Frequency characteristics of cortical activity associated with perturbations to upright stability

Varghese, Jessy Parokaran; Marlin, Amanda; Beyer, Kit B.; Staines, Richard; Mochizuki, George; McIlroy, William E.

doi:10.1016/j.neulet.2014.06.017

Cited by 77 publications

(84 citation statements)

References 28 publications

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“…The negative potential occurring around 100 ms following an event, such as mechanical perturbations, is termed the N100 potential. The N100 response over the fronto-central area has been observed in a wide range of balance tasks, and N100 amplitude increases in challenging balance control tasks, including unpredictable or surprise perturbations, and in balance challenges with low sensory inputs (Adkin et al, 2006, 2008; Mochizuki et al, 2008; Huang et al, 2014; Varghese et al, 2014, 2015). However, Mochizuki et al (2009a) observed no difference in N100 latency and amplitude in sitting and standing instability conditions, suggesting that there may be more general processes that underlie stability, regardless of sensory, motor, or postural aspects of response.…”

Section: Resultsmentioning

confidence: 99%

“…Activation of the prefrontal cortex (PFC), supplementary motor area (SMA), and Premotor cortex (PMC) frequently occurred in response to the static balance challenges. Although more common in dynamic balance control studies, seven of the static balance control studies invoking mechanical perturbations used advanced signal processing methodologies such as ICA to reduce movement artifacts (Mochizuki et al, 2009a; Smith et al, 2012, 2014; Marlin et al, 2014; Varghese et al, 2014, 2015; Hülsdünker et al, 2016). …”

Section: General Discussion and Conclusionmentioning

confidence: 99%

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Neuroimaging of Human Balance Control: A Systematic Review

Wittenberg

Thompson

Nam

et al. 2017

Front. Hum. Neurosci.

121

106

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This review examined 83 articles using neuroimaging modalities to investigate the neural correlates underlying static and dynamic human balance control, with aims to support future mobile neuroimaging research in the balance control domain. Furthermore, this review analyzed the mobility of the neuroimaging hardware and research paradigms as well as the analytical methodology to identify and remove movement artifact in the acquired brain signal. We found that the majority of static balance control tasks utilized mechanical perturbations to invoke feet-in-place responses (27 out of 38 studies), while cognitive dual-task conditions were commonly used to challenge balance in dynamic balance control tasks (20 out of 32 studies). While frequency analysis and event related potential characteristics supported enhanced brain activation during static balance control, that in dynamic balance control studies was supported by spatial and frequency analysis. Twenty-three of the 50 studies utilizing EEG utilized independent component analysis to remove movement artifacts from the acquired brain signals. Lastly, only eight studies used truly mobile neuroimaging hardware systems. This review provides evidence to support an increase in brain activation in balance control tasks, regardless of mechanical, cognitive, or sensory challenges. Furthermore, the current body of literature demonstrates the use of advanced signal processing methodologies to analyze brain activity during movement. However, the static nature of neuroimaging hardware and conventional balance control paradigms prevent full mobility and limit our knowledge of neural mechanisms underlying balance control.

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Section: Resultsmentioning

confidence: 99%

Section: General Discussion and Conclusionmentioning

confidence: 99%

Neuroimaging of Human Balance Control: A Systematic Review

Wittenberg

Thompson

Nam

et al. 2017

Front. Hum. Neurosci.

121

106

View full text Add to dashboard Cite

show abstract

“…Time-frequency analyses of the ERN (Luu, Tucker, & Makeig, 2004) and balance N1 (Peterson & Ferris, 2018;Varghese et al, 2014) have been used to suggest that both of these ERPs may reflect a transient synchronization of theta frequency (4-7 Hz) brain activity. However, in simulated data sets, such analyses are unable to distinguish synchronization of oscillatory components from discrete component peaks (Yeung, Bogacz, Holroyd, Nieuwenhuis, & Cohen, 2007).…”

Section: Balance N1 and Ernmentioning

confidence: 99%

Do sensorimotor perturbations to standing balance elicit an error‐related negativity?

2019

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Detecting and correcting errors is essential to successful action. Studies on response monitoring have examined scalp ERPs following the commission of motor slips in speeded‐response tasks, focusing on a frontocentral negativity (i.e., error‐related negativity or ERN). Sensorimotor neurophysiologists investigating cortical monitoring of reactive balance recovery behavior observe a strikingly similar pattern of scalp ERPs following externally imposed postural errors, including a brief frontocentral negativity that has been referred to as the balance N1. We integrate and review relevant literature from these discrepant fields to suggest shared underlying mechanisms and potential benefits of collaboration across fields. Unlike the cognitive tasks leveraged to study the ERN, balance perturbations afford precise experimental control of postural errors to elicit balance N1s that are an order of magnitude larger than the ERN and drive robust and well‐characterized adaptation of behavior within an experimental session. Many factors that modulate the ERN, including motivation, perceived consequences, perceptual salience, expectation, development, and aging, are likewise known to modulate the balance N1. We propose that the ERN and balance N1 reflect common neural activity for detecting errors. Collaboration across fields could help clarify the functional significance of the ERN and poorly understood interactions between motor and cognitive impairments.

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“…A previous study reported a cortical theta power increase in frontal and central areas, primarily located along the midline cortical, which is correlated with sudden postural perturbation [14]. Other studies suggested that theta band activity in frontal and central cortical areas are involved in the monitoring of postural stability and dangerous events during balance control [15,16]. Although it has been found that postural perturbation can lead to changes in neurophysiological signals of the brain, there is no research attempting to use brain signals as a biomarker to detect it.…”

Section: Introductionmentioning

confidence: 99%

A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features

Jiang

Zhang

et al. 2019

Sensors

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In the human-robot hybrid system, due to the error recognition of the pattern recognition system, the robot may perform erroneous motor execution, which may lead to falling-risk. While, the human can clearly detect the existence of errors, which is manifested in the central nervous activity characteristics. To date, the majority of studies on falling-risk detection have focused primarily on computer vision and physical signals. There are no reports of falling-risk detection methods based on neural activity. In this study, we propose a novel method to monitor multi erroneous motion events using electroencephalogram (EEG) features. There were 15 subjects who participated in this study, who kept standing with an upper limb supported posture and received an unpredictable postural perturbation. EEG signal analysis revealed a high negative peak with a maximum averaged amplitude of −14.75 ± 5.99 μV, occurring at 62 ms after postural perturbation. The xDAWN algorithm was used to reduce the high-dimension of EEG signal features. And, Bayesian linear discriminant analysis (BLDA) was used to train a classifier. The detection rate of the falling-risk onset is 98.67%. And the detection latency is 334ms, when we set detection rate beyond 90% as the standard of dangerous event onset. Further analysis showed that the falling-risk detection method based on postural perturbation evoked potential features has a good generalization ability. The model based on typical event data achieved 94.2% detection rate for unlearned atypical perturbation events. This study demonstrated the feasibility of using neural response to detect dangerous fall events.

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Frequency characteristics of cortical activity associated with perturbations to upright stability

Cited by 77 publications

References 28 publications

Neuroimaging of Human Balance Control: A Systematic Review

Neuroimaging of Human Balance Control: A Systematic Review

Do sensorimotor perturbations to standing balance elicit an error‐related negativity?

A Pilot Study on Falling-Risk Detection Method Based on Postural Perturbation Evoked Potential Features

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