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The earliest recorded discussion of the basic structure of matter comes from ancient Greek philosophers. Leucippus and Democritus argued that all matter was composed of small, finite particles that they called atomos, meaning “indivisible.” Later, Aristotle and others came to the conclusion that matter consisted of various combinations of the four “elements” — fire, earth, air, and water — and could be infinitely divided. Interestingly, these philosophers...
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Related Experiment Video

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Modeling Alcohol Consumption in Rodents Using Two-Bottle Choice Home Cage Drinking and Microstructural Analysis
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Decoding diagnosis and lifetime consumption in alcohol dependence from grey-matter pattern information.

M Guggenmos1, M Scheel2, M Sekutowicz1

  • 1Department of Psychiatry and Psychotherapy, Campus Charité Mitte, Charité Universitätsmedizin Berlin, Berlin, Germany.

Acta Psychiatrica Scandinavica
|January 30, 2018
PubMed
Summary

Computer models accurately predict alcohol dependence (AD) diagnosis and alcohol consumption using brain grey matter. This approach shows promise as a sensitive screening tool for AD.

Keywords:
alcohol drinkinggrey mattermachine learningneuroimagingradiologists

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Psychiatry

Background:

  • Alcohol dependence (AD) diagnosis and severity assessment remain challenging.
  • Machine learning in psychiatric neuroimaging faces scrutiny regarding generalization and transparency.

Purpose of the Study:

  • To evaluate computer-based models for decoding alcohol dependence diagnosis and lifetime alcohol consumption from grey-matter patterns.
  • To address methodological criticisms of machine learning in neuroimaging research.

Main Methods:

  • Structural MRI data from 119 individuals with AD and 97 controls (ages 20-65) were analyzed.
  • Machine learning models were applied to predict diagnosis and lifetime alcohol consumption based on regional grey matter.

Main Results:

  • The classification scheme achieved 74% diagnostic accuracy and predicted lifetime consumption with high accuracy (r = 0.56).
  • Algorithmic transparency identified cingulate, insular, and inferior frontal cortices as key brain regions.
  • Successful validation on an independent dataset confirmed generalization.
  • Computer-based classification demonstrated higher accuracy and sensitivity than a blinded radiologist, with reduced biases.

Conclusions:

  • Computer-based models using whole-brain grey matter effectively predict diagnosis and consumption in alcohol dependence.
  • This method shows potential as a highly sensitive screening tool for alcohol dependence.