Predicting Skill-Based Task Performance and Learning with fMRI Motor and Subcortical Network Connectivity

Aki Nikolaidis, Drew Goatz, Paris Smaragdis, Arthur Kramer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Procedural learning is the process of skill acquisition that is regulated by the basal ganglia, and this learning becomes automated over time through cortico-striatal and cortico-cortical connectivity. In the current study, we use a common machine learning regression technique to investigate how fMRI network connectivity in the subcortical and motor networks are able to predict initial performance and traininginduced improvement in a skill-based cognitive training game, Space Fortress, and how these predictions interact with the strategy the trainees were given during training. To explore the reliability and validity of our findings, we use a range of regression lambda values, sizes of model complexity, and connectivity measurements.

Original languageEnglish (US)
Title of host publicationProceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages93-96
Number of pages4
ISBN (Electronic)9781467371452
DOIs
StatePublished - Sep 16 2015
Event5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015 - Stanford, United States
Duration: Jun 10 2015Jun 12 2015

Publication series

NameProceedings - 2015 International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015

Other

Other5th International Workshop on Pattern Recognition in NeuroImaging, PRNI 2015
Country/TerritoryUnited States
CityStanford
Period6/10/156/12/15

Keywords

  • basal ganglia
  • cognitive training
  • motor control
  • procedural learning; ridge regression
  • skill

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Radiology Nuclear Medicine and imaging

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