scholarly journals Right Dorsolateral Prefrontal Cortex Activation during a Time Production Task: A Functional Near-Infrared Spectroscopy Study

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Asato Morita ◽  
Yasunori Morishima ◽  
David W. Rackham
Keyword(s):  
Working Memory ◽  
Infrared Spectroscopy ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Time Estimation ◽  
Production Task ◽  
Control Task ◽  
Functional Near Infrared Spectroscopy ◽  
Time Production ◽  
Dorsolateral Prefrontal

Accurate time estimation is crucial for many human activities and necessitates the use of working memory, in which the dorsolateral prefrontal cortex (DLPFC) plays a critical role. We tested the hypothesis that the DLPFC is activated in participants attempting time estimations that require working memory. Specifically, we used functional near-infrared spectroscopy (fNIRS) to investigate prefrontal cortical activity in the brains of individuals performing a prospective time production task. We measured cerebral hemodynamic responses in 26 healthy right-handed university students while they marked the passage of specified time intervals (3, 6, 9, 12, or 15 s) or performed a button-pressing (control) task. The behavioral results indicated that participants’ time estimations were accurate with minimal variability. The fNIRS data showed that activity was significantly higher in the right DLPFC during the time estimation task compared to the control task. Theoretical considerations and the results of this study suggest that DLPFC activation resulting from time estimation indicates that the working memory system is in use.

Social hyperscanning with fNIRS

Gesture ◽  
2020 ◽  
Vol 19 (2-3) ◽  
pp. 196-222
Author(s):  
Michela Balconi ◽  
Angela Bartolo ◽  
Giulia Fronda
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Brain Connectivity ◽  
Gestural Communication ◽  
Functional Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal ◽  
Posterior Parietal

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.

scholarly journals What Does Sleeping Brain Tell About Stress? A Pilot Functional Near-Infrared Spectroscopy Study Into Stress-Related Cortical Hemodynamic Features During Sleep

2021 ◽  
Vol 3 ◽  
Author(s):  
Zilu Liang
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Pilot Study ◽  
Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Stress Indicators ◽  
Measured Stress ◽  
Dorsolateral Prefrontal

People with mental stress often experience disturbed sleep, suggesting stress-related abnormalities in brain activity during sleep. However, no study has looked at the physiological oscillations in brain hemodynamics during sleep in relation to stress. In this pilot study, we aimed to explore the relationships between bedtime stress and the hemodynamics in the prefrontal cortex during the first sleep cycle. We tracked the stress biomarkers, salivary cortisol, and secretory immunoglobulin A (sIgA) on a daily basis and utilized the days of lower levels of measured stress as natural controls to the days of higher levels of measured stress. Cortical hemodynamics was measured using a cutting-edge wearable functional near-infrared spectroscopy (fNIRS) system. Time-domain, frequency-domain features as well as nonlinear features were derived from the cleaned hemodynamic signals. We proposed an original ensemble algorithm to generate an average importance score for each feature based on the assessment of six statistical and machine learning techniques. With all channels counted in, the top five most referred feature types are Hurst exponent, mean, the ratio of the major/minor axis standard deviation of the Poincaré plot of the signal, statistical complexity, and crest factor. The left rostral prefrontal cortex (RLPFC) was the most relevant sub-region. Significantly strong correlations were found between the hemodynamic features derived at this sub-region and all three stress indicators. The dorsolateral prefrontal cortex (DLPFC) is also a relevant cortical area. The areas of mid-DLPFC and caudal-DLPFC both demonstrated significant and moderate association to all three stress indicators. No relevance was found in the ventrolateral prefrontal cortex. The preliminary results shed light on the possible role of the RLPCF, especially the left RLPCF, in processing stress during sleep. In addition, our findings echoed the previous stress studies conducted during wake time and provides supplementary evidence on the relevance of the dorsolateral prefrontal cortex in stress responses during sleep. This pilot study serves as a proof-of-concept for a new research paradigm to stress research and identified exciting opportunities for future studies.

scholarly journals Functional Effects of Bilateral Dorsolateral Prefrontal Cortex Modulation During Sequential Decision-Making: A Functional Near-Infrared Spectroscopy Study With Offline Transcranial Direct Current Stimulation

2021 ◽  
Vol 14 ◽  
Author(s):  
Iryna Schommartz ◽  
Annika Dix ◽  
Susanne Passow ◽  
Shu-Chen Li
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Direct Current ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Sequential Decision Making ◽  
Sequential Decision ◽  
Functional Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal

The ability to learn sequential contingencies of actions for predicting future outcomes is indispensable for flexible behavior in many daily decision-making contexts. It remains open whether such ability may be enhanced by transcranial direct current stimulation (tDCS). The present study combined tDCS with functional near-infrared spectroscopy (fNIRS) to investigate potential tDCS-induced effects on sequential decision-making and the neural mechanisms underlying such modulations. Offline tDCS and sham stimulation were applied over the left and right dorsolateral prefrontal cortex (dlPFC) in young male adults (N = 29, mean age = 23.4 years, SD = 3.2) in a double-blind between-subject design using a three-state Markov decision task. The results showed (i) an enhanced dlPFC hemodynamic response during the acquisition of sequential state transitions that is consistent with the findings from a previous functional magnetic resonance imaging (fMRI) study; (ii) a tDCS-induced increase of the hemodynamic response in the dlPFC, but without accompanying performance-enhancing effects at the behavioral level; and (iii) a greater tDCS-induced upregulation of hemodynamic responses in the delayed reward condition that seems to be associated with faster decision speed. Taken together, these findings provide empirical evidence for fNIRS as a suitable method for investigating hemodynamic correlates of sequential decision-making as well as functional brain correlates underlying tDCS-induced modulation. Future research with larger sample sizes for carrying out subgroup analysis is necessary in order to decipher interindividual differences in tDCS-induced effects on sequential decision-making process at the behavioral and brain levels.

scholarly journals Scarcity Mindset Neuro Network Decoding With Reward: A Tree-Based Model and Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 15 ◽  
Author(s):  
Xiaowei Jiang ◽  
Chenghao Zhou ◽  
Na Ao ◽  
Wenke Gu ◽  
Jingyi Li ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Infrared Spectroscopy ◽  
Cognitive Performance ◽  
Near Infrared Spectroscopy ◽  
Dorsolateral Prefrontal Cortex ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Ventrolateral Prefrontal Cortex ◽  
Dorsolateral Prefrontal ◽  
The Right

Resource scarcity imposes challenging demands on the human cognitive system. Insufficient resources cause the scarcity mindset to affect cognitive performance, while reward enhances cognitive function. Here, we examined how reward and scarcity simultaneously contribute to cognitive performance. Experimental manipulation to induce a polar scarcity mindset and reward conditions within participants under functional near-infrared spectroscopy (fNIRS) recording was implemented to explore the mechanism underlying the scarcity mindset and reward in terms of behavior and neurocognition. Participants showed decreased functional connectivity from the dorsolateral prefrontal cortex (DLPFC) to the ventrolateral prefrontal cortex (VLPFC) with a scarcity mindset, a region often implicated in cognitive control. Moreover, under reward conditions, the brain activation of the maximum total Hb bold signal was mainly located in the left hemisphere [channels 1, 3, and 4, left ventrolateral prefrontal cortex (L-VLPFC) and channel 6, left dorsolateral prefrontal cortex (L-DLPFC)], and there was also significant brain activation of the right dorsolateral prefrontal cortex (R-DLPFC) in the right hemisphere (channel 17). Furthermore, these data indicate the underlying neural changes of the scarcity mentality and demonstrate that brain activities may underlie reward processing. Additionally, the base-tree machine learning model was trained to detect the mechanism of reward function in the prefrontal cortex (PFC). According to SHapley Additive exPlanations (SHAP), channel 8 contributed the most important effect, as well as demonstrating a high-level interrelationship with other channels.

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