Performance Baseline of Phase Transfer Entropy Methods for Detecting Animal Brain Area Interactions

<b><i>Objective:</i></b> Phase transfer entropy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><msup><mi>E</mi><mi>θ</mi></m...

Full description

Bibliographic Details
Main Authors: Jun-Yao Zhu, Meng-Meng Li, Zhi-Heng Zhang, Gang Liu, Hong Wan
Format: Article
Language:English
Published: MDPI AG 2023-06-01
Series:Entropy
Subjects:
Online Access:https://www.mdpi.com/1099-4300/25/7/994
Description
Summary:<b><i>Objective:</i></b> Phase transfer entropy (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><msup><mi>E</mi><mi>θ</mi></msup></mrow></semantics></math></inline-formula>) methods perform well in animal sensory–spatial associative learning. However, their advantages and disadvantages remain unclear, constraining their usage. <b><i>Method:</i></b> This paper proposes the performance baseline of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><msup><mi>E</mi><mi>θ</mi></msup></mrow></semantics></math></inline-formula> methods. Specifically, four <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><msup><mi>E</mi><mi>θ</mi></msup></mrow></semantics></math></inline-formula> methods are applied to the simulated signals generated by a neural mass model and the actual neural data from ferrets with known interaction properties to investigate the accuracy, stability, and computational complexity of the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><msup><mi>E</mi><mi>θ</mi></msup></mrow></semantics></math></inline-formula> methods in identifying the directional coupling. Then, the most suitable method is selected based on the performance baseline and used on the local field potential recorded from pigeons to detect the interaction between the hippocampus (Hp) and nidopallium caudolaterale (NCL) in visual–spatial associative learning. <b><i>Results:</i></b> (1) This paper obtains a performance baseline table that contains the most suitable method for different scenarios. (2) The <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><msup><mi>E</mi><mi>θ</mi></msup></mrow></semantics></math></inline-formula> method identifies an information flow preferentially from Hp to NCL of pigeons at the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">θ</mi></semantics></math></inline-formula> band (4–12 Hz) in visual–spatial associative learning. <b><i>Significance:</i></b> These outcomes provide a reference for the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>T</mi><msup><mi>E</mi><mi>θ</mi></msup></mrow></semantics></math></inline-formula> methods in detecting the interactions between brain areas.
ISSN:1099-4300