scholarly journals Measures of prefrontal functional near-infrared spectroscopy in visuomotor learning

Author(s):  
Angelica M. Tinga ◽  
Maria-Alena Clim ◽  
Tycho T. de Back ◽  
Max M. Louwerse

AbstractFunctional near-infrared spectroscopy (fNIRS) is a promising technique for non-invasively assessing cortical brain activity during learning. This technique is safe, portable, and, compared to other imaging techniques, relatively robust to head motion, ocular and muscular artifacts and environmental noise. Moreover, the spatial resolution of fNIRS is superior to electroencephalography (EEG), a more commonly applied technique for measuring brain activity non-invasively during learning. Outcomes from fNIRS measures during learning might therefore be both sensitive to learning and to feedback on learning, in a different way than EEG. However, few studies have examined fNIRS outcomes in learning and no study to date additionally examined the effects of feedback. To address this apparent gap in the literature, the current study examined prefrontal cortex activity measured through fNIRS during visuomotor learning and how this measure is affected by task feedback. Activity in the prefrontal cortex decreased over the course of learning while being unaffected by task feedback. The findings demonstrate that fNIRS in the prefrontal cortex is valuable for assessing visuomotor learning and that this measure is robust to task feedback. The current study highlights the potential of fNIRS in assessing learning even under different task feedback conditions.

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Atsumichi Tachibana ◽  
J. Adam Noah ◽  
Yumie Ono ◽  
Daisuke Taguchi ◽  
Shuichi Ueda

Abstract Understanding how the brain modulates improvisation has been the focus of numerous studies in recent years. Models have suggested regulation of activity between default mode and executive control networks play a role in improvisational execution. Several studies comparing formulaic to improvised sequences support this framework and document increases in activity in medial frontal lobe with decreased activity in the dorsolateral prefrontal cortex (DLPFC). These patterns can be influenced through training and neural responses may differ between in beginner and expert musicians. Our goal was to test the generalizability of this framework and determine similarity in neural activity in the prefrontal cortex during improvisation. Twenty guitarists performed improvised and formulaic sequences in a blues rock format while brain activity was recorded using functional near-infrared spectroscopy. Results indicate similar modulation in DLPFC as seen previously. Specific decreases of activity from left DLPFC in the end compared to beginning or middle of improvised sequences were also found. Despite the range of skills of participants, we also found significant correlation between subjective feelings of improvisational performance and modulation in left DLPFC. Processing of subjective feelings regardless of skill may contribute to neural modulation and may be a factor in understanding neural activity during improvisation.


2021 ◽  
Vol 11 (6) ◽  
pp. 701
Author(s):  
Cheng-Hsuan Chen ◽  
Kuo-Kai Shyu ◽  
Cheng-Kai Lu ◽  
Chi-Wen Jao ◽  
Po-Lei Lee

The sense of smell is one of the most important organs in humans, and olfactory imaging can detect signals in the anterior orbital frontal lobe. This study assessed olfactory stimuli using support vector machines (SVMs) with signals from functional near-infrared spectroscopy (fNIRS) data obtained from the prefrontal cortex. These data included odor stimuli and air state, which triggered the hemodynamic response function (HRF), determined from variations in oxyhemoglobin (oxyHb) and deoxyhemoglobin (deoxyHb) levels; photoplethysmography (PPG) of two wavelengths (raw optical red and near-infrared data); and the ratios of data from two optical datasets. We adopted three SVM kernel functions (i.e., linear, quadratic, and cubic) to analyze signals and compare their performance with the HRF and PPG signals. The results revealed that oxyHb yielded the most efficient single-signal data with a quadratic kernel function, and a combination of HRF and PPG signals yielded the most efficient multi-signal data with the cubic function. Our results revealed superior SVM analysis of HRFs for classifying odor and air status using fNIRS data during olfaction in humans. Furthermore, the olfactory stimulation can be accurately classified by using quadratic and cubic kernel functions in SVM, even for an individual participant data set.


Gesture ◽  
2020 ◽  
Vol 19 (2-3) ◽  
pp. 196-222
Author(s):  
Michela Balconi ◽  
Angela Bartolo ◽  
Giulia Fronda

Abstract The interest of neuroscience has been aimed at the investigation of the neural bases underlying gestural communication. This research explored the intra- and inter-brain connectivity between encoder and decoder. Specifically, adopting a “hyperscanning paradigm” with the functional Near-infrared Spectroscopy (fNIRS) cerebral connectivity in oxygenated (O2Hb) and deoxygenated (HHb) hemoglobin levels were revealed during the reproduction of affective, social, and informative gestures of different valence. Results showed an increase of intra- and inter-brain connectivity in dorsolateral prefrontal cortex for affective gestures, in superior frontal gyrus for social gestures and in frontal eyes field for informative gestures. Moreover, encoder showed a higher intra-brain connectivity in posterior parietal areas more than decoder. Finally, an increasing of inter-brain connectivity more than intra-brain (ConIndex) was observed in left regions for positive gestures. The present research has explored how the individuals neural tuning mechanisms turn out to be strongly influenced by the nature of specific gestures.


Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2362 ◽  
Author(s):  
Alexander E. Hramov ◽  
Vadim Grubov ◽  
Artem Badarin ◽  
Vladimir A. Maksimenko ◽  
Alexander N. Pisarchik

Sensor-level human brain activity is studied during real and imaginary motor execution using functional near-infrared spectroscopy (fNIRS). Blood oxygenation and deoxygenation spatial dynamics exhibit pronounced hemispheric lateralization when performing motor tasks with the left and right hands. This fact allowed us to reveal biomarkers of hemodynamical response of the motor cortex on the motor execution, and use them for designing a sensing method for classification of the type of movement. The recognition accuracy of real movements is close to 100%, while the classification accuracy of imaginary movements is lower but quite high (at the level of 90%). The advantage of the proposed method is its ability to classify real and imaginary movements with sufficiently high efficiency without the need for recalculating parameters. The proposed system can serve as a sensor of motor activity to be used for neurorehabilitation after severe brain injuries, including traumas and strokes.


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