Quantifying cerebellar signal detectability in MEG and EEG in epilepsy using anatomically informed source modeling

The cerebellum is increasingly recognized as a key component of large-scale brain networks implicated in epilepsy, yet its electrophysiological characterization remains limited in non-invasive recordings. This limitation arises from the cerebellum's depth, complex folding, and unfavorable source orientations, which challenge conventional magnetoencephalography (MEG) and electroencephalography (EEG). Here, we quantitatively characterize cerebellar signal detectability across modalities and sensor configurations using anatomically informed source modeling at the population level. We analyzed clinical MEG and EEG recordings from a large cohort of patients with epilepsy undergoing presurgical evaluation (n = 54), selected from a larger consecutive clinical population. Cerebellar and cerebral source spaces were constructed using subject-specific anatomical models derived from routine clinical MRI, enabling consistent forward modeling across individuals. The signal-to-noise ratio (SNR) was estimated at individual source locations and summarized at the regional level. In addition to clinical superconducting quantum interference device (SQUID)-MEG and EEG, multiple on-scalp optically pumped magnetometer (OPM) configurations were evaluated through simulations, including layouts matched to clinical sensor geometries and layouts optimized for posterior fossa coverage. The effects of source orientation, source-to-sensor distance, and head size on SNR were systematically investigated. In routine clinical recordings, cerebellar SNR was consistently lower than superficial cortical reference levels, confirming challenges in detecting cerebellar activity using standard SQUID-MEG and EEG. Reducing source-to-sensor distance by placing OPMs at SQUID-equivalent locations, i.e., projecting SQUID sensor locations to the scalp, did not improve cerebellar SNR, indicating that proximity alone is insufficient for better detectability of deeper sources. In contrast, cerebellar-optimized OPM layouts produced substantial SNR gains in posterior cerebellar regions. The effects of source orientation influenced SNR differences between OPM and EEG (under identical sensor/electrode coverage) but were secondary to depth- and geometry-related constraints. Mediation analysis further demonstrated that relative sensor distance significantly mediated OPM-related advantages in posterior cerebellar regions, particularly in individuals with smaller head sizes. These findings demonstrate that cerebellar signal detectability is governed primarily by anatomical depth and geometry rather than sensor proximity alone. By combining anatomically informed source modeling with flexible, region-specific sensor layouts, this work provides a principled framework for evaluating and improving MEG and EEG sensitivity to cerebellar activity, with implications extending beyond epilepsy to non-invasive mapping of deep and highly folded brain structures.