Analysis of the aperiodic component in the mouse neocortex

Abstract

Neuronal activity that underlies the conscious and unconscious aspects of animal life can manifest and be measured in different ways. The understanding of what is happening in the brain is a paramount objective of neuroscience. With the increasing availability of data and better analysis tools, we are seeing this objective becoming closer to our reach. The electrical activity recorded from the brain can display oscillations at specific frequencies in conjunction with physiological or behavioral states. These periodic components have been associated to animal (including human) behavior and even used to diagnose physiological abnormalities. In more recent years, the notion that only periodic components provide a view into brain activity has been somewhat challenged. Brain signals can also show changes in wider ranges of the spectrum, not linked to any periodic process. This aperiodic component has been already associated to changes in age, but recent studies have begun to show how they could provide a window to more physiological phenomena occurring in the brain. In this work we investigate how two different types of physiological changes are reflected by changes in the aperiodic component. To that end we analysed data recorded in the primary sensory and motor cortex of mice. To analyze the aperiodic component changes, we used a novel tool that extracts it from a signal, separating it from the periodic components. In our first study, we observed that sensory stimulation correlates with an increase of the aperiodic component in the sensory cortex. In our second analysis we focused on changes occurring under the effect of a receptor blocking drug applied to the primary sensory cortex. Within the area affected by the drug, we observed a decrease in the aperiodic offset and a decrease in correlation of the aperiodic components extracted at different layers. In two out of three mice we also observed this change in the primary motor cortex. These results help to develop our understanding of the mechanisms underlying the aperiodic contributions to the brain’s recorded activity. At the same time, they could potentially enable the use of metrics based on aperiodic activity as a diagnostic tool for mental conditions in health and disease.