Neighboring Pallidal Neurons Do Not Exhibit more Synchronous Oscillations than Remote Ones in the MPTP Primate Model of Parkinson's Disease.

In the healthy primate, neurons of the external and internal segments of the globus pallidus (GP) present a primarily irregular firing pattern, and a negligible level of synchrony is observed between pairs of neurons. This holds even for neighboring cells, despite their higher probability to receive common inputs and to innervate each other via lateral connectivity. In the Parkinsonian primate, this changes drastically, and many pairs of GP cells show synchronous oscillations. To address the relation between distance and synchrony in the Parkinsonian state, we compared the synchrony of discharge of close pairs of neurons, recorded by the same electrode, with remote pairs, recorded by different ones. However, spike trains of neighboring cells recorded by the same extracellular electrode exhibit the shadowing effect; i.e., lack of detection of spikes that occur within a few milliseconds of each other. Here, we demonstrate that the shadowing artifact can both induce apparent correlations between non-correlated neurons, as well as conceal existing correlations between neighboring ones. We therefore introduced artificial shadowing in the remote pairs, similar to the effect we observed in the close ones. After the artificial shadowing, neighboring cells did not show a higher tendency to oscillate synchronously than remote ones. On the contrary, the average percentage (over all sessions) of artificially shadowed remote pairs exhibiting synchronous oscillations was 35.4% compared to 17.2% in the close ones. Similar trend was found when the unshadowed remote pairs were separated according to the estimated distance between electrode tips: 29.9% of pairs at approximate distance of less than 750 µm were significantly synchronized, in comparison with 28.5% of the pairs whose distance was more than 750 µm. We conclude that the synchronous oscillations in the GP of MPTP treated primates are homogenously distributed.

The accelerated failure time model under biased sampling.

Chen (2009, Biometrics) studies the semi-parametric accelerated failure time model for data that are size biased. Chen considers only the uncensored case and uses hazard-based estimation methods originally developed for censored observations. However, for uncensored data, a simple linear regression on the log scale is more natural and provides better estimators.