AI Medical Compendium Journal:
Progress in neuro-psychopharmacology & biological psychiatry

Showing 1 to 10 of 21 articles

Swimming into the future: Machine learning in zebrafish behavioral research.

Progress in neuro-psychopharmacology & biological psychiatry
The zebrafish (Danio rerio) has emerged as a powerful organism in behavioral neuroscience, offering invaluable insights into the neural circuits and molecular pathways underlying complex behaviors. Although the knowledge of zebrafish behavioral reper...

Neuroimaging pattern interactions for suicide risk in depression captured by ensemble learning over transcriptome-defined parcellation.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: For suicide in major depression disorder, it is urgent to seek for a reliable neuroimaging biomarker with interpretable links to molecular tissue signatures. Accordingly, we developed an ensemble learning scheme over transcriptome-defined...

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.

Progress in neuro-psychopharmacology & biological psychiatry
Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral ...

Predicting drug craving among ketamine-dependent users through machine learning based on brain structural measures.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Craving is a core factor driving drug-seeking and -taking, representing a significant risk factor for relapse. This study aims to identify neuroanatomical biomarkers for quantifying and predicting craving.

Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Schizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and invest...

Identification of patients with internet gaming disorder via a radiomics-based machine learning model of subcortical structures in high-resolution T1-weighted MRI.

Progress in neuro-psychopharmacology & biological psychiatry
It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted r...

Predicting cognitive impairment in chronic kidney disease patients using structural and functional brain network: An application study of artificial intelligence.

Progress in neuro-psychopharmacology & biological psychiatry
OBJECTIVE: To develop and validate artificial intelligence models for the prediction of cognitive impairment in chronic kidney disease (CKD) patients using structural and functional brain network.

Dysgraphia disorder forecasting and classification technique using intelligent deep learning approaches.

Progress in neuro-psychopharmacology & biological psychiatry
Writing abilities are impacted by dysgraphia, a condition of learning disability. It might be challenging to diagnose dysgraphia at an initial point of a child's upbringing. Problematic abilities linked to Dysgraphia difficulties that is utilized in ...

Online music-assisted rehabilitation system for depressed people based on deep learning.

Progress in neuro-psychopharmacology & biological psychiatry
The processing of negative emotions is closely related to the occurrence of depression, and improving the mood of patients with depression has an important effect on improving symptoms. This article applies deep learning to the diagnosis and treatmen...

Research on intelligent analysis strategies to improve athletes' psychological experience in the era of artificial intelligence.

Progress in neuro-psychopharmacology & biological psychiatry
In order to improve the psychological quality of athletes, this paper combines artificial intelligence technology to quantitatively analyze the psychological experience of athletes. Moreover, in view of the insufficiency of the ASI method to standard...