BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …

Psychology has historically been concerned, first and foremost, with explaining the causal
mechanisms that give rise to behavior. Randomized, tightly controlled experiments are …

The ability to make decisions and act quickly without hesitation can be advantageous in
many settings. However, when persistently expressed, impulsive decisions and actions are …

A new data-driven computational framework is developed to assist in the design and
modeling of new material systems and structures. The proposed framework integrates three …

Although attention plays a ubiquitous role in perception and cognition, researchers lack a
simple way to measure a person's overall attentional abilities. Because behavioral …

To date, fMRI research has been concerned primarily with evincing generic principles of
brain function through averaging data from multiple subjects. Given rapid developments in …

Background Adolescence is a critical biological, psychological, and social developmental
stage involving heightened risk for substance use and associated adverse consequences …

Importance Disrupted reward processing, mainly driven by striatal dysfunction, is a key
characteristic of addictive behaviors. However, functional magnetic resonance imaging …

Artificial intelligence (AI) is increasingly employed in health care fields such as oncology,
radiology, and dermatology. However, the use of AI in mental health care and …

Neuroimaging has greatly enhanced the cognitive neuroscience understanding of the
human brain and its variation across individuals (neurodiversity) in both health and disease …