Multi-Modal Spectral Parametrization Method (MMSPM) for analyzing EEG activity with distinct scaling regimes

Aperiodic neural activity has been the subject of intense research interest lately as it could reflect on the cortical excitation/inhibition ratio, which is suspected to be affected in numerous clinical conditions. This phenomenon is characterized via the aperiodic scaling exponent $\beta$, equal to the spectral slope following log-log transformation of power spectra. Despite recent progress, however, most current methods do not take into consideration the plausible multimodal nature in the power spectra of neurophysiological recordings - i.e., $\beta$ might be different in low- ($\beta_{lo}$) and high-frequency ($\beta_{hi}$) regimes -, especially in case of $|\beta_{lo}|>|\beta_{hi}|$. Here we propose an algorithm, the multi-modal spectral parametrization method (MMSPM) that aims to account for this issue. MMSPM estimates $\beta_{lo}$ and $\beta_{hi}$ separately using a constrained, piece-wise regression technique, and also assesses if they are significantly different or instead the spectrum is indeed unimodal and can be characterized simply with broadband $\beta$. Here we present the MMSPM algorithm and evaluate its performance in silico on simulated power spectra. Then, we use MMSPM on resting-state electroencephalography (EEG) data collected from 19 young, healthy volunteers, as well as on a separate dataset of EEG recordings from 30 schizophrenia patients and 31 healthy controls, and demonstrate that broadband (0.1-100 Hz and 0.5-45 Hz) EEG spectra can indeed present a bimodality pattern with significantly steeper low-range ($<\sim2$ Hz) and flatter high-range scaling regimes (i.e., $|\beta_{lo}|>|\beta_{hi}|$). Clinical relevance: The MMSPM method characterizes aperiodic neural activity in distinct scaling regimes, which can be relevant in numerous pathological conditions such as dementia or schizophrenia.