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Front Comput Neurosci. 2017 Jul 27;11:70. doi: 10.3389/fncom.2017.00070. eCollection 2017.

Striatal Network Models of Huntington's Disease Dysfunction Phenotypes.

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Computational Neuroscience and Multiscale Brain Modeling, Computational Biology Center, IBM Research Division, IBM T. J. Watson Research CenterNew York, NY, United States.


We present a network model of striatum, which generates "winnerless" dynamics typical for a network of sparse, unidirectionally connected inhibitory units. We observe that these dynamics, while interesting and a good match to normal striatal electrophysiological recordings, are fragile. Specifically, we find that randomly initialized networks often show dynamics more resembling "winner-take-all," and relate this "unhealthy" model activity to dysfunctional physiological and anatomical phenotypes in the striatum of Huntington's disease animal models. We report plasticity as a potent mechanism to refine randomly initialized networks and create a healthy winnerless dynamic in our model, and we explore perturbations to a healthy network, modeled on changes observed in Huntington's disease, such as neuron cell death and increased bidirectional connectivity. We report the effect of these perturbations on the conversion risk of the network to an unhealthy state. Finally we discuss the relationship between structural and functional phenotypes observed at the level of simulated network dynamics as a promising means to model disease progression in different patient populations.


Huntington's disease; STDP; dynamics; homeostatic plasticity; medium spiny neuron; network; neurodegenerative; striatum

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