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Related Experiment Video

Updated: Jun 25, 2026

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice
07:10

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Published on: July 1, 2018

Spatially Masked Regression Reveals Local and Distributed Predictability in Electrophysiological Recordings.

Maryam Ostadsharif Memar1, Nima Dehghani2

  • 1Department of Electrical and Computer Engineering, IUT.

Arxiv
|June 24, 2026
PubMed
Summary

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Open Data In Neurophysiology: Advancements, Solutions & Challenges.

eNeuro·2025

Neural recordings capture both local and distributed network activity. A new Spatially Masked Regression (SMR) framework quantifies this balance, revealing significant information beyond immediate neighbors.

Area of Science:

  • Neuroscience
  • Signal Processing
  • Computational Biology

Background:

  • Neural recordings are often treated as purely local measurements.
  • However, signals at individual sensors can reflect complex, distributed network activity.
  • Understanding the spatial scale of neural information is crucial for interpreting brain activity.

Purpose of the Study:

  • To develop a method for quantifying the balance between local and distributed information in neural recordings.
  • To determine how much of an electrode's signal reflects its immediate neighborhood versus broader network activity.
  • To assess the generalizability of this framework across different recording modalities.

Main Methods:

  • Introduction of the Spatially Masked Regression (SMR) framework.
Keywords:
cross-subject generalizationdistance correlationdistributed predictabilityfunctional connectivitylocal maskingneural signal reconstructionspatial redundancyvolume conduction

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Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Related Experiment Videos

Last Updated: Jun 25, 2026

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice
07:10

Recording Spatially Restricted Oscillations in the Hippocampus of Behaving Mice

Published on: July 1, 2018

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

  • SMR reconstructs an electrode's signal from others, progressively masking local neighbors.
  • Application of SMR to intracranial EEG (iEEG) and scalp EEG data.

    • Main Results:

    • Strong within-subject signal reconstruction was observed in both iEEG and EEG.
    • Significant predictive information remained even after excluding local neighbors.
    • Cross-subject information transfer was notably higher in EEG compared to iEEG.
    • SMR's performance relied on structured temporal and cross-channel organization, not just marginal statistics.

    Conclusions:

    • Individual neural recording channels contain both local and distributed information.
    • SMR provides an interpretable method to quantify the spatial scale of neural information.
    • The findings highlight differences in information distribution between iEEG and EEG.