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Neurocognitive dysfunction in addiction: Testing hypotheses of diffuse versus selective phenotypic dysfunction with a classification-based approach.

Abstract Neurocognitive dysfunctions are frequently identified in the addictions. These dysfunctions may indicate either diffuse dysfunction or may represent separate facets that have differential importance to the addiction phenotype. In a sample (n = 260) of alcohol and/or stimulant users and controls we measured responses across 7 diverse neurocognitive measures. These measures were Continuous Performance, Delay Discounting, Iowa Gambling, Stroop, Tower, Wisconsin Card Sorting, and Letter Number Sequencing. Comparisons were then made between the drug-dependent groups and controls using analysis of variance and also using a machine learning approach to classify participants based on task performance as substance-dependent or controls in 1 tree and as alcohol and/or stimulant users or controls in a second tree. The analysis of variance showed significant differences between groups on the Delay Discounting (p < .001), Iowa Gambling (p < .001), Letter Number Sequencing (p < .001), and Wisconsin Card Sorting (p < .05) tasks. The first classification tree correctly classified between substance-dependent or controls for 88.3% of participants and classified between alcohol and/or stimulant users or controls for 63.9% of participants. Delay discounting was the first split in both trees and in the substance-dependent and control tree. The analysis of variance results largely replicate previous findings. The machine learning classification tree analysis provides evidence to support the hypothesis that different measures of neurocognitive dysfunction represent different processes. Among them, delay discounting was the most robust in categorizing drug dependence. (PsycINFO Database Record
PMID
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Authors

Mayor MeshTerms

Delay Discounting

Keywords
Journal Title experimental and clinical psychopharmacology
Publication Year Start




PMID- 28782983
OWN - NLM
STAT- MEDLINE
DA  - 20170807
DCOM- 20170904
LR  - 20170904
IS  - 1936-2293 (Electronic)
IS  - 1064-1297 (Linking)
VI  - 25
IP  - 4
DP  - 2017 Aug
TI  - Neurocognitive dysfunction in addiction: Testing hypotheses of diffuse versus
      selective phenotypic dysfunction with a classification-based approach.
PG  - 322-332
LID - 10.1037/pha0000115 [doi]
AB  - Neurocognitive dysfunctions are frequently identified in the addictions. These
      dysfunctions may indicate either diffuse dysfunction or may represent separate
      facets that have differential importance to the addiction phenotype. In a sample 
      (n = 260) of alcohol and/or stimulant users and controls we measured responses
      across 7 diverse neurocognitive measures. These measures were Continuous
      Performance, Delay Discounting, Iowa Gambling, Stroop, Tower, Wisconsin Card
      Sorting, and Letter Number Sequencing. Comparisons were then made between the
      drug-dependent groups and controls using analysis of variance and also using a
      machine learning approach to classify participants based on task performance as
      substance-dependent or controls in 1 tree and as alcohol and/or stimulant users
      or controls in a second tree. The analysis of variance showed significant
      differences between groups on the Delay Discounting (p &lt; .001), Iowa Gambling (p 
      &lt; .001), Letter Number Sequencing (p &lt; .001), and Wisconsin Card Sorting (p &lt;
      .05) tasks. The first classification tree correctly classified between
      substance-dependent or controls for 88.3% of participants and classified between 
      alcohol and/or stimulant users or controls for 63.9% of participants. Delay
      discounting was the first split in both trees and in the substance-dependent and 
      control tree. The analysis of variance results largely replicate previous
      findings. The machine learning classification tree analysis provides evidence to 
      support the hypothesis that different measures of neurocognitive dysfunction
      represent different processes. Among them, delay discounting was the most robust 
      in categorizing drug dependence. (PsycINFO Database Record
CI  - (c) 2017 APA, all rights reserved).
FAU - Bickel, Warren K
AU  - Bickel WK
AD  - Addiction Recovery Research Center, Virginia Tech Carilion Research Institute.
FAU - Moody, Lara N
AU  - Moody LN
AD  - Addiction Recovery Research Center, Virginia Tech Carilion Research Institute.
FAU - Eddy, Celia R
AU  - Eddy CR
AD  - Department of Statistics, Virginia Tech.
FAU - Franck, Christopher T
AU  - Franck CT
AD  - Addiction Recovery Research Center, Virginia Tech Carilion Research Institute.
LA  - eng
PT  - Journal Article
PL  - United States
TA  - Exp Clin Psychopharmacol
JT  - Experimental and clinical psychopharmacology
JID - 9419066
RN  - 0 (Central Nervous System Stimulants)
SB  - IM
MH  - Adult
MH  - Alcohol-Related Disorders/classification/*psychology
MH  - Analysis of Variance
MH  - Central Nervous System Stimulants/administration &amp; dosage/adverse effects
MH  - Cognitive Dysfunction/classification/*epidemiology
MH  - *Delay Discounting
MH  - Female
MH  - Humans
MH  - Machine Learning
MH  - Male
MH  - Middle Aged
MH  - Neuropsychological Tests
MH  - Phenotype
MH  - Substance-Related Disorders/classification/*psychology
MH  - Task Performance and Analysis
EDAT- 2017/08/08 06:00
MHDA- 2017/09/05 06:00
CRDT- 2017/08/08 06:00
AID - 2017-33271-002 [pii]
AID - 10.1037/pha0000115 [doi]
PST - ppublish
SO  - Exp Clin Psychopharmacol. 2017 Aug;25(4):322-332. doi: 10.1037/pha0000115.