Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recording
Summary
In this paper we introduce “Spatially Masked Regression” (SMR) as a reconstruction-based framework for separating local redundancy from distributed predictability in electrophysiological recordings. Instead of asking whether pairs of electrodes are connected, SMR asks how well one channel can be reconstructed from the rest of the array while progressively masking its local neighborhood. Applied to scalp EEG and intracranial EEG, the results show that nearby electrodes carry the strongest predictive information, but substantial structure remains even after local channels are excluded. The contrast between EEG and iEEG further reveals how measurement geometry shapes reconstructability: EEG is more spatially redundant and transferable across subjects, whereas iEEG is more focal and individualized. Overall, the work reframes “local field” signals as locally anchored but distributedly embedded observations of neural dynamics.
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@article{memarDehghani2026spatiallymaskedregressionreveals,
title={Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings},
author={Maryam Ostadsharif Memar and Nima Dehghani},
year={2026},
eprint={2606.11415},
archivePrefix={arXiv},
primaryClass={q-bio.NC},
url={https://arxiv.org/abs/2606.11415},
}
Code & Data
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Abstract
Neural recordings are often interpreted as local measurements, yet the signal observed at any one sensor can also reflect structured activity distributed across the broader network. This raises a basic question: to what extent does an electrode’s signal reflect local versus distributed information in the underlying network? More specifically, how much of an electrode’s signal is carried by its immediate neighborhood, and how much is embedded more broadly across the array? We address this question with a Spatially Masked Regression (SMR) framework that reconstructs each electrode’s time series from the remaining electrodes while explicitly excluding a configurable neighborhood around the target. By progressively increasing this mask, spatial locality becomes an experimental control for quantifying how much predictive information survives after nearby channels are withheld. We apply SMR to multi-day intracranial EEG (iEEG) recordings with heterogeneous electrode coverage and to scalp EEG recordings with standardized montages over sensorimotor cortex. Using distance correlation between original and reconstructed signals, we find strong within-subject reconstruction in both modalities, substantial residual predictability even when local neighbors are excluded, and markedly stronger cross-subject transfer in EEG than in iEEG. Masking analyses show that nearby electrodes contribute strongly to reconstruction but do not account for all of it, indicating that individual channels reflect both local redundancy and broader distributed structure. Surrogate-data controls that preserve selected marginal or spectral properties while disrupting phase structure or temporal ordering substantially reduce performance, supporting the conclusion that SMR depends on structured temporal and cross-channel organization rather than on marginal statistics alone. Together, these results position SMR as an interpretable framework for quantifying the balance between local and distributed contributions in neural recordings.
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