Attention-deficit/hyperactivity disorder (ADHD) is highly heritable and the most common
neurodevelopmental disorder in childhood. In recent decades, it has been appreciated that …

Deep learning (DL) is a family of machine learning methods that has gained considerable
attention in the scientific community, breaking benchmark records in areas such as speech …

Importance Identifying neurobiological differences between patients with major depressive
disorder (MDD) and healthy individuals has been a mainstay of clinical neuroscience for …

In this work, we expand the normative model repository introduced in Rutherford et al.,
2022a to include normative models charting lifespan trajectories of structural surface area …

Predictive models ground many state-of-the-art developments in statistical brain image
analysis: decoding, MVPA, searchlight, or extraction of biomarkers. The principled approach …

Neuroimaging-based single subject prediction of brain disorders has gained increasing
attention in recent years. Using a variety of neuroimaging modalities such as structural …

Objective: Neuroimaging studies show structural differences in both cortical and subcortical
brain regions in children and adults with autism spectrum disorder (ASD) compared with …

Importance Schizophrenia and bipolar disorder are severe and complex brain disorders
characterized by substantial clinical and biological heterogeneity. However, case-control …

Normative models are a class of emerging statistical techniques useful for understanding the
heterogeneous biology underlying psychiatric disorders at the level of the individual …

This review focuses on the cognitive neuroscience of Attention Deficit Hyperactivity Disorder
(ADHD) based on functional magnetic resonance imaging (fMRI) studies and on recent …