Modeling motor learning connectivity using coordinate-based meta-analysis and TMS/PET

A. R. Laird, K. Li, S. Narayana, L. R. Price, R. W. Laird, J. Xiong, P. T. Fox

Producción científica: Conference contribution

Resumen

BrainMap is a database of peak activation locations and metadata reported in functional neuroimaging studies, which was designed to develop and promote coordinate-based meta-analysis techniques. Here, we demonstrate the activation likelihood estimation (ALE) method in a meta-analysis of published TMS/PET studies. Using the results of this meta-analysis, we constructed a data-driven model of motor connectivity in TMS/PET data in which stimulation was delivered to RM1 before and after motor skill acquisition. A hybrid motor connectivity model of pre- and post-learning was generated to identify specific pathways most affected by the mechanisms involved in the motor learning process.

Idioma originalEnglish (US)
Título de la publicación alojadaConference Record - 43rd Asilomar Conference on Signals, Systems and Computers
Páginas1079-1082
Número de páginas4
DOI
EstadoPublished - 2009
Evento43rd Asilomar Conference on Signals, Systems and Computers - Pacific Grove, CA, United States
Duración: nov 1 2009nov 4 2009

Serie de la publicación

NombreConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (versión impresa)1058-6393

Other

Other43rd Asilomar Conference on Signals, Systems and Computers
País/TerritorioUnited States
CiudadPacific Grove, CA
Período11/1/0911/4/09

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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