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

Laird, AR, Li, K, Narayana, S et al. (2009). Modeling motor learning connectivity using coordinate-based meta-analysis and TMS/PET . Conference Record - Asilomar Conference on Signals, Systems and Computers, 1079-1082. 10.1109/ACSSC.2009.5470062

cited authors

  • Laird, AR; Li, K; Narayana, S; Price, LR; Laird, RW; Xiong, J; Fox, PT

abstract

  • 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. © 2009 IEEE.

authors

publication date

  • December 1, 2009

published in

Digital Object Identifier (DOI)

start page

  • 1079

end page

  • 1082