# Detection of latent brain states from spontaneous neural activity in the amygdala

## TL;DR

This study shows that the amygdala's spontaneous neural activity contains information about social context, detectable using machine learning.

## Contribution

The novel use of machine learning to decode social context from spontaneous local field potentials in the amygdala.

## Key findings

- Machine learning can reliably decode social vs. non-social context from spontaneous LFP spectrograms.
- Context-related signals in the amygdala are not specific to particular nuclei.
- Contextual information in the amygdala may arise from shared interoceptive inputs.

## Abstract

The amygdala responds to a large variety of socially and emotionally salient environmental and interoceptive stimuli. The context in which these stimuli occur determines their social and emotional significance. In canonical neurophysiological studies, the fast-paced succession of stimuli and events induce phasic changes in neural activity. During inter-trial intervals, neural activity is expected to return to a stable and featureless level of spontaneous activity, often called baseline. In previous studies we found that context, such as the presence of a social partner, induces brain states that can transcend the fast-paced succession of stimuli and can be recovered from the spontaneous, inter-trial firing rate of neurons. Indeed, the spontaneous firing rates of neurons in the amygdala are different during blocks of gentle grooming touches delivered by a trusted social partner, and during blocks of non-social airflow stimuli delivered by a computer-controlled air valve. Here, we examine local field potentials (LFPs) recorded during periods of spontaneous activity to determine whether information about context can be extracted from these signals. We found that information about social vs. non-social context is present in the local field potential during periods of spontaneous activity between the application of grooming and airflow stimuli, as machine learning techniques can reliably decode context from spectrograms of spontaneous LFPs. No significant differences were detected between the nuclei of the amygdala that receive direct or indirect inputs from areas of the prefrontal cortex known to coordinate flexible, context-dependent behaviors. The lack of nuclear specificity suggests that context-related synaptic inputs arise from a shared source, possibly interoceptive inputs, that signal the physiological state of the body during social and non-social blocks of tactile stimulation.

The amygdala responds to a large array of socially and emotionally salient stimuli, both external (e.g., affective or neutral touch) and internal (e.g., heart rate), and the context in which these stimuli are processed. It thus plays an important role in how social animals like primates interact. Recent work showed that individual cells in the amygdala carry contextual information during the periods between stimuli, characterized by spontaneous baseline activity, i.e., socially significant interactions lead to detectable, persistent changes in activity in the amygdala. Here, we show that using modern machine learning techniques, we can identify, during this period of spontaneous activity, context-related signals not only from the individual neurons but also from the collective activity of large groups of brain cells. As a central task of computational neuroscience is to identify cognitive states and their neural and behavioral correlates, this study hints at how contextual information is encoded in the amygdala and contributes to the growing understanding of the potentials and pitfalls of machine learning methods in neuroscience.