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Updated: Apr 28, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Canonical information flow decomposition among neural structure subsets.

Daniel Y Takahashi¹, Luiz A Baccalá², Koichi Sameshima³

¹Psychology Department, Neuroscience Institute, Princeton University Princeton, NJ, USA.

Frontiers in Neuroinformatics

|June 10, 2014

Summary

This summary is machine-generated.

We introduce canonical partial directed coherence (cPDC) and canonical directed coherence (cDC) to analyze complex information flow between brain regions. These methods improve upon existing techniques by revealing dominant interaction modes in the frequency domain.

Keywords:

canonical decomposition directed connectivity measures frequency domain generalized coherence information flow

Related Experiment Videos

Last Updated: Apr 28, 2026

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Neuroscience
Time Series Analysis
Information Theory

Background:

Partial Directed Coherence (PDC) and Directed Coherence (DC) analyze information flow in time series.
Generalized versions (bPDC/bDC) extend this to subsets of time series, crucial for neuroscience.
Current methods struggle with irrelevant or opposing interactions within subsets, complicating interpretation.

Purpose of the Study:

To develop a novel method (cPDC/cDC) for analyzing directed information flow between vector subsets of time series.
To address limitations of bPDC/bDC in handling complex interactions within subsets.
To reveal dominant frequency domain modes of interaction between physiological regions of interest.

Main Methods:

Canonical decomposition applied to time series analysis.
Development of canonical partial directed coherence (cPDC) and canonical directed coherence (cDC).
Demonstration of the relationship between bPDC/bDC and cPDC/cDC, including mutual information rate interpretations.

Main Results:

cPDC/cDC effectively reveal main frequency domain modes of interaction between vector subsets.
The proposed methods offer improved interpretability compared to bPDC/bDC for complex systems.
Numerical examples and real data analysis validate the efficacy of cPDC/cDC.

Conclusions:

Canonical decomposition provides a powerful framework for analyzing frequency-domain information flow between multivariate time series.
cPDC/cDC offer a more nuanced understanding of directed interactions in complex systems like the brain.
This work presents the first canonical decomposition of frequency-domain information flow.