Modeling motor connectivity using TMS/PET and structural equation modeling

Abstract

Structural equation modeling (SEM) was applied to positron emission tomographic (PET) images acquired during transcranial magnetic stimulation (TMS) of the primary motor cortex (M1(hand)). 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 M1(hand) region, and (3) that remote effects must be caused, either directly or indirectly, by the M1(hand) excitation. The path model thus derived exhibited an exceptional level of goodness (chi(2)=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.

Evoked potentials in large-scale cortical networks elicited by TMS of the visual cortex

“…Theta activity is maximal over frontal cortex, and alpha activity is maximal in frontal and posterior cortex. Bohning et al 1998;Baudewig et al 2001;PET: Fox et al 1997;Laird et al 2008;Paus et al 1997;and SPECT: Okabe et al 2003). Our results extend this literature by providing evidence that the time course of the cortical response to a single pulse of TMS is sufficiently long (ϳ700 ms) to impact multiple cognitive functions and that stimulating one region of cortex has broad impact on a network of brain areas.…”

“…However, evidence inconsistent with this hypothesis demonstrates that the action of TMS is much more complex than simply disabling a region of cortex (e.g., Sack et al 2006). Brain mapping studies using concurrent imaging techniques (e.g., PET, EEG) have measured responses to the TMS pulse distal from the stimulation site, typically in regions believed to be functionally connected to the stimulation site [functional (f)MRI: Bohning et al 1998;Baudewig et al 2001;PET: Fox et al 1997;Laird et al 2008;Paus et al 1997; and singlephoton emission computed tomography (SPECT): Okabe et al 2003]. For example, TMS over the frontal eye field engages a whole network of brain areas as measured by PET, including regions in occipital and parietal cortex (Paus et al 1997).…”

Discovering effective connectivity among brain regions from functional MRI data

“…Usually, SEMs are used as a confirmatory tool for a hypothesised causal model, or to assess the strength of the relationships in different experimental setups (e.g., Walsh et al, 2008;Tsubomi et al, 2009;Tourville et al, 2008). But some studies have tried to use SEMs as an exploratory tool (e.g., Laird et al, 2008;Zhuang et al, 2008). Granger (1969) causality is used to determine if a time series in one variable forecasts a time series in other variables.…”

Modeling of Circuits within Networks by fMRI

“…An increasing number of PET, fMRI and transcranial magnetic stimulation (TMS) studies have used SEM to investigate large-scale functional brain networks [90][91][92][93] and show specific networks involved in either working memory [94][95][96][97][98][99][100], attentional processes [64,[101][102][103], face perception [104][105][106], motor movement processing [61,[107][108][109][110][111][112], language [32,113,114] or processing of painful stimuli [62].…”