With the aging population, prevalence of neurodegenerative diseases is increasing, thus
placing a growing burden on individuals and the whole society. However, individual rates of …

The brain changes as we age and these changes are associated with functional
deterioration and neurodegenerative disease. It is vital that we better understand individual …

Abstract Machine learning algorithms can be trained to estimate age from brain structural
MRI. The difference between an individual's predicted and chronological age, predicted age …

Life history theory argues that exposure to early life adversity (ELA) accelerates
development, although existing evidence for this varies. We present a meta-analysis and …

The rise of machine learning has unlocked new ways of analysing structural neuroimaging
data, including brain age prediction. In this state-of-the-art review, we provide an …

Abstract Machine learning techniques are increasingly being used in making relevant
predictions and inferences on individual subjects neuroimaging scan data. Previous studies …

Alzheimer's disease (AD), the most common form of dementia, shares many aspects of
abnormal brain aging. We present a novel magnetic resonance imaging (MRI)-based …

Structural brain abnormalities are central to schizophrenia (SZ), but it remains unknown
whether they are linked to dysmaturational processes crossing diagnostic boundaries …

Traditionally sex hormones have been associated with reproductive and developmental
processes only. Since the 1950s we know that hormones can have organizational effects on …

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 …