional fingerprints” obtained by correlation measures on resting-state functional Magnetic Resonance Imaging (fMRI) data to parcellate brain regions. In other terms, brain regions are parcellated based on the similarity of their connection – as reflected by correlation during resting-state – to the whole brain. The proposed method was used to parcellate the medial frontal cortex (MFC) into supplementary motor areas (SMA) and pre-SMA subregions. In agreement with anatomical landmark-based parcellation, we find that functional fingerprint clustering of the MFC results in anterior and posterior clusters. The probabilistic maps from 12 subjects showed that the anterior cluster is mainly located rostral to the vertical commissure anterior (VCA) line, whereas the posterior cluster is mainly located caudal to VCA line, suggesting the homologues of pre-SMA and SMA. The functional connections from the putative pre-SMA cluster were connected to brain regions which are responsible for complex/cognitive motor control, whereas those from the putative SMA cluster were connected to brain regions which are related to the simple motor control. These findings demonstrate the feasibility of the functional connectivity-based parcellation of the human cerebral cortex using resting state fMRI.

The locally deepest regions of major sulci, the sulcal pits, are thought to be the first cortical folds to develop and are closely related to functional areas. We examined the spatial distribution of sulcal pits across the entire cortical region, and assessed the hemispheric asymmetry in their frequency and distribution in a large group of normal adult brains. We automatically extracted sulcal pits from magnetic resonance imaging data using surface-based methods and constructed a group map from 148 subjects. The spatial distribution of the sulcal pits was relatively invariant between individuals, showing high frequency and density in specific focal areas. The left and right sulcal pits were spatially covariant in the regions of the earliest developed sulci. The sulcal pits with great spatial invariance appear to be useful as stable anatomical landmarks. We showed the most significant asymmetry in the frequency and spatial variance of sulcal pits in the superior temporal sulcus, which might be related to the lateralization of language function to the left hemisphere, developing more consistently and strongly than for the right. Our analyses support previous empirical and theoretical studies, and provide additional insights concerning the anatomical and functional development of the brain.

Fractal dimension (FD) has been widely used to provide a quantitative description of structural complexity in the cerebral cortex. FD is an extremely compact measure of shape complexity, condensing all details into a single numeric value. We interpreted the variation of the FD in the cortical surface of normal controls through multiple regression analysis with cortical thickness, sulcal depth, and folding area related to cortical complexity. We used a cortical surface showing a reliable representation of folded gyri and manually parcellated it into frontal, parietal, temporal, and occipital regions for regional analysis. In both hemispheres the mean cortical thickness and folding area showed significant combination effects on cortical complexity and accounted for about 50% of its variance. The folding area was significant in accounting for the FD of the cortical surface, with positive coefficients in both hemispheres and several lobe regions, while sulcal depth was significant only in the left temporal region. The results may suggest that human cortex develops a complex structure through the thinning of cortical thickness and by increasing the frequency of folds and the convolution of gyral shape rather than by deepening sulcal regions. Through correlation analysis of FD with IQ and the number of years of education, the results showed that a complex shape of the cortical surface has a significant relationship with intelligence and education. Our findings may indicate the structural characteristics that are revealed in the cerebral cortex when the FD in human brain is increased, and provide important information about brain development.

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