T1 white/gray contrast as a predictor of chronological age, and an index of cognitive performance

Abstract

The maturational schedule of human brain development appears to be narrowly confined. The chronological age of an individual can be predicted from brain images with considerable accuracy, and deviation from the typical pattern of brain maturation has been related to cognitive performance. Methods using multi-modal data, or complex measures derived from voxels throughout the brain have shown the greatest accuracy, but are difficult to interpret in terms of the biology. Measures based on the cortical surface(s) have yielded less accurate predictions, suggesting that perhaps developmental changes related to cortical gray matter are not strongly related to chronological age, and that perhaps development is more strongly related to changes in subcortical regions or in deep white matter. We show that a simple metric based on the white/gray contrast at the inner border of the cortical gray-matter is a comPreprint submitted to NeuroImage August 2, 2017 . CC-BY-NC-ND 4.0 International license peer-reviewed) is the author/funder. It is made available under aThe copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/171892 doi: bioRxiv preprint first posted online parably good predictor of chronological age, and our usage of an elastic net penalized linear regression model reveals the brain regions which contribute most to age-prediction. We demonstrate this in two large datasets: the NIH Pediatric Data, with 832 scans of typically developing children, adolescents, and young adults; and the Pediatric Imaging, Neurocognition, and Genetics data, with 760 scans of individuals in a similar age-range. Moreover, we show that the residuals of age-prediction based on this white/gray contrast metric are more strongly related to IQ than are those from cortical thickness, suggesting that this metric is more sensitive to aspects of brain development that reflect cognitive performance.

Mapping the neuroanatomical impact of very preterm birth across childhood

“…showing general trends with development, is variable and is not reliably associated with age (Lewis et al, 2018).…”

“…White matter increases steadily into the third decade of life. These developmental patterns can be used to predict brain maturation and relate to behaviour (Khundrakpam et al, 2015;Lewis et al, 2018). These developmental patterns can be used to predict brain maturation and relate to behaviour (Khundrakpam et al, 2015;Lewis et al, 2018).…”

“…Subcortical structures show variable maturational changes through childhood and adolescence (Sussman, Leung, Chakravarty, Lerch, & Taylor, 2016;Wierenga et al, 2014). These developmental patterns can be used to predict brain maturation and relate to behaviour (Khundrakpam et al, 2015;Lewis et al, 2018). There has thus been considerable interest in understanding these developmental trajectories in VPT born children, as an indicator or predictor of the cognitive difficulties that many experience.…”

Brain Age Prediction: Cortical and Subcortical Shape Covariation in the Developing Human Brain

“…The model with JIVE joint and individual components along with total volumes produced a mean absolute age prediction error of 1.41 years and explained 71% of total variation in age, making it one of the best age prediction models in the recent literature (Brown et al, 2012;Erus et al, 2015;Franke et al, 2012;Khundrakpam et al, 2015;Lewis et al, 2018). We also found that the JIVE joint and individual components together predicted sex with high accuracy (AUC=0.85) and explained 10% of total variation in FSIQ, achieving better prediction results than a recent study using T1 white/gray contrast to predict FSIQ (Lewis et al, 2018). To test the validity of the model's predictive performance, we applied it to an independent dataset (NKI-RS) and the results showed that the model generalized to the new dataset, has excellent test-retest reliability, and was able to capture longitudinal changes.…”

“…Brain-based age prediction is thought to be important for understanding normal brain developmental process in healthy individuals, as well as atypical brain structural/functional developmental patterns that might have clinical implications (Davatzikos et al, 2009;Dosenbach et al, 2010). Among brain structural features, cortical thickness (Khundrakpam et al, 2015;Lewis et al, 2018) and GM volume (Franke et al, 2010) have been used to predict brain age. An increasingly broad range of possible measures exist for characterizing brain cortical structures, including surface area, mean curvature, travel depth, and WM volume; additionally, some studies have used the GM/WM difference or contrast (Franke et al, 2012;Lewis et al, 2018) or developed composite metrics incorporating multiple features to increase the brain age prediction accuracy (Brown et al, 2012;Erus et al, 2015;Lewis et al, 2018).…”