Modeling Huntington’s Disease Considering the Theory of Central Pattern Generators (CPG)

Abstract

In this study, we present a novel model for Huntington’s disease (HD) gait disorder. We consider the mathematical relations between blocks. The number of inputs and outputs of each block are designated due to the physiological findings. The connection types between blocks are modeled by gains. Inner structure of each block is modeled using a central pattern generator neural network. Our model is able to simulate the normal and HD strides time intervals and shows how diazepam is able to ameliorate the gait disorder; however, we believe that this treatment is somehow irrational. Using GABA blockers recovers the symptoms but it means omitting BG from motor control loop. Our model shows that increment of GABA aggravates the gait disorder. Our novel idea about BG treatment is to reduce glutamate. Experimental studies are needed for evaluating this novel treatment. This validation would implement a milestone in treatment of such a debilitating disease. It seems that synchronization of a number of neurons is the major disturbance in HD. The synchronization was modeled as CPG (Central Pattern Generator) structure. We supposed that the disorder will recover if the wrong synchronization of the neurons is diminished. Therefore, deep brain stimulation may be useful in this regard.