Psychiatry

EEG-Based Computer Aided Diagnosis of Autism Spectrum Disorder Using Wavelet, Entropy, and ANN.

Autism spectrum disorder (ASD) is a type of neurodevelopmental disorder with core impairments in the social relationships, communication, imagination, or flexibility of thought and restricted repertoire of activity and interest. In this work, a new computer aided diagnosis (CAD) of autism ‎based on electroencephalography (EEG) signal analysis is investigated. The proposed method is based on discre...

Multi-center machine learning in imaging psychiatry: A meta-model approach.

One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and co...

Altered Pituitary Gland Structure and Function in Posttraumatic Stress Disorder.

OBJECTIVES: Posttraumatic stress disorder (PTSD) is associated with hypothalamus-pituitary-adrenal (HPA) axis response to stressors, but links to neur...

Depressive Symptoms and Their Interactions With Emotions and Personality Traits Over Time: Interaction Networks in a Psychiatric Clinic.

OBJECTIVE: Associations between depression, personality traits, and emotions are complex and reciprocal. The aim of this study is to explore these int...

Predicting healthcare trajectories from medical records: A deep learning approach.

Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencie...

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder.

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rath...

Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The incons...

Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.

Sensors in everyday devices, such as our phones, wearables, and computers, leave a stream of digital traces. Personal sensing refers to collecting and...

Mimicking Neurotransmitter Release in Chemical Synapses via Hysteresis Engineering in MoS Transistors.

Neurotransmitter release in chemical synapses is fundamental to diverse brain functions such as motor action, learning, cognition, emotion, perception...

Screening of autism based on task-free fMRI using graph theoretical approach.

Studies on autism spectrum disorder (ASD) have indicated several dysfunctions in the structure, and functional organization of the brain. However, fin...

Classification of suicide attempters in schizophrenia using sociocultural and clinical features: A machine learning approach.

OBJECTIVE: Suicide is a major concern for those afflicted by schizophrenia. Identifying patients at the highest risk for future suicide attempts remai...

Antidepressant-like effects of Gan-Mai-Dazao-Tang via monoamine regulatory pathways on forced swimming test in rats.

Depression is a highly prevalent and recurrent mental disorder that impacts all aspects of human life. Undesirable effects of the antidepressant drugs...

A symptom network structure of the psychosis spectrum.

Current diagnostic systems mainly focus on symptoms needed to classify patients with a specific mental disorder and do not take into account the varia...

Diagnosis of major depressive disorder by combining multimodal information from heart rate dynamics and serum proteomics using machine-learning algorithm.

OBJECTIVE: Major depressive disorder (MDD) is a systemic and multifactorial disorder that involves abnormalities in multiple biochemical pathways and ...

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in som...

Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study.

BACKGROUND: Generalized anxiety disorder (GAD) is difficult to recognize and hard to separate from major depression (MD) in clinical settings. Biomark...

A First Step towards a Clinical Decision Support System for Post-traumatic Stress Disorders.

PTSD is distressful and debilitating, following a non-remitting course in about 10% to 20% of trauma survivors. Numerous risk indicators of PTSD have ...

Association between abnormal brain functional connectivity in children and psychopathology: A study based on graph theory and machine learning.

OBJECTIVES: One of the major challenges facing psychiatry is how to incorporate biological measures in the classification of mental health disorders. ...

Improving therapeutic outcomes in autism spectrum disorders: Enhancing social communication and sensory processing through the use of interactive robots.

For children with autism spectrum disorders (ASDs), social robots are increasingly utilized as therapeutic tools in order to enhance social skills and...

Deep dreaming, aberrant salience and psychosis: Connecting the dots by artificial neural networks.

Why some individuals, when presented with unstructured sensory inputs, develop altered perceptions not based in reality, is not well understood. Machi...