Recurrence eigenvalues of movements from brain signals

Tuan D Pham

doi:10.1186/s40708-021-00143-3

Abstract

The ability to characterize muscle activities or skilled movements controlled by signals from neurons in the motor cortex of the brain has many useful implications, ranging from biomedical perspectives to brain–computer interfaces. This paper presents the method of recurrence eigenvalues for differentiating moving patterns in non-mammalian and human models. The non-mammalian models of Caenorhabditis elegans have been studied for gaining insights into behavioral genetics and discovery of human disease genes. Systematic probing of the movement of these worms is known to be useful for these purposes. Study of dynamics of normal and mutant worms is important in behavioral genetic and neuroscience. However, methods for quantifying complexity of worm movement using time series are still not well explored. Neurodegenerative diseases adversely affect gait and mobility. There is a need to accurately quantify gait dynamics of these diseases and differentiate them from the healthy control to better understand their pathophysiology that may lead to more effective therapeutic interventions. This paper attempts to explore the potential application of the method for determining the largest eigenvalues of convolutional fuzzy recurrence plots of time series for measuring the complexity of moving patterns of Caenorhabditis elegans and neurodegenerative disease subjects. Results obtained from analyses demonstrate that the largest recurrence eigenvalues can differentiate phenotypes of behavioral dynamics between wild type and mutant strains of Caenorhabditis elegans; and walking patterns among healthy control subjects and patients with Parkinson’s disease, Huntington’s disease, or amyotrophic lateral sclerosis.

Highlights

Caenorhabditis elegans is a nematode or roundworm about 1 mm in length [1] and commonly used as a model organism in the study of genetics because of its powerful genetics and fully characterized simple nervous system [2, 3]
3.1 Eigenworm behavioral phenotypes To determine the largest eigenvalues from the time series of the C. elegans behavioral phenotypes presented in the foregoing section, the embedding dimension m = 4 was selected based on the identification of the four principal dimensions of the eigenworms, time delay τ = 1, and number of clusters c = 3, 5, and 7
To compute the largest eigenvalues, the size of the final cFRPs for the five C. elegans classes was set as n = 2, that is a 2 × 2 matrix, to allow the deepest feature extraction based on the deep-learning approach

Summary

Introduction

Caenorhabditis elegans is a nematode or roundworm about 1 mm in length [1] and commonly used as a model organism in the study of genetics because of its powerful genetics and fully characterized simple nervous system [2, 3]. The nervous system of the C. elegans hermaphrodite consists of 302 neurons that form 118 morphologically distinct neuron classes [4] These neurons activate many distinct stimulus modalities and combine them to produce distinct patterns of behavior [5, 6]. It is known that behavior is a visual display of sensitive and integrative information of nervous system function and plays as an effective measure for evaluating the effects of mutation or efficacy of drug treatment for animals [7]. Such knowledge is expected to provide potential alternatives for better diagnosis and therapeutics of human disease [8]

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Conclusion