Modeling motor connectivity using TMS/PET and structural equation modeling

  • Laird A
  • Robbins J
  • Li K
 et al. 
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Abstract

Structural equation modeling (SEM) was applied to positron emission tomographic (PET) images acquired during transcranial magnetic stimulation (TMS) of the primary motor cortex (M1hand). TMS was applied across a range of intensities, and responses both at the stimulation site and remotely connected brain regions covaried with stimulus intensity. Regions of interest (ROIs) were identified through an activation likelihood estimation (ALE) meta-analysis of TMS studies. That these ROIs represented the network engaged by motor planning and execution was confirmed by an ALE meta-analysis of finger movement studies. Rather than postulate connections in the form of an a priori model (confirmatory approach), effective connectivity models were developed using a model-generating strategy based on improving tentatively specified models. This strategy exploited the experimentally imposed causal relations: (1) that response variations were caused by stimulation variations, (2) that stimulation was unidirectionally applied to the M1handregion, and (3) that remote effects must be caused, either directly or indirectly, by the M1handexcitation. The path model thus derived exhibited an exceptional level of goodness (χ2= 22.150, df = 38, P = 0.981, TLI = 1.0). The regions and connections derived were in good agreement with the known anatomy of the human and primate motor system. The model-generating SEM strategy thus proved highly effective and successfully identified a complex set of causal relationships of motor connectivity. © 2008 Elsevier Inc. All rights reserved.

Author-supplied keywords

  • ALE
  • Activation likelihood estimation
  • Effective connectivity
  • Meta-analysis
  • Motor
  • Path analysis
  • SEM
  • Structural equation modeling
  • TMS
  • Transcranial magnetic stimulation

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