Hemodynamic response to intestinal pH stimulation measured with spectroscopic video imaging.

To investigate the relationship between the gut and skin (gut-skin axis), head skin hemodynamic responses to gut stimulation including the injection of acetic acid in nude mice were measured by spectroscopic video imaging, which was calculated using a modified Beer-Lambert formula. The relationship with blood proteins was also analyzed. The blood volume changes in three mice injected with acetic acid were highly reproducible in the mathematical model equation. Four proteins correlated with blood volume changes were all related to immunity. These results suggest that intestinal pH can alter the blood volume in the skin and induce immune-related responses.

A 0.01-degree gridded precipitation dataset for Japan, 1926-2020.

We developed a 0.01-degree gridded precipitation dataset of Japan based on historical observation datasets covering 1926 to 2020. Historical observations conducted by the Japan Meteorological Agency and other Japanese bureaucratic agencies were spatially interpolated using the inverse distance weighting method at daily and hourly temporal resolutions. Optimal parameterization for our interpolation process was selected by comparing interpolated results of various parameter combinations with precipitation observation conducted by the University of Tokyo Forests. We conducted cross-validation for over 1,000 stations with sufficient data throughout our data period and verified our product can reproduce the temporal variability of local precipitation. The strong points of our precipitation dataset are its high spatiotemporal resolution and the abundance of point precipitation source data. We expect our dataset to be highly relevant to various future studies as it can serve multiple purposes such as forcing data for hydrological models or a database for analyzing the characteristics of historical rainfall events.

Projection of Damaged Visual and Language Regions on Low Trail Making Test Part-B Performance in Stroke Patients.

Background: The Trail Making Test Part-B (TMT-B) is an attention functional test to investigate cognitive dysfunction. It requires the ability to recognize not only numbers but also letters. We analyzed the relationship between brain lesions in stroke patients and their TMT-B performance. Methods: From the TMT-B, two parameters (score and completion time) were obtained. The subjects were classified into several relevant groups by their scores and completion times through a data-driven analysis (k-means clustering). The score-classified groups were characterized by low (≤10), moderate (10 < score < 25), and high (25) scores. In terms of the completion time, the subjects were classified into four groups. The lesion degree in the brain was calculated for each of the 116 regions classified by automated anatomical labeling (AAL). For each group, brain sites with a significant difference (corrected p < 0.1) between each of the 116 regions were determined by a Wilcoxon Rank-Sum significant difference test. Results: Lesions at the cuneus and the superior occipital gyrus, which are mostly involved in visual processing, were significant (corrected p < 0.1) in the low-score group. Furthermore, the moderate-score group showed more-severe lesion degrees (corrected p < 0.05) in the regions responsible for the linguistic functions, such as the superior temporal gyrus and the supramarginal gyrus. As for the completion times, lesions in the calcarine, the cuneus, and related regions were significant (corrected p < 0.1) in the fastest group as compared to the slowest group. These regions are also involved in visual processing. Conclusion: The TMT-B results revealed that the subjects in the low-score group or the slowest- group mainly had damage in the visual area, whereas the subjects in the moderate-score group mainly had damage in the language area. These results suggest the potential utility of TMT-B performance in the lesion site.

Artificially-reconstructed brain images with stroke lesions from non-imaging data: modeling in categorized patients based on lesion occurrence and sparsity.

Brain imaging is necessary for understanding disease symptoms, including stroke. However, frequent imaging procedures encounter practical limitations. Estimating the brain information (e.g., lesions) without imaging sessions is beneficial for this scenario. Prospective estimating variables are non-imaging data collected from standard tests. Therefore, the current study aims to examine the variable feasibility for modelling lesion locations. Heterogeneous variables were employed in the multivariate logistic regression. Furthermore, patients were categorized (i.e., unsupervised clustering through k-means method) by the charasteristics of lesion occurrence (i.e., ratio between the lesioned and total regions) and sparsity (i.e., density measure of lesion occurrences across regions). Considering those charasteristics in models improved estimation performances. Lesions (116 regions in Automated Anatomical Labeling) were adequately predicted (sensitivity: 80.0-87.5% in median). We confirmed that the usability of models was extendable to different resolution levels in the brain region of interest (e.g., lobes, hemispheres). Patients' charateristics (i.e., occurrence and sparsity) might also be explained by the non-imaging data as well. Advantages of the current approach can be experienced by any patients (i.e., with or without imaging sessions) in any clinical facilities (i.e., with or without imaging instrumentation).

Working Memory Performance under a Negative Affect Is More Susceptible to Higher Cognitive Workloads with Different Neural Haemodynamic Correlates.

The effect of stress on task performance is complex, too much or too little stress negatively affects performance and there exists an optimal level of stress to drive optimal performance. Task difficulty and external affective factors are distinct stressors that impact cognitive performance. Neuroimaging studies showed that mood affects working memory performance and the correlates are changes in haemodynamic activity in the prefrontal cortex (PFC). We investigate the interactive effects of affective states and working memory load (WML) on working memory task performance and haemodynamic activity using functional near-infrared spectroscopy (fNIRS) neuroimaging on the PFC of healthy participants. We seek to understand if haemodynamic responses could tell apart workload-related stress from situational stress arising from external affective distraction. We found that the haemodynamic changes towards affective stressor- and workload-related stress were more dominant in the medial and lateral PFC, respectively. Our study reveals distinct affective state-dependent modulations of haemodynamic activity with increasing WML in n-back tasks, which correlate with decreasing performance. The influence of a negative effect on performance is greater at higher WML, and haemodynamic activity showed evident changes in temporal, and both spatial and strength of activation differently with WML.

Proposal of layered mental healthcare for mental well-being.

A new concept, 'Layered mental healthcare' for keeping employees mental well-being in the workplace to avoid losses caused by both absenteeism and presenteeism is proposed. A key factor forming the basis of the concept is the biometric measurements over three layers, i.e., behaviour, physiology, and brain layers, for monitoring mental/distress conditions of employees. Here, the necessity of measurements in three layers was validated by the data-driven approach using the preliminary dataset measured in the office environment. Biometric measurements were supported by an activity tracker, a PC logger, and the optical topography; mental/distress conditions were quantified by the brief job stress questionnaire. The biometric features obtained 1 week before the measurement of mental/distress scores were selected for the best regression model. The feature importance of each layer was obtained in the learning process of the best model using the light graded boosting machine and was compared between layers. The ratio of feature importance of behaviour:physiology:brain layers was found to be 4:3:3. The study results suggest the contribution and necessity of the three-layer features in predicting mental/distress scores.

Detection of Emotional Sensitivity Using fNIRS Based Dynamic Functional Connectivity.

In this study, we proposed an analytical framework to identify dynamic task-based functional connectivity (FC) features as new biomarkers of emotional sensitivity in nursing students, by using a combination of unsupervised and supervised machine learning techniques. The dynamic FC was measured by functional Near-Infrared Spectroscopy (fNIRS), and computed using a sliding window correlation (SWC) analysis. A k -means clustering technique was applied to derive four recurring connectivity states. The states were characterized by both graph theory and semi-metric analysis. Occurrence probability and state transition were extracted as dynamic FC network features, and a Random Forest (RF) classifier was implemented to detect emotional sensitivity. The proposed method was trialled on 39 nursing students and 19 registered nurses during decision-making, where we assumed registered nurses have developed strategies to cope with emotional sensitivity. Emotional stimuli were selected from International Affective Digitized Sound System (IADS) database. Experiment results showed that registered nurses demonstrated single dominant connectivity state of task-relevance, while nursing students displayed in two states and had higher level of task-irrelevant state connectivity. The results also showed that students were more susceptive to emotional stimuli, and the derived dynamic FC features provided a stronger discriminating power than heart rate variability (accuracy of 81.65% vs 71.03%) as biomarkers of emotional sensitivity. This work forms the first study to demonstrate the stability of fNIRS based dynamic FC states as a biomarker. In conclusion, the results support that the state distribution of dynamic FC could help reveal the differentiating factors between the nursing students and registered nurses during decision making, and it is anticipated that the biomarkers might be used as indicators when developing professional training related to emotional sensitivity.

Optimizing Mental Workload Estimation by Detecting Baseline State Using Vector Phase Analysis Approach.

Improper baseline return from the previous task-evoked hemodynamic response (HR) can contribute to a large variation in the subsequent HR, affecting the estimation of mental workload in brain-computer interface systems. In this study, we proposed a method using vector phase analysis to detect the baseline state as being optimal or suboptimal. We hypothesize that selecting neuronal-related HR as observed in the optimal-baseline blocks can lead to an improvement in estimating mental workload. Oxygenated and deoxygenated hemoglobin concentration changes were integrated as parts of the vector phase. The proposed method was applied to a block-design functional near-infrared spectroscopy dataset (total blocks = 1384), measured on 24 subjects performing multiple difficulty levels of mental arithmetic task. Significant differences in hemodynamic signal change were observed between the optimal- and suboptimal-baseline blocks detected using the proposed method. This supports the effectiveness of the proposed method in detecting baseline state for better estimation of mental workload. The results further highlight the need of customized recovery duration. In short, the proposed method offers a practical approach to detect task-evoked signals, without the need of extra probes.

fNIRS-based functional connectivity estimation using semi-metric analysis to study decision making by nursing students and registered nurses.

This study aims to investigate the generalizability of the semi-metric analysis of the functional connectivity (FC) for functional near-infrared spectroscopy (fNIRS) by applying it to detect the dichotomy in differential FC under affective and neutral emotional states in nursing students and registered nurses during decision making. The proposed method employs wavelet transform coherence to construct FC networks and explores semi-metric analysis to extract network redundancy features, which has not been considered in conventional fNIRS-based FC analyses. The trials of the proposed method were performed on 19 nursing students and 19 registered nurses via a decision-making task under different emotional states induced by affective and neutral emotional stimuli. The cognitive activities were recorded using fNIRS, and the emotional stimuli were adopted from the International Affective Digitized Sound System (IADS). The induction of emotional effects was validated by heart rate variability (HRV) analysis. The experimental results by the proposed method showed significant difference (FDR-adjusted p = 0.004) in the nursing students' cognitive FC network under the two different emotional conditions, and the semi-metric percentage (SMP) of the right prefrontal cortex (PFC) was found to be significantly higher than the left PFC (FDR-adjusted p = 0.036). The benchmark method (a typical weighted graph theory analysis) gave no significant results. In essence, the results support that the semi-metric analysis can be generalized and extended to fNIRS-based functional connectivity estimation.

Mental Condition Monitoring Based on Multimodality Biometry.

We have developed a system with multimodality that monitors objective biomarkers for screening the mental distress in the office. A field study using a prototype of the system was performed over four months with 39 volunteers. We obtained PC operation patterns using a PC logger, sleeping time and activity levels using a wrist-band-type activity tracker, and brain activity and behavior data during a working memory task using optical topography. We also administered two standard questionnaires: the Brief Job Stress Questionnaire (BJS) and the Kessler 6 scale (K6). Supervised machine learning and cross validation were performed. The objective variables were mental scores obtained from the questionnaires and the explanatory variables were the biomarkers obtained from the modalities. Multiple linear regression models for mental scores were comprehensively searched and the optimum models were selected from 2,619,785 candidates. Each mental score estimated with each optimum model was well correlated with each mental score obtained with the questionnaire (correlation coefficient = 0.6-0.8) within a 24% of estimation error. Mental scores obtained by means of questionnaires have been in general use in mental health care for a while, so our multimodality system is potentially useful for mental healthcare due to the quantitative agreement on the mental scores estimated with biomarkers and the mental scores obtained with questionnaires.

A Unified Analytical Framework With Multiple fNIRS Features for Mental Workload Assessment in the Prefrontal Cortex.

Knowing the actual level of mental workload is important to ensure the efficacy of brain-computer interface (BCI) based cognitive training. Extracting signals from limited area of a brain region might not reveal the actual information. In this study, a functional near-infrared spectroscopy (fNIRS) device equipped with multi-channel and multi-distance measurement capability was employed for the development of an analytical framework to assess mental workload in the prefrontal cortex (PFC). In addition to the conventional features, e.g. hemodynamic slope, we introduced a new feature - deep contribution ratio which is the proportion of cerebral hemodynamics to the fNIRS signals. Multiple sets of features were examined by a simple logical operator to suppress the false detection rate in identifying the activated channels. Using the number of activated channels as input to a linear support vector machine (SVM), the performance of the proposed analytical framework was assessed in classifying three levels of mental workload. The best set of features involves the combination of hemodynamic slope and deep contribution ratio, where the identified number of activated channels returned an average accuracy of 80.6% in predicting mental workload, compared to a single conventional feature (accuracy: 59.8%). This suggests the feasibility of the proposed analytical framework with multiple features as a means towards a more accurate assessment of mental workload in fNIRS-based BCI applications.

Automated Thresholding Method for fNIRS-Based Functional Connectivity Analysis: Validation With a Case Study on Alzheimer's Disease.

While functional integration has been suggested to reflect brain health, non-standardized network thresholding methods complicate network interpretation. We propose a new method to analyze functional near-infrared spectroscopy-based functional connectivity (fNIRS-FC). In this study, we employed wavelet analysis for motion correction and orthogonal minimal spanning trees (OMSTs) to derive the brain connectivity. The proposed method was applied to an Alzheimer's disease (AD) dataset and was compared with a number of well-known thresholding techniques. The results demonstrated that the proposed method outperformed the benchmarks in filtering cost-effective networks and in differentiation between patients with mild AD and healthy controls. The results also supported the proposed method as a feasible technique to analyze fNIRS-FC, especially with cost-efficiency, assortativity and laterality as a set of effective features for the diagnosis of AD.

Atypical Dynamic-Connectivity Recruitment in Attention-Deficit/Hyperactivity Disorder Children: An Insight Into Task-Based Dynamic Connectivity Through an fNIRS Study.

Connectivity between brain regions has been redefined beyond a stationary state. Even when a person is in a resting state, brain connectivity dynamically shifts. However, shifted brain connectivity under externally evoked stimulus is still little understood. The current study, therefore, focuses on task-based dynamic functional-connectivity (FC) analysis of brain signals measured by functional near-infrared spectroscopy (fNIRS). We hypothesize that a stimulus may influence not only brain connectivity but also the occurrence probabilities of task-related and task-irrelevant connectivity states. fNIRS measurement (of the prefrontal-to-inferior parietal lobes) was conducted on 21 typically developing (TD) and 21 age-matched attention-deficit/hyperactivity disorder (ADHD) children performing an inhibitory control task, namely, the Go/No-Go (GNG) task. It has been reported that ADHD children lack inhibitory control; differences between TD and ADHD children in terms of task-based dynamic FC were also evaluated. Four connectivity states were found to occur during the temporal task course. Two dominant connectivity states (states 1 and 2) are characterized by strong connectivities within the frontoparietal network (occurrence probabilities of 40%-56% and 26%-29%), and presumptively interpreted as task-related states. A connectivity state (state 3) shows strong connectivities in the bilateral medial frontal-to-parietal cortices (occurrence probability of 7-15%). The strong connectivities were found at the overlapped regions related the default mode network (DMN). Another connectivity state (state 4) visualizes strong connectivities in all measured regions (occurrence probability of 10%-16%). A global effect coming from cerebral vascular may highly influence this connectivity state. During the GNG stimulus interval, the ADHD children tended to show decreased occurrence probability of the dominant connectivity state and increased occurrence probability of other connectivity states (states 3 and 4). Bringing a new perspective to explain neuropathophysiology, these findings suggest atypical dynamic network recruitment to accommodate task demands in ADHD children.

Assessing Neural Compensation With Visuospatial Working Memory Load Using Near-Infrared Imaging.

Alzheimer's disease is characterized by the progressive deterioration of cognitive abilities particularly working memory while mild cognitive impairment (MCI) represents its prodrome. It is generally believed that neural compensation is intact in MCI but absent in Alzheimer's disease. This study investigated the effects of increasing task load as a means to induce neural compensation through a novel visual working memory (VSWM) task using functional near-infrared spectroscopy (fNIRS). The bilateral prefrontal cortex (PFC) was explored due to its relevance in VSWM and neural compensation. A total of 31 healthy controls (HC), 12 patients with MCI and 18 patients with mild Alzheimer's disease (mAD) were recruited. Although all groups showed sensitivity in terms of behavioral performance (i.e. score) towards increasing task load (level 1 to 3), only in MCI load effect on cortical response (as measured by fNIRS) was significant. At lower task load, bilateral PFC activation did not differ between MCI and HC. Neural compensation in the form of hyperactivation was only noticeable in MCI with a moderate task load. Lack of hyperactivation in mAD, coupled with significantly poorer task performance across task loads, suggested the inability to compensate due to a greater degree of neurodegeneration. Our findings provided an insight into the interaction of cognitive load theory and neural compensatory mechanisms. The experiment results demonstrated the feasibility of inducing neural compensation with the proposed VSWM task at the right amount of cognitive load. This may provide a promising avenue to develop an effective cognitive training and rehabilitation for dementia population.

Exploring attentive task-based connectivity for screening attention deficit/hyperactivity disorder children: a functional near-infrared spectroscopy study.

Connectivity impairment has frequently been associated with the pathophysiology of attention-deficit/hyperactivity disorder (ADHD). Although the connectivity of the resting state has mainly been studied, we expect the transition between baseline and task may also be impaired in ADHD children. Twenty-three typically developing (i.e., control) and 36 disordered (ADHD and autism-comorbid ADHD) children were subjected to connectivity analysis. Specifically, they performed an attention task, visual oddball, while their brains were measured by functional near-infrared spectroscopy. The results of the measurements revealed three key findings. First, the control group maintained attentive connectivity, even in the baseline interval. Meanwhile, the disordered group showed enhanced bilateral intra- and interhemispheric connectivities while performing the task. However, right intrahemispheric connectivity was found to be weaker than those for the control group. Second, connectivity and activation characteristics might not be positively correlated with each other. In our previous results, disordered children lacked activation in the right middle frontal gyrus. However, within region connectivity of the right middle frontal gyrus was relatively strong in the baseline interval and significantly increased in the task interval. Third, the connectivity-based biomarker performed better than the activation-based biomarker in terms of screening. Activation and connectivity features were independently optimized and cross validated to obtain the best performing threshold-based classifier. The effectiveness of connectivity features, which brought significantly higher training accuracy than the optimum activation features, was confirmed (88% versus 76%). The optimum screening features were characterized by two trends: (1) strong connectivities of right frontal, left frontal, and left parietal lobes and (2) weak connectivities of left frontal, left parietal, and right parietal lobes in the control group. We conclude that the attentive task-based connectivity effectively shows the difference between control and disordered children and may represent pathological characteristics to be feasibly implemented as a supporting tool for clinical screening.

Multiple-Time-Scale Analysis of Attention as Revealed by EEG, NIRS, and Pupil Diameter Signals During a Free Recall Task: A Multimodal Measurement Approach.

Attention plays a fundamental role in acquiring and understanding information. Therefore, it is useful to evaluate attention objectively in such fields as education and mental health. Aimed at extracting objective indicators of attention from physiological signals, this study examined the characteristics of electroencephalography (EEG), near-infrared spectroscopy (NIRS), and pupil diameter signals during a free recall task. The objective was to clarify the temporal characteristics of these signals in relation to attention. We used a free recall task as a cognitive task with an attentional load. The participants attempted to memorize and then recall 13 serially presented words. Our hypothesis was that the significant physiological responses should differ depending on the time scale of the attention evaluation. The physiological responses were compared on the basis of differences between success and failure to recall a word on a short time scale, in terms of the attentional state among five serial position groups on a middle time scale, and on the basis of differences between trials with many and few words recalled on a long time scale. We found that the response of each physiological signal depended on the attention in the different time-scale comparisons. (1) The P300 amplitudes of the EEG signals for the words that were recalled were significantly higher than those for the words that were not recalled. (2) Pupillary dilation differed significantly depending on the serial position group. (3) Functional connectivity in the right hemisphere revealed by NIRS was significantly stronger in trials with many words recalled than in those with few words recalled. Different temporal characteristics of physiological signals with respect to attention were suggested by multimodal measurement and multiple-time-scale analysis. Consideration of these characteristics should help in the development of applications requiring objective attention evaluation.

Prefrontal cortex activation of return-to-work trainees in remission of mental disorders with depressive symptoms compared to that of healthy controls.

The increase in the number of patients with mental disorders with depressive symptoms has become a significant problem. To prevent people developing those disorders and help with the effective recovery, it is important to quantitatively and objectively monitor an individual's mental state. Previous studies have shown the relationship between negative or depressive mood state and human prefrontal cortex (PFC) activation during verbal and spatial working memory tasks based on a near-infrared spectroscopy imaging technique. In this study, we aimed to explore a biomarker of the mental state of people in remission of mental disorders with depressive symptoms using this technique. We obtained the PFC activation of return-to-work (RTW) trainees in remission of those disorders, compared that of healthy controls, and obtained subjective questionnaire scores with the Profile of Mood States. We compared the PFC activation with the questionnaire scores by receiver operating characteristic analysis using a logistic-regression model. The results showed that the PFC activation indicates a healthy state compared to that of the RTW trainees evaluated by area-under-curve analysis. This study demonstrates that our PFC measurement technique will be useful as a quantitative and objective assessment of mental state.

Distinct Methylphenidate-Evoked Response Measured Using Functional Near-Infrared Spectroscopy During Go/No-Go Task as a Supporting Differential Diagnostic Tool Between Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder Comorbid Children.

Attention deficit/hyperactivity disorder (ADHD) has been frequently reported as co-occurring with autism spectrum disorder (ASD). However, ASD-comorbid ADHD is difficult to diagnose since clinically significant symptoms are similar in both disorders. Therefore, we propose a classification method of differentially recognizing the ASD-comorbid condition in ADHD children. The classification method was investigated based on functional brain imaging measured by near-infrared spectroscopy (NIRS) during a go/no-go task. Optimization and cross-validation of the classification method was carried out in medicated-naïve and methylphenidate (MPH) administered ADHD and ASD-comorbid ADHD children (randomized, double-blind, placebo-controlled, and crossover design) to select robust parameters and cut-off thresholds. The parameters could be defined as either single or averaged multi-channel task-evoked activations under an administration condition (i.e., pre-medication, post-MPH, and post-placebo). The ADHD children were distinguished by significantly high MPH-evoked activation in the right hemisphere near the midline vertex. The ASD-comorbid ADHD children tended to have low activation responses in all regions. High specificity (86 ± 4.1%; mean ± SD), sensitivity (93 ± 7.3%), and accuracy (82 ± 1.6%) were obtained using the activation of oxygenated-hemoglobin concentration change in right middle frontal, angular, and precentral gyri under MPH medication. Therefore, the significantly differing MPH-evoked responses are potentially effective features and as supporting differential diagnostic tools.

Denoising of neuronal signal from mixed systemic low-frequency oscillation using peripheral measurement as noise regressor in near-infrared imaging.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive functional imaging technique measuring hemodynamic changes including oxygenated ( O 2 Hb ) and deoxygenated (HHb) hemoglobin. Low frequency (LF; 0.01 to 0.15 Hz) band is commonly analyzed in fNIRS to represent neuronal activation. However, systemic physiological artifacts (i.e., nonneuronal) likely occur also in overlapping frequency bands. We measured peripheral photoplethysmogram (PPG) signal concurrently with fNIRS (at prefrontal region) to extract the low-frequency oscillations (LFOs) as systemic noise regressors. We investigated three main points in this study: (1) the relationship between prefrontal fNIRS and peripheral PPG signals; (2) the denoising potential using these peripheral LFOs, and (3) the innovative ways to avoid the false-positive result in fNIRS studies. We employed spatial working memory (WM) and control tasks (e.g., resting state) to illustrate these points. Our results showed: (1) correlation between signals from prefrontal fNIRS and peripheral PPG is region-dependent. The high correlation with peripheral ear signal (i.e., O 2 Hb ) occurred mainly in frontopolar regions in both spatial WM and control tasks. This may indicate the finding of task-dependent effect even in peripheral signals. We also found that the PPG recording at the ear has a high correlation with prefrontal fNIRS signal than the finger signals. (2) The systemic noise was reduced by 25% to 34% on average across regions, with a maximum of 39% to 58% in the highly correlated frontopolar region, by using these peripheral LFOs as noise regressors. (3) By performing the control tasks, we confirmed that the statistically significant activation was observed in the spatial WM task, not in the controls. This suggested that systemic (and any other) noises unlikely violated the major statistical inference. (4) Lastly, by denoising using the task-related signals, the significant activation of region-of-interest was still observed suggesting the manifest task-evoked response in the spatial WM task.

Erratum: Adaptive algorithm utilizing acceptance rate for eliminating noisy epochs in block-design functional near-infrared spectroscopy data: application to study in attention deficit/hyperactivity disorder children.

[This corrects the article DOI: 10.1117/1.NPh.5.4.045001.].