scholarly journals Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryan Smith ◽  
◽  
Justin S. Feinstein ◽  
Rayus Kuplicki ◽  
Katherine L. Forthman ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Individual Differences ◽  
Substance Use Disorders ◽  
Breath Hold ◽  
Healthy Individuals ◽  
Performance Improvements ◽  
Heartbeat Perception ◽  
Minimal Improvement ◽  
Healthy Participants

AbstractThis study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N = 221), and substance use disorders (N = 136) is less flexible than that of healthy individuals (N = 53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety and/or substance use disorder showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.

scholarly journals Perceptual insensitivity to the modulation of interoceptive signals in depression, anxiety, and substance use disorders

2020 ◽  
Author(s):  
Ryan Smith ◽  
Justin Feinstein ◽  
Rayus Kuplicki ◽  
Katherine Lynne Forthman ◽  
Jennifer Stewart ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Individual Differences ◽  
Substance Use Disorders ◽  
Breath Hold ◽  
Healthy Individuals ◽  
Performance Improvements ◽  
Heartbeat Perception ◽  
Minimal Improvement ◽  
Healthy Participants

This study employed a series of heartbeat perception tasks to assess the hypothesis that cardiac interoceptive processing in individuals with depression/anxiety (N=221), and substance use disorders (N=136) is less flexible than that of healthy individuals (N=53) in the context of physiological perturbation. Cardiac interoception was assessed via heartbeat tapping when: (1) guessing was allowed; (2) guessing was not allowed; and (3) experiencing an interoceptive perturbation (inspiratory breath hold) expected to amplify cardiac sensation. Healthy participants showed performance improvements across the three conditions, whereas those with depression/anxiety, and substance use showed minimal improvement. Machine learning analyses suggested that individual differences in these improvements were negatively related to anxiety sensitivity, but explained relatively little variance in performance. These results reveal a perceptual insensitivity to the modulation of interoceptive signals that was evident across several common psychiatric disorders, suggesting that interoceptive deficits in the realm of psychopathology manifest most prominently during states of homeostatic perturbation.

scholarly journals A Bayesian computational model reveals a failure to adapt interoceptive precision estimates across depression, anxiety, eating, and substance use disorders

2020 ◽  
Vol 16 (12) ◽  
pp. e1008484
Author(s):  
Ryan Smith ◽  
Rayus Kuplicki ◽  
Justin Feinstein ◽  
Katherine L. Forthman ◽  
Jennifer L. Stewart ◽  
...  
Keyword(s):  
Substance Use ◽  
Computational Model ◽  
Substance Use Disorders ◽  
The Body ◽  
Breath Holding ◽  
Healthy Individuals ◽  
Prior Beliefs ◽  
Future Studies ◽  
Psychiatric Conditions ◽  
Processing Mechanisms

Recent neurocomputational theories have hypothesized that abnormalities in prior beliefs and/or the precision-weighting of afferent interoceptive signals may facilitate the transdiagnostic emergence of psychopathology. Specifically, it has been suggested that, in certain psychiatric disorders, interoceptive processing mechanisms either over-weight prior beliefs or under-weight signals from the viscera (or both), leading to a failure to accurately update beliefs about the body. However, this has not been directly tested empirically. To evaluate the potential roles of prior beliefs and interoceptive precision in this context, we fit a Bayesian computational model to behavior in a transdiagnostic patient sample during an interoceptive awareness (heartbeat tapping) task. Modelling revealed that, during an interoceptive perturbation condition (inspiratory breath-holding during heartbeat tapping), healthy individuals (N = 52) assigned greater precision to ascending cardiac signals than individuals with symptoms of anxiety (N = 15), depression (N = 69), co-morbid depression/anxiety (N = 153), substance use disorders (N = 131), and eating disorders (N = 14)–who failed to increase their precision estimates from resting levels. In contrast, we did not find strong evidence for differences in prior beliefs. These results provide the first empirical computational modeling evidence of a selective dysfunction in adaptive interoceptive processing in psychiatric conditions, and lay the groundwork for future studies examining how reduced interoceptive precision influences visceral regulation and interoceptively-guided decision-making.

scholarly journals Can population registry data predict which children with ADHD are at risk of later substance use disorders?

2020 ◽  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Child Psychiatry ◽  
Registry Data ◽  
Early Prediction ◽  
Children With Adhd ◽  
Youth With Adhd

The first study to examine the potential of machine learning in early prediction of later substance use disorders (SUDs) in youth with ADHD has been published in the Journal of Child Psychiatry and Psychology.

scholarly journals Machine-Learning Prediction of Comorbid Substance Use Disorders in ADHD Youth Using Swedish Registry Data

2019 ◽  
Author(s):  
Yanli Zhang-James ◽  
Qi Chen ◽  
Ralf Kuja-Halkola ◽  
Paul Lichtenstein ◽  
Henrik Larsson ◽  
...  
Keyword(s):  
Machine Learning ◽  
At Risk ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Registry Data ◽  
Longitudinal Models ◽  
Cross Sectional ◽  
Using Data

AbstractBackgroundChildren with attention-deficit/hyperactivity disorder (ADHD) have a high risk for substance use disorders (SUDs). Early identification of at-risk youth would help allocate scarce resources for prevention programs.MethodsPsychiatric and somatic diagnoses, family history of these disorders, measures of socioeconomic distress and information about birth complications were obtained from the national registers in Sweden for 19,787 children with ADHD born between 1989-1993. We trained 1) cross-sectional machine learning models using data available by age 17 to predict SUD diagnosis between ages 18-19; and 2) a longitudinal model to predict new diagnoses at each age.ResultsThe area under the receiver operating characteristic curve (AUC) was 0.73 and 0.71 for the random forest and multilayer perceptron cross-sectional models. A prior diagnosis of SUD was the most important predictor, accounting for 25% of correct predictions. However, after excluding this predictor, our model still significantly predicted the first-time diagnosis of SUD during age 18-19 with an AUC of 0.67. The average of the AUCs from longitudinal models predicting new diagnoses one, two, five and ten years in the future was 0.63.ConclusionsSignificant predictions of at-risk co-morbid SUDs in individuals with ADHD can be achieved using population registry data, even many years prior to the first diagnosis. Longitudinal models can potentially monitor their risks over time. More work is needed to create prediction models based on electronic health records or linked population-registers that are sufficiently accurate for use in the clinic.

scholarly journals Machine‐Learning prediction of comorbid substance use disorders in ADHD youth using Swedish registry data

2020 ◽  
Vol 61 (12) ◽  
pp. 1370-1379
Author(s):  
Yanli Zhang‐James ◽  
Qi Chen ◽  
Ralf Kuja‐Halkola ◽  
Paul Lichtenstein ◽  
Henrik Larsson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Registry Data

scholarly journals Personality, Self-Esteem, and Perceived Stress in Communal Residences Supporting Recovery

2017 ◽  
Vol 47 (3-4) ◽  
pp. 108-120
Author(s):  
Angela Reilly ◽  
Edward B. Stevens ◽  
Leonard A. Jason
Keyword(s):  
Substance Use ◽  
Longitudinal Study ◽  
Individual Differences ◽  
Substance Use Disorders ◽  
Perceived Stress ◽  
Adult Population ◽  
Self Esteem ◽  
Demographic Variables ◽  
Recovery Homes ◽  
The Mean

The current study examined the relationships between a personality metatrait (Stability consisting of conscientiousness, agreeableness, and neuroticism), self-esteem, and stress in an adult population of individuals with substance use disorders living in recovery homes. Adults ( N = 229) residing in 42 residential recovery settings were interviewed as part of the first wave of a longitudinal study in three sites. Standard error of the mean analysis found significant effects for several demographic variables on Stability, and Stability was significantly related both directly and indirectly to stress. These findings suggest that individual differences at entry may influence recovery home effects and may be important to developing more effective aftercare systems.

scholarly journals Predicting hospital readmission in patients with mental or substance use disorders: A machine learning approach

2020 ◽  
Vol 139 ◽  
pp. 104136 ◽  
Author(s):  
Didier Morel ◽  
Kalvin C. Yu ◽  
Ann Liu-Ferrara ◽  
Ambiorix J. Caceres-Suriel ◽  
Stephan G. Kurtz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Substance Use ◽  
Substance Use Disorders ◽  
Hospital Readmission ◽  
Learning Approach ◽  
Machine Learning Approach
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