Quantifying Cerebellar Signal Detectability in MEG and EEG in Epilepsy Using Anatomically Informed Source Modeling.
bioRxiv : the preprint server for biology – January 14, 2026
Source: PubMed
Summary
Detecting deep brain activity requires more than just close sensors. Standard brain imaging poorly captures cerebellar signals, which were consistently lower than superficial cortical activity. Simply reducing sensor distance did not improve detection. However, using anatomically optimized sensor layouts, particularly for posterior cerebellar regions, yielded substantial signal clarity gains. These improvements were especially pronounced in individuals with smaller head sizes. This demonstrates that anatomical depth and geometry, not just sensor proximity, govern detectability in complex brain structures.
Abstract
The cerebellum is increasingly recognized as a key component of large-scale brain networks implicated in epilepsy, yet its electrophysiological characterization remains limited in noninvasive 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. Cerebellar and cerebral source spaces were constructed using subject-specific anatomical models derived from routine clinical MRI, enabling consistent forward modeling across individuals. 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 simulation, including layouts matched to clinical sensor geometries and layouts optimized for posterior fossa coverage. The effects of source orientation, sensor-source distance, and head size on SNR were systematically investigated. In routine clinical recordings, cerebellar SNR was consistently lower than superficial cortical reference levels, confirming the limited detectability of cerebellar activity with standard SQUID-MEG and EEG. Reducing sensor-source 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 influence 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. Anatomically informed source modeling, combined with flexible and region-specific sensor layouts, enables meaningful improvements in cerebellar SNR that are not achievable with fixed-helmet systems. While directly motivated by epilepsy, this framework advances human brain mapping beyond the cerebrum by providing a principled approach for evaluating MEG and EEG sensitivity in deep and highly folded brain structures.