Psychiatry

Large-Scale Structural Covariance Networks Predict Age in Middle-to-Late Adulthood: A Novel Brain Aging Biomarker.

The aging process is accompanied by changes in the brain's cortex at many levels. There is growing interest in summarizing these complex brain-aging profiles into a single, quantitative index that could serve as a biomarker both for characterizing individual brain health and for identifying neurodegenerative and neuropsychiatric diseases. Using a large-scale structural covariance network (SCN)-bas...

Computational framework for detection of subtypes of neuropsychiatric disorders based on DTI-derived anatomical connectivity.

Many brain disorders - such as Alzheimer's disease, Parkinson's disease, schizophrenia and autism - are heterogeneous, that is, they may have several subtypes. Traditionally, clinicians have identified subtypes, such as subtypes of psychosis, using clinical criteria. Neuroimaging has the potential to detect subtypes based on objective biomarker-based criteria; however, there are no studies that ev...

AI enabled suicide prediction tools: a qualitative narrative review.

Suicide poses a significant health burden worldwide. In many cases, people at risk of suicide do not engage with their doctor or community due to con...

Depression Severity Assessment for Adolescents at High Risk of Mental Disorders.

Recent progress in artificial intelligence has led to the development of automatic behavioral marker recognition, such as facial and vocal expressions...

Automated Detection of Enhanced DBS Device Settings.

Continuous deep brain stimulation (DBS) of the ventral striatum (VS) is an effective treatment for severe, treatment-refractory obsessive-compulsive d...

[A comparative study on treatment of lumbar degenerative disease with osteoporosis by manual and robot-assisted cortical bone trajectory screws fixation].

OBJECTIVE: To compare the safety and accuracy of manual and robot-assisted cortical bone trajectory (CBT) screws fixation in the treatment of lumbar d...

Leveraging Machine Learning to Identify Predictors of Receiving Psychosocial Treatment for Attention Deficit/Hyperactivity Disorder.

This study aimed to identify factors associated with receiving psychosocial treatment for ADHD in a nationally representative sample. Participants wer...

Improving Mental Health Services: A 50-Year Journey from Randomized Experiments to Artificial Intelligence and Precision Mental Health.

This conceptual paper describes the current state of mental health services, identifies critical problems, and suggests how to solve them. I focus on ...

Can Artificial Intelligence Improve Psychotherapy Research and Practice?

Leonard Bickman's article on the future of artificial intelligence (AI) in psychotherapy research paints an encouraging picture of the progress to be ...

A Randomized Controlled Trial of an Intelligent Robotic Response to Joint Attention Intervention System.

Although there has been growing interest in utilizing robots for intervention in autism spectrum disorder (ASD), there have been very few controlled t...

Signs of the times: universalism and localism.

At a time when nationalism has reappeared in Europe, when COVID-19 is not yet quarantined and when compassion coexists with grief, there is a need to ...

Can machine-learning methods really help predict suicide?

PURPOSE OF REVIEW: In recent years there has been interest in the use of machine learning in suicide research in reaction to the failure of traditiona...

MRI signatures of brain age and disease over the lifespan based on a deep brain network and 14 468 individuals worldwide.

Deep learning has emerged as a powerful approach to constructing imaging signatures of normal brain ageing as well as of various neuropathological pro...

Machine learning and natural language processing in psychotherapy research: Alliance as example use case.

Artificial intelligence generally and machine learning specifically have become deeply woven into the lives and technologies of modern life. Machine l...

Emerging Clinical Technology: Application of Machine Learning to Chronic Pain Assessments Based on Emotional Body Maps.

Depression and anxiety co-occur with chronic pain, and all three are thought to be caused by dysregulation of shared brain systems related to emotiona...

Depression Scale Prediction with Cross-Sample Entropy and Deep Learning.

A two-stage deep learning-based scheme is presented to predict the Hamilton Depression Scale (HAM-D) in this study. First, the cross-sample entropy (C...

Predicting Early Stage Drug Induced Parkinsonism using Unsupervised and Supervised Machine Learning.

Drug Induced Parkinsonism (DIP) is the most common, debilitating movement disorder induced by antipsychotics. There is no tool available in clinical p...

Anxiety and Depression Diagnosis Method Based on Brain Networks and Convolutional Neural Networks.

At present, only professional doctors can use the professional scales to diagnose depression and anxiety in clinical practice. In recent years, the pr...

Unsupervised stratification in neuroimaging through deep latent embeddings.

There is growing evidence that the use of stringent and dichotomic diagnostic categories in many medical disciplines (particularly 'brain sciences' as...

Antidepressant pathways of the Chinese herb through genetic ontology analysis.

Active compounds and corresponding targets of the traditional Chinese herb, were obtained from systems pharmacological database and placed into ClueG...