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 …

Major depressive disorder (MDD) is characterized by impairments in mood and cognitive
functioning, and it is a prominent source of global disability and stress. A functional magnetic …

Background Major depressive disorder (MDD) is an overbroad and heterogeneous
diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning …

Multimodal fusion has become an important research technique in neuroscience that
completes downstream tasks by extracting complementary information from multiple …

Major depressive disorder (MDD) has been associated with aberrant effective connectivity
(EC) among the default mode network (DMN), salience network (SN), and central executive …

Background Depression is a common mental disorder and the diagnosis is still based on the
descriptions of symptoms. Biomarkers can reveal disease characteristics for diagnosis …

Introduction Previous studies have shown disrupted effective connectivity in the large-scale
brain networks of individuals with major depressive disorder (MDD). However, it is unclear …

Objective. The excellent signal-to-noise ratio (SNR) is the premise of electroencephalogram
(EEG) research and applications. This study aims to use innovative method to swiftly remove …

Most neuroimaging studies of the pathogenesis of early mild cognitive impairment (EMCI)
rely on a node‐centric network model, which only calculates correlations between brain …