Transfer Entropy as a Measure of Brain Connectivity: A Critical Analysis With the Help of Neural Mass Models

Knowledge of brain connectivity is an important aspect of modern neuroscience, to understand how the brain realizes its functions. In this work, neural mass models including four groups of excitatory and inhibitory neurons are used to estimate the connectivity among three cortical regions of interests (ROIs) during a foot-movement task. Real data were obtained via high-resolution scalp EEGs on two populations: healthy volunteers and tetraplegic patients. A 3-shell Boundary Element Model of the head was used to estimate the cortical current density and to derive cortical EEGs in the three ROIs. The model assumes that each ROI can generate an intrinsic rhythm in the beta range, and receives rhythms in the alpha and gamma ranges from other two regions. Connectivity strengths among the ROIs were estimated by means of an original genetic algorithm that tries to minimize several cost functions of the difference between real and model power spectral densities. Results show that the stronger connections are those from the cingulate cortex to the primary and supplementary motor areas, thus emphasizing the pivotal role played by theCMA_Lduring the task. Tetraplegic patients exhibit higher connectivity strength on average, with significant statistical differences in some connections. The results are commented and virtues and limitations of the proposed method discussed.

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Assessment of Anesthesia Depth Using Effective Brain Connectivity Based on Transfer Entropy on EEG Signal

Basic and Clinical Neuroscience Journal ◽

10.32598/bcn.12.2.2034.2 ◽

2021 ◽

Vol 12 (2) ◽

pp. 269-280

Author(s):

Neda Sanjari ◽

◽

Ahmad Shalbaf ◽

Reza Shalbaf ◽

Jamie Sleigh ◽

...

Keyword(s):

Brain Connectivity ◽

Effective Connectivity ◽

Doa Estimation ◽

Brain Regions ◽

Transfer Entropy ◽

Effect Site ◽

Anesthetic Drugs ◽

Key Factor ◽

The Relationship ◽

Better Than

Introduction: Ensuring an adequate Depth of Anesthesia (DOA) during surgery is essential for anesthesiologists. Since the effect of anesthetic drugs is on the central nervous system, brain signals such as Electroencephalogram (EEG) can be used for DOA estimation. Anesthesia can interfere among brain regions, so the relationship among different areas can be a key factor in the anesthetic process. Methods: In this paper, by combining the Wiener causality concept and the conditional mutual information, a nonlinear effective connectivity measure called Transfer Entropy (TE) is presented to describe the relationship between EEG signals at frontal and temporal regions from eight volunteers in three anesthetic states (awake, unconscious and recovery). This index is also compared with Granger causality and partial directional coherence methods as common effective connectivity indexes. Results: Based on a statistical analysis of the probability predictive value and Kruskal-Wallis statistical method, TE can effectively fallow the effect-site concentration of propofol and distinguish the anesthetic states well, and perform better than the other effective connectivity indexes. This index is also better than Bispectral Index (BIS) as commercial DOA monitor because of the faster response and higher correlation with the drug concentration effect-site, less irregularity in the unconscious state and better ability to distinguish three states of anesthestesia. Conclusion: TE index is a confident indicator for designing a new monitoring system of the two EEG channels for DOA estimation.

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Modern Methods for Interrogating the Human Connectome

Journal of the International Neuropsychological Society ◽

10.1017/s1355617716000060 ◽

2016 ◽

Vol 22 (2) ◽

pp. 105-119 ◽

Cited By ~ 14

Author(s):

Mark J. Lowe ◽

Ken E. Sakaie ◽

Erik B. Beall ◽

Vince D. Calhoun ◽

David A. Bridwell ◽

...

Keyword(s):

Resting State ◽

Brain Function ◽

Critical Analysis ◽

Diffusion Mri ◽

Brain Connectivity ◽

Structural Connectivity ◽

Scientific Methods ◽

Modern Methods ◽

Analytical Approaches ◽

Neuroimaging Techniques

AbstractObjectives: Connectionist theories of brain function took hold with the seminal contributions of Norman Geschwind a half century ago. Modern neuroimaging techniques have expanded the scientific interest in the study of brain connectivity to include the intact as well as disordered brain. Methods: In this review, we describe the most common techniques used to measure functional and structural connectivity, including resting state functional MRI, diffusion MRI, and electroencephalography and magnetoencephalography coherence. We also review the most common analytical approaches used for examining brain interconnectivity associated with these various imaging methods. Results: This review presents a critical analysis of the assumptions, as well as methodological limitations, of each imaging and analysis approach. Conclusions: The overall goal of this review is to provide the reader with an introduction to evaluating the scientific methods underlying investigations that probe the human connectome. (JINS, 2016, 22, 105–119)

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