Joint Analysis of Cortical Area and Thickness as a Replacement for the Analysis of the Volume of the Cerebral Cortex

Abstract

Cortical surface area is an increasingly used brain morphology metric that is ontogenetically and phylogenetically distinct from cortical thickness and offers a separate index of neurodevelopment and disease. However, the various existing methods for assessment of cortical surface area from magnetic resonance images have never been systematically compared. We show that the surface area method implemented in FreeSurfer corresponds closely to the exact, but computationally more demanding, mass-conservative (pycnophylactic) method, provided that images are smoothed. Thus, the data produced by this method can be interpreted as estimates of cortical surface area, as opposed to areal expansion. In addition, focusing on the joint analysis of thickness and area, we compare an improved, analytic method for measuring cortical volume to a permutation-based nonparametric combination (NPC) method. We use the methods to analyze area, thickness and volume in young adults born preterm with very low birth weight, and show that NPC analysis is a more sensitive option for studying joint effects on area and thickness, giving equal weight to variation in both of these 2 morphological features.

Structural Variability in the Human Brain Reflects Fine-Grained Functional Architecture at the Population Level

“…Imaging processing. As we aim to further our understanding of what the contributing factors to the gray matter volume networks might be, for example, as described in Seeley et al (2009), we assessed cortical thickness and area in addition to volume, as these are the two modalities typically used to disentangle the different contributions to, and possible interpretations of, gray matter volume in numerous previous studies (Douaud et al, 2007;Voets et al, 2008;Jalbrzikowski et al, 2013;Storsve et al, 2014;Winkler et al, 2018).…”

Morphometric correlates of anomia in patients with small left temporopolar lesions

“…This approach allowed us to identify morphological changes in brain structures close, but also remote from, the lesion, as has been previously demonstrated in patients with temporal lobe epilepsy (Alvim et al ., ; Bell, Lin, Seidenberg, & Hermann, ; Keller & Roberts, ; Lin et al ., ). Specifically, we chose to explore the cortex throughout two independent morphological features: (1) cortical thickness, which represents the combined thickness of the layers of the cerebral cortex; and (2) gyrification index, which is a normalized metric of the cortical surface area (Winkler et al ., ). Based on previous studies (Chen et al ., ; Halai et al ., ; Mesulam et al ., ; Reilly, Peelle, Antonucci, & Grossman, ), we hypothesized that both types of naming errors will be associated with abnormalities in ATL regions.…”

Searching for replicable associations between cortical thickness and psychometric variables in healthy adults: empirical facts

“…GMV is frequently seen in the neuroimaging community as an impure, multidetermined, and hence, crude estimate of grey matter tissue (Ashburner, 2009;Winkler et al, 2018). Since variabilities in the volumetric measures are mostly caused by surface area variations than by thickness variation itself, cortical thickness estimates (CT) may be seen as a more straightforward measure of brain structural features (Winkler et al, 2018(Winkler et al, , 2010. Accordingly, variability in cortical thickness could be expected to show relatively straightforward and reliable associations with behavioural measurements (But also see (Natu et al, 2019)).…”