Cortical complexity as a measure of age-related brain atrophy

Abstract

The structure of the human brain changes in a variety of ways as we age. While a sizeable literature has examined age-related differences in cortical thickness, and to a lesser degree, gyrification, here we examined differences in cortical complexity, as indexed by fractal dimensionality in a sample of over 400 individuals across the adult lifespan. While prior studies have shown differences in fractal dimensionality between patient populations and age-matched, healthy controls, it is unclear how well this measure would relate to age-related cortical atrophy. Initially computing a single measure for the entire cortical ribbon, i.e., unparcellated gray matter, we found fractal dimensionality to be more sensitive to age-related differences than either cortical thickness or gyrification index. We additionally observed regional differences in age-related atrophy between the three measures, suggesting that they may index distinct differences in cortical structure. We also provide a freely available MATLAB toolbox for calculating fractal dimensionality.

The neurocognitive correlates of academic diligence in adolescent girls

“…Unlike other studies in adults (e.g. Madan & Kensinger, 2016;Yotter et al, 2011), we did not find that fractal dimension was a particularly sensitive measure, but this index of surface area is, at present, underexplored in developmental populations.…”

“…We analyzed cortical thickness, surface area (gyrification), and surface complexity (fractional dimension; Yotter, Nenaduc, Ziegler, Thompson & Gaser, 2011) of the right IFG. All of these measures have been shown to provide complimentary information about individual differences and development (Madan & Kensinger, 2016). Cortical thickness is an established surfacebased measure known to be associated with conscientiousness (Riccelli, Toschi, Nigro, Terracciano, & Passamonti, 2017), just like gyrification (Riccelli et al, 2017), but gyrification may be particularly sensitive to developmental differences (Klein et al, 2014).…”

Age-related differences in the structural complexity of subcortical and ventricular structures

“…Recently we demonstrated that age-related differences in the shape, i.e., structural complexity, of cortical regions were more pronounced than in cortical thickness or gyrification (Madan & Kensinger, 2016). Moreover, we found that complexity statistically accounted for all of the age-related differences associated with cortical thickness and gyrification.…”

Masks2Metrics (M2M): A Matlab Toolbox for Gold Standard Morphometrics

“…Human brains undergo morphometric changes over a lifetime, from conception through to birth, infancy, adolescence, adulthood, and old age (Thambisetty et al (2010); Madan and Kensinger (2016)). This is further compounded by the changes associated with various brain pathologies such as tumours (e.g.…”