Education/Training:
Ph.D. Physics, Institute for Applied Physics, Russian Academy of Science, (2000)
M.S. University of California, SanDiego, (1997)

Dynamics and Synchronization of Neuronal Networks and Physiology of Basal Ganglia in Health and Parkinson's diseases.

My research interests lie in the area of mathematical and computational neuroscience. I am using mathematical and computational methods to study the dynamics of the nervous system to get insights into its function. My current research is concentrated on the dynamics of basal ganglia - brain nuclei, which, among other things, control motor programs and are impacted in Parkinson's disease (characterized by tremor and hypokinetic behavior). Despite the large amount of factual knowledge about basal ganglia at all levels - from cells to behavior, the principles of function of basal ganglia in Parkinson's disease (and other diseases involving basal ganglia) and even in normal conditions are far from being fully understood. There is mounting experimental evidence that complex collective dynamics of interaction of various ionic channels and various cells are responsible for normal basal ganglia operation and pathological variations of these dynamics are responsible for its pathophysiology.

Models of basal ganglia motor control circuits
I work on the development of biophysically-based models of basal ganglia motor control, which connect the cellular biophysics with the motor program execution and, thus, behavior. The modeling makes it possible to study basal ganglia motor control with mathematical and numerical methods. One cannot easily predict how a changes in the kinetics of several channels or changes in the circuitry (changes that result from the loss of dopaminergic innervation) will affect the behavior of a network composed of many neurons, particularly across several nuclei with various patterns of connectivity. This is where biophysically-based modeling not only helps to integrate and interpret current data, but also can suggest future experiments or potential therapy strategies.

Synchronous oscillatory dynamics of tremor supporting networks in Parkinsons' disease
Synchronization phenomena in the clinically observed oscillatory electrophysiological data from parkinsonian patients are studied to reconstruct the structure of the neuronal circuits, which control motor programs in health and support tremor in pathology. While quantitative characterization of tremor in limbs and tremor-related activity in brain is of interest for neurology, Parkinson's disease and its motor symptoms constitute a unique nature-created experiment, which presents a window for looking at the basic principles of the basal ganglia motor control operation. The methods we developed to detect short-term phase synchrony between tremor in the limbs and tremor-related activity in the brain point to the intermittent character of tremor-related oscillations and synchronization of them. The goal here is to understand the nature of this unstable, variable behavior and I am working on hypothesis why transiently synchronous oscillations can arise in the networks, controlling motor programs.
Ultimately, both modeling and data analysis research should be advanced to the level at which model networks will be able to adequately describe complex dynamics of synchronization observed in vivo. Understanding of basal ganglia dynamics may open the way for developing treatment techniques, such as novel deep brain stimulation techniques, which would be a low-amplitude control of pathological basal ganglia dynamics. This approach can be potentially extended to other types of neuronal systems.

Recent Publications:

LL Rubchinsky, AS Kuznetsov, VL Wheelock, KA Sigvardt (2007) Tremor. Scholarpedia, p.22658

AS Kuznetsov, LL Rubchinsky, N Kopell, C Wilson (2007) Models of Midbrain Dopaminergic Neurons. Scholarpedia, p.23551

L.L. Rubchinsky, N. Kopell, K.A. Sigvardt. Conductance-based models of STN-GPe-GPi circuits: from biophysics to behavior. In Recent Breakthroughs in Basal Ganglia Research, ed. E. Bezard. Nova Publishers, 17-25, 2006.

J.A. Hurtado, L.L. Rubchinsky, K.A. Sigvardt. The dynamics of tremor networks in Parkinson's disease. In Recent Breakthroughs in Basal Ganglia Research, ed. E Bezard. Nova Publishers, 249-266, 2006.

Hurtado JM, Rubchinsky LL, Sigvardt KA, Wheelock VL, Pappas CT. Temporal evolution of oscillations and synchrony in GPi/muscle pairs in Parkinson's disease.
J Neurophysiol. 2005 Mar;93(3):1569-84.

J.M. Hurtado, L.L. Rubchinsky, K.A. Sigvardt. Statistical method for detection of phase locking episodes in neural oscillations. J. Neurophysiol. 91:1883-1898, 2004.

L.L. Rubchinsky, N. Kopell, K.A. Sigvardt. Modeling facilitation and inhibition of competing motor programs in basal ganglia subthalamic nucleus - pallidal circuits. Proc. Nat. Acad. Sci. USA 100: 14427-14432, 2003