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Craving

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Cathodal Transcranial Direct Current Stimulation Does Not Change Implicit Associations Against Alcohol in Alcohol Use Disorder: A Preregistered Clinical Trial.

Addiction biology
Addictive behaviour is shaped by the dynamic interaction of implicit, bottom-up and explicit, top-down cognitive processes. In alcohol use disorder (AUD), implicit alcohol-related associations have been shown to predict increased subsequent alcohol c...

Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use.

Sensors (Basel, Switzerland)
Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing ...

Modeling motivation for alcohol in humans using traditional and machine learning approaches.

Addiction biology
Given the significant cost of alcohol use disorder (AUD), identifying risk factors for alcohol seeking represents a research priority. Prominent addiction theories emphasize the role of motivation in the alcohol seeking process, which has largely bee...

Analysis of addiction craving onset through natural language processing of the online forum Reddit.

PloS one
AIMS: Alcohol cravings are considered a major factor in relapse among individuals with alcohol use disorder (AUD). This study aims to investigate the frequency and triggers of cravings in the daily lives of people with alcohol-related issues. Large a...

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.

Spatial Craving Patterns in Marijuana Users: Insights From fMRI Brain Connectivity Analysis With High-Order Graph Attention Neural Networks.

IEEE journal of biomedical and health informatics
The excessive consumption of marijuana can induce substantial psychological and social consequences. In this investigation, we propose an elucidative framework termed high-order graph attention neural networks (HOGANN) for the classification of Marij...

Applying machine learning to ecological momentary assessment data to identify predictors of loss-of-control eating and overeating severity in adolescents: A preliminary investigation.

Appetite
OBJECTIVE: Several factors (e.g., interpersonal stress, affect) predict loss-of-control (LOC) eating and overeating in adolescents, but most past research has tested predictors separately. We applied machine learning to simultaneously evaluate multip...