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.

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 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

Classification of affect using deep learning on brain blood flow data

2019 ◽  
Vol 27 (3) ◽  
pp. 206-219 ◽  
Author(s):  
Danushka Bandara ◽  
Leanne Hirshfield ◽  
Senem Velipasalar
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Time Series Data ◽  
Self Report ◽  
Series Data ◽  
Spectroscopy Data ◽  
Short Term ◽  
Functional Near Infrared Spectroscopy

We present a convolutional neural network- and long short-term memory-based method to classify the valence level of a computer user based on functional near infrared spectroscopy data. Convolutional neural networks are well suited for capturing the spatial characteristics of functional near infrared spectroscopy data. And long short-term memories are demonstrated to be good at learning temporal patterns of unknown length in time series data. We explore these methods in a combined layered architecture in order to improve classification accuracy. We conducted an experiment with 20 participants, wherein they were subjected to emotion inducing stimuli while their brain activity was measured using functional near infrared spectroscopy. Self-report surveys were administered after each stimulus to gauge participants' self-assessment of their valence. The resulting classification using these survey labels as ground truth provided a three-class classification accuracy 77.89% in across subject cross-validation. This method also shows promise for generalization to other classification tasks using functional near infrared spectroscopy data.

scholarly journals Interbrain Synchrony in the Expectation of Cooperation Behavior: A Hyperscanning Study Using Functional Near-Infrared Spectroscopy

2020 ◽  
Vol 11 ◽  
Author(s):  
Mingming Zhang ◽  
Huibin Jia ◽  
Mengxue Zheng
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Mirror Neuron System ◽  
Cooperative Behavior ◽  
Inferior Frontal Gyrus ◽  
Neural Substrates ◽  
Environmental Cues ◽  
Functional Near Infrared Spectroscopy ◽  
The Individual

Expectation of others’ cooperative behavior plays a core role in economic cooperation. However, the dynamic neural substrates of expectation of cooperation (hereafter EOC) are little understood. To fully understand EOC behavior in more natural social interactions, the present study employed functional near-infrared spectroscopy (fNIRS) hyperscanning to simultaneously measure pairs of participants’ brain activations in a modified prisoner’s dilemma game (PDG). The data analysis revealed the following results. Firstly, under the high incentive condition, team EOC behavior elicited higher interbrain synchrony (IBS) in the right inferior frontal gyrus (rIFG) than individual EOC behavior. Meanwhile, the IBS in the IFG could predict the relationship between empathy/agreeableness and EOC behavior, and this prediction role was modulated by social environmental cues. These results indicate the involvement of the human mirror neuron system (MNS) in the EOC behavior and the different neural substrates between team EOC and individual EOC, which also conform with theory that social behavior was affected by internal (i.e., empathy/agreeableness) and external factors (i.e., incentive). Secondly, female dyads exhibited a higher IBS value of cooperative expectation than male dyads in the team EOC than the individual EOC in the dorsal medial prefrontal cortex (DMPFC), while in the individual EOC stage, the coherence value of female dyads was significantly higher than that of male dyads under the low incentive reward condition in the rIFG. These sex effects thus provide presumptive evidence that females are more sensitive to environmental cues and also suggest that during economic social interaction, females’ EOC behavior depends on more social cognitive abilities. Overall, these results raise intriguing questions for future research on human cooperative behaviors.

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