Functional connectivity analysis using fNIRS in healthy subjects during prolonged simulated driving

Xu, Long; Wang, Bitian; Xu, Gelin; Wang, Wei; Liu, Zhian; Li, Zengyong

doi:10.1016/j.neulet.2017.01.018

Cited by 80 publications

(53 citation statements)

References 41 publications

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“…The main features of fNIRS, such as its portability and lower susceptibility to motion artifacts, allow researchers to measure changes in cortical activity during diverse motor tasks, ranging from moving the fingers and hands in clicking a mouse or keyboard during a Stroop task or computer game ( Carrieri et al, 2016 ; Harmat et al, 2015 ; Kashou et al, 2016 ; Shortz et al, 2015 ) to those body movements requiring coordination, such as juggling ( Carius et al, 2016 ) and balancing on a board ( Herold et al, 2017 ), and even to those requiring fine motor skills, such as simulating surgery ( Andreu-Perez et al, 2016 ) and flight ( Choe et al, 2016 ; Gateau et al, 2015 ). In addition, several recent studies have investigated the effects of bodily and road conditions, such as fatigue ( Xu et al, 2017 ), age ( Foy et al, 2016 ), and road curve ( Oka et al, 2015 ), on the hemodynamic responses of subjects during a driving simulation test. These studies found an activation of a number of brain regions that are known to be involved in cognitive and motor functions, including the prefrontal cortex (PFC), motor cortex, premotor cortex (PMC), and supplementary motor area (SMA).…”

Section: Applications Of Fnirs To Human Subjectsmentioning

confidence: 99%

Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models

Kim

Seo

Jeon

et al. 2017

Molecules and Cells

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Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical imaging technique that indirectly assesses neuronal activity by measuring changes in oxygenated and deoxygenated hemoglobin in tissues using near-infrared light. fNIRS has been used not only to investigate cortical activity in healthy human subjects and animals but also to reveal abnormalities in brain function in patients suffering from neurological and psychiatric disorders and in animals that exhibit disease conditions. Because of its safety, quietness, resistance to motion artifacts, and portability, fNIRS has become a tool to complement conventional imaging techniques in measuring hemodynamic responses while a subject performs diverse cognitive and behavioral tasks in test settings that are more ecologically relevant and involve social interaction. In this review, we introduce the basic principles of fNIRS and discuss the application of this technique in human and animal studies.

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Section: Applications Of Fnirs To Human Subjectsmentioning

confidence: 99%

Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models

Kim

Seo

Jeon

et al. 2017

Molecules and Cells

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“…Typically, DFC studies are conducted using fMRI, which is not appropriate for protocols requiring supine positions and/or non-elicited task execution. Recent studies have demonstrated that fNIRS is well positioned in such scenarios [47][48][49] . We foresee that implementing such approaches in the context of bimanual skill assessment can lead to refined skill level assessment metrics as well as potentially provide predictive models of skill acquisition.…”

Section: Submission Date: February 6thmentioning

confidence: 99%

Assessing bimanual motor skills with optical neuroimaging

Nemani

Yücel

Krüger

et al. 2017

Preprint

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Measuring motor skill proficiency is critical for the certification of highly-skilled individuals in numerous fields. However, conventional measures use subjective metrics that often cannot distinguish between expertise levels. Here, we present an advanced optical neuroimaging methodology that can objectively and successfully classify subjects with different expertise levels associated with bimanual motor dexterity. The methodology was tested by assessing laparoscopic surgery skills within the framework of the fundamentals of laparoscopic surgery program, which is a pre-requisite for certification in general surgery. We demonstrate that optical-based metrics outperformed current metrics for surgical certification in classifying subjects with varying surgical expertise. Moreover, we report that optical neuroimaging allows for the successful classification of subjects during the acquisition of such skills.Motor skills that involve bimanual motor coordination are essential in performing numerous tasks ranging from simple daily activities to complex motor actions performed by highly skilled individuals. Hence, metrics to assess motor task performance are critical in numerous fields including neuropathology and neurological recovery, surgical training and certification, and athletic performance [1][2][3][4][5][6][7] . In the vast majority of fields, however, current metrics are human-administered, subjective, and require significant personnel resources and time. Thus, there is critical need for more automated, analytical, and objective evaluation methods 4,[8][9][10][11] . From a neuroscience perspective, bimanual task assessment provides insights into motor skill expertise, motor dysfunctions, interconnectivity between brain regions, and higher cognitive and executive functions, such as motor perception, motor action, and task multitasking 7,12 . Therefore, incorporating the underlying neurological responses in bimanual motor skill assessment is a logical step towards providing robust, objective metrics, which ultimately may lead to greatly improving our understanding of motor skill processes and facilitating bimanual-based task certification.Among all non-invasive functional brain imaging techniques, functional near infrared spectroscopy (fNIRS) offers the unique ability to monitor and quantify fast functional brain activations over numerous cortical areas without constraining and interfering with bimanual task execution. Hence, fNIRS is a promising neuroimaging modality to study cortical brain activations but to date, only a very limited number of studies have been reported in regards to assessing fine surgical motor skills 13 . These exploratory studies have reported differentiation in functional cortical activations between groups with varying surgical motor skills [13][14][15][16][17] . However, they suffer from recognized limitations 13 such as such as the lack of signal specificity between scalp and cortical hemodynamics 18,19 , the lack of multivariate statistical approaches that leverage changes in function...

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“…In this way, it can make the interaction process more flexible and labor-saving. Monitoring people's mental states is an effective approach to mitigate human error and enhance the safety of the HRI process [6]. This method of introducing physiological data into the HRI process, called "Physiological Computing", could guide the system through taking the operator's states into account [7].…”

Section: The Smart House Based On Multiple Welfare Robotsmentioning

confidence: 99%

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

et al. 2020

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The multi-robot system (MRS) and relevant control strategy are a potential and effective approach to assist people with weak motion capability for various forms of assisted living. However, the rising transfer, a frequent and strenuous behavior, and its human-robot interaction (HRI) process with MRS, especially mental state, has never been researched, although it directly determines the user experience and security. In this paper, Functional Near-InfraRed Spectroscopy (fNIRS), a brain imaging technique to perform a continuous measure of the mental state, is introduced to monitor the user’s mental fatigue when implementing a behavior transfer in two difficulty levels assisted by multiple welfare-robots. Twenty-five subjects performed self-rising transfer and multiple welfare robots-assisted rising transfer. After removing physiological noises, six features of oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively) features, which included the mean, slope, variance, peak, skewness, and kurtosis, were calculated. To maximize the distinction of fNIRS between self-rising transfer and assisted-rising transfer (multiple welfare robots assisted rising transfer), the optimal statistical feature combination for linear discriminant analysis (LDA) classification was proposed. In addition, the classification accuracy is regarded as a standard to quantify the difference of mental states between two contrasting behaviors. By fitting the index, we established the mental fatigue model that grows exponentially as the workload increases. Finally, the mental fatigue model is applied to guide the nursing mode of caregivers and the control strategy of the MRS. Our findings disclose that the combinations containing mean and peak values significantly yielded higher classification accuracies for both HbO and HbR than the entire other combinations did, across all the subjects. They effectively quantify mental fatigue to provide an evaluation with a theoretical foundation for enhancing the user experience and optimizing the control strategy of MRS.

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Functional connectivity analysis using fNIRS in healthy subjects during prolonged simulated driving

Cited by 80 publications

References 41 publications

Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models

Application of Functional Near-Infrared Spectroscopy to the Study of Brain Function in Humans and Animal Models

Assessing bimanual motor skills with optical neuroimaging

Quantitative Estimation of Differentiated Mental Fatigue between Self-Rising Transfer and Multiple Welfare Robots-Assisted Rising Transfer

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