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Statistical inference for the effect of magnetic brain stimulation on a motoneurone

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A stochastic model is developed for the possible excitatory and inhibitory effects of a stimulus to the brain on the activity of single human motoneurones. The model consists of a Wiener process for the build-up of underlying potential, a deterministic effect due to the stimulus and a random lag from brain to muscle. Direct likelihood inference for its parameters seems impossible, and we study the use of simulation to estimate the log-likelihood for the parameters of substantive interest. Monte Carlo methods yield point and confidence interval estimates of the membrane excitability underlying excitatory and inhibitory effects. The main qualitative conclusion is that both excitatory and inhibitory effects are unambiguously present. The contribution of statistical analysis to this problem is to provide accurate and apparently reliable inference for the quantities of neurophysiological interest. More generally, our methodology has the potential to make accurate likelihood-based inferences in challenging problems, but the computational burden can be large, particularly if the model is not fully adequate for the data, as in our application.

Keywords: Backward recurrence time; Excitatory post-synaptic potential; Forward recurrence time; Implicit statistical model; Importance sampling; Inhibitory post-synaptic potential; Inverse Gaussian distribution; Length-biased sampling; Likelihood; Magnetic brain stimulation; Point process; Response surface; Simulation; Wiener process

Document Type: Original Article


Affiliations: 1: Carnegie Mellon University, Pittsburgh, USA, 2: Swiss Federal Institute of Technology, Lausanne, Switzerland, 3: Addenbrooke’s Hospital, Cambridge, UK

Publication date: January 1, 1998


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