File Name: principles techniques and limitations of near infrared spectroscopy .zip
In the data analysis of functional near-infrared spectroscopy fNIRS , linear model frameworks, in particular mass univariate analysis, are often used when researchers consider examining the difference between conditions at each sampled time point. However, some statistical issues, such as assumptions of linearity, autocorrelation and multiple comparison problems, influence statistical inferences when mass univariate analysis is used on fNIRS time course data. In order to address these issues, the present study proposes a novel perspective, multi-time-point analysis MTPA , to discriminate signal differences between conditions by combining temporal information from multiple time points in fNIRS.
- Novel method for shark age estimation using near infrared spectroscopy
- Using Near-Infrared Spectroscopy in Agricultural Systems
- Principles, Techniques, and Limitations of Near Infrared Spectroscopy
- Multi-time-point analysis: A time course analysis with functional near-infrared spectroscopy
Metrics details. Near-infrared spectroscopy NIRS has become an increasingly valuable tool to monitor tissue oxygenation T oxy in vivo.
Novel method for shark age estimation using near infrared spectroscopy
In the data analysis of functional near-infrared spectroscopy fNIRS , linear model frameworks, in particular mass univariate analysis, are often used when researchers consider examining the difference between conditions at each sampled time point.
However, some statistical issues, such as assumptions of linearity, autocorrelation and multiple comparison problems, influence statistical inferences when mass univariate analysis is used on fNIRS time course data.
In order to address these issues, the present study proposes a novel perspective, multi-time-point analysis MTPA , to discriminate signal differences between conditions by combining temporal information from multiple time points in fNIRS. In addition, MTPA adopts the random forest algorithm from the statistical learning domain, followed by a series of cross-validation procedures, providing reasonable power for detecting significant time points and ensuring generalizability.
Using a real fNIRS data set, the proposed MTPA outperformed mass univariate analysis in detecting more time points, showing significant differences between experimental conditions. The data set and all source code are available for researchers to replicate the analyses and to adapt the program for their own needs in future fNIRS studies. Functional near-infrared spectroscopy fNIRS is a noninvasive tool for recording hemodynamic activity along the scalp time-locked to response events.
Previous studies suggest that fNIRS data are highly consistent with data from the most widely used neuroimaging modality, functional magnetic resonance imaging fMRI Strangman, Boas, et al.
Based on its temporal resolution, fNIRS can thus provide specific time course information for physical or mental events in the human brain. In fNIRS statistical analysis, the most common approach is to average signals across all time points of the event of interest, but this technique is limited by loss of time course information. Statistical methods such as the t test or analysis of variance ANOVA would then be applied on these averaged values in order to draw a conclusion Germon et al.
In other words, the relationship between cognitive states and the time course of brain signals may not be revealed when using this approach. However, the general linear model is still limited in that it converts hemodynamic response function HRF curves into a single beta value, which only represents an activation level rather than real-time course information of individual trials.
In order to obtain the time course information for fNIRS data, researchers usually implement mass univariate analysis. Under the linear model framework, mass univariate analysis can examine the differences between different experimental conditions at each individual time point H.
For example, H. Chen et al. For instance, in the aforementioned study by Chen et al. Based on these procedures of mass univariate analysis, it seems that neuroscientists can make further inferences regarding the time course of fNIRS data. Although findings obtained by mass univariate analysis have increased our understanding of fNIRS time course, several methodological issues still exist that actually limit inference and generalizability.
First, it is apparent that adjusting the level of statistical significance, which is necessary for multiple testing in an fNIRS time course, will decrease the power of the testing procedure under a given effect size. This autocorrelation issue is not considered in mass univariate analysis, which assumes that the covariance across neighboring time points is not informative. Furthermore, mass univariate analysis assumes that the relationships between brain signals and psychological states are linear.
However, the assumptions of a linear relationship between the brain and behavior are inevitably limited by the problem of individual differences. Consider a situation in which 10 of 20 people have higher hemodynamic changes in the concentration of oxyhemoglobin during one condition and lower during another condition, but the reverse is shown for the other 10 people.
These differences will not be detected using mass univariate analysis. Thus these shortcomings must be taken into consideration when using mass univariate analysis, which may not be able to correctly detect the differences between experimental conditions. The objective of the current study was to provide a novel perspective that we call multi-time-point analysis MTPA.
This method aims to extract fNIRS time course information to discriminate signal differences between different underlying cognitive states i. In general, MTPA treats fNIRS data analysis as a supervised classification problem, where the experimental condition is the variable of primary interest and the signals at each time point are predictors.
A tree-based statistical learning method, the random forest Breiman, , was implemented as the major algorithm. The following sections are organized as a series of demonstrations on an fNIRS data set.
We first apply mass univariate analysis to the data set. The proposed MTPA is then used to analyze the same data set.
Finally, we compare the MTPA with the mass univariate analysis, and describe the pros and cons of the two methods. The data for one subject was excluded due to severe motion.
The remaining 14 subjects were right-handed, with normal or corrected-to-normal vision, and no specific disease or cognitive disorder. The experiment was conducted following local institutional review board regulations, and all participants provided their written consent before the experiment.
The subjects were asked to complete a meaning judgment task. In the task, two visual Chinese characters were presented sequentially, and the subjects had to quickly and accurately indicate whether or not the character pair was related in meaning related or unrelated by pressing the yes or no buttons with their right hand.
Furthermore, 24 pairs of non-characters, which were made by replacing radicals of real characters with other radicals that did not form real Chinese characters, were included as the perceptual control condition. Participants were asked to indicate whether or not the two stimuli were identical by pressing a yes or no button as quickly and as accurately as possible. There were also 24 baseline trials with the first stimulus as a solid square and the second stimulus as a hollow square.
Participants were to press a button as soon as the solid square turned into the hollow square. Each participant received a different randomized sequence of these stimulus pairs. Before the formal experiment, subjects received 15 practice trials to become familiar with the procedure.
During the task, the optical signals were simultaneously collected by the fNIRS electronic control box serving as both the source and the receiver of the near-infrared light. The present study focused on left inferior frontal gyrus IFG and left middle temporal gyrus MTG , which were the most two consistently activated regions in previous language studies Gow, ; Hagoort, ; Jefferies, , as the primary regions of interest.
Therefore, we utilized four emitters to direct the two wavelengths of near-infrared light through the scalp and four detectors to receive the returning near-infrared light. Eight channels thus formed by pairs of emitters and detectors covered these regions of interest.
Illustration of emitter and detector positions. Filled circles with letters indicate laser detectors, open circles with letters indicate laser emitters, and the gray squares with numbers indicate channels.
Specifically, detector A FP1 received the near-infrared light from emitter A AF7 and emitter B 3 cm dorsal sides of FP1 , and respectively formed channel 2 and channel 1. Detector B 3 cm dorsal sides of AF7 formed channel 4 and channel 3. Similarly, detector C TP7 received the near-infrared light from emitter C P7 and emitter D 3 cm dorsal sides of TP7 , and formed channel 6 and channel 5.
Detector D P7 also formed channel 8 and channel 7. The distance 3 cm between a detector and an emitter was suggested by Hebden and Delpy The converted data were averaged from each individual trial and analyzed in s epochs including the onset 0 s to the end 16 s of the stimuli, indicating that the total number of sampling is time points in each curve.
Furthermore, the present study aggregated the signal from channel 1 to channel 4, which covered the IFG in the left hemisphere, as the primary estimated region of interest. In other words, there was only one curve per condition for each subject in the left IFG after aggregating.
The major purpose of this fNIRS experiment was to investigate the differences between these two conditions. Note that the analysis and the results of the left MTG channel 5 to channel 8 is not shown in the following sections in order to simplify the demonstration steps. Panel a shows the individual fNIRS curves by condition. Panel b shows the confidence intervals for mean fNIRS curves in two conditions. Conducting mass univariate analysis, such as a paired t test, at each sampled time point is a widely used approach to obtain the time course information of fNIRS.
Because there were time points in a curve for a condition, the mass univariate analysis required hypothesis testing times using a paired t test. The p value corrections must be performed because of multiple comparisons. With a FWER controlled by the Bonferroni test, the present study did not reveal any significant differences between these two conditions. Significance testing results between related and unrelated conditions for mass univariate analysis. The time series is shown on the x -axis.
It is thus crucial to note that the larger value on the y -axis indicates a smaller p value. The central idea in the use of nonparametric permutation frameworks was to use the collected data itself to generate an empirical null distribution of the maximum statistics. An empirical null distribution of the maximum statistic could be accordingly provided by repeating this process many times.
That is, each curve was randomly relabeled with a new label using a combination of subject and condition. There were 28! For each relabeling, paired t tests were computed, and the maximum t -statistic was recorded. Thus, a permutation-based null distribution of the maximum t -statistic was generated to assess the significance of the experiment.
The significance threshold was 3. Any time point with a t value greater than 3. With a FWER controlled by this maximum t- statistic framework, nonparametric permutation results showed no significant difference between two conditions. The results of nonparametric permutation frameworks. The null distribution of the maximum t- statistic and the distribution of the maximum suprathreshold temporal cluster size are shown. No significant difference was revealed in these two nonparametric permutation frameworks.
Spatial clusters were defined by connected suprathreshold regions e. Large spatial clusters suggested significant differences between the two conditions. Likewise, in the present study, temporal clusters were defined by connected suprathreshold time points. Hence, large temporal clusters could suggest a significant difference between two conditions.
We used a permutation procedure similar to the aforementioned one. The only difference was that the recorded maximum statistic was the max STCS. Similarly, a permutation-based null distribution of max STCS was generated.
The 95th percentile of this max STCS distribution was used as the significance threshold, which was 37 in the present study Fig.
In this framework, no significant temporal cluster was revealed. In addition to the issues of power, mass univariate analysis assumes that the covariance across neighboring time points is not informative, but this is not the case in highly auto-correlated fNIRS data. Also, the assumption of linear relationships between the brain fNIRS data and behaviors conditions would also influence the inferences by mass univariate analysis.
To briefly sum up, it was demonstrated that, regardless of the correction method used i. Due to the aforementioned issues including low power, autocorrelation problems, and the assumptions of linearity, mass univariate analysis was not able to detect the significant differences between conditions, and might further bias the results, conclusions and implications. In order to address these issues, the present MTPA method provided a different perspective that uses time course information to classify two conditions, instead of examining the difference between two conditions at each time point, and adopts a popular ensemble-learning model, the random forest Breiman, Thus, we treated each time point as a predictor but not a dependent variable, and experimental conditions were considered as the dependent variable but not an independent variable.
In general, this concept of classification was somehow similar to some much more well-known methods, such as logistic regression and discriminant analysis.
Using Near-Infrared Spectroscopy in Agricultural Systems
Fourier-transform infrared spectroscopy FTIR  is a technique used to obtain an infrared spectrum of absorption or emission of a solid, liquid or gas. An FTIR spectrometer simultaneously collects high-resolution spectral data over a wide spectral range. This confers a significant advantage over a dispersive spectrometer, which measures intensity over a narrow range of wavelengths at a time. The term Fourier-transform infrared spectroscopy originates from the fact that a Fourier transform a mathematical process is required to convert the raw data into the actual spectrum. The most straightforward way to do this, the "dispersive spectroscopy" technique, is to shine a monochromatic light beam at a sample, measure how much of the light is absorbed, and repeat for each different wavelength. This is how some UV—vis spectrometers work, for example.
Marcelo V. Fernanda S. Costa c. The aim of this study was to quantitatively determine the olanzapine in a pharmaceutical formulation for assessing the potentiality of near infrared spectroscopy NIR combined with partial least squares PLS regression. The method was developed with samples based on a commercial formulation containing olanzapine and seven excipients. The method was validated in the range from 1.
The system can't perform the operation now. Try again later. Citations per year. Duplicate citations. The following articles are merged in Scholar. Their combined citations are counted only for the first article. Merged citations.
This review summarizes the most recent literature about the principles, techniques, advantages, limitations, and applications of NIRS in exercise physiology and.
Principles, Techniques, and Limitations of Near Infrared Spectroscopy
Continuous wave near infrared spectroscopy CW NIRS provides non-invasive technology to measure relative changes in oxy- and deoxy-haemoglobin in a dynamic environment. NIRS parameters that measure O 2 delivery and capacity to utilise O 2 in the muscle have been developed based on response to physiological interventions and exercise. NIRS has good reproducibility and agreement with gold standard techniques and can be used in clinical populations where muscle oxidative capacity or oxygen delivery or both are impaired. CW NIRS has limitations including: the unknown contribution of myoglobin to the overall signals, the impact of adipose tissue thickness, skin perfusion during exercise, and variations in skin pigmentation. These, in the main, can be circumvented through appropriate study design or measurement of absolute tissue saturation.
Near Infrared Spectroscopy: fundamentals, practical aspects and analytical applications. It is addressed to the reader who does not have a profound knowledge of vibrational spectroscopy but wants to be introduced to the analytical potentialities of this fascinating technique and, at same time, be conscious of its limitations. Essential theory background, an outline of modern instrument design, practical aspects, and applications in a number of different fields are presented.
This chapter provides a review on the state of art of the use of the visible near-infrared vis-NIR spectroscopy technique to determine mineral nutrients, organic compounds, and other physical and chemical characteristics in samples from agricultural systems—such as plant tissues, soils, fruits, cocomposted sewage sludge and wastes, cereals, and forage and silage. Currently, all this information is needed to be able to carry out the appropriate fertilization of crops, to handle agricultural soils, determine the organoleptic characteristics of fruit and vegetable products, discover the characteristics of the various substrates obtained in composting processes, and characterize byproducts from the industrial sector. All this needs a large number of samples that must be analyzed; this is a time-consuming work, leading to high economic costs and, obviously, having a negative environmental impact owing to the production of noxious chemicals during the analyses.
Multi-time-point analysis: A time course analysis with functional near-infrared spectroscopy
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Сьюзан снова завладели прежние сомнения: правильно ли они поступают, решив сохранить ключ и взломать Цифровую крепость. Ей было не по себе, хотя пока, можно сказать, им сопутствовала удача. Чудесным образом Северная Дакота обнаружился прямо под носом и теперь попал в западню. Правда, оставалась еще одна проблема - Дэвид до сих пор не нашел второй экземпляр ключа.
В некотором отдалении от него возникла фигура человека, приближавшегося медленно и неотвратимо. В руке его поблескивал пистолет. Беккер, отступая к стене, вновь обрел способность мыслить четко и ясно.