scholarly journals Clinical Utility of Functional Near-Infrared Spectroscopy for Assessment and Prediction of Suicidality: A Systematic Review

2021 ◽  
Vol 12 ◽  
Author(s):  
Y. Q. Lee ◽  
Gabrielle W. N. Tay ◽  
Cyrus S. H. Ho
Keyword(s):  
Systematic Review ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Clinical Utility ◽  
Near Infrared ◽  
Cochrane Library ◽  
Functional Near Infrared Spectroscopy ◽  
Cross Sectional ◽  
Cerebral Hemodynamic ◽  
The Brain

Introduction: Suicide is a pressing psychiatric concern worldwide with no established biomarker. While there is some evidence of the clinical utility of functional near-infrared spectroscopy (fNIRS) in assessing and predicting suicidality, no systematic review of such evidence has been conducted to date. Therefore, this review aimed to systematically review and gather evidence from existing studies that used fNIRS signals to assess suicidality and its associated changes in the brain, and those that examined how such signals correlated with suicide symptomatology.Methods: PubMed, EMBASE, and Cochrane Library databases were used in a systematic literature search for English-language articles published between 2000 and December 19, 2020 that focused on the utility of fNIRS for (i) assessing suicidality and its associated changes in the brain, and (ii) correlating with suicide symptomatology. Studies were included if they utilised fNIRS to evaluate variations in fNIRS-measured cerebral hemodynamic responses in patients with different mental disorders (e.g., major depressive disorder, schizophrenia), as well as in healthy controls, of any age group. Quality of evidence was assessed using the Newcastle-Ottawa quality assessment scale.Results: A total of 7 cross-sectional studies were included in this review, all of which had acceptable quality. Across all studies, fNIRS demonstrated reduced cerebral hemodynamic changes in suicidal individuals when compared to non-suicidal individuals. One study also demonstrated the potential of fNIRS signals in correlating with the severity of suicidality.Conclusions: This review provides a comprehensive, updated review of evidence supporting the clinical utility of fNIRS in the assessment and prediction of suicidality. Further studies involving larger sample sizes, standardised methodology, and longitudinal follow-ups are needed.

scholarly journals Functional Near Infrared Spectroscopy (fNIRS) based Odor Detection and Classification using Functional Data Analysis

2021 ◽  
Author(s):  
Faezeh Moradi ◽  
Shima T. Moein ◽  
Issa Zakeri ◽  
Kambiz Pourrezaei
Keyword(s):  
Infrared Spectroscopy ◽  
Data Analysis ◽  
Near Infrared Spectroscopy ◽  
Functional Data Analysis ◽  
Functional Data ◽  
Near Infrared ◽  
Stimulus Condition ◽  
Functional Near Infrared Spectroscopy ◽  
Odor Detection ◽  
The Brain

AbstractAn objective approach for odor detection is to analyze the brain activity using imaging techniques during the odor stimulation. In this study, Functional Near Infrared Spectroscopy (fNIRS) is used to record hemodynamic response from the frontal region of the brain by using a 4-channel fNIRS system. The fNIRs data is collected during the odor detection task in which the subjects were asked to press a button when they detect the given odor. Functional Data Analysis (FDA) was applied on fNIRs data to convert discrete measured samples of data to continuous smooth curves. The FDA method enables us to use the bases coefficients of fNIRS smoothed curves for features that represent the shape of the raw fNIRS signal. With the learning algorithm that we proposed, these features were used to train the support vector machine classifier. We evaluated the odor detection problem, in two binary classification cases: odorant vs. non-odorant and odorant vs. fingertapping. The model achieved a classification accuracy of 94.12% and 97.06% over the stimulus condition in the two cases, respectively. Moreover to find the actual predictors we used the extracted defined features (slope, standard deviation, and delta) to train our classifier. We achieved an average accuracy of 91.18 % on classifying odorant vs. non-odorant and an accuracy of 94.12% for odorant vs. fingertapping on the stimulus condition. The results determined that fNIRs signals of odorant and non-odorant are distinguishable without being affected by the motor activity during the experiment.These findings suggest that fNIRs measurement on the forehead could be potentially used for objective and comparably inexpensive assessment of odor detection in cases that the subjective report is unreliable.

scholarly journals Brain–Computer Interfacing Using Functional Near-Infrared Spectroscopy (fNIRS)

Biosensors ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 389
Author(s):  
Kogulan Paulmurugan ◽  
Vimalan Vijayaragavan ◽  
Sayantan Ghosh ◽  
Parasuraman Padmanabhan ◽  
Balázs Gulyás
Keyword(s):  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Brain Function ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Non Invasive ◽  
Neuron Function ◽  
Brain Computer Interfacing ◽  
The Brain ◽  
Computer Interfacing

Functional Near-Infrared Spectroscopy (fNIRS) is a wearable optical spectroscopy system originally developed for continuous and non-invasive monitoring of brain function by measuring blood oxygen concentration. Recent advancements in brain–computer interfacing allow us to control the neuron function of the brain by combining it with fNIRS to regulate cognitive function. In this review manuscript, we provide information regarding current advancement in fNIRS and how it provides advantages in developing brain–computer interfacing to enable neuron function. We also briefly discuss about how we can use this technology for further applications.

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.

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