Chaotic Desynchronization as the Therapeutic Mechanism of Deep Brain Stimulation

High frequency deep-brain stimulation of the subthalamic nucleus (deep brain stimulation, DBS) relieves many of the symptoms of Parkinson's disease in humans and animal models. Although the treatment has seen widespread use, its therapeutic mechanism remains paradoxical. The subthalamic nucleus is excitatory, so its stimulation at rates higher than its normal firing rate should worsen the disease by increasing subthalamic excitation of the globus pallidus. The therapeutic effectiveness of DBS is also frequency and intensity sensitive, and the stimulation must be periodic; aperiodic stimulation at the same mean rate is ineffective. These requirements are not adequately explained by existing models, whether based on firing rate changes or on reduced bursting. Here we report modeling studies suggesting that high frequency periodic excitation of the subthalamic nucleus may act by desynchronizing the firing of neurons in the globus pallidus, rather than by changing the firing rate or pattern of individual cells. Globus pallidus neurons are normally desynchronized, but their activity becomes correlated in Parkinson's disease. Periodic stimulation may induce chaotic desynchronization by interacting with the intrinsic oscillatory mechanism of globus pallidus neurons. Our modeling results suggest a mechanism of action of DBS and a pathophysiology of Parkinsonism in which synchrony, rather than firing rate, is the critical pathological feature

Synchronization from Second Order Network Connectivity Statistics

We investigate how network structure can influence the tendency for a neuronal network to synchronize, or its synchronizability, independent of the dynamical model for each neuron. The synchrony analysis takes advantage of the framework of second order networks, which defines four second order connectivity statistics based on the relative frequency of two-connection network motifs. The analysis identifies two of these statistics, convergent connections, and chain connections, as highly influencing the synchrony. Simulations verify that synchrony decreases with the frequency of convergent connections and increases with the frequency of chain connections. These trends persist with simulations of multiple models for the neuron dynamics and for different types of networks. Surprisingly, divergent connections, which determine the fraction of shared inputs, do not strongly influence the synchrony. The critical role of chains, rather than divergent connections, in influencing synchrony can be explained by their increasing the effective coupling strength. The decrease of synchrony with convergent connections is primarily due to the resulting heterogeneity in firing rates