Music and cortical blood flow: A functional near-infrared spectroscopy (fNIRS) study.

2014 ◽  
Vol 7 (4) ◽  
pp. 545-550 ◽  
Author(s):  
Marcelo Bigliassi ◽  
Vinícius Barreto-Silva ◽  
Thiago Ferreira Dias Kanthack ◽  
Leandro Ricardo Altimari
Keyword(s):  
Blood Flow ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Cortical Blood Flow

scholarly journals Analyzing Brain Functions by Subject Classification of Functional Near-Infrared Spectroscopy Data Using Convolutional Neural Networks Analysis

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Satoru Hiwa ◽  
Kenya Hanawa ◽  
Ryota Tamura ◽  
Keisuke Hachisuka ◽  
Tomoyuki Hiroyasu
Keyword(s):  
Blood Flow ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Inferior Frontal Gyrus ◽  
Memory Storage ◽  
Series Data ◽  
Analysis Method ◽  
Functional Near Infrared Spectroscopy ◽  
Brain Functions

Functional near-infrared spectroscopy (fNIRS) is suitable for noninvasive mapping of relative changes in regional cortical activity but is limited for quantitative comparisons among cortical sites, subjects, and populations. We have developed a convolutional neural network (CNN) analysis method that learns feature vectors for accurate identification of group differences in fNIRS responses. In this study, subject gender was classified using CNN analysis of fNIRS data. fNIRS data were acquired from male and female subjects during a visual number memory task performed in a white noise environment because previous studies had revealed that the pattern of cortical blood flow during the task differed between males and females. A learned classifier accurately distinguished males from females based on distinct fNIRS signals from regions of interest (ROI) including the inferior frontal gyrus and premotor areas that were identified by the learning algorithm. These cortical regions are associated with memory storage, attention, and task motor response. The accuracy of the classifier suggests stable gender-based differences in cerebral blood flow during this task. The proposed CNN analysis method can objectively identify ROIs using fNIRS time series data for machine learning to distinguish features between groups.

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.

scholarly journals Brain function changes induced by intermittent sequential pneumatic compression in patients with stroke as assessed by functional near-infrared spectroscopy

2020 ◽  
Author(s):  
Hui Xie ◽  
Gongcheng Xu ◽  
Congcong Huo ◽  
Wenhao Li ◽  
Haihong Zhao ◽  
...  
Keyword(s):  
Blood Flow ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Resting State ◽  
Near Infrared ◽  
Tissue Concentration ◽  
Personalized Treatment ◽  
Functional Near Infrared Spectroscopy ◽  
Functional Rehabilitation ◽  
Significant Difference

Abstract Background. Intermittent sequential pneumatic compression (ISPC) can effectively promote blood flow and improve microcirculation. The increase in pressure gradient and blood flow velocity by ISPC has been suggested to be a possible mechanism to improve the microcirculation of patients. However, the effects of ISPC on cerebral oscillations are still unclear.Methods. The tissue concentration of oxyhemoglobin and deoxyhemoglobin oscillations were measured by functional near-infrared spectroscopy under resting and ISPC conditions in 27 right-handed adult patients with stroke. Five characteristic frequency signals (I, 0.6–2 Hz; II, 0.145–0.6 Hz; III, 0.052–0.145 Hz; IV, 0.021–0.052 Hz; and V, 0.0095–0.021 Hz) were identified using the wavelet method. The wavelet amplitude (WA) and laterality index (LI) were calculated to describe the frequency-specific cortical activities.Results. The WA values of the ipsilesional motor cortex (MC) in the frequency intervals III (F = 4.378, p = 0.041), IV (F = 4.281, p = 0.044), and V (F = 5.33, p = 0.025) and those of the contralesional MC in III (F = 10.122, p = 0.002), IV (F = 9.275, p = 0.004), and V (F = 8.373, p = 0.006) were significantly higher when the patients were under the ISPC state than when they were under the resting state. Also, the LI value of the prefrontal cortex (PFC) and MC of the patients decreased more obviously in the ISPC state compared with the resting state despite there was no significant difference.Conclusions. ISPC could induce the activation of bilateral MCs in myogenic and neurogenic innervations and endothelial cell metabolic activities. The decreased LI values in the PFC and MC indicated that the ISPC had a positive effect on these regions’ functional rehabilitation. The ISPC of 0.03 Hz is not suitable for all patients with stroke, and personalized treatment options should be considered in subsequent ISPC intervention. This study provides a method for assessing the effects of ISPC on cerebral oscillations, and the results benefit the optimization of ISPC parameters in the personalized treatment for the functional recovery of patients with stroke.

scholarly journals Removing signals from scalp blood flow for functional near-infrared spectroscopy

BIOPHILIA ◽  
2016 ◽  
Vol 2016 (2) ◽  
pp. 27-27
Author(s):  
Mariko Sakamoto ◽  
Yoshiyasu Takefuji ◽  
Shigeo Takizawa ◽  
Toshiyuki Tanaka
Keyword(s):  
Blood Flow ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy

A Study of Frontal Signals in Brain Computer Interfaces: Interpretation of EEG & FNIRS

2020 ◽  
pp. 136-142
Author(s):  
S. Srilekha ◽  
B. Vanathi
Keyword(s):  
Infrared Spectroscopy ◽  
Healthy Subject ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Economic Conditions ◽  
Brain Computer Interfaces ◽  
Functional Near Infrared Spectroscopy ◽  
Computer Interfaces ◽  
Rehabilitation Devices

This paper focuses on electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) comparison to help the rehabilitation patients. Both methods have unique techniques and placement of electrodes. Usage of signals are different in application based on the economic conditions. This study helps in choosing the signal for the betterment of analysis. Ten healthy subject datasets of EEG & FNIRS are taken and applied to plot topography separately. Accuracy, Sensitivity, peaks, integral areas, etc are compared and plotted. The main advantages of this study are to prompt their necessities in the analysis of rehabilitation devices to manage their life as a typical individual.

61-LB: Use of Functional Near-Infrared Spectroscopy (fNIRS) to Assess Cognitive Effort in KATP-Related Neonatal Diabetes (KATP-NDM)

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 61-LB
Author(s):  
LISA R. LETOURNEAU-FREIBERG ◽  
KIMBERLY L. MEIDENBAUER ◽  
ANNA M. DENSON ◽  
PERSEPHONE TIAN ◽  
KYOUNG WHAN CHOE ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Neonatal Diabetes ◽  
Cognitive Effort ◽  
Functional Near Infrared Spectroscopy
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