Segmentation of Target Nuclei in Parkinson's Disease Based on Fuzzy Connectedness

With the development of nerve stereotactic technology, brain stereotactic surgery has become an effective method for the current treatment of Parkinson's disease. The accurate localization of the target nuclei is the key issue of the treatment. In this paper, we constructed a dependence tree model to identify the target nuclei further. Theory of fuzzy connectedness was used in the segmentation. Experimental results show it was more desirable and suitable to the clinical applications.

The Costs of Ignoring High-Order Correlations in Populations of Model Neurons

Investigators debate the extent to which neural populations use pairwise and higher-order statistical dependencies among neural responses to represent information about a visual stimulus. To study this issue, three statistical decoders were used to extract the information in the responses of model neurons about the binocular disparities present in simulated pairs of left-eye and right-eye images: (1) the full joint probability decoder considered all possible statistical relations among neural responses as potentially important; (2) the dependence tree decoder also considered all possible relations as potentially important, but it approximated high-order statistical correlations using a computationally tractable procedure; and (3) the independent response decoder, which assumed that neural responses are statistically independent, meaning that all correlations should be zero and thus can be ignored. Simulation results indicate that high-order correlations among model neuron responses contain significant information about binocular disparities and that the amount of this high-order information increases rapidly as a function of neural population size. Furthermore, the results highlight the potential importance of the dependence tree decoder to neuroscientists as a powerful but still practical way of approximating high-order correlations among neural responses.