A defining aspect of brain organization is its spatial heterogeneity, which gives rise to
multiple topographies at different scales. Brain parcellation—defining distinct partitions in …

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 …

As our brains age, we tend to experience cognitive decline and are at greater risk of
neurodegenerative disease and dementia. Symptoms of chronic neuropsychiatric diseases …

Resting-state functional magnetic resonance imaging (rs-fMRI) allows estimation of
individual-specific cortical parcellations. We have previously developed a multi-session …

There is significant interest in the development and application of deep neural networks
(DNNs) to neuroimaging data. A growing literature suggests that DNNs outperform their …

Both normal ageing and neurodegenerative diseases cause morphological changes to the
brain. Age-related brain changes are subtle, nonlinear, and spatially and temporally …

Healthy aging has been associated with a decrease in functional network specialization.
Importantly, variability of alterations of functional connectivity is especially high across older …

As evidenced by the present special issue, movie fMRI is emerging as a powerful tool for
exploring brain function and characterizing its variation across individuals. Here, we provide …

The difference between age predicted using anatomical brain scans and chronological age,
ie, the brain-age delta, provides a proxy for atypical aging. Various data representations and …

Over the past decade, there has been an abundance of research on the difference between
age and age predicted using brain features, which is commonly referred to as the “brain age …