Activities in the frontal cortex and gait performance are modulated by preparation. An fNIRS study

Suzuki, Mitsuo; Miyai, Ichiro; Ono, Takeshi; Kubota, Kisou

doi:10.1016/j.neuroimage.2007.08.044

Cited by 190 publications

(157 citation statements)

References 51 publications

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“…However, power increases in the left/right sensorimotor cortices were more pronounced for contralateral limb push-off than for ipsilateral limb push-off. Studies carried out with fNIRS also showed involvement of frontal, premotor, and supplementary motor areas during walking (Harada et al 2009;Miyai et al 2001;Suzuki et al 2004Suzuki et al , 2008. These results support the idea that walking is represented across a plurality of cortical brain areas.…”

Section: Discussionsupporting

confidence: 64%

“…Neuroimaging studies show that rhythmic foot or leg movements recruit primary motor cortex (Christensen et al 2001;Dobkin et al 2004;Heuninckx et al 2005Heuninckx et al , 2008Luft et al 2002;Sahyoun et al 2004), whereas electrophysiological investigations demonstrate electrocortical potentials related to lower limb movements (Wieser et al 2010), as well as a greater involvement of human cortex during steady-speed locomotion than previously thought (Gwin et al , 2011. In this regard, studies using functional near-infrared spectroscopy (fNIRS) show involvement of frontal, premotor, and supplementary motor areas during walking (Harada et al 2009;Miyai et al 2001;Suzuki et al 2004Suzuki et al , 2008. That primary sensorimotor cortices carry information about bipedal locomotion has been directly proven by the work of Nicolelis and colleagues (Fitzsimmons et al 2009), who demonstrated that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to predict the kinematics of bipedal walking in rhesus macaques.…”

mentioning

confidence: 99%

“…In this regard, studies using functional near-infrared spectroscopy (fNIRS) show involvement of frontal, premotor, and supplementary motor areas during walking (Harada et al 2009;Miyai et al 2001;Suzuki et al 2004Suzuki et al , 2008. That primary sensorimotor cortices carry information about bipedal locomotion has been directly proven by the work of Nicolelis and colleagues (Fitzsimmons et al 2009), who demonstrated that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to predict the kinematics of bipedal walking in rhesus macaques.…”

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confidence: 99%

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Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Presacco

Goodman²,

Forrester

et al. 2011

Journal of Neurophysiology

191

199

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Here we show that the linear and angular kinematics of the ankle, knee, and hip joints during both normal and precision (attentive) human treadmill walking can be inferred from noninvasive scalp electroencephalography (EEG) with decoding accuracies comparable to those from neural decoders based on multiple single-unit activities (SUAs) recorded in nonhuman primates. Six healthy adults were recorded. Participants were asked to walk on a treadmill at their self-selected comfortable speed while receiving visual feedback of their lower limbs (i.e., precision walking), to repeatedly avoid stepping on a strip drawn on the treadmill belt. Angular and linear kinematics of the left and right hip, knee, and ankle joints and EEG were recorded, and neural decoders were designed and optimized with cross-validation procedures. Of note, the optimal set of electrodes of these decoders were also used to accurately infer gait trajectories in a normal walking task that did not require subjects to control and monitor their foot placement. Our results indicate a high involvement of a fronto-posterior cortical network in the control of both precision and normal walking and suggest that EEG signals can be used to study in real time the cortical dynamics of walking and to develop brain-machine interfaces aimed at restoring human gait function. brain computer interface; brain-machine interface; electroencephalography LITTLE IS KNOWN about the organization, neural network mechanisms, and computations underlying the control of walking in humans (Choi and Bastian 2007). Although central pattern generators for locomotion are important in the control of walking, supraspinal networks, including the brain stem, cerebellum, and cortex, must be critical, as demonstrated by the changing motor and cognitive (i.e., spatial attention) demands imposed by bipedal walking in unknown or cluttered dynamic environments (Choi and Bastian 2007;Grillner et al. 2008;Nielsen 2003;Rossignol et al. 2007). Neuroimaging studies show that rhythmic foot or leg movements recruit primary motor cortex (Christensen et al. 2001;Dobkin et al. 2004;Heuninckx et al. 2005Heuninckx et al. , 2008Luft et al. 2002;Sahyoun et al. 2004), whereas electrophysiological investigations demonstrate electrocortical potentials related to lower limb movements (Wieser et al. 2010), as well as a greater involvement of human cortex during steady-speed locomotion than previously thought (Gwin et al. , 2011. In this regard, studies using functional near-infrared spectroscopy (fNIRS) show involvement of frontal, premotor, and supplementary motor areas during walking (Harada et al. 2009;Miyai et al. 2001;Suzuki et al. 2004Suzuki et al. , 2008. That primary sensorimotor cortices carry information about bipedal locomotion has been directly proven by the work of Nicolelis and colleagues (Fitzsimmons et al. 2009), who demonstrated that chronic recordings from ensembles of cortical neurons in primary motor (M1) and primary somatosensory (S1) cortices can be used to predict the kinematics of bipedal wa...

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Section: Discussionsupporting

confidence: 64%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Presacco

Goodman²,

Forrester

et al. 2011

Journal of Neurophysiology

191

199

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show abstract

“…Because it is non-ionizing and non-confining, fNIRS is particularly well suited for non-invasive studies in children or infants. In addition, the portability of fNIRS and its ability to record brain signals during moderate subject movement has allowed its use during various tasks such as walking [3,4], balance [5][6][7], or social interaction [8], which otherwise could not be imaged by conventional fMRI.…”

Section: Introductionmentioning

confidence: 99%

Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS

Barker

Aarabi

Huppert

2013

Biomed. Opt. Express

276

334

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Abstract:Systemic physiology and motion-induced artifacts represent two major sources of confounding noise in functional near infrared spectroscopy (fNIRS) imaging that can reduce the performance of analyses and inflate false positive rates (i.e., type I errors) of detecting evoked hemodynamic responses. In this work, we demonstrated a general algorithm for solving the general linear model (GLM) for both deconvolution (finite impulse response) and canonical regression models based on designing optimal pre-whitening filters using autoregressive models and employing iteratively reweighted least squares. We evaluated the performance of the new method by performing receiver operating characteristic (ROC) analyses using synthetic data, in which serial correlations, motion artifacts, and evoked responses were controlled via simulations, as well as using experimental data from children (3-5 years old) as a source baseline physiological noise and motion artifacts. The new method outperformed ordinary least squares (OLS) with no motion correction, wavelet based motion correction, or spline interpolation based motion correction in the presence of physiological and motion related noise. In the experimental data, false positive rates were as high as 37% when the estimated p-value was 0.05 for the OLS methods. The false positive rate was reduced to 5-9% with the proposed method. Overall, the method improves control of type I errors and increases performance when motion artifacts are present.

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“…This characteristic makes fNIRS suitable for studying the temporal characteristics of the hemodynamic signal. Furthermore, fNIRS is nonrestraining, making it suitable for infants and small children and for various tasks, such as walking, 4,5 balance, [6][7][8] and social interaction, 9 where fMRI is not practical.…”

Section: Introductionmentioning

confidence: 99%

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

et al. 2016

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Abstract. Functional near-infrared spectroscopy (fNIRS) is a relatively low-cost, portable, noninvasive neuroimaging technique for measuring task-evoked hemodynamic changes in the brain. Because fNIRS can be applied to a wide range of populations, such as children or infants, and under a variety of study conditions, including those involving physical movement, gait, or balance, fNIRS data are often confounded by motion artifacts. Furthermore, the high sampling rate of fNIRS leads to high temporal autocorrelation due to systemic physiology. These two factors can reduce the sensitivity and specificity of detecting hemodynamic changes. In a previous work, we showed that these factors could be mitigated by autoregressive-based prewhitening followed by the application of an iterative reweighted least squares algorithm offline. This current work extends these same ideas to real-time analysis of brain signals by modifying the linear Kalman filter, resulting in an algorithm for online estimation that is robust to systemic physiology and motion artifacts. We evaluated the performance of the proposed method via simulations of evoked hemodynamics that were added to experimental resting-state data, which provided realistic fNIRS noise. Last, we applied the method post hoc to data from a standing balance task. Overall, the new method showed good agreement with the analogous offline algorithm, in which both methods outperformed ordinary least squares methods.

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Activities in the frontal cortex and gait performance are modulated by preparation. An fNIRS study

Cited by 190 publications

References 51 publications

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Neural decoding of treadmill walking from noninvasive electroencephalographic signals

Autoregressive model based algorithm for correcting motion and serially correlated errors in fNIRS

Correction of motion artifacts and serial correlations for real-time functional near-infrared spectroscopy

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