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

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.

Microglia inhibition rescues developmental hypofrontality in a mouse model of mental illness

“…1a). In line with our previous findings 5,20,26,27 , the first patterns of network activity of the neonatal PL in all investigated control and dual-hit GE mice were discontinuous, i.e. spindle-shaped oscillations switching between theta and beta-gamma frequency components alternated with long periods of network silence (Fig.…”

“…The detection of discontinuous patterns of activity in the neonatal PL were performed using a modified version of the previously developed algorithm for unsupervised analysis of neonatal oscillations 26 . Briefly, deflections of the root mean square of band-pass filtered signals (1–100 Hz) exceeding a variance-depending threshold were considered as network oscillations.…”

Developmental dysfunction of prefrontal–hippocampal networks in mouse models of mental illness

“…Discontinuous oscillatory events were detected using a previously developed unsupervised algorithm (Cichon, Denker, Grun, & Hanganu‐Opatz, ). Briefly, deflections of the root‐mean‐square of band‐pass (3–100 Hz) filtered signals exceeding a variance‐depending threshold were assigned as network oscillations.…”

Identifying neurophysiological features associated with anesthetic state in newborn mice and humans

“…Discontinuous active periods were detected with a modified version of a previously developed algorithm for unsupervised analysis of neonatal oscillations ( 55 ). Briefly, deflections of the root mean square of band-pass filtered signals (1–100 Hz) exceeding a variance-depending threshold were considered as network oscillations.…”