Steve Robbins | Search PubMed

PubMed2005

Precentral sulcal complex of the human brain: morphology and statistical probability maps.

The morphology of the precentral sulcus was examined via 3D visualization in 40 structural magnetic resonance images of the human brain to define its common features and their variability. The precentral sulcus is composed of two distinct sulcal configurations: 1) the inferior precentral sulcus (IP), situated caudal to the inferior frontal sulcus, and 2) the superior precentral sulcus (SP), caudal to the superior frontal sulcus. The SP was usually a single connected structure, and only in 24% of cases studied did it consist of two separate folds. The caudal end of the superior frontal sulcus and the SP merge on the surface of the brain. However, a clear separation can be established in 72% of cases by analyzing the morphology of the depth of this region in serial sections. Two or three small sulci can be identified in the area between the SP and the midline: the medial precentral sulcus, the marginal precentral sulcus, and a paramidline sulcus. The IP is composed of three different parts: a dorsal and a ventral branch, both oriented vertically, and a short horizontal extension. The ventral branch forms the caudal border of the inferior frontal gyrus, the dorsal branch forms the caudal border of the ventral portion of the middle frontal gyrus, and the horizontal extension runs in a rostrodorsal direction into the middle frontal gyrus. These three structures are closely related to each other but can, in most cases, be separated in the depth of the inferior precentral sulcal complex.

Adult↗

PubMed2003

Deformation-based surface morphometry applied to gray matter deformation.

We present a unified statistical approach to deformation-based morphometry applied to the cortical surface. The cerebral cortex has the topology of a 2D highly convoluted sheet. As the brain develops over time, the cortical surface area, thickness, curvature, and total gray matter volume change. It is highly likely that such age-related surface changes are not uniform. By measuring how such surface metrics change over time, the regions of the most rapid structural changes can be localized. We avoided using surface flattening, which distorts the inherent geometry of the cortex in our analysis and it is only used in visualization. To increase the signal to noise ratio, diffusion smoothing, which generalizes Gaussian kernel smoothing to an arbitrary curved cortical surface, has been developed and applied to surface data. Afterward, statistical inference on the cortical surface will be performed via random fields theory. As an illustration, we demonstrate how this new surface-based morphometry can be applied in localizing the cortical regions of the gray matter tissue growth and loss in the brain images longitudinally collected in the group of children and adolescents.

Adolescent↗