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

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

Neuroimaging‐based approaches have been extensively applied to study mental illness in
recent years and have deepened our understanding of both cognitively healthy and …

Psychiatric disorders are increasingly being recognised as having a biological basis, but
their diagnosis is made exclusively behaviourally. A promising approach for …

Neuropsychiatric disorders are diagnosed based on behavioral criteria, which makes the
diagnosis challenging. Objective biomarkers such as neuroimaging are needed, and when …

Unlike neurologic conditions, such as brain tumors, dementia, and stroke, the neural
mechanisms for all psychiatric disorders remain unclear. A large body of research obtained …

Multivariate pattern recognition approaches have recently facilitated the search for reliable
neuroimaging-based biomarkers in psychiatric disorders such as schizophrenia. By taking …

Background Diagnosis of schizophrenia (SCZ) is made exclusively clinically, since specific
biomarkers that can predict the disease accurately remain unknown. Machine learning (ML) …

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

Quantitative abnormalities of brain structure in patients with major depressive disorder have
been reported at a group level for decades. However, these structural differences appear …