Involvement of the Rostromedial Prefrontal Cortex in Human-Robot Interaction: fNIRS Evidence From a Robot-Assisted Motor Task

Abstract

Assistive exoskeleton robots are being widely applied in neurorehabilitation to improve upper-limb motor and somatosensory functions. During robot-assisted exercises, the central nervous system appears to highly attend to external information-processing (IP) to efficiently interact with robotic assistance. However, the neural mechanisms underlying this process remain unclear. The rostromedial prefrontal cortex (rmPFC) may be the core of the executive resource allocation that generates biases in the allocation of processing resources toward an external IP according to current behavioral demands. Here, we used functional near-infrared spectroscopy to investigate the cortical activation associated with executive resource allocation during a robot-assisted motor task. During data acquisition, participants performed a right-arm motor task using elbow flexion-extension movements in three different loading conditions: robotic assistive loading (ROB), resistive loading (RES), and non-loading (NON). Participants were asked to strive for kinematic consistency in their movements. A one-way repeated measures analysis of variance and general linear model-based methods were employed to examine task-related activity. We demonstrated that hemodynamic responses in the ventral and dorsal rmPFC were higher during ROB than during NON. Moreover, greater hemodynamic responses in the ventral rmPFC were observed during ROB than during RES. Increased activation in ventral and dorsal rmPFC subregions may be involved in the executive resource allocation that prioritizes external IP during human-robot interactions. In conclusion, these findings provide novel insights regarding the involvement of executive control during a robot-assisted motor task.

A study of dynamic hand orthosis combined with unilateral task-oriented training in subacute stroke: A functional near-infrared spectroscopy case series

“…It has been reported that head motion can affect the results of cerebral blood flow measured by fNIRS. In recent research, dense fNIRS probes were set to obtain more precise information about task-related neural signals, and motion artifacts were corrected using a technique based on moving standard deviation and spline interpolation or an applied use of smartphones ( 61 , 62 ). In our design, the participants' head movements were strictly suppressed during the whole task using a chinrest, and the light probes were fixed through an integral plastic sheet to deal with motion artifacts.…”

“…At present, it is generally admitted that the NIRS-Hb signal mainly reflects task-related hemodynamic changes in the gray matter, so care must be taken when comparing the NIRS-Hb signal with the extracranial blood flow. In some studies, short-separation channels that measured hemodynamic signals from extracranial tissues were used; the blood flow difference between long and short channels could distinguish regional cortical blood flow or scalp blood flow ( 62 ).In verbal fluency tasks or cognitive tasks, skin blood flow must be considered because that would induce sympathetic hyperactivation. In our study, the influence of this factor was not distinguished.…”

Neural Mechanisms of Neuro-Rehabilitation Using Transcranial Direct Current Stimulation (tDCS) over the Front-Polar Area

“…The role played by the most anterior portion of the prefrontal cortex (PFC) (i.e., FPA) in motor learning is unique in various regards. Some previous noninvasive imaging studies have demonstrated that the FPA is mainly activated when subjects acquire novel motor task(s) [21][22][23]. Recently, Kobayashi et al (2021) examined FPA activity during the acquisition of a sequential motor task with near-infrared spectroscopy (NIRS) [16].…”

Interdisciplinary views of fNIRS: Current advancements, equity challenges, and an agenda for future needs of a diverse fNIRS research community

“…Since then, fNIRS technology has advanced rapidly with newer, wearable, high-density systems developed all over the world from both large manufacturers and laboratory-developed systems including from Mexico (Gorostieta-Esperon and Jiménez-Ángeles, 2019), Japan (Kubo and Kubo, 2015), Europe (Piper et al, 2014;Pinti et al, 2015), China (Liang et al, 2016), and the U.S. (Ayaz et al, 2013;Tsow et al, 2021). Robust improvements in hardware have allowed investigation of unique domains not previously explored, such as movement-heavy activities like yoga (Dev et al, 2019;Dybvik and Steinert, 2021), unpredictable outdoor environments (McKendrick et al, 2016), and other naturalistic environments (Pinti et al, 2018), humanrobot interaction (Le et al, 2022), collaborations between two or more agents (Czeszumski et al, 2020), and in sensitive populations (Arenth et al, 2007). Because these systems remain lightweight and portable, they are ideal for non-invasive brain measurement in a number of complex real-world scenarios (Le et al, 2022).…”

“…Robust improvements in hardware have allowed investigation of unique domains not previously explored, such as movement-heavy activities like yoga (Dev et al, 2019;Dybvik and Steinert, 2021), unpredictable outdoor environments (McKendrick et al, 2016), and other naturalistic environments (Pinti et al, 2018), humanrobot interaction (Le et al, 2022), collaborations between two or more agents (Czeszumski et al, 2020), and in sensitive populations (Arenth et al, 2007). Because these systems remain lightweight and portable, they are ideal for non-invasive brain measurement in a number of complex real-world scenarios (Le et al, 2022).…”