Investigation of cerebral hemodynamic changes during repeated sit-stand maneuver using functional near-infrared spectroscopy

2011 ◽  
Author(s):  
Haijing Niu ◽  
Lin Li ◽  
Gauri S. Bhave ◽  
Zi-jing Lin ◽  
Fenghua Tian ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Hemodynamic Changes ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Hemodynamic

scholarly journals Multimodal Functional Near-Infrared Spectroscopy in Monitoring Cerebral Haemodynamic: A Review Article

2020 ◽  
Vol 4 (1) ◽  
pp. 47-52
Author(s):  
Fairuz Mohd Nasir ◽  
Hiroshi Watabe
Keyword(s):  
Computed Tomography ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Blood Oxygenation ◽  
Imaging Modalities ◽  
Emission Computed Tomography ◽  
Functional Near Infrared Spectroscopy ◽  
Xenon Ct ◽  
Cerebral Hemodynamic

Functional near-infrared spectroscopy (fNIRS) is an optical imaging tool to study brain activities. Moreover, many researchers combined fNIRS with other modalities to gain a better understanding of the brain. This paper provides an overview of the combination of fNIRS with other imaging modalities in the detection and measurement of the cerebral hemodynamic. Cerebral haemodynamic such as the cerebral blood flow (CBF), cerebral blood volume (CBV) and cerebral blood oxygenation (CBO) are the important parameters in many neuroimaging studies. Cerebral hemodynamic had been studied by various medical imaging modalities.  Initially, Xenon enhanced Computed Tomography (Xenon CT), Computed Tomography (CT) perfusion; Single Photon Emission Computed Tomography (SPECT), Positron Emission Tomography (PET) are used to measure the cerebral hemodynamic. Recently, fNIRS is used to optically observe the changes in cerebral haemodynamic during brain activities and the combination of fNIRS with other modalities also become an interest to study the relations within brain activities and the cerebral hemodynamic. Therefore, this paper provides an overview of existing multimodal fNIRS in detection of cerebral haemodynamic changes and provides an important insight on how multimodal fNIRS aid in advancing modern investigations of human brain function.       Keywords: multimodal imaging, fNIRS-fMRI, fNIRS-PET, fNIRS-EEG

scholarly journals Decoding Three Different Preference Levels of Consumers Using Convolutional Neural Network: A Functional Near-Infrared Spectroscopy Study

2021 ◽  
Vol 14 ◽  
Author(s):  
Kunqiang Qing ◽  
Ruisen Huang ◽  
Keum-Shik Hong
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Convolutional Neural Network ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Classification Performance ◽  
Hemodynamic Responses ◽  
Functional Near Infrared Spectroscopy ◽  
Cerebral Hemodynamic

This study decodes consumers' preference levels using a convolutional neural network (CNN) in neuromarketing. The classification accuracy in neuromarketing is a critical factor in evaluating the intentions of the consumers. Functional near-infrared spectroscopy (fNIRS) is utilized as a neuroimaging modality to measure the cerebral hemodynamic responses. In this study, a specific decoding structure, called CNN-based fNIRS-data analysis, was designed to achieve a high classification accuracy. Compared to other methods, the automated characteristics, constant training of the dataset, and learning efficiency of the proposed method are the main advantages. The experimental procedure required eight healthy participants (four female and four male) to view commercial advertisement videos of different durations (15, 30, and 60 s). The cerebral hemodynamic responses of the participants were measured. To compare the preference classification performances, CNN was utilized to extract the most common features, including the mean, peak, variance, kurtosis, and skewness. Considering three video durations, the average classification accuracies of 15, 30, and 60 s videos were 84.3, 87.9, and 86.4%, respectively. Among them, the classification accuracy of 87.9% for 30 s videos was the highest. The average classification accuracies of three preferences in females and males were 86.2 and 86.3%, respectively, showing no difference in each group. By comparing the classification performances in three different combinations (like vs. so-so, like vs. dislike, and so-so vs. dislike) between two groups, male participants were observed to have targeted preferences for commercial advertising, and the classification performance 88.4% between “like” vs. “dislike” out of three categories was the highest. Finally, pairwise classification performance are shown as follows: For female, 86.1% (like vs. so-so), 87.4% (like vs. dislike), 85.2% (so-so vs. dislike), and for male 85.7, 88.4, 85.1%, respectively.

scholarly journals The Temporal Confounding Effects of Extra-cerebral Contamination Factors on the Hemodynamic Signal Measured by Functional Near-Infrared Spectroscopy

2019 ◽  
Vol 10 (5) ◽  
pp. S73-S81
Author(s):  
Mehrdad Zarei ◽  
Mohammad Ali Ansari ◽  
Kourosh Zare
Keyword(s):  
Blood Flow ◽  
Monte Carlo ◽  
Infrared Spectroscopy ◽  
Brain Imaging ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Temporal Changes ◽  
Hemodynamic Changes ◽  
Functional Near Infrared Spectroscopy ◽  
Large Source

Introduction: Functional near-infrared spectroscopy (fNIRS) has been broadly applied for optical brain imaging. This method is hemodynamic-based functional brain imaging relying on the measurement of the neurovascular coupling to detect changes in cerebral neuronal activities. The extra-cerebral hemodynamic changes are important contaminating factors in fNIRS measurements. This error signal can be misinterpreted as cerebral activities during fNIRS studies. Recently, it was assumed that temporal changes in deoxygenated hemoglobin concentration [HHb] was hardly affected by superficial blood flow, and it was proposed that the activation maps could be determined from [HHb] at large source-detector separation. Methods: In the current study, we measured the temporal changes in [HHb] using a continueswave fNIRS device at large source-detector separation, while superficial blood flow was stimulated by infrared lasers. A mesh-based Monte Carlo code was applied to estimate fNIRS sensitivity to superficial hemodynamic changes in a realistic 3D MRI-based brain phantom. Results: First, we simulated photon migration in a four-layered human-head slab model to calculate PPLs and fNIRS sensitivity. Then, the localization of the infrared laser inside a realistic brain model was studied using the Monte Carlo method. Finally, the changes in [HHb] over the prefrontal cortex of six adult males were measured by fNIRS at a source-detector separation of 3 cm. The results demonstrated that the relation between fNIRS sensitivity and an increase in S-D separation was nonlinear and a correlation between shallow and deep signals was observed. Conclusion: The presented results demonstrated that the temporal changes in the superficial blood flow could strongly affect HHb measurement at large source-detector separation. Hence, the cerebral activity map extracted from the [HHb] signal was mainly contaminated by superficial blood flow.

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