Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data

Abstract

The analysis of cell type-specific activity patterns during behaviors is important for better understanding of how neural circuits generate cognition, but has not been well explored from in vivo neurophysiological datasets. Here, we describe a computational approach to uncover distinct cell subpopulations from in vivo neural spike datasets. This method, termed “inter-spike-interval classification-analysis” (ISICA), is comprised of four major steps: spike pattern feature-extraction, pre-clustering analysis, clustering classification, and unbiased classification-dimensionality selection. By using two key features of spike dynamic - namely, gamma distribution shape factors and a coefficient of variation of inter-spike interval - we show that this ISICA method provides invariant classification for dopaminergic neurons or CA1 pyramidal cell subtypes regardless of the brain states from which spike data were collected. Moreover, we show that these ISICA-classified neuron subtypes underlie distinct physiological functions. We demonstrate that the uncovered dopaminergic neuron subtypes encoded distinct aspects of fearful experiences such as valence or value, whereas distinct hippocampal CA1 pyramidal cells responded differentially to ketamine-induced anesthesia. This ISICA method should be useful to better data mining of large-scale in vivo neural datasets, leading to novel insights into circuit dynamics associated with cognitions.

Dopamine Rebound-Excitation Theory: Putting Brakes on PTSD

“…For example, multi-phasic temporal dynamics in DAergic neurons have been reported in studies using unexpected (unconditioned) aversive stimuli ( 40 , 53 ). Consistent with such complex dynamics, we recently described computational classifications of DAergic subtypes based on their distinct inter-spike-interval dynamics ( 54 ). Such classifications were further verified by optogenetic methods ( 54 ).…”

“…Consistent with such complex dynamics, we recently described computational classifications of DAergic subtypes based on their distinct inter-spike-interval dynamics ( 54 ). Such classifications were further verified by optogenetic methods ( 54 ). In addition, downstream targets receiving DAergic projections can send feedback projections to modulate DA activities ( 55 – 57 ).…”

“…On the other hand, large-scale in vivo electrophysiological recordings in freely behaving laboratory animals now allows single neural unit activity to be accessed in real time with high temporal and spatial resolution ( 65 ). In addition, reliable identification of DAergic neuron subtypes can be achieved by using optogenetics and computational analysis ( 54 ). Therefore, initial efforts to test the Rebound-Excitation Theory may be fruitful in animal models.…”

Spike-timing pattern operates as gamma-distribution across cell types, regions and animal species and is essential for naturally-occurring cognitive states

“…To further evaluate the general utility of the gamma distribution model, we then analyzed neuromodulatory projection neurons, such as dopaminergic (DA) neurons recorded from the mouse VTA region. The DA neuron dataset was collected from freelybehaving mice during the awake period as we have previously described [27,38]. DA The line in the plot denotes the linear regression between D and mean firing rates.…”

“…It has been shown that in spite of "channel noise" [9], deterministic neuronal models can accurately predict stimulus responses [15].The irregularity of inter-spike intervals has long been suggested to be a fundamental process of cortical communication [6,[16][17][18][19][20], and it was often modeled as a Poisson-like random process. Intriguingly, a growing number of observations have shown that the neuronal spike pattern in many cortical areas seems to be inconsistent with the Poisson process, indicating that the Poisson process can either under-or overestimate the variabilities of the neuronal spike patterns [21][22][23][24][25][26][27][28]. Despite the importance in regulating synaptic plasticity and neural coding, spike-time patterns across a wide range of brain regions and animal species have not been examined or compared in a systematic manner.…”