Unsupervised classification of neocortical activity patterns in neonatal and pre-juvenile rodents.

Nicole B Cichon,Ileana L Hanganu-Opatz,Michael Denker,Sonja Grã¼N

doi:10.3389/fncir.2014.00050

Abstract

Flexible communication within the brain, which relies on oscillatory activity, is not confined to adult neuronal networks. Experimental evidence has documented the presence of discontinuous patterns of oscillatory activity already during early development. Their highly variable spatial and time-frequency organization has been related to region specificity. However, it might be equally due to the absence of unitary criteria for classifying the early activity patterns, since they have been mainly characterized by visual inspection. Therefore, robust and unbiased methods for categorizing these discontinuous oscillations are needed for increasingly complex data sets from different labs. Here, we introduce an unsupervised detection and classification algorithm for the discontinuous activity patterns of rodents during early development. For this, in a first step time windows with discontinuous oscillations vs. epochs of network “silence” were identified. In a second step, the major features of detected events were identified and processed by principal component analysis for deciding on their contribution to the classification of different oscillatory patterns. Finally, these patterns were categorized using an unsupervised cluster algorithm. The results were validated on manually characterized neonatal spindle bursts (SB), which ubiquitously entrain neocortical areas of rats and mice, and prelimbic nested gamma spindle bursts (NG). Moreover, the algorithm led to satisfactory results for oscillatory events that, due to increased similarity of their features, were more difficult to classify, e.g., during the pre-juvenile developmental period. Based on a linear classification, the optimal number of features to consider increased with the difficulty of detection. This algorithm allows the comparison of neonatal and pre-juvenile oscillatory patterns in their spatial and temporal organization. It might represent a first step for the unbiased elucidation of activity patterns during development.

Highlights

Neuronal oscillations are a ubiquitous and robust phenomenon that is observed in various measures of brain activity
By inspecting the feature decomposition of these four principal components (PCs), we found that PC1 (Figure 3A) is homogenously composed of all features, indicating that all extracted features appear to find a linear bisection of the data cloud
Two patterns of activity that correspond to spindle bursts (SB) and nested gamma spindle bursts (NG) have been classified in the prefrontal cortex (PFC) of neonatal mice (Figure 5B) and confirmed by the calculated reliability and yield as well as by the bimodal distribution observed in all feature histograms (Figure 5D)

Summary

Introduction

Neuronal oscillations are a ubiquitous and robust phenomenon that is observed in various measures of brain activity. While the function and major underlying mechanisms of oscillatory activity within adult neuronal networks have been largely investigated and partially elucidated, much less is known about the activity patterns during neocortical maturation Both human and animal research showed that coupling of neuronal networks in oscillatory rhythms emerges early during brain development (Anderson et al, 1985; Khazipov et al, 2004; Hanganu-Opatz, 2010). In the primary sensory cortices with a columnar organization, spindle-shaped burst oscillations termed as SB represent the dominant activity pattern observed in the local field potential (LFP) and alternate with “silent” inter-burst intervals (Khazipov et al, 2004; Hanganu et al, 2006) They result from intracortical activation, Frontiers in Neural Circuits www.frontiersin.org

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