Exploring Brain Functional Connectivity in Hand Motion and Motor Imagery through fNIRS Signals: A Graph Theory Approach

Mahsan  Hajihosseini; Omid  Asadi; Sima  Shirzadi; Zahra  Einalou; Mehrdad  Dadgostar

doi:10.18502/fbt.v13i2.21926

Mahsan Hajihosseini Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
Omid Asadi Department of Biomedical Engineering, North Tehran Branch, Islamic Azad University, Tehran, Iran
Sima Shirzadi Department of Biomedical Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
Zahra Einalou Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States
Mehrdad Dadgostar Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, United States

DOI: https://doi.org/10.18502/fbt.v13i2.21926

Keywords: Functional Near-Infrared Spectroscopy; Motor Imagery; Graph Theory; Small World Network; Functional Connectivity.

Abstract

Purpose: Functional Near-Infrared Spectroscopy (fNIRS) is a valuable and cost-effective neuroimaging technique, particularly in the context of sensorimotor tasks and its applications in brain-computer interface (BCI) research. While numerous studies have explored brain functional connectivity during sensorimotor tasks, they have often primarily focused on electrical brain activity. In this study, we present a signal processing algorithm utilizing fNIRS-HbO2 data to identify active brain regions involved in both actual motor execution and motor imagery within a motor imagery task.

Materials and Methods: Our algorithm incorporates several key steps: firstly, the application of wavelet transform to eliminate noise and preprocess the fNIRS signal. Subsequently, we employ correlation analysis to extract functional connectivity matrices for both motor execution and motor imagery. Finally, we compute global efficiency (GE) values, a significant graph theory parameter, to analyze network properties. Additionally, we investigate the small-world network characteristics within the connectivity matrices and classify motor execution and motor imagery using a t-test.

Results: To gather data, we recorded 20-channel fNIRS signals, measuring changes in HbO2 concentration in the motor cortex, from 12 healthy participants at a sampling frequency of 10 Hz. Our findings not only confirm the presence of small-world network properties in the correlation matrices but also reveal that meaningful classification between motor execution and motor imagery of both right and left hands occurs when we select the top 40% of the strongest connections between channels. Furthermore, the results indicate a tendency towards stronger connectivity between channels in the left hemisphere.

Conclusion: In summary, our study demonstrates that brain networks are organized as small-world networks during sensorimotor tasks and underscores the prominent role of the dominant hemisphere in executing these tasks.