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Illustration of the baseline-correction method (Ch. 14, Subject 5). (a) An HbO signal before applying the baseline-correction method (blue thin curve) and the fitted signal f(x) (red thick curve); (b) the corrected HbO signal (blue thick curve).
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In this paper, a theory for detection of the absolute concentrations of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) from hemodynamic responses using a bundled-optode configuration in functional near-infrared spectroscopy (fNIRS) is proposed. The proposed method is then applied to the identification of two fingers (i.e., little and thumb) during...
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This paper presents a new mobile near-infrared functional spectroscopy (fNIRS) device, with digital detectors that can be placed anywhere on the head and fit into standard caps to measure cortical brain activation. The device's functionality was evaluated in two steps, i.e. first, by means of simple pulse measurements and second, in a motor cortex...
Stuttering affects nearly 1% of the population worldwide and often has life-altering negative consequences, including poorer mental health and emotional well-being, and reduced educational and employment achievements. Over two decades of neuroimaging research reveals clear anatomical and physiological differences in the speech neural networks of ad...
The mirror neuron system (MNS), mainly including the premotor cortex (PMC), inferior frontal gyrus (IFG), superior parietal lobule (SPL), and rostral inferior parietal lobule (IPL), has attracted extensive attention as a possible neural mechanism of social interaction. Owing to high ecological validity, functional near-infrared spectroscopy (fNIRS)...
Citations
... 456 The future of portable, noninvasive, and wearable fNIRS-based BCIs for neurorehabilitation and neurofeedback applications lies with the use of hybrid EEG-NIRS systems and bundled-type NIRS probes and the detection of the initial dip and improved sampling rate. 457,458 The availability of wearable NIRS devices has paved the way for new and revolutionary neuroimaging investigations that might grow over the following years, such as wearable high-density systems for studies in naturalistic settings. 459 ...
This report is the second part of a comprehensive two-part series aimed at reviewing an extensive and diverse toolkit of novel methods to explore brain health and function. While the first report focused on neurophotonic tools mostly applicable to animal studies, here, we highlight optical spectroscopy and imaging methods relevant to noninvasive human brain studies. We outline current state-of-the-art technologies and software advances, explore the most recent impact of these technologies on neuroscience and clinical applications, identify the areas where innovation is needed, and provide an outlook for the future directions.
... In addition, the activation map in Figure 5 can only provide information about the location and intensity of brain activation and cannot reveal the information about the peak value or peak time of the HbO response [22]. Figure 6 shows that the peak value and the peak time to reach the peak value of the HbO response signal of the different imagined actions were different [53], and the time of leftward movement involving the right forearm and right-hand clenching was between 9-10 s and 8-9 s, respectively. Therefore, the peak value of the HbO signal and the time to reach the peak value can be used to classify different imagined actions. ...
Functional near-infrared spectroscopy (fNIRS) is a type of functional brain imaging. Brain-computer interfaces (BCIs) based on fNIRS have recently been implemented. Most existing fNIRS-BCI studies have involved off-line analyses, but few studies used online performance testing. Furthermore, existing online fNIRS-BCI experimental paradigms have not yet carried out studies using different imagined movements of the same side of a single limb. In the present study, a real-time fNIRS-BCI system was constructed to identify two imagined movements of the same side of a single limb (right forearm and right hand). Ten healthy subjects were recruited and fNIRS signal was collected and real-time analyzed with two imagined movements (leftward movement involving the right forearm and right-hand clenching). In addition to the mean and slope features of fNIRS signals, the correlation coefficient between fNIRS signals induced by different imagined actions was extracted. A support vector machine (SVM) was used to classify the imagined actions. The average accuracy of real-time classification of the two imagined movements was 72.25 ± 0.004%. The findings suggest that different imagined movements on the same side of a single limb can be recognized real-time based on fNIRS, which may help to further guide the practical application of online fNIRS-BCIs.
... Therefore, methods to extract information on cerebral blood flows and hemoglobin distributions in cerebral blood vessels, for instance, Doppler ultrasound imaging and vascular magnetic resonance imaging (MRI), can be employed as a noninvasive diagnostic method for stroke. The optical imaging technology, defined as a functional near-infrared spectroscopy (f NIRS), provides an ability to obtain distribution information of oxy-/deoxy-hemoglobin and total blood concentrations using infrared light sources [57][58][59][60]. f NIRS does not use specific indicators, unlike fluorescence imaging. ...
Stroke is the second most common cause of death and third most common cause of disability worldwide. Therefore, it is an important disease from a medical standpoint. For this reason, various studies have developed diagnostic and therapeutic techniques for stroke. Among them, developments and applications of optical modalities are being extensively studied. In this article, we explored three important optical modalities for research, diagnostic, and therapeutics for stroke and the brain injuries related to it: (1) photochemical thrombosis to investigate stroke animal models; (2) optical imaging techniques for in vivo preclinical studies on stroke; and (3) optical neurostimulation based therapy for stroke. We believe that an exploration and an analysis of previous studies will help us proceed from research to clinical applications of optical modalities for research, diagnosis, and treatment of stroke.
... There are also concerns about potential contamination by the extracranial circulation or artefacts caused by intracranial blood. [41][42][43][44] Other methods used in the retrieved studies are invasive brain tissue oxygen pressure (PtO2) and intracranial pressure (ICP), [45][46][47][48] Although these techniques provide robust parameters, they are invasive techniques that can be applied only in critically ill patients. 47,49 PtO2 measures changes in the local oxygen tension in the tissue surrounding the probe, which can be used as a surrogate for CBF. ...
Acute stroke is associated with high morbidity and mortality. In the last decades, new therapies have been investigated with the aim of improving clinical outcomes in the acute phase post stroke onset. However, despite such advances, a large number of patients do not demonstrate improvement, furthermore, some unfortunately deteriorate. Thus, there is a need for additional treatments targeted to the individual patient. A potential therapeutic target is interventions to optimize cerebral perfusion guided by cerebral hemodynamic parameters such as dynamic cerebral autoregulation (dCA). This narrative led to the development of the INFOMATAS (Identifying New targets FOr Management And Therapy in Acute Stroke) project, designed to foster interventions directed towards understanding and improving hemodynamic aspects of the cerebral circulation in acute cerebrovascular disease states. This comprehensive review aims to summarize relevant studies on assessing dCA in patients suffering acute ischemic stroke, intracerebral haemorrhage, and subarachnoid haemorrhage. The review will provide to the reader the most consistent findings, the inconsistent findings which still need to be explored further and discuss the main limitations of these studies. This will allow for the creation of a research agenda for the use of bedside dCA information for prognostication and targeted perfusion interventions.
... Compared to TCD, NIRS has the advantage that is easier to install and is not limited to the bone window; but is limited by the potential contamination of the NIRS signal by the extracranial circulation and/or changes in systemic circulation, leading to erroneous interpretations [26,42]. Spatial resolution is another concern of the method, but significant advances have been made in this area [43,44]. Despite some of its potential advantages, NIRS has not been used extensively for the assessment of CA in stroke studies [37], and should be further explored. ...
Ischemic stroke (IS) is one of the most impacting diseases in the world. In the last decades, new therapies have been introduced to improve outcomes after IS, most of them aiming for recanalization of the occluded vessel. However, despite this advance, there are still a large number of patients that remain disabled. One interesting possible therapeutic approach would be interventions guided by cerebral hemodynamic parameters such as dynamic cerebral autoregulation (dCA). Supportive hemodynamic therapies aiming to optimize perfusion in the ischemic area could protect the brain and may even extend the therapeutic window for reperfusion therapies. However, the knowledge of how to implement these therapies in the complex pathophysiology of brain ischemia is challenging and still not fully understood. This comprehensive review will focus on the state of the art in this promising area with emphasis on the following aspects: (1) pathophysiology of CA in the ischemic process; (2) methodology used to evaluate CA in IS; (3) CA studies in IS patients; (4) potential non-reperfusion therapies for IS patients based on the CA concept; and (5) the impact of common IS-associated comorbidities and phenotype on CA status. The review also points to the gaps existing in the current research to be further explored in future trials.
... A potential difference is caused by the propagation of the current flow induced by synchronized postsynaptic potentials in pyramidal neuron cell membranes. fNIRS is a promising noninvasive neuroimaging technique featuring the advantages of safety, low cost, mobility, excellent temporal resolution (compared with fMRI), moderate spatial resolution, and tolerance to motion artifacts (Nguyen et al., 2016;Ghafoor et al., 2019). The fNIRS principle is based on the absorption characteristics of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) in the spectrum ranging from 650 to 1,000 nm, for which brain tissues are more translucent than HbO or HbR (Aqil et al., 2012a,b). ...
Background
Brain disorders are gradually becoming the leading cause of death worldwide. However, the lack of knowledge of brain disease’s underlying mechanisms and ineffective neuropharmacological therapy have led to further exploration of optimal treatments and brain monitoring techniques.
Objective
This study aims to review the current state of brain disorders, which utilize transcranial electrical stimulation (tES) and daily usable noninvasive neuroimaging techniques. Furthermore, the second goal of this study is to highlight available gaps and provide a comprehensive guideline for further investigation.
Method
A systematic search was conducted of the PubMed and Web of Science databases from January 2000 to October 2020 using relevant keywords. Electroencephalography (EEG) and functional near-infrared spectroscopy were selected as noninvasive neuroimaging modalities. Nine brain disorders were investigated in this study, including Alzheimer’s disease, depression, autism spectrum disorder, attention-deficit hyperactivity disorder, epilepsy, Parkinson’s disease, stroke, schizophrenia, and traumatic brain injury.
Results
Sixty-seven studies (1,385 participants) were included for quantitative analysis. Most of the articles (82.6%) employed transcranial direct current stimulation as an intervention method with modulation parameters of 1 mA intensity (47.2%) for 16–20 min (69.0%) duration of stimulation in a single session (36.8%). The frontal cortex (46.4%) and the cerebral cortex (47.8%) were used as a neuroimaging modality, with the power spectrum (45.7%) commonly extracted as a quantitative EEG feature.
Conclusion
An appropriate stimulation protocol applying tES as a therapy could be an effective treatment for cognitive and neurological brain disorders. However, the optimal tES criteria have not been defined; they vary across persons and disease types. Therefore, future work needs to investigate a closed-loop tES with monitoring by neuroimaging techniques to achieve personalized therapy for brain disorders.
... In comparison with other non-invasive neuroimaging modalities (i.e., fMRI, EEG, and MEG), fNIRS has the advantages of safety, lower cost, portability, tolerance of motion artifacts, good temporal resolution, and moderate spatial resolution [22]. Additionally, the development of initial dip detection [23][24][25], bundled-optodes configurations [26][27][28][29], and adaptive algorithms [30][31][32][33] have offered further opportunities to improve the temporal and spatial resolution of fNIRS. ...
Background:
Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors.
Objective:
This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals.
Methods:
In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration.
Results:
There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% % and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input.
Conclusion:
The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.
... PNU IRB/2016_101_HR). Written consent was obtained from all subjects prior to starting, and the experimental procedure was conducted in accordance with the ethical standards stipulated in the latest Declaration of Helsinki (Santosa et al., 2013;Nguyen et al., 2016). ...
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.
... In comparison with other non-invasive neuroimaging modalities (i.e., fMRI, EEG, and MEG), fNIRS has the advantages of safety, lower cost, portability, tolerance of motion artifacts, good temporal resolution, and moderate spatial resolution [16]. Additionally, the development of initial dip detection [17][18][19], bundled-optodes configurations [20][21][22][23], and adaptive algorithms [24][25][26][27] have offered further opportunities to improve the temporal and spatial resolution of fNIRS. ...
Background: Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer’s disease, which is the sixth leading cause of death in the United State. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. Currently, the combination of machine learning and neuroimaging plays a role in identifying and understanding neuropathological diseases. However, some challenges still remain, and these limitations need to be optimized for clinical MCI diagnosis.
Methods: In this study, for stable identification with functional near-infrared spectroscopy (fNIRS) using the minimum resting-state time, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated based on 30 s intervals using a traditional machine learning approach and graph theory analysis. The machine learning methods were trained using temporal features of the resting-state fNIRS signal and included linear discriminant analysis (LDA), support vector machine, and K-nearest neighbor (KNN) algorithms. To enhance the diagnostic accuracy, feature representation- and classification-based transfer learning (TL) methods were used to detect MCI from the healthy controls through the input of connectivity maps with 30 and 90 s durations.
Results: As in the results of the traditional machine learning and graph theory analysis, there was no significant difference among the different time windows. The accuracy of the conventional machine learning methods ranged from 55.76% (KNN, 120 s) to 67.00% (LDA, 90 s). The feature representation-based TL showed improved accuracy in both the 30 and 90 s cases (i.e., mean accuracy of 30 s: 79.37%, mean accuracy of 30 s: 74.05%). Notably, the classification-based TL method achieved the highest accuracy of 97.01% using the VGG19 pre-trained CNN model trained with the 30 s duration connectivity map.
Conclusion: The results indicate that a 30 s measurement of the resting state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) may be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.
... LEDs are arranged at variable distances from the SiPD starting from 20 mm, which results in channels reaching different depths in the brain compared to the channels reaching the same depth in the conventional fNIRS imaging; this allows 3D brain imaging using fNIRS. To realize a image, the measured point by each channel is assumed to be the midpoint between the source and th depth of the measured point is assumed to be distance [47]. Using this approach, the channel information can be displayed in the 3D space. ...
... The LEDs are arranged at variable distances from the SiPD starting from 20 mm, which results in channels reaching ths in the brain compared to the channels conventional fNIRS imaging; ging using fNIRS. To realize a 3D image, the measured point by each channel is assumed to be the detector, and the assumed to be half of the [47]. Using this approach, the channel information 3D space. ...
... The values of differential pathlength factor based on the wavelengths, were taken as 6.3125 and 5.235 for 735 and 850 nm. The software can compute the absolute concentration of HbO and HbR because of the novel detector configuration, in which the absolute value computed by implementing the bundled-optode theory [38,47]. The HbO and HbR values for the short channels are not used for real-time noise removal by the software. ...
Advancements in functional near-infrared spectroscopy (fNIRS) neuroimaging have extended its usage from clinical settings to real-life applications. We developed a wearable and wireless fNIRS-based neuroimaging system that can be applied for brain disease diagnostics and during daily life activities. We utilized 128 compact dual-wavelength LEDs of 735 nm and 850 nm and a silicon photodiode (SiPD). The LEDs and SiPD were closely assembled in a 3D-printed flexible configuration holder fitted in a soft neoprene cap. The developed configuration has 128 channels in a 7 × 7 cm area with source-detector separations ranging from 5 mm to 38 mm, which intends to provide a high-density device. The system acquires data from multiple brain depths to generate a 3D activation image. Short-separation channels provide the physiological noise in the superficial layer, which can be used to enhance fNIRS signals. The experimental data are stored in a USB flash disk attached to the device. We developed software that is connected to the device using Wi-Fi to display the oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) levels in real-time. The system architecture allows the selection of channels and the use of multiple devices simultaneously. The system was tested using a silicone phantom as well as human subjects. The LED intensity is dynamically calibrated within the safety range once the device is set up, so that every channel can acquire stable data. The system provided high-quality signals with a signal-to-noise ratio of more than 64 dB, high optical sensitivity, and dynamic optical range of 140 dB in the phantom test. A sampling rate of 30 Hz was achieved with all the channels used. The system exhibited valid HbO and HbR responses during the arterial occlusion test and a motor task. These results show that the developed system can provide precise, robust, and multi-depth measurements resulting in high-density images. With these novel characteristics, the system favors clinical usage and outdoor activities as well as brain-computer interface applications.
