Capturing movement responses of single cells in the human basal ganglia using hidden Markov models

We used hidden Markov models (HMMs) to characterise the firing activity of single neurons in the basal ganglia recorded during voluntary limb movements made by patients undergoing surgery for Parkinson´s disease. The parameters of these HMMs were then used as input patterns for training the generative topographic mapping (GTM) algorithm. Data were strongly clustered in the GTM latent space, and separated both by brain region and by movement type, when as few as two states were modelled in the HMM. Our results support the hypotheses that (1) cells in these brain regions are not involved in the preparation or execution of a single type of movement but rather participate to some extent in many different movements, and (2) neural networks in these regions operate as systems with distinct and relatively stable configurations of activity