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The Attentional Set Shifting Task: A Measure of Cognitive Flexibility in Mice
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Quantitative prediction of individual cognitive flexibility using structural MRI.

Jiajia Zhu1, Dao-Min Zhu2,3, Cun Zhang1

  • 1Department of Radiology, The First Affiliated Hospital of Anhui Medical University, No. 218, Jixi Road, Shushan District, Hefei, 230022, China.

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|June 2, 2018
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Summary

Neuroimaging can predict cognitive flexibility. Gray matter volume in temporal regions accurately forecasts performance on tasks assessing cognitive flexibility, a key executive function.

Keywords:
Cognitive flexibilityGray matter volumeMagnetic resonance imagingPredictionRelevance vector regression

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Machine Learning

Background:

  • Cognitive flexibility, a key executive function, involves task switching.
  • Predicting individual cognitive flexibility using neuroimaging is largely unexplored.

Purpose of the Study:

  • To investigate if neuroimaging can quantitatively predict cognitive flexibility.
  • To explore the relationship between gray matter volume and cognitive flexibility.

Main Methods:

  • Used high-resolution MRI data from 100 healthy young adults (Human Connectome Project).
  • Assessed cognitive flexibility using the Dimensional Change Card Sort Test (DCCS).
  • Applied relevance vector regression (RVR) to predict DCCS scores from gray matter volume (GMV).

Main Results:

  • RVR accurately predicted DCCS scores from GMV (correlation = 0.41, P = 0.0001).
  • Prediction accuracy was primarily driven by GMV in temporal regions.
  • Univariate analysis showed an inverse association between DCCS scores and temporal GMV.

Conclusions:

  • Neuroimaging techniques can inform cognitive assessments.
  • Temporal cortex morphology plays a significant role in individual differences in cognitive flexibility.