Parkinsonism-related features of neuronal discharge in primates

Abstract

Parkinson's disease is known to be associated with abnormal electrical spiking activities of basal ganglia neurons, including changes in firing rate, bursting activities and oscillatory firing patterns and changes in entropy. We explored the relative importance of these measures through optimal feature selection and discrimination analysis methods. We identified key characteristics of basal ganglia activity that predicted whether the neurons were recorded in the normal or parkinsonian state. Starting with 29 features extracted from the spike timing of neurons recorded in normal and parkinsonian monkeys in the internal or external segment of the globus pallidus or the subthalamic nucleus (STN), we used a method that incorporates a support vector machine algorithm to find feature combinations that optimally discriminate between the normal and parkinsonian states. Our results demonstrate that the discrimination power of combinations of specific features is higher than that of single features, or of all features combined, and that the most discriminative feature sets differ substantially between basal ganglia structures. Each nucleus or class of neurons in the basal ganglia may react differently to the parkinsonian condition, and the features used to describe this state should be adapted to the neuron type under study. The feature that was overall most predictive of the parkinsonian state in our data set was a high STN intraburst frequency. Interestingly, this feature was not correlated with parameters describing oscillatory firing properties in recordings made in the normal condition but was significantly correlated with spectral power in specific frequency bands in recordings from the parkinsonian state (specifically with power in the 8-13 Hz band).

Keywords: external pallidal segment; feature; internal pallidal segment; subthalamic nucleus; support vector machine.

Fig. 1.

Pearson correlation between features in normal (left) and parkinsonian (right) states for external pallidal segment (GPe, top), internal pallidal segment (GPi, middle), and subthalamic nucleus (STN, bottom). See Table 1 for definitions for features 1–29. The strengths of the correlation between features on the x-axis with those on the y-axis are color-coded.

Fig. 2.

Proportion of GPe data samples correctly identified as originating from either the normal or the parkinsonian state with radial basis function (RBF) support vector machine (SVM) discrimination for single features (bars) and with features cumulatively incorporated from best to worst single feature performance (line) (A); with features cumulatively incorporated by F score ranking (highest to lowest F score) (B); with features cumulatively incorporated by relevance and redundancy ranking (C); and for features cumulatively incorporated by adding the 2 features that increased performance the most at each step (D). The GPe results were chosen as the representative set since they yielded the middle discrimination performance. Results for GPi and STN are similar in appearance.

Fig. 3.

A: RBF SVM discrimination performance using single features (shown for features 1–29). Discrimination performance is shown for features 1–29 for GPe, GPi, and STN. B: summary of RBF SVM discrimination performance for all feature selection methods, applied to data from the GPe, GPi, or STN.

Fig. 4.

Scatterplot of the 2 best features for all cells, GPe (A), GPi (B), and STN (C). Linear discrimination analysis (LDA) of the 2-dimensional data yielded 76% for GPe, 71% for GPi, and 82% for the STN data (dashed lines show LDA discrimination boundaries). MPTP, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine.

Fig. 5.

Scatterplots with lines showing least-squares fits to baseline (Control) and parkinsonian state (MPTP) data from the STN for median intraburst frequency vs. 30–100 Hz (A) and median intraburst frequency vs. 3–8 Hz (B). Note that the slopes of the least-squares fit lines for the data from the parkinsonian state show a negative correlation between intraburst frequency and the 30–100 Hz power in A and a positive correlation between intraburst frequency and the 3–8 Hz power in B.