Psychiatry

Latest AI and machine learning research in psychiatry for healthcare professionals.

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Identifying children with autism spectrum disorder based on their face processing abnormality: A machine learning framework.

The atypical face scanning patterns in individuals with Autism Spectrum Disorder (ASD) has been repe...

Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls.

Neuroimaging-based single subject prediction of brain disorders has gained increasing attention in r...

Genes with high penetrance for syndromic and non-syndromic autism typically function within the nucleus and regulate gene expression.

BACKGROUND: Intellectual disability (ID), autism, and epilepsy share frequent yet variable comorbidi...

Accelerated Brain Aging in Schizophrenia: A Longitudinal Pattern Recognition Study.

OBJECTIVE: Despite the multitude of longitudinal neuroimaging studies that have been published, a ba...

A multi-layer network approach to MEG connectivity analysis.

Recent years have shown the critical importance of inter-regional neural network connectivity in sup...

Minimalistic toy robot to analyze a scenery of speaker-listener condition in autism.

Atypical neural architecture causes impairment in communication capabilities and reduces the ability...

Use of machine learning for behavioral distinction of autism and ADHD.

Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue...

Fusing Data Mining, Machine Learning and Traditional Statistics to Detect Biomarkers Associated with Depression.

BACKGROUND: Atheoretical large-scale data mining techniques using machine learning algorithms have p...

Robots Learn to Recognize Individuals from Imitative Encounters with People and Avatars.

Prior to language, human infants are prolific imitators. Developmental science grounds infant imitat...

Reflections of Low-Income, Second-Generation Latinas About Experiences in Depression Therapy.

Depression is higher among second-generation Latinas compared with immigrants, but mental health tre...

Cross-trial prediction of treatment outcome in depression: a machine learning approach.

BACKGROUND: Antidepressant treatment efficacy is low, but might be improved by matching patients to ...

Decreased Intra- and Inter-Salience Network Functional Connectivity is Related to Trait Anxiety in Adolescents.

OBJECTIVE: Adolescence is a critical period for the vulnerability of anxiety. Imaging studies focusi...

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.

OBJECTIVE: A growing body of evidence has put forward clinical risk factors associated with patients...

Retrieving Binary Answers Using Whole-Brain Activity Pattern Classification.

Multivariate pattern analysis (MVPA) has been successfully employed to advance our understanding of ...

GC-MS analysis of the designer drug α-pyrrolidinovalerophenone and its metabolites in urine and blood in an acute poisoning case.

α-Pyrrolidinovalerophenone (α-PVP) is a synthetic cathinone belonging to the group of "second genera...

Machine learning of structural magnetic resonance imaging predicts psychopathic traits in adolescent offenders.

Classification models are becoming useful tools for finding patterns in neuroimaging data sets that ...

Classification of first-episode psychosis in a large cohort of patients using support vector machine and multiple kernel learning techniques.

First episode psychosis (FEP) patients are of particular interest for neuroimaging investigations be...

Training and testing ERP-BCIs under different mental workload conditions.

OBJECTIVE: As one of the most popular and extensively studied paradigms of brain-computer interfaces...

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