Aperiodicity in mouse CA1 and DG power spectra

Rodent hippocampal power spectra comprise of periodic and aperiodic components. The periodic components (brain rhythms) contain information about the behavioral or cognitive state of the animal. The aperiodic components are rarely studied and their functionality is not well understood, though have shown to be correlated with animal’s age or the excitation-inhibition ratio of the brain region. To study these components in the mouse hippocampus we modified the existing open-source FOOOF toolbox, which was originally optimized for EEG data. First, using simulated data, we show that our modifications decrease the error in assessment of the low frequency periodic components from 3% to 0.1%. Second, using tetrode electrophysiological signals from adult males, we compare the aperiodic activity within mice hippocampal sub-regions, CA1 and dentate gyrus (DG). Our optimization of FOOOF improved the aperiodic assessment errors by about 50% and were critical in making the first assessment of the aperiodic components in these brain regions. Our results show significantly larger aperiodic exponents and knee frequencies in the DG than in CA1, which have been proposed to correlate with lower excitation and shorter timescales. Our work highlights the subtle differences in electrophysiology field potentials between hippocampal sub-regions, and presents the improvements needed in the existing open-source toolbox to be able to see such differences. Significance statement Electrical brain signals comprise of neuronal spiking activity and voltage fluctuations arising from extra-somatic and transmembrane processes, termed local-field potentials (LFP). The LFP comprises of periodic brain rhythms overlaid on aperiodic background. Though spiking activity and brain rhythms have been under investigation for decades, the aperiodicity in these signals was often cast aside as “noise”, due to its ubiquitous nature. Our work estimates the aperiodic nature of rodent hippocampal signals for the first time, which suggests different neuronal timescales between hippocampal sub-regions. Our study will be important to assess changes in periodic and aperiodic parameters with hippocampal cognitive states, such as during hippocampal memory tasks, and sleep states.