Optimizing Experimental Design for Comparing Models of Brain Function

This article presents the first attempt to formalize the optimization of experimental design with the aim of comparing models of brain function based on neuroimaging data. We demonstrate our approach in the context of Dynamic Causal Modelling (DCM), which relates experimental manipulations to observed network dynamics (via hidden neuronal states) and provides an inference framework for selecting among candidate models. Here, we show how to optimize the sensitivity of model selection by choosing among experimental designs according to their respective model selection accuracy. Using Bayesian decision theory, we (i) derive the Laplace-Chernoff risk for model selection, (ii) disclose its relationship with classical design optimality criteria and (iii) assess its sensitivity to basic modelling assumptions. We then evaluate the approach when identifying brain networks using DCM. Monte-Carlo simulations and empirical analyses of fMRI data from a simple bimanual motor task in humans serve to demonstrate the relationship between network identification and the optimal experimental design. For example, we show that deciding whether there is a feedback connection requires shorter epoch durations, relative to asking whether there is experimentally induced change in a connection that is known to be present. Finally, we discuss limitations and potential extensions of this work

Transcranial Magnetic Stimulation Intensities in Cognitive Paradigms

BACKGROUND: Transcranial magnetic stimulation (TMS) has become an important experimental tool for exploring the brain's functional anatomy. As TMS interferes with neural activity, the hypothetical function of the stimulated area can thus be tested. One unresolved methodological issue in TMS experiments is the question of how to adequately calibrate stimulation intensities. The motor threshold (MT) is often taken as a reference for individually adapted stimulation intensities in TMS experiments, even if they do not involve the motor system. The aim of the present study was to evaluate whether it is reasonable to adjust stimulation intensities in each subject to the individual MT if prefrontal regions are stimulated prior to the performance of a cognitive paradigm. METHODS AND FINDINGS: Repetitive TMS (rTMS) was applied prior to a working memory task, either at the 'fixed' intensity of 40% maximum stimulator output (MSO), or individually adapted at 90% of the subject's MT. Stimulation was applied to a target region in the left posterior middle frontal gyrus (pMFG), as indicated by a functional magnetic resonance imaging (fMRI) localizer acquired beforehand, or to a control site (vertex). Results show that MT predicted the effect size after stimulating subjects with the fixed intensity (i.e., subjects with a low MT showed a greater behavioral effect). Nevertheless, the individual adaptation of intensities did not lead to stable effects. CONCLUSION: Therefore, we suggest assessing MT and account for it as a measure for general cortical TMS susceptibility, even if TMS is applied outside the motor domain

Convergence of human brain mapping tools: neuronavigated TMS Parameters and fMRI activity in the hand motor area

Functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS) are well-established tools for investigating the human motor system in-vivo. We here studied the relationship between movement-related fMRI signal changes in the primary motor cortex (M1) and electrophysiological properties of the hand motor area assessed with neuronavigated TMS in 17 healthy subjects. The voxel showing the highest task-related BOLD response in the left hand motor area during right hand movements was identified for each individual subject. This fMRI peak voxel in M1 served as spatial target for coil positioning during neuronavigated TMS. We performed correlation analyses between TMS parameters, BOLD signal estimates and effective connectivity parameters of M1 assessed with dynamic causal modeling (DCM). The results showed a negative correlation between the movement-related BOLD signal in left M1 and resting as well as active motor threshold (MT) obtained for left M1. The DCM analysis revealed that higher excitability of left M1 was associated with a stronger coupling between left supplementary motor area (SMA) and M1. Furthermore, BOLD activity in left M1 correlated with ipsilateral silent period (ISP), i.e. the stronger the task-related BOLD response in left M1, the higher interhemispheric inhibition effects targeting right M1. DCM analyses revealed a positive correlation between the coupling of left SMA with left M1 and the duration of ISP. The data show that TMS parameters assessed for the hand area of M1 do not only reflect the intrinsic properties at the stimulation site but also interactions with remote areas in the human motor system. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.link_to_subscribed_fulltex