In silico Hierarchical Clustering of Neuronal Populations in the Rat Ventral Tegmental Area Based on Extracellular Electrophysiological Properties

The ventral tegmental area (VTA) is a heterogeneous brain region, containing different neuronal populations. During in vivo recordings, electrophysiological characteristics are classically used to distinguish the different populations. However, the VTA is also considered as a region harboring neurons with heterogeneous properties. In the present study, we aimed to classify VTA neurons using in silico approaches, in an attempt to determine if homogeneous populations could be extracted. Thus, we recorded 291 VTA neurons during in vivo extracellular recordings in anesthetized rats. Initially, 22 neurons with high firing rates (>10 Hz) and short-lasting action potentials (AP) were considered as a separate subpopulation, in light of previous studies. To segregate the remaining 269 neurons, presumably dopaminergic (DA), we performed in silico analyses, using a combination of different electrophysiological parameters. These parameters included: (1) firing rate; (2) firing rate coefficient of variation (CV); (3) percentage of spikes in a burst; (4) AP duration; (5) Δt1 duration (i.e., time from initiation of depolarization until end of repolarization); and (6) presence of a notched AP waveform. Unsupervised hierarchical clustering revealed two neuronal populations that differed in their bursting activities. The largest population presented low bursting activities (<17.5% of total spikes in burst), while the remaining neurons presented higher bursting activities (>17.5%). Within non-high-firing neurons, a large heterogeneity was noted concerning AP characteristics. In conclusion, this analysis based on conventional electrophysiological criteria clustered two subpopulations of putative DA VTA neurons that are distinguishable by their firing patterns (firing rates and bursting activities) but not their AP properties.