Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous
neurodevelopmental disorder in children and has a high chance of persisting in adulthood …

Functional magnetic resonance imaging without an explicit task (ie, resting-state functional
magnetic resonance imaging) of individuals with attention-deficit/hyperactivity disorder …

Abstract In 2011, the “ADHD-200 Global Competition” was held with the aim of identifying
biomarkers of attention-deficit/hyperactivity disorder from resting-state functional magnetic …

Background Digital phenotyping is the use of data from smartphones and wearables
collected in situ for capturing a digital expression of human behaviors. Digital phenotyping …

Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

Background The development of machine learning models for aiding in the diagnosis of
mental disorder is recognized as a significant breakthrough in the field of psychiatry …

In the multimodal neuroimaging framework, data on a single subject are collected from
inherently different sources such as functional MRI, structural MRI, behavioral and/or …

Attention Deficit Hyperactive Disorder (ADHD) is getting a lot of attention recently for two
reasons. First, it is one of the most commonly found childhood disorders and second, the …

Objective: Machine learning (ML) has been applied to develop magnetic resonance imaging
(MRI)-based diagnostic classifiers for attention-deficit/hyperactivity disorder (ADHD). This …

There has been a recent upsurge of reports about applications of pattern-recognition
techniques from the field of machine learning to functional MR imaging data as a diagnostic …