Typical brain development follows a protracted trajectory throughout childhood and
adolescence. Deviations from typical growth trajectories have been implicated in …

Cortical development is characterized by distinct spatial and temporal patterns of
maturational changes across various cortical shape measures. There is a growing interest in …

Brain age prediction based on imaging data and machine learning (ML) methods has great
potential to provide insights into the development of cognition and mental disorders. Though …

Abstract Machine learning has been increasingly applied to neuroimaging data to predict
age, deriving a personalized biomarker with potential clinical applications. The scientific and …

The maturational schedule of typical brain development is tightly constrained; deviations
from it are associated with cognitive atypicalities, and are potentially predictive of …

Estimating age based on neuroimaging‐derived data has become a popular approach to
developing markers for brain integrity and health. While a variety of machine‐learning …

3. Conclusions Two main conclusions can be drawn based on the examples in this
commentary: I) The method proposed by Behesti et al. provides age-bias correction that is …

Brain age prediction using machine‐learning techniques has recently attracted growing
attention, as it has the potential to serve as a biomarker for characterizing the typical brain …

Brain morphology varies across the ageing trajectory and the prediction of a person's age
using brain features can aid the detection of abnormalities in the ageing process. Existing …

Brain structural morphology varies over the aging trajectory, and the prediction of a person's
age using brain morphological features can help the detection of an abnormal aging …