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Related Concept Videos

CNS Depressants: Alcohol and Nicotine01:27

CNS Depressants: Alcohol and Nicotine

241
Ethanol, a clear colorless alcohol, has been consumed by humans for millennia, but its effects on the body are far from benign. At lower doses, it induces decreased inhibitions and loquaciousness, leading to its social appeal. However, it can cause severe consequences at higher doses, such as coma and respiratory depression, due to its zero-order elimination kinetics. Chronic ethanol abuse wreaks havoc on multiple organ systems, particularly the CNS and the liver. Abrupt cessation of ethanol...
241

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Related Experiment Video

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Chronic Intermittent Ethanol Vapor Exposure Paired with Two-Bottle Choice to Model Alcohol Use Disorder
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Machine Learning of Functional Connectivity to Biotype Alcohol and Nicotine Use Disorders.

Tan Zhu1, Wuyi Wang2, Yu Chen3

  • 1Department of Computer Science and Engineering, School of Engineering, University of Connecticut, Storrs, Connecticut.

Biological Psychiatry. Cognitive Neuroscience and Neuroimaging
|September 11, 2023
PubMed
Summary

This study identified three distinct biotypes for alcohol use disorder (AUD) and nicotine use disorder (NUD) using neuroimaging and genetic data. These biotypes improve diagnostic accuracy and reveal key brain circuit differences in AUD and NUD.

Keywords:
Alcohol use disorderBiotypingGenetic association analysisMachine learningNicotine use disorderResting-state functional magnetic resonance imaging (MRI)

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Area of Science:

  • Neuroscience
  • Genetics
  • Psychiatry

Background:

  • Magnetic resonance imaging (MRI) offers noninvasive methods for studying alcohol use disorder (AUD) and nicotine use disorder (NUD).
  • A transdiagnostic, data-driven approach can illuminate the neurobiology underlying AUD and NUD.
  • Understanding shared and distinct neural phenotypes is crucial for developing targeted interventions.

Purpose of the Study:

  • To identify distinct biotypes of AUD and NUD using a data-driven approach.
  • To integrate clinical, neuroimaging, and genetic data for a comprehensive understanding of AUD and NUD.
  • To develop and evaluate a multitask artificial neural network for diagnosing AUD and NUD based on identified biotypes.

Main Methods:

  • Utilized UK Biobank data from individuals with AUD (n=140), NUD (n=249), and healthy controls (n=461).
  • Integrated resting-state functional connectivity (FC) features with clinical metrics to partition participants into biotypes.
  • Trained a multitask artificial neural network to classify biotypes and jointly infer AUD and NUD diagnoses.

Main Results:

  • Identified three biotypes: primary NUD, mixed NUD/AUD with depression and anxiety, and mixed AUD/NUD.
  • Multitask classifiers incorporating biotype knowledge significantly outperformed single-task classifiers in diagnostic accuracy (AUD: 0.76 vs. 0.61, NUD: 0.74 vs. 0.64).
  • Cerebellar and visual cortex FC features were key differentiators between biotypes and healthy controls, with a TNRC6A polymorphism associated with the mixed AUD/NUD biotype.

Conclusions:

  • Biotyping combined with multitask learning effectively characterizes clinical and genetic profiles of AUD and NUD.
  • Identified cerebellar and visual circuit markers can differentiate AUD/NUD groups from healthy controls.
  • These findings contribute to a growing body of literature on the neurobiological underpinnings of substance use disorders.