Measuring and comparing brain cortical surface area and other areal quantities

Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
NeuroImage (Impact Factor: 6.36). 03/2012; 61(4):1428-43. DOI: 10.1016/j.neuroimage.2012.03.026
Source: PubMed


Structural analysis of MRI data on the cortical surface usually focuses on cortical thickness. Cortical surface area, when considered, has been measured only over gross regions or approached indirectly via comparisons with a standard brain. Here we demonstrate that direct measurement and comparison of the surface area of the cerebral cortex at a fine scale is possible using mass conservative interpolation methods. We present a framework for analyses of the cortical surface area, as well as for any other measurement distributed across the cortex that is areal by nature. The method consists of the construction of a mesh representation of the cortex, registration to a common coordinate system and, crucially, interpolation using a pycnophylactic method. Statistical analysis of surface area is done with power-transformed data to address lognormality, and inference is done with permutation methods. We introduce the concept of facewise analysis, discuss its interpretation and potential applications.

Download full-text


Available from: Anderson Winkler
  • Source
    • "After these 3D surfaces are constructed , CT is estimated as the shortest distance from thewhite surface to the pial surface at each surface vertex. SA is measured by assigning an area to each vertex equal to the average of its surrounding triangles on the white sur- face[67]. CV is measured by the amount of gray matter volume that lies between the white surface and the pial surface[68]. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Individuals with autism spectrum disorder (ASD) have been characterized by altered cerebral cortical structures; however, the field has yet to identify consistent markers and prior studies have included mostly adolescents and adults. While there are multiple cortical morphological measures, including cortical thickness, surface area, cortical volume, and cortical gyrification, few single studies have examined all these measures. The current study analyzed all of the four measures and focused on pre-adolescent children with ASD. We employed the FreeSurfer pipeline to examine surface-based morphometry in 60 high-functioning boys with ASD (mean age = 8.35 years, range = 4–12 years) and 41 gender-, age-, and IQ-matched typically developing (TD) peers (mean age = 8.83 years), while testing for age-by-diagnosis interaction and between-group differences. During childhood and in specific regions, ASD participants exhibited a lack of normative age-related cortical thinning and volumetric reduction and an abnormal age-related increase in gyrification. Regarding surface area, ASD and TD exhibited statistically comparable age-related development during childhood. Across childhood, ASD relative to TD participants tended to have higher mean levels of gyrification in specific regions. Within ASD, those with higher Social Responsiveness Scale total raw scores tended to have greater age-related increase in gyrification in specific regions during childhood. ASD is characterized by cortical neuroanatomical abnormalities that are age-, measure-, statistical model-, and region-dependent. The current study is the first to examine the development of all four cortical measures in one of the largest pre-adolescent samples. Strikingly, Neurosynth-based quantitative reverse inference of the surviving clusters suggests that many of the regions identified above are related to social perception, language, self-referential, and action observation networks—those frequently found to be functionally altered in individuals with ASD. The comprehensive, multilevel analyses across a wide range of cortical measures help fill a knowledge gap and present a complex but rich picture of neuroanatomical developmental differences in children with ASD.
    Full-text · Article · Dec 2016 · Molecular Autism
  • Source
    • "The WM surface is inflated in an areapreserving transformation and subsequently homeomorphically transformed to a sphere (Fischl, Sereno, Tootell, et al., 1999), and matching of cortical geometry across subjects is achieved by way of registration to a spherical atlas based on individual cortical folding patterns. Cortical thickness and surface area estimates were obtained as described in previous publications and the thickness and area maps from each hemisphere were smoothed with a full-width-half-maximum Gaussian kernel of 30 mm (662 iterations) (Fischl & Dale, 2000; Rimol et al., 2012; Winkler et al., 2012). "
    [Show abstract] [Hide abstract]
    ABSTRACT: While cross-sectional neuroimaging studies on cortical development predict reductions in cortical volume (surface area and thickness) during adolescence, this is the first study to undertake a longitudinal assessment of cortical surface area changes across the continuous cortical surface during this period. We studied the developmental dynamics of cortical surface area and thickness in adolescents and young adults (aged 15–20) born with very low birth weight (VLBW; <1500 g) as well in term-born controls. Previous studies have demonstrated brain structural abnormalities in cortical morphology, as well as long-term motor, cognitive and behavioral impairments, in adolescents and young adults with VLBW, but the developmental dynamics throughout adolescence have not been fully explored. T1-weighted MRI scans from 51 VLBW (27 scanned twice) and 79 term-born adolescents (37 scanned twice) were used to reconstruct the cortical surface and produce longitudinal estimates of cortical surface area and cortical thickness. Linear mixed model analyses were performed, and the main effects of time and group, as well as time × group interaction effects, were investigated. In both groups, cortical surface area decreased up to 5% in some regions, and cortical thickness up to 8%, over the five-year period. The most affected regions were located on the lateral aspect of the hemispheres, in posterior temporal, parietal and to some extent frontal regions. There was no significant interaction between time and group for either morphometry variable. In conclusion, cortical thickness decreases from 15 to 20 years of age, in a similar fashion in the clinical and control groups. Moreover, we show for the first time that developmental trajectories of cortical surface area in preterm and term-born adolescents do not diverge during adolescence.
    Full-text · Article · Dec 2015 · Cortex
  • Source
    • "Statistical analyses are now often conducted both using standard univariate methods and multi-voxel pattern analysis (MVPA) (Haynes and Rees, 2006; Kriegeskorte et al., 2006; Norman et al., 2006). Brain structure is often analyzed using voxel-(Ashburner, 2009) and surface-based (Winkler et al., 2012) morphometry, and gyrification indices (Schaer et al., 2008). Registration between individuals can use relatively low-dimensional warping to a template, or higher dimensional registration (Ashburner, 2007, 2009). "
    [Show abstract] [Hide abstract]
    ABSTRACT: Recent years have seen neuroimaging data sets becoming richer, with larger cohorts of participants, a greater variety of acquisition techniques, and increasingly complex analyses. These advances have made data analysis pipelines complicated to set up and run (increasing the risk of human error) and time consuming to execute (restricting what analyses are attempted). Here we present an open-source framework, automatic analysis (aa), to address these concerns. Human efficiency is increased by making code modular and reusable, and managing its execution with a processing engine that tracks what has been completed and what needs to be (re)done. Analysis is accelerated by optional parallel processing of independent tasks on cluster or cloud computing resources. A pipeline comprises a series of modules that each perform a specific task. The processing engine keeps track of the data, calculating a map of upstream and downstream dependencies for each module. Existing modules are available for many analysis tasks, such as SPM-based fMRI preprocessing, individual and group level statistics, voxel-based morphometry, tractography, and multi-voxel pattern analyses (MVPA). However, aa also allows for full customization, and encourages efficient management of code: new modules may be written with only a small code overhead. aa has been used by more than 50 researchers in hundreds of neuroimaging studies comprising thousands of subjects. It has been found to be robust, fast, and efficient, for simple-single subject studies up to multimodal pipelines on hundreds of subjects. It is attractive to both novice and experienced users. aa can reduce the amount of time neuroimaging laboratories spend performing analyses and reduce errors, expanding the range of scientific questions it is practical to address.
    Full-text · Article · Jan 2015 · Frontiers in Neuroinformatics
Show more