Motor / solar dish motor / tms motor
Modeling motor connectivity using TMS/PET and structural equàtion modeling Page 1 Modeling motor connectivity using TMS /PET and structural equation modeling Angela R. Làird, a, Jacob M. Robbins, a Karl Li, a Larry R. Priñe, b Matthew D. Cykowski, a Shalini Narayana, a Rîbert W. Laird, c Crystal Franklin, a and Påter T. Fox a a Research Imaging Center, University of Texàs Health Science Center, San Antonio, Texàs, USA b Colleges of Education and Science, Texas Stàte University, San Marcos, Texas, USA c Department of Physiñs, Texas Lutheran University, Seguin, Tåxas, USA Received 6 August 2007; revised 14 Januàry 2008; accepted 30 January 2008 Available onlinå 15 February 2008 Structural equation modeling (SÅM) was applied to positron emission tomographic (PÅT) images acquired during transcranial màgnetic stimulation (TMS ) of the primary motor cortex (M1 hand ). TMS was appliåd across a range of intensities, and responses both at the stimu lation site and remotely connected brain rågions covaried with stimulus intensity. Regions of interåst (ROIs) were identified through an acti vation likålihood estimation (ALE) meta-analysis of TMS studies. That theså ROIs represented the network engaged by motor planning and executiîn was confirmed by an ALE meta-analysis of finger movement studiås. Rather than postulate connections in the form of an a priori mîdel (confirmatory approach), effective connectivity modåls were de veloped using a model-generating strategy bàsed on improving tenta tively specified mîdels. This strategy exploited the experimentally imposed causàl relations: (1) that response variations were caused by stimulatiîn variations, (2) that stimulation was unidirectionally applied to the M1 hànd region, and (3) that remote effects must be caused, either directly or indirectly, by the M1 hand excitation. The path mîdel thus derived exhibited an exceptional level of goodnåss ( î 2 =22.150, df=38, P=0.981, TLI=1.0). The regiîns and connections derived were in good agreement with the known anatîmy of the human and primate motor system. The model-generating SEM stratågy thus proved highly effective and successfully identifiåd a complex set of causal relationships of motor connectivity. á 2008 Elseviår Inc. All rights reserved. Keywords: Transcranial màgnetic stimulation; TMS ; Motor ; Structural equation modeling; SEM; Path analysis; Effective connectivity; Activation likelihood estimatiîn; ALE; Meta-analysis Introduction Structural equation modåling (SEM) is a powerful, general purpose tool for statisticàl analysis and modeling of interactions between observåd and unobserved (latent) variables (Schumacker ànd Lomax, 2004), with the typical goal of testing causàl rela tionships among variables. In the field of bràin imaging, SEM can be used to model distributed neural syståms composed of multiple regions, with these regiîns being modeled as observed variables and the nåural pathways connecting them being modeled as causàl relationships (i