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Measuring spectrally-resolved information transfer.

Edoardo Pinzuti1, Patricia Wollstadt2, Aaron Gutknecht3

  • 1Leibniz Institute for Resilience Research, Mainz, Germany.

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|December 28, 2020
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Summary
This summary is machine-generated.

This study introduces a new method to track information flow across different brain frequencies. The algorithm reveals complex, frequency-specific communication patterns in human and animal brain activity.

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Area of Science:

  • Neuroscience
  • Information Theory
  • Computational Neuroscience

Background:

  • Information transfer is crucial for distributed computation in natural systems.
  • Understanding frequency-specific information flow is essential for deciphering neural computation.
  • Existing methods for analyzing frequency-resolved information transfer face limitations.

Purpose of the Study:

  • To develop a novel algorithm for identifying frequency-specific information sending and receiving nodes in a network.
  • To address limitations of traditional filtering methods in information-theoretic analyses.
  • To investigate complex spectral configurations of information transmission in biological systems.

Main Methods:

  • Utilized the invertible maximum overlap discrete wavelet transform (MODWT) for surrogate data generation.
  • Computed transfer entropy without filtering original signals to avoid phase shifts and ineffectiveness.
  • Evaluated the algorithm on simulated data, human magnetoencephalography (MEG), and ferret local field potential (LFP) recordings.

Main Results:

  • Demonstrated top-down information flow in human temporal cortex from high frequencies (>100Hz) to both high and 20Hz frequencies.
  • Revealed prefrontal cortex sending low-frequency (4-8Hz) information to early visual cortex (V1) in ferrets.
  • Identified V1 receiving information at high frequencies (>125Hz) in the ferret model.

Conclusions:

  • The developed algorithm effectively identifies frequency-specific information flow in neural networks.
  • The findings highlight complex, previously undescribed spectral patterns of cortical information transmission.
  • Frequency-resolved information transfer analysis is a partial information decomposition problem requiring further resolution.