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Identifying cognitive deficits in cocaine dependence using standard tests and machine learning.

Said Jiménez1, Diego Angeles-Valdez1, Viviana Villicaña2

  • 1Subdirección de Investigaciones Clínicas, Instituto Nacional de Psiquiatría "Ramón de la Fuente Muñiz", Mexico City, Mexico; Faculty of Psychology, National Autonomous University of Mexico (UNAM), Mexico City, Mexico.

Progress in Neuro-Psychopharmacology & Biological Psychiatry
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Summary

Machine learning effectively identifies cognitive deficits in cocaine dependence (CD). Elastic Net (GlmNet) outperformed other algorithms, pinpointing key cognitive markers for accurate classification of CD and non-dependent controls (NDC).

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Area of Science:

  • Neuroscience
  • Computer Science
  • Psychiatry

Background:

  • Standard cognitive tests show variability in detecting deficits in cocaine dependence (CD).
  • More effective methods are needed for categorizing CD and non-dependent controls (NDC).

Purpose of the Study:

  • To identify cognitive deficits in CD using Machine Learning (ML) algorithms.
  • To compare the performance of Generalized Linear Model (Glm), Random Forest (Rf), and Elastic Net (GlmNet) for CD classification.
  • To identify reliable cognitive markers for CD.

Main Methods:

  • Trained three ML algorithms (Glm, Rf, GlmNet) on a dataset of 87 participants (53 CD, 34 NDC).
  • Validated algorithms on an independent dataset of 40 participants (20 CD, 20 NDC).
  • Utilized 40 cognitive variables from neuropsychological tests for classification.

Main Results:

  • All ML algorithms showed receiver operating curve (ROC) performance above 50%.
  • GlmNet demonstrated superior performance in both training (ROC=0.71) and testing (ROC=0.85) datasets.
  • GlmNet identified eight key predictors of group assignment, including cognitive flexibility and inhibition domains.

Conclusions:

  • ML, particularly GlmNet, is effective in highlighting relevant cognitive test sections for CD.
  • ML can identify generalizable cognitive markers for accurate CD classification.
  • This approach aids in addressing methodological challenges in cognitive deficit detection in CD.