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Statistical Analysis of fNIRS Data: A Comprehensive Review.
Abstract
Functional near-infrared spectroscopy (fNIRS) is a non-invasive method to measure brain activities using the changes of optical absorption in the brain through the intact skull. fNIRS has many advantages over other neuroimaging modalities such as positron emission tomography (PET), functional magnetic resonance imaging (fMRI), or magnetoencephalography (MEG), since it can directly measure blood oxygenation level changes related to neural activation with high temporal resolution. However, fNIRS signals are highly corrupted by measurement noises and physiology-based systemic interference. Careful statistical analyses are therefore required to extract neuronal activity-related signals from fNIRS data. In this paper, we provide an extensive reviewof historical developments of statistical analysis for fNIRS signal, which includes motion artifact correction, short source-detector separation correction, principal component analysis (PCA)/independent component analysis (ICA), false discovery rate (FDR), serially-correlated errors, as well as inference techniques such as the standard t-test, F-test, analysis of variance (ANOVA), and statistical parameter mapping (SPM) framework. In addition, to provide a unified view of various existing inference techniques, we explain a linear mixed effect model with restricted maximum likelihood (ReML) variance estimation, and show that most of the existing inference methods for fNIRS analysis can be derived as special cases. Some of the open issues in statistical analysis are also described.
... For example, fNIRS studies have used the False Discovery Rate (FDR) (Benjamini & Hochberg, 1995;Singh & Dan, 2006), Bonferroni correction (Dunn, 1961), spatially contiguous activation (Sarah Lloyd-Fox, Blasi, Everdell, Elwell, & Johnson, 2011;Southgate, Begus, Lloyd-Fox, di Gangi, & Hamilton, 2014), and Monte Carlo simulation (de Klerk, Bulgarelli, Hamilton, & Southgate, 2019). While the baseline-corrected averaging method is relatively easy to implement with fNIRS infant data, and avoids assumptions about the shape of the haemodynamic signal and its time course, it also disregards important timing information (Tak & Ye, 2014). Conversely, the General Linear Model (GLM) considers the whole-time course, thus proving a more powerful approach to the analysis of fNIRS data by making use of its good temporal resolution (Pinti et al., 2017). ...
... While the GLM approach was originally implemented with fMRI data (Friston et al., 1996) it has been adapted for use with optical data (e.g., the NIRS-SPM toolbox; Ye, Tak, Jang, Jung, & Jang, 2009), based on the similarities between fMRI and NIRS designs and their reliance on the haemodynamic response. The GLM approach consists of modelling pre-specified regressors, which are then convolved with the expected haemodynamic response function (HRF) and fitted to the data (Pinti et al., 2017;Tak & Ye, 2014). However, the disadvantage of the GLM is that it assumes a predefined HRF, which e.g., can differ both within-and between-subjects and is not well established in newborns and young infants. ...
... Specifically, thirty 4-to-6-month-old infants were presented with upright and inverted face stimuli in a block design. We relied on previous procedures used with infant fNIRS data to perform our baseline-corrected averaging (de Klerk, Hamilton, & Southgate, 2018) and GLM-based analyses (de Klerk et al, 2019;Pinti et al., 2017;Tak & Ye, 2014). For the multivariate analysis we used the MVPA method combined with machine learning techniques (by making use of support vector machine; SVMa supervised learning model) which are frequently employed in multivariate analysis of adult fNIRS (Ichikawa et al., 2014) and fMRI data (Norman, Polyn, Detre, & Haxby, 2006). ...
In the last decade, fNIRS has provided a non-invasive method to investigate neural activation in developmental populations. Despite its increasing use in developmental cognitive neuroscience, there is little consistency or consensus on how to pre-process and analyse infant fNIRS data. With this registered report, we investigated the feasibility of applying more advanced statistical analyses to infant fNIRS data and compared the most commonly used baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches, to show how the conclusions one would draw based on these different analysis approaches converge or differ. The different analysis methods were tested using a face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured in thirty 4-to-6-month-old infants. By including more standard approaches together with recent machine learning techniques, we aim to inform the fNIRS community on alternative ways to analyse infant fNIRS datasets.
... 4 However, current techniques for movement artifact correction (e.g., wavelet filtering, decomposition, spline interpolation, and so on) typically assume that the behavior of both wavelengths is similar in time, thus do not take advantage of the structured information offered by both wavelengths. [5][6][7] Twodimensional (2D) analyses require that data with more dimensions, such as fNIRS data, undergo superficial unfolding before processing, e.g., treating both wavelengths or HbO and HbR independently. Hence, some of these 2D analysis tools are forced to impose other nonphysiological constraints, such as orthogonality in the case of principal component analysis (PCA) or statistical independence for independent component analysis (ICA). ...
... Although there are several ways to approach PCA, e.g., dimensionality reduction, 8 classification, 9 from the signal decomposition point of view, PCA aims at extracting the so-called principal components, i.e., those components that explain the greatest amount of variance of the signal 10 activities in fNIRS. 6,7,10,11 In temporal PCA, the data is decomposed into a sum of components, each one formed by the product of two vectors: one representing the temporal principal component and the other, the corresponding topography (scores for each channel). A basic problem with PCA is that the components defined by only two signatures (time and space) are not uniquely determined. ...
... Therefore, orthogonality is imposed between the corresponding temporal signatures of the different components. 7,12,13 Orthogonality among brain signals is, however, a rather nonphysiological constraint. Even with this restriction, the extracted principal components are not completely unique, given that the arbitrary rotation of axes does not change the explained variance of the data. ...
Significance:
Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields.
Aim:
We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength).
Approach:
We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techniques, i.e., target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions.
Results:
PARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifact's characteristics.
Conclusions:
This study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.
... Accordingly, Pouliot et al. [7] concluded that GLM could be used as a legal analysis to examine fNIRS data for spikes and seizures. In 2014, Tak and Ye [8] systematically reviewed the commonly used statistics such as principal component analysis, independent component analysis, false discovery rate, and inference statistics such as the standard t-test, F-test, analysis of variance, and statistical parameter mapping framework. Eventually, they proposed adopting the GLM mixed-effect model with restricted maximum likelihood variance estimation to model hemodynamic changes [8]. ...
... In 2014, Tak and Ye [8] systematically reviewed the commonly used statistics such as principal component analysis, independent component analysis, false discovery rate, and inference statistics such as the standard t-test, F-test, analysis of variance, and statistical parameter mapping framework. Eventually, they proposed adopting the GLM mixed-effect model with restricted maximum likelihood variance estimation to model hemodynamic changes [8]. ...
General linear modeling (GLM) has been widely employed to estimate the hemodynamic changes observed by functional near infrared spectroscopy (fNIRS) technology, which are found to be nonlinear rather than linear, however. Therefore, GLM might not be appropriate for modeling the hemodynamic changes evoked by cognitive processing in developmental neurocognitive studies. There is an urgent need to identify a better statistical model to fit into the nonlinear fNIRS data. This study addressed this need by developing a quadratic equation model to reanalyze the existing fNIRS data (N = 38, Mage = 5.0 years, SD = 0.69 years, 17 girls) collected from the mixed-order design Dimensional Change Card Sort (DCCS) task and verified the model with a new set of data with the Habit-DisHabit design. First, comparing the quadratic and cubic modeling results of the mixed-order design data indicated that the proposed quadratic equation was better than GLM and cubic regression to model the oxygenated hemoglobin (HbO) changes in this task. Second, applying this quad-ratic model with the Habit-DisHabit design data verified its suitability and indicated that the new design was more effective in identifying the neural correlates of cognitive shifting than the mixed-order design. These findings jointly indicate that Habit-DisHabit Design with a quadratic equation might better model the hemodynamic changes in preschoolers during the DCCS task.
... Furthermore, p unc < 0.05 and p unc < 0.001, uncorrected for multiple comparisons, were considered as statistical thresholds for further reliability analyses as detailed below. These different statistical thresholds were chosen since they represent the most employed critical values in both fNIRS and fMRI studies (Poldrack et al., 2008;Tak and Ye, 2014;Yeung, 2018;Yücel et al., 2021). ...
... We focused on bilateral pre-and post-central gyri. The proposed surface-based approach was applied according to three different statistical thresholds (i.e., p FDR <0.05, p unc <0.05, p unc <0.001), which represent the most commonly utilized statistical thresholds in fNIRS-fMRI analyses (Poldrack et al., 2008;Tak and Ye, 2014;Yeung, 2018;Yücel et al., 2021). ...
Introduction:
Studies integrating functional near-infrared spectroscopy (fNIRS) with functional MRI (fMRI) employ heterogeneous methods in defining common regions of interest in which similarities are assessed. Therefore, spatial agreement and temporal correlation may not be reproducible across studies. In the present work, we address this issue by proposing a novel method for integration and analysis of fNIRS and fMRI over the cortical surface.
Materials and methods:
Eighteen healthy volunteers (age mean±SD 30.55±4.7, 7 males) performed a motor task during non-simultaneous fMRI and fNIRS acquisitions. First, fNIRS and fMRI data were integrated by projecting subject- and group-level source maps over the cortical surface mesh to define anatomically constrained functional ROIs (acfROI). Next, spatial agreement and temporal correlation were quantified as Dice Coefficient (DC) and Pearson's correlation coefficient between fNIRS-fMRI in the acfROIs.
Results:
Subject-level results revealed moderate to substantial spatial agreement (DC range 0.43 - 0.64), confirmed at the group-level only for blood oxygenation level-dependent (BOLD) signal vs. HbO2 (0.44 - 0.69), while lack of agreement was found for BOLD vs. HbR in some instances (0.05 - 0.49). Subject-level temporal correlation was moderate to strong (0.79 - 0.85 for BOLD vs. HbO2 and -0.62 - -0.72 for BOLD vs. HbR), while an overall strong correlation was found for group-level results (0.95 - 0.98 for BOLD vs. HbO2 and -0.91 - -0.94 for BOLD vs. HbR).
Conclusion:
The proposed method directly compares fNIRS and fMRI by projecting individual source maps to the cortical surface. Our results indicate spatial and temporal correspondence between fNIRS and fMRI, and promotes the use of fNIRS when more ecological acquision settings are required, such as longitudinal monitoring of brain activity.
... In comparison to alternative methodologies such as electroencephalography (EEG) and functional magnetic resonance imaging (fMRI), fNIRS exhibits less susceptibility to motion artifacts and offers higher temporal resolution. Furthermore, it facilitates data acquisition within ecologically valid settings, making it wellsuited for investigating the intricate landscape of emotion processing (Ferrari & Quaresima, 2012;Pinti et al., 2020;Quaresima & Ferrari, 2019;Tak & Ye, 2014). The portability, userfriendliness, and minimal interference associated with fNIRS systems further enhance their appeal for BCI designs. ...
... The PFC plays a pivotal role in the evaluation of emotional stimuli, the generation of appropriate emotional responses, and the modulation of emotional intensity. Extensive investigation utilizing various neuroimaging techniques, including fNIRS, consistently highlights the involvement of the PFC in the regulation of emotions (Glotzbach et al., 2011;Ozawa et al., 2019;Tak & Ye, 2014). By integrating fNIRS into affective BCIs, we stand to gain valuable insights into the specific contributions of the PFC in these intricate processes. ...
Emotion regulation, a fundamental aspect of human functioning, involves the ability to monitor, evaluate, and modify emotional responses. Understanding the neural mechanisms underlying emotion regulation holds significant implications across various disciplines, including psychology, neuroscience, and clinical psychiatry. This study aims to explore the neural correlates of emotion regulation using functional near-infrared spectroscopy (fNIRS) with a specific focus on the prefrontal cortex (PFC). fNIRS, a non-invasive and portable brain imaging technology, offers an excellent opportunity to investigate real-life emotion processing with high temporal resolution. Twenty participants underwent an experimental protocol where they viewed emotional pictures from the International Affective Picture System (IAPS) database, varying in valence (positive and negative) and arousal (high and low). fNIRS data were collected during the picture presentation, and the hemodynamic responses in the PFC were analyzed. The findings demonstrated distinct spatiotemporal patterns of activation associated with different emotional states. Positive valence stimuli elicited higher hemodynamic activation in bilateral dorsolateral prefrontal cortex (DLPFC) and orbitofrontal cortex (OFC) regions when compared to negative valence stimuli. On the other hand, negative valence stimuli induced higher activation in the medial prefrontal cortex (mPFC) when compared to positive valence stimuli. Moreover, high arousal positive valence stimuli evoked higher activation in the left DLPFC region when compared to high arousal negative valence stimuli. These results shed light on the differential neural processing of positive and negative emotions within the PFC, supporting the notion of lateralized emotional processing. The study validates the feasibility of fNIRS for objectively capturing emotion-related neural activity, providing valuable insights for future applications in emotion recognition and affective brain-computer interfaces (BCIs). Understanding the neural basis of emotion regulation has significant implications for designing targeted interventions for individuals experiencing emotion dysregulation disorders. Additionally, the integration of fNIRS technology into affective BCIs may offer new possibilities for real-time emotion detection and communication in populations with communication challenges.
... The most critical step in analyzing fNIRS data is estimating cortical activity and localizing it. By adjusting the recorded HR to the predetermined dHRF, cortical activity may be determined [45], and its presence may be inferred from the associated channels' t-values. A higher t-value specifies that the measured channel signal (i.e., ∆HbO) and dHRF have a strong correlation. ...
... All the images were constructed at 227 × 227 pixel size. Only ∆HbO data were used for further analysis since several prior research has demonstrated that ∆HbO is a more sensitive and accurate indicator [3,26,45]. ...
This work investigates the classification of finger-tapping task images constructed for the initial dip duration of hemodynamics (HR) associated with the small brain area of the left motor cortex using functional near-infrared spectroscopy (fNIRS). Different layers (i.e., 16-layers, 19-layers, 22-layers, and 25-layers) of isolated convolutional neural network (CNN) designed from scratch are tested to classify the right-hand thumb and little finger-tapping tasks. Functional t-maps of finger-tapping tasks (thumb, little) were constructed for various durations (0.5 to 4 s with a uniform interval of 0.5 s) for the initial dip duration using a three gamma functions-based designed HR function. The results show that the 22-layered isolated CNN model yielded the highest classification accuracy of 89.2% with less complexity in classifying the functional t-maps of thumb and little fingers associated with the same small brain area using the initial dip. The results further demonstrated that the active brain area of the two tapping tasks from the same small brain area are highly different and well classified using functional t-maps of the initial dip (0.5 to 4 s) compared to functional t-maps generated for delayed HR (14 s). This study shows that the images constructed for initial dip duration can be helpful in the future for fNIRS-based diagnosis or cortical analysis of abnormal cerebral oxygen exchange in patients.
... It can thereby protect the researcher from implicit biases that may arise during data analysis (e.g., incorrectly recalling that the only significant effect is the one that was originally predicted). In fNIRS research, several recent efforts have been made to develop guidelines for preprocessing and analyzing fNIRS data, 9,35,36 as well as for reporting practices. 19 Similar guidance would be desirable for the planning stage of an fNIRS experiment. ...
... Furthermore, adequate correction methods for multiple comparisons should be stated (e.g., false discovery rate, Holm correction, and Bonferroni correction). 35,[164][165][166] To limit the number of statistical tests performed, researchers may first combine signals across multiple neighboring channels, yielding a smaller number of ROIs. 167 However, this approach requires careful specification of the methods used to define (1) which channel belongs to which ROI and how was this defined (cf., Sec. ...
Significance
The expansion of functional near-infrared spectroscopy (fNIRS) methodology and analysis tools gives rise to various design and analytical decisions that researchers have to make. Several recent efforts have developed guidelines for preprocessing, analyzing, and reporting practices. For the planning stage of fNIRS studies, similar guidance is desirable. Study preregistration helps researchers to transparently document study protocols before conducting the study, including materials, methods, and analyses, and thus, others to verify, understand, and reproduce a study. Preregistration can thus serve as a useful tool for transparent, careful, and comprehensive fNIRS study design.
Aim
We aim to create a guide on the design and analysis steps involved in fNIRS studies and to provide a preregistration template specified for fNIRS studies.
Approach
The presented preregistration guide has a strong focus on fNIRS specific requirements, and the associated template provides examples based on continuous-wave (CW) fNIRS studies conducted in humans. These can, however, be extended to other types of fNIRS studies.
Results
On a step-by-step basis, we walk the fNIRS user through key methodological and analysis-related aspects central to a comprehensive fNIRS study design. These include items specific to the design of CW, task-based fNIRS studies, but also sections that are of general importance, including an in-depth elaboration on sample size planning.
Conclusions
Our guide introduces these open science tools to the fNIRS community, providing researchers with an overview of key design aspects and specification recommendations for comprehensive study planning. As such it can be used as a template to preregister fNIRS studies or merely as a tool for transparent fNIRS study design.
... The purpose of this operation is to reduce the influence of the previous block on the hemodynamic activity of this block [40]. The degree of correlation between HbO2 changes and the experimentally designed time-series model was calculated using a generalized linear model (GLM) [41]. GLM can quantify the amplitude of the hemodynamic response and the significance of cortical activation, and we performed a statistical analysis of the HbO2 data based on GLM. ...
... A one-sample t-test can be used to test the difference of means in fNIRS experiments [43]. False discovery rate (FDR) can reduce the errors in performing statistical tests in multiple comparison problems [41]. FDR has been applied to channel-wise analysis of fNIRS data, for making inferences about the activated region. ...
Tinnitus is an auditory phantom percept that affects the perception of sound in the patient’s ears, and the incidence of prolonged tinnitus is as high as ten to fifteen percent. Acupuncture is a unique treatment method in Chinese medicine, and it has great advantages in the treatment of tinnitus. However, tinnitus is a subjective symptom of patients, and there is currently no objective detection method to reflect the improvement effect of acupuncture on tinnitus. We used functional near-infrared spectroscopy (fNIRS) to explore the effect of acupuncture on the cerebral cortex of tinnitus patients. We collected the scores of the tinnitus disorder inventory (THI), tinnitus evaluation questionnaire (TEQ), hamilton anxiety scale (HAMA), and hamilton depression scale (HAMD) of eighteen subjects before and after acupuncture treatment, and the fNIRS signals of these subjects in sound-evoked activity before and after acupuncture treatment. According to the fNIRS detection results of tinnitus patients, acupuncture increased the concentration of oxygenated hemoglobin in the temporal lobe of tinnitus patients, and affected the activation of the auditory cortex. The study may reflect the neural mechanisms of acupuncture treatment for tinnitus and ultimately help to provide an objective evaluation method for the therapeutic effect of acupuncture treatment for tinnitus.
... Many Design Science studies with fNIRS and fMRI focused on commonly applied analysis methods such as EEG power spectral analyses or block-average and GLM analyses. Generalized linear modeling is widely applied in fMRI and fNIRS research (Monti, 2011;Tak & Ye, 2014) and allows brain activation assessment within regions and networks. This method attempts to model small portions of an expected hemodynamic response through convolution onto the recorded fMRI or fNIRS data (Monti, 2011;Tak & Ye, 2014). ...
... Generalized linear modeling is widely applied in fMRI and fNIRS research (Monti, 2011;Tak & Ye, 2014) and allows brain activation assessment within regions and networks. This method attempts to model small portions of an expected hemodynamic response through convolution onto the recorded fMRI or fNIRS data (Monti, 2011;Tak & Ye, 2014). In contrast, Neuroscience publications tended to conduct more advanced analyses such as functional connectivity and network analyses. ...
Design Neurocognition is an emerging research field that aims to elucidate the "black-box" of a designer's mind through the use of brain imaging tools such as electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS) and magnetic resonance imaging (MRI). More than a decade worth of study has led to many interesting research findings. Here, we systematically review the existing literature in the field. We identify 82 publications and provide an overview of nine main research topics that have been studied. We uncover a large variety of methodological approaches that currently hamper the evaluation of research findings through meta-analysis. Finally, we provide recommendations to advance the field and collaboratively generate a more complete understanding of human behavior during design activities.
... Although this type of setup is very easy to implement, it has the disadvantage that it is not always easy to distinguish when changes in intensity are produced by hemodynamics of the brain or by other physiology, e.g., by hemodynamic changes in the scalp or motion artifacts. 6,7 One of the reasons for this is that all photons reaching the detector are modulated by skin hemodynamics produced by respiration and the cardiac pulse. Furthermore, CW fNIRS detectors integrate photons from a large number of paths in the tissue, many of which did not reach the brain, reducing the contrast to brain ratio. ...
... We performed multiple simulations with different absorption coefficients from 0.009 to 0.03 mm −1 (in intervals of 0.001 mm −1 ). The simulated number of photons was 1 × 10 8 with the max number of detected photons set as 1 × 10 6 . ...
Significance
Advances in electronics have allowed the recent development of compact, high channel count time domain functional near-infrared spectroscopy (TD-fNIRS) systems. Temporal moment analysis has been proposed for increased brain sensitivity due to the depth selectivity of higher order temporal moments. We propose a general linear model (GLM) incorporating TD moment data and auxiliary physiological measurements, such as short separation channels, to improve the recovery of the HRF.
Aims
We compare the performance of previously reported multi-distance TD moment techniques to commonly used techniques for continuous wave (CW) fNIRS hemodynamic response function (HRF) recovery, namely block averaging and CW GLM. Additionally, we compare the multi-distance TD moment technique to TD moment GLM.
Approach
We augmented resting TD-fNIRS moment data (six subjects) with known synthetic HRFs. We then employed block averaging and GLM techniques with “short-separation regression” designed both for CW and TD to recover the HRFs. We calculated the root mean square error (RMSE) and the correlation of the recovered HRF to the ground truth. We compared the performance of equivalent CW and TD techniques with paired t-tests.
Results
We found that, on average, TD moment HRF recovery improves correlations by 98% and 48% for HbO and HbR respectively, over CW GLM. The improvement on the correlation for TD GLM over TD moment is 12% (HbO) and 27% (HbR). RMSE decreases 56% and 52% (HbO and HbR) for TD moment compared to CW GLM. We found no statistically significant improvement in the RMSE for TD GLM compared to TD moment.
Conclusions
Properly covariance-scaled TD moment techniques outperform their CW equivalents in both RMSE and correlation in the recovery of the synthetic HRFs. Furthermore, our proposed TD GLM based on moments outperforms regular TD moment analysis, while allowing the incorporation of auxiliary measurements of the confounding physiological signals from the scalp.
... SPSS 25.0 software was used for statistical analysis (Tak and Ye, 2014), and GraphPad 8.0 was used to draw data results. All subjects' MMSE, FC value of brain network, reaction time of judgment test and correct rate data were tested normally, and all measurement data were expressed by mean standard deviation. ...
Objective: To study the brain network mechanism of cognitive control in the
elderly with brain aging.
Materials and methods: 21 normal young people and 20 elderly people were
included in this study. Mini-mental State Examination and functional near-infrared
spectroscopy (fNIRS) synchronous judgment test (including forward tests and
reverse judgment tests) were performed on all subjects. To observe and compare
differences in brain region activation and brain functional connectivity between
subjects and forward and reverse trials by recording functional connectivity (FC)
in different task paradigms and calculating bilateral prefrontal and primary motor
cortical (PMC) areas.
Results: In the forward and reverse judgment tests, the reaction time of the elderly
group was significantly longer than the young group (P < 0.05), and there was no
significant difference in the correct rate. In the homologous regions of interest
(ROI) data, the FC of PMC and prefrontal cortex (PFC) in the elderly group was
significantly decreased (P < 0.05). In the heterologous ROI data, except for left
primary motor cortex (LPMC)-left prefrontal cortex (LPFC), the other PMC and
PFC of the elderly group were significantly lower than the young group (P < 0.05)
while processing the forward judgment test. However, the heterologous ROI data
of LPMC-right prefrontal cortex (RPFC), LPMC-LPFC and RPFC-LPFC in the elderly
group were significantly lower than the young group (P < 0.05) while processing
the reverse judgment test.
Conclusion: The results suggest that brain aging affected degeneration of whole
brain function, which reduce the speed of information processing and form a
brain network functional connection mode different from that of young people.
... The regions of the brain that can be examined via fNIRS include the lateral prefrontal cortex (LPFC) and the medial prefrontal cortex (MPFC). Since a delay occurs between response to a stimulus and peak oxygenation readout on an fNIRS device (Tak and Ye 2014), most fNIRS researchers (Kovelman et al. 2008;Minagawa-Kawai et al. 2007;Zinszer et al. 2015) have employed block designs to capture data. These designs measure peak oxygenation levels during blocks of time starting approximately 5 s after the initial presentation of target stimuli and are measured against a baseline of oxygenation data. ...
The study investigated word recognition during neural activation in monolinguals and bilinguals. We specifically examined word retrieval and blood-oxygenation changes in the prefrontal cortex during a code-mixed word recognition task. Participants completed a gating task incorporating monolingual sentences and Spanish-English code-mixed sentences while using functional near-infrared spectroscopy (fNIRS) to measure blood-oxygenation changes. Word recognition contained four phonotactic conditions: (1) voiceless initial consonants, (2) voiced initial consonants, (3) CV-tense words, and (4) CV-lax words. Bilingual speakers had word-recognition capabilities similar to monolingual speakers even when identifying English words. Word recognition outcomes suggested that prefrontal cortex functioning is similar for early age of acquisition (AOA) bilinguals and monolinguals when identifying words in both code-mixed and monolingual sentences. Monolingual speakers experienced difficulty with English-voiced consonant sounds; while bilingual speakers experienced difficulties with English-lax vowels. Results suggest that localization of speech perception may be similar for both monolingual and bilingual populations, yet levels of activation differed. Our findings suggest that this parity is due to early age of acquisition (AoA) bilinguals finding a balance of language capabilities (i.e., native-like proficiency) and that in some instances the bilingual speakers processed language in the same areas dedicated to first language processing.
... Only data between 0.01 and 0.03 Hz were retained for the subsequent steps (Niu et al., 2013). The optical density data was further converted to the concentrations of HbO based on the modified Beer-Lambert law (Delpy et al., 1988;Tak & Ye, 2014). The first five seconds of data within each block were discarded due to unstable fluctuations, resulting in the averaging of thirty-six seconds of data across different blocks. ...
Developmental dyscalculia (DD) is a heterogenous mathematics learning difficulty, affecting approximately 4 to 7% of children. Despite its prevalence, our current understanding of the neural underpinnings of DD remains limited. This study probed DD’s neural heterogeneity through case study comparative analyses between dyscalculia-at-risk children (DR) with non-dyscalculia-at-risk (NDR) children. Utilizing functional near-infrared spectroscopy, brain data from resting states and a mathematical computation task (addition) were acquired and analysed, using Graph theory assessing brain global and nodal network indicators. By comparing DR cases’ network indicators and activation with NDR children’s data, three DR cases demonstrated lower nodal efficiency, providing insights into potential early biomarkers of DD. Moreover, the thorough investigation of single cases can offer valuable insights for devising personalized interventions for children with DD.
Research Highlights
Although the behavioural and cognitive heterogeneity of developmental dyscalculia (DD) has been investigated, its neural heterogeneity is under-researched.
Case-control design empowers researchers to probe individual idiosyncrasies, transcending the constraints imposed by summary statistics derived from group comparisons.
Graph theory metrics provided insights into the topological organization of the brain areas that underpins mathematical tasks, extending researchers’ understandings of their brain activations.
Three dyscalculia-at-risk cases not only demonstrated different behaviorual and neural profiles, but also showed similar neural deficits, providing insights into potential early biomarkers of DD.
... The two-bridge route refers to the first bridge between cognition and education, and the second bridge between cognition and neuroscience. Due to the tremendous progress made in neuroscience, neuroimaging technologies such as electroencephalogram (EEG), event-related potentials (ERP), functional magnetic resonance imaging (fMRI), and functional Beer-Lambert law (Tak & Ye, 2014), thereby obtaining neural hemodynamic data. ...
Functional near-infrared spectroscopy (fNIRS) has been applied in educational studies during the past decade, arousing tremendous attention, but lack of a systematic review, which prompted this paper to fill the gap. A systematic search with snowball approach identified 99 peer-reviewed journal papers for in-depth content analysis. The findings revealed that considerable attention was devoted to cognitive domain, while a discernible void was observed in the affective domain, accounting for a mere 10.1% of articles. Most participants were aged between 7 and 11 years old, while the adolescents were not sufficiently investigated. Most studies on infants investigated the temporal region, which showed the great potential of fNIRS exploring language function in the younger age group. More wearable or wireless fNIRS devices applied in education suggested its practicability of cognitive evaluation in physical education and skilled training. Finally, this paper proposed potential prospects for future trends adopting fNIRS in education research (e.g., learning science in real educational context, facilitating brain science in early education, learning analytics based on multi-modal data fusion).
... This may lead to missing important TOIs and ROIs when studying significant differences in the brain's hemodynamic response across conditions. Another fNIRS signal analysis approach that has been gaining popularity is the general linear model (GLM) (Tak and Ye, 2014;McCullagh and Nelder, 2019;Pinti et al., 2019;von Lühmann et al., 2020). The GLM aims to model the relationship between the fNIRS signals and experimental conditions. ...
The analysis of functional near-infrared spectroscopy (fNIRS) signals has not kept pace with the increased use of fNIRS in the behavioral and brain sciences. The popular grand averaging method collapses the oxygenated hemoglobin data within a predefined time of interest window and across multiple channels within a region of interest, potentially leading to a loss of important temporal and spatial information. On the other hand, the tensor decomposition method can reveal patterns in the data without making prior assumptions of the hemodynamic response and without losing temporal and spatial information. The aim of the current study was to examine whether the tensor decomposition method could identify significant effects and novel patterns compared to the commonly used grand averaging method for fNIRS signal analysis. We used two infant fNIRS datasets and applied tensor decomposition (i.e., canonical polyadic and Tucker decompositions) to analyze the significant differences in the hemodynamic response patterns across conditions. The codes are publicly available on GitHub. Bayesian analyses were performed to understand interaction effects. The results from the tensor decomposition method replicated the findings from the grand averaging method and uncovered additional patterns not detected by the grand averaging method. Our findings demonstrate that tensor decomposition is a feasible alternative method for analyzing fNIRS signals, offering a more comprehensive understanding of the data and its underlying patterns.
... The analysis of variance (ANOVA) is a common technique to determine localised brain activation based on changes in simultaneous HbO and HbR concentrations in repeated measures and block designs (Balters et al., 2021;Tak & Ye, 2014). Although it is common in the literature to report only HbO, HbR or HbT, the haemodynamic response is bi-dimensional. ...
Objective:
Using brain haemodynamic responses to measure perceived risk from traffic complexity during automated driving.
Background:
Although well-established during manual driving, the effects of driver risk perception during automated driving remain unknown. The use of fNIRS in this paper for assessing drivers' states posits it could become a novel method for measuring risk perception.
Methods:
Twenty-three volunteers participated in an empirical driving simulator experiment with automated driving capability. Driving conditions involved suburban and urban scenarios with varying levels of traffic complexity, culminating in an unexpected hazardous event. Perceived risk was measured via fNIRS within the prefrontal cortical haemoglobin oxygenation and from self-reports.
Results:
Prefrontal cortical haemoglobin oxygenation levels significantly increased, following self-reported perceived risk and traffic complexity, particularly during the hazardous scenario.
Conclusion:
This paper has demonstrated that fNIRS is a valuable research tool for measuring variations in perceived risk from traffic complexity during highly automated driving. Even though the responsibility over the driving task is delegated to the automated system and dispositional trust is high, drivers perceive moderate risk when traffic complexity builds up gradually, reflected in a corresponding significant increase in blood oxygenation levels, with both subjective (self-reports) and objective (fNIRS) increasing further during the hazardous scenario.
Application:
Little is known regarding the effects of drivers' risk perception with automated driving. Building upon our experimental findings, future work can use fNIRS to investigate the mental processes for risk assessment and the effects of perceived risk on driving behaviours to promote the safe adoption of automated driving technology.
... It accommodates a wide range of psychometrically validated WM tasks that require movement-related responses, and despite its lower spatial resolution relative to traditional functional neuroimaging approaches, fNIRS is a well-validated technique that compares favorably with fMRI (Cui et al., 2011;Huppert et al., 2006;Strangman et al., 2002). Additional advantages of fNIRS include its low cost, portability, relative comfort, brief set-up time, and potential for widespread availability (Buss et al., 2014;Cutini & Brigadoi, 2014;Ehlis et al., 2014;Tak & Ye, 2014;Xu et al., 2015). Consistent with the fMRI literature, previous studies using fNIRS reveal a general pattern of hypoactivation in the dorsolateral PFC for children with ADHD relative to typically developing children (cf. ...
Working memory impairments are an oft-reported deficit among children with ADHD, and complementary neuroimaging studies implicate reductions in prefrontal cortex (PFC) structure and function as a neurobiological explanation. Most imaging studies, however, rely on costly, movement-intolerant, and/or invasive methods to examine cortical differences. This is the first study to use a newer neuroimaging tool that overcomes these limitations, functional Near Infrared Spectroscopy (fNIRS), to investigate hypothesized prefrontal differences. Children (aged 8-12) with ADHD (N = 22) and typically developing (N = 18) children completed phonological working memory (PHWM) and short-term memory (PHSTM) tasks. Children with ADHD evinced poorer performance on both tasks, with greater differences observed in PHWM (Hedges' g = 0.67) relative to PHSTM (g = 0.39). fNIRS revealed reduced hemodynamic response among children with ADHD in the dorsolateral PFC while completing the PHWM task, but not within the anterior or posterior PFC. No between-group fNIRS differences were observed during the PHSTM task. Findings suggest that children with ADHD exhibit an inadequate hemodynamic response in a region of the brain that underlies PHWM abilities. The study also highlights the use of fNIRS as a cost-effective, noninvasive neuroimaging technique to localize/quantify neural activation patterns associated with executive functions.
... We have advocated strongly for these measures' generation and use since our first iteration of HAPPE software ( Gabard-Durnam, 2018 ). However, EEG research is well behind other human neuroscience modalities that have shifted normative practice to include 1) reporting empirical data quality metrics in manuscripts, and 2) evaluating artifact-related measures' impacts on brain measure variables of interest (e.g., Fishburn et al., 2019 ;Gratton et al., 2020 ;Parkes et al., 2018 ;Power et al., 2015 ;Tak and Ye, 2014 ). Though many EEG manuscripts report the number of artifactfree segments included in analyses, few studies report testing whether segment retention impacts their EEG measure estimates or include any information about data quality within those retained segments. ...
Electroencephalographic (EEG) methods have great potential to serve both basic and clinical science approaches to understand individual differences in human neural function. Importantly, the psychometric properties of EEG data, such as internal consistency and test-retest reliability, constrain their ability to differentiate individuals successfully. Rapid and recent technological and computational advancements in EEG research make it timely to revisit the topic of psychometric reliability in the context of individual difference analyses. Moreover, pediatric and clinical samples provide some of the most salient and urgent opportunities to apply individual difference approaches, but the changes these populations experience over time also provide unique challenges from a psychometric perspective. Here we take a developmental neuroscience perspective to consider progress and new opportunities for parsing the reliability and stability of individual differences in EEG measurements across the lifespan. We first conceptually map the different profiles of measurement reliability expected for different types of individual difference analyses over the lifespan. Next, we summarize and evaluate the state of the field's empirical knowledge and need for testing measurement reliability, both internal consistency and test-retest reliability, across EEG measures of power, event-related potentials, nonlinearity, and functional connectivity across ages. Finally, we highlight how standardized pre-processing software for EEG denoising and empirical metrics of individual data quality may be used to further improve EEG-based individual differences research moving forward. We also include recommendations and resources throughout that individual researchers can implement to improve the utility and reproducibility of individual differences analyses with EEG across the lifespan.
... For group analysis, mixed effects model was used to determine effects of the condition as fixed effects, and subject as a random effect (formula='beta -1 + cond + (1|subject)). The advantage of using mixed effects models is that they allow modelling both fixed and random effects in a data and therefore increase power of a model [95]. The false discovery rate (FDR) correction was used with the significance level set at 0.05 ( ≤ 0.05) [9]. ...
... The first is committed to constructing a realistic neuronal model and the second uses the concept of conditional independence among variables to define causal relationships. The article by Tak and Ye (2014) contains a detailed review of some statistical methods for the analysis of the fNIRS signal, which include signal processing methods: such as correlation-based methods (Cooper et al., 2012) and Principal Component Analysis (PCA)/Independent Component Analysis (ICA) (Wilcox et al., 2008;Patel et al., 2011); as well as statistical analysis methods: such as Analysis Of Variance (ANOVA) (Okamoto et al., 2004) and Statistical Parameter Mapping (SPM) (Friston et al., 1994). Recent applications use regression models to measure human social interaction, as in Barreto et al. (2021) where the authors propose a student fNIRS signal prediction model using the teachers' signal as predictors. ...
... SPSS 25.0 software was used for statistical analysis (Tak and Ye, 2014), and GraphPad 8.0 was used to draw data results. All subjects' MMSE, FC value of brain network, reaction time of judgment test and correct rate data were tested normally, and all measurement data were expressed by mean standard deviation. ...
Objective:
To study the brain network mechanism of cognitive control in the elderly with brain aging.
Materials and methods:
21 normal young people and 20 elderly people were included in this study. Mini-mental State Examination and functional near-infrared spectroscopy (fNIRS) synchronous judgment test (including forward tests and reverse judgment tests) were performed on all subjects. To observe and compare differences in brain region activation and brain functional connectivity between subjects and forward and reverse trials by recording functional connectivity (FC) in different task paradigms and calculating bilateral prefrontal and primary motor cortical (PMC) areas.
Results:
In the forward and reverse judgment tests, the reaction time of the elderly group was significantly longer than the young group (P < 0.05), and there was no significant difference in the correct rate. In the homologous regions of interest (ROI) data, the FC of PMC and prefrontal cortex (PFC) in the elderly group was significantly decreased (P < 0.05). In the heterologous ROI data, except for left primary motor cortex (LPMC)-left prefrontal cortex (LPFC), the other PMC and PFC of the elderly group were significantly lower than the young group (P < 0.05) while processing the forward judgment test. However, the heterologous ROI data of LPMC-right prefrontal cortex (RPFC), LPMC-LPFC and RPFC-LPFC in the elderly group were significantly lower than the young group (P < 0.05) while processing the reverse judgment test.
Conclusion:
The results suggest that brain aging affected degeneration of whole brain function, which reduce the speed of information processing and form a brain network functional connection mode different from that of young people.
... Modified Beer-Lambert law was used to transform measured optical densities into hemoglobin concentration. For each channel, the original fNIRS data were lowpass filtered and high-pass filtered through wavelet-based methods of hemodynamic response function (HRF) and Discrete Cosine Transform (DCT) with a cutoff period of 128 s to remove motion artifacts and physiological noise induced by heartbeat, breathing cycle and low frequency oscillations of blood pressure (Ye et al., 2009;Tak and Ye, 2014). The mean changes in HbO 2 and HbR concentration were obtained using the last 30 s of the resting state before the beginning of the task as a baseline. ...
Hands-on learning is proposed as a prerequisite for mathematics learning in kindergarten and primary school. However, it remains unclear that whether hands-on experience aids understanding of geometry knowledge for middle school students. We also know little about the neural basis underlying the value of hands-on experience in math education. In this study, 40 right-handed Chinese students (20 boys and 20 girls) with different academic levels were selected from 126 seventh-grade students in the same school, who learnt “Axisymmetric of an Isosceles Triangle” in different learning style (hands-on operation vs. video observation). Half of them operated the concrete manipulatives while the other half watched the instructional videos. The learning-test paradigm and functional near-infrared spectroscopy (fNIRS) technique were used to compare the differences in geometry reasoning involved in solving well-structured problems and ill-structured problems. Behavioral results showed that hands-on experience promoted students’ performances of geometry problem-solving. Students with lower academic level were more dependent on hands-on experience than those with higher academic level. The fNIRS results showed that meaningful hands-on experience with concrete manipulatives related to learning contents increased reactivation of the somatosensory association cortex during subsequent reasoning, which helped to improve the problem-solving performance. Hands-on experience also reduced students’ cognitive load during the well-structured problem-solving process. These findings contribute to better understand the value of hands-on experience in geometry learning and the implications for future mathematics classroom practices.
... In recent years, functional near-infrared spectroscopy (fNIRS) has emerged as a well-established imaging tool for neuroscience research (Eastmond et al., 2022). It has a high temporal resolution (Tak and Ye, 2014) and the capacity for monitoring in real clinical settings (Dybvik and Steinert, 2021;Gossé et al., 2022). fNIRS has gained increasing attention and application in acupuncture research (Fernandez Rojas et al., 2019;Ghafoor et al., 2019;Wong et al., 2021). ...
Background: Acupuncture reinforcing-reducing manipulation (ARRM) is a necessary procedure of traditional Chinese acupuncture and an essential factor affecting the therapeutic effect of acupuncture. Shaoshanhuo reinforcing method (SSH) and Toutianliang reducing method (TTL) are the most representative ARRMs. They integrate six single ARRMs and pose distinguished therapeutic effects of acupuncture. However, due to the complexity, diversity, and variation, investigating the mechanism of these two classic manipulations is insufficient. The neuroimaging technique is an important method to explore the central mechanism of SSH and TTL. This study attempted to design a randomized crossover trial based on functional near-infrared spectroscopy (fNIRS) to explore the mechanism of SSH and TTL, meanwhile, provide valuable methodological references for future studies.
Methods: A total of 30 healthy subjects were finally included and analyzed in this study. fNIRS examination was performed to record the neural responses during the two most representative ARRMs. The cortical activation and the inter-network functional connectivity (FC) were explored.
Results: The results found that SSH and TTL could elicit significant cerebral responses, respectively, but there was no difference between them.
Conclusion: Neuroimaging techniques with a higher spatiotemporal resolution, combinations of therapeutic effects, and strict quality control are important to neuroimaging studies on SSH and TTL.
... (a) Figure 1: (a) fNIRS cap on participant, (b) prefrontal cortex channel placement While wearing the fNIRS cap, students were asked to first complete a word tracing task to record baseline activation in their brain. This type of baseline recording is typical among neurocognitive studies [15], [16]. Subsequent to the word tracing, participants were asked to rest for thirty seconds by staring at a cross-hair. ...
Neuroimaging provides a relatively new approach for advancing engineering education by
exploring changes in neurocognition from educational interventions. The purpose of the research
described in this paper is to present the results of a preliminary study that measured students’
neurocognition while concept mapping. Engineering design is an iterative process of exploring
both the problem and solution spaces. To aid students in exploring these spaces, half of the 66
engineering students who participated in the study were first asked to develop a concept map and
then construct a design problem statement. The concept mapping activity significantly reduced
neurocognitive activation in the students’ left prefrontal cortex (PFC) compared to students who
did not receive this intervention when constructing their problem statement. The sub-region in
the left PFC that elicited less activation is generally associated with analytical judgment and
goal-directed planning. The group of students who completed the concept mapping activity had
greater focused neurocognitive activation in their right PFC. The right PFC is often associated
with divergent thinking and ill-structured representation. Patterns of functional connectivity
across students’ PFC also differed between the groups. The concept mapping activity reduced the
network density in students’ PFC. Lower network density is one measure of lower cognitive
effort. These results provide new insight into the neurocognition of engineering students when
designing and how educational interventions can change engineering students’ neurocognition. A
better understanding of how interventions like concept mapping shape students’ neurocognition,
and how this relates to learning, can lay the groundwork for novel advances in engineering
education that support new tools and pedagogy for engineering design.
... The 22 channels on the fNIRS cap were placed in accordance with the 10-20 system, shown in Figure 1 While wearing the fNIRS cap, students were asked to complete a word tracing exercise. This type of recording is typical among neurocognitive studies [38], [39]. The neuroimaging data collected during the word tracing excise was used as a baseline level of activation when writing and subtracted from the neuroimaging data when participants were writing their problem statements. ...
The research presented in this paper tested whether drawing concept maps changes how engineering students construct design problem statements and whether these differences are observable in their brains. The process of identifying and constructing problem statements is a critical step in engineering design. Concept mapping has the potential to expand the problem space that students explore through the attention given to the relationship between concepts. It
helps integrate existing knowledge in new ways. Engineering students (n=66) were asked to construct a problem statement to improve mobility on campus. Half of these students were randomly chosen to first receive instructions about how to develop a concept map and were asked to draw a concept map about mobility systems on campus. The semantic similarity of concepts in the students’ problem statements, the length of their problem statements, and their neurocognition when developing their statements were measured. The results indicated that students who were asked to first draw concept maps produced a more diverse problem statement with less semantically similar words. The students who first developed concept maps also produce significantly longer problem statements. Concept mapping changed students’ neurocognition. The students who used concept mapping elicited less cognitive activation in their left prefrontal cortex (PFC) and more concentrated activation in their right PFC. The right PFC is generally associated with divergent thinking and the left PFC is generally associated with
convergent and analytical thinking. These results provide new insight into how educational interventions, like concept mapping, can change students’ cognition and neurocognition. Better understanding how concept maps, and other tools, help students approach complex problems and the associated changes that occur in their brain can lay the groundwork for novel advances in engineering education that support new tools and pedagogy development for design.
... Multiple methods have been developed to control the FWER 1,2 for positron emission tomography (PET) and functional magnetic resonance imaging (fMRI). As statistical rigor has become a greater focus in the optical neuroimaging community, 3 multiple groups have adapted such methods to functional near-infrared spectroscopy [4][5][6] and widefield optical neuroimaging (WOI). 7 Such methods are often demonstrated using a task in which a known response is expected or with two groups between which a difference is expected. ...
Significance:
Statistical inference in functional neuroimaging is complicated by the multiple testing problem and spatial autocorrelation. Common methods in functional magnetic resonance imaging to control the familywise error rate (FWER) include random field theory (RFT) and permutation testing. The ability of these methods to control the FWER in optical neuroimaging has not been evaluated.
Aim:
We attempt to control the FWER in optical intrinsic signal imaging resting-state functional connectivity using both RFT and permutation inference at a nominal value of 0.05. The FWER was derived using a mass empirical analysis of real data in which the null is known to be true.
Approach:
Data from normal mice were repeatedly divided into two groups, and differences between functional connectivity maps were calculated with pixel-wise t -tests. As the null hypothesis was always true, all positives were false positives.
Results:
Gaussian RFT resulted in a higher than expected FWER with either cluster-based (0.15) or pixel-based (0.62) methods. t -distribution RFT could achieve FWERs of 0.05 (cluster-based or pixel-based). Permutation inference always controlled the FWER.
Conclusions:
RFT can lead to highly inflated FWERs. Although t -distribution RFT can be accurate, it is sensitive to statistical assumptions. Permutation inference is robust to statistical errors and accurately controls the FWER.
... The General Linear Model is the standard approach for analysing and interpreting hemodynamic responses [54], [62]. Among the range of possibilities this approach offers, the wellknown analysis of variance (ANOVA) is a common technique to determine localised brain activation based on changes in simultaneous HbO and HbR concentrations in repeated measures designs [63]. Although it is common in the related literature to report only HbO, HbR or HbT -i.e. the combination of both-the hemodynamic is a bi-dimensional response and both chromophores, HbO and HbR, usually correlate negatively during brain stimulation. ...
... MA, United States). In statistical parametric mapping analysis, a generalized linear model with standard hemodynamic response curves was established to simulate the hypothesized oxyHb response and examined to determine significant cortical activation during the experiment (Tak and Ye, 2014). At the group level, data were statistically analyzed on the basis of individual-level beta values to identify activated channels (corrected p < 0.05; Benjamini and Hochberg, 1995). ...
Objective
Gait is a complex behavior that involves not only the musculoskeletal system, but also higher-order brain functions, including cognition. This study was performed to investigate the correlation between lower limb muscle activity and cortical activation during treadmill walking in two groups of elderly people: the young-old (aged 65–74 years) and the old-old (aged 75–84 years).
Methods
Thirty-one young-old and 31 old-old people participated in this study. All participants were sequentially subjected to three gait conditions on a treadmill: (1) comfortable walking, (2) fast walking, and (3) cognitive dual-task walking. During treadmill walking, the activity of the lower limb muscles was measured using a surface electromyography system, and cortical activation was measured using a functional near-infrared spectroscopy system. The correlation between muscle activity and cortical activation during treadmill walking was analyzed and compared between the two groups.
Results
During comfortable walking, lower extremity muscle activity had a strong correlation with cortical activation, especially in the swing phase; this was significantly stronger in the young-old than the old-old. During fast walking, the correlations between lower limb muscle activity and cortical activation were stronger than those during comfortable walking in both groups. In cognitive dual-task walking, cortical activation in the frontal region and motor area was increased, although the correlation between muscle activity and cortical activation was weaker than that during comfortable walking in both groups.
Conclusion
The corticomotor correlation differed significantly between the old-old and the young-old. These results suggest that gait function is compensated by regulating corticomotor correlation as well as brain activity during walking in the elderly. These results could serve as a basis for developing gait training and fall prevention programs for the elderly.
... We approached the subject-level statistical analysis (SLSA) of the fNIRS data according to the general linear model (GLM). The GLM parameter estimation required us to avoid low-pass smoothing filtering (added to the mentioned high-pass filtering) to prevent artifactual temporal correlations and to obtain white noise residuals [63,64]. We specifically employed the autoregressive iteratively reweighted least squares (AR-IRLS) by Barker et al. [65]. ...
Functional near-infrared spectroscopy (fNIRS) is increasingly employed as an ecological neuroimaging technique in assessing age-related chronic neurological disorders, such as Parkinson’s disease (PD), mainly providing a cross-sectional characterization of clinical phenotypes in ecological settings. Current fNIRS studies in PD have investigated the effects of motor and non-motor impairment on cortical activity during gait and postural stability tasks, but no study has employed fNIRS as an ecological neuroimaging tool to assess PD at different stages. Therefore, in this work, we sought to investigate the cortical activity of PD patients during a motor grasping task and its relationship with both the staging of the pathology and its clinical variables. This study considered 39 PD patients (age 69.0 ± 7.64, 38 right-handed), subdivided into two groups at different stages by the Hoehn and Yahr (HY) scale: early PD (ePD; N = 13, HY = [1; 1.5]) and moderate PD (mPD; N = 26, HY = [2; 2.5; 3]). We employed a whole-head fNIRS system with 102 measurement channels to monitor brain activity. Group-level activation maps and region of interest (ROI) analysis were computed for ePD, mPD, and ePD vs. mPD contrasts. A ROI-based correlation analysis was also performed with respect to contrasted subject-level fNIRS data, focusing on age, a Cognitive Reserve Index questionnaire (CRIQ), disease duration, the Unified Parkinson’s Disease Rating Scale (UPDRS), and performances in the Stroop Color and Word (SCW) test. We observed group differences in age, disease duration, and the UPDRS, while no significant differences were found for CRIQ or SCW scores. Group-level activation maps revealed that the ePD group presented higher activation in motor and occipital areas than the mPD group, while the inverse trend was found in frontal areas. Significant correlations with CRIQ, disease duration, the UPDRS, and the SCW were mostly found in non-motor areas. The results are in line with current fNIRS and functional and anatomical MRI scientific literature suggesting that non-motor areas—primarily the prefrontal cortex area—provide a compensation mechanism for PD motor impairment. fNIRS may serve as a viable support for the longitudinal assessment of therapeutic and rehabilitation procedures, and define new prodromal, low-cost, and ecological biomarkers of disease progression.
... Bununla birlikte elektrotların üstüne pasta, jel gibi iletkenliği artırıcı maddelerin sürülmesinin gerekliliği ve verilerin elektromanyetik alanlardan, hareketten ve gürültüden dolayı kolayca bozulması (artefakt), bu tekniğin dezavantajlarıdır. fNIRS, beyin aktivitelerini ölçmek için invazif, yani ilaç veya cerrahi müdahale gerektirmeyen bir yöntemdir (Tak & Ye, 2013). Bununla birlikte fNIRS'de, fMRI tarafından üretilen yüksek seslere maruz kalınmaz. ...
Hizmetin özellikleri (soyutluk, ayrılmazlık vb.) ve tüketicilerin duygusal, bilinçsiz ve bilinçaltı durumlarından dolayı, turizm pa-zarlaması karmaşık bir olgudur (Boz vd., 2017). Turizm endüstrisi içerisinde işletmelerin içinde bulunduğu yoğun rekabet ortamı da düşünüldüğünde, pazarlamaya yönelik karmaşık analizleri doğru yapabilmek işletmeler için hayati önem taşımaktadır. Pazarlama araştırmaları, yöneticilere ihtiyaç ve talepleri anlama konusunda önemli veriler sunmakla beraber, kullanılan geleneksel yöntemle-rin başarısı sorgulanabilmektedir. Katılımcıların farklı nedenlerle kendini doğru ifade etmemesi veya edememesi hatalı pazarlama faaliyetlerine neden olabilmekte, bu da kaynakların verimsiz kul-lanılmasının yanı sıra, mevcut müşterilerin dahi kaybedilmesine neden olabilmektedir. Bu durum, pazarlamacılarda "tüketici ger-çekte ne ister?" sorusunun doğmasına neden olmuştur. Pazarlamadaki en önemli sorulardan bir diğeri ise tüketicileri bir ürün yerine başka bir ürüne karar vermeye iten şeyin ne olduğu-dur (Jordão vd., 2017). Pepsi yerine neden Coca Cola tercih edil-mektedir? Kadınlar neden bilimkurgu filmlerini tercih etmez? Er-kekler neden spor arabaları tercih eder? İşletmeler tüketicileri sa-tın almaya ikna etmek için bu tür soruları yanıtlamaya çalışmalı ve her zaman tüketicilerin nasıl düşündüklerini öğrenmenin yeni yollarını bulmalıdır (Ciprian-Marcel vd., 2004).
... Specifically, fMRI has limited temporal resolution (∼0.5 Hz) and is prone to motion artifacts. On the other hand, fNIRS has a relatively low spatial resolution (∼1 cm) and a low signal-to-noise ratio (SNR) since light has to pass through several layers of tissue before attaining the brain [14]. Moreover, the use of scalp EEG may lower the sensitivity to detect spikes as the electrical signals may be attenuated or cancelled by soft tissue/bone and are frequently degraded by muscle artifacts. ...
Interictal epileptiform discharges (IEDs) are brief neuronal discharges occurring between seizures in patients with epilepsy. The characterization of the hemodynamic response function (HRF) specific to IEDs could increase the accuracy of other functional imaging techniques to localize epileptiform activity, including functional near-infrared spectroscopy and functional magnetic resonance imaging. This study evaluated the possibility of using an intraoperative multispectral imaging system combined with electrocorticography (ECoG) to measure the average HRF associated with IEDs in eight patients. Inter-patient variability of the HRF is illustrated in terms of oxygenated hemoglobin peak latency, oxygenated hemoglobin increase/decrease following IEDs, and signal-to-noise ratio. A sub-region was identified using an unsupervised clustering algorithm in three patients that corresponded to the most active area identified by ECoG.
... The term 'functional' signifies lots of pictures of the brain over the course of time to identify the changes in the activity of the brain. The main advantage of fNIRS compared to other technologies like positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) is its compact measurement, which reduces strain on the subjects [3]. ...
Functional near infrared spectroscopy (fNIRS) is a non-invasive tool for monitoring functional brain activation that records changes in oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentrations. fNIRS is well accepted in the cognitive study where the signals are intended to measure cognitive load in the human brain. Concentration changes in HbO and HbR help in classifying the cognitive states of human brain. There are several machine learning classification techniques to distinguish different cognitive states. Some conventional machine learning methods, which are easier to implement, undergo a complex processing phase before training the network and also suffer from low accuracy due to inappropriate data preprocessing. Deep learning based convolutional neural network (CNN) having automatic feature engineering capability plays a very important role in efficiently classifying different cognitive states. The present work uses two open-access datasets on fNIRS signal. The datasets are taken for two cognitive states: mental task (MT) and resting state or baseline task (BL). The concentration changes of HbO and HbR are computed using the modified Beer–Lambert law. The band-pass filter is used to remove additional noise from the signals. Here, topographical brain images are generated from the data of 2 s window with 1 s overlapping for both HbO and HbR. Global normalization is applied to the filtered data for better visualization of the images. The brain images are fed to the proposed CNN model in order to classify them into MT or BL. The accuracy of the classification and the comparative study shows the superiority of the proposed model over two existing models.
... A typical signal processing pipeline should include the following steps: signal quality check, removal of motion artifact and physiological components, filtering, and conversion to Oxy and DeOxy signals. While the definition of an optimal standardized procedure is still an open research question, some works collect the main approaches and algorithms [59][60][61] and can be used as a reference to design the signal processing pipeline. Many software and toolboxes are available to implement the signal processing pipelines (see Almajidy and colleagues' review 62 , including, but not limited to, homer 57 , NirsToolbox 63 , nirs-spm 64 and mne-fnirs 60 . ...
The term “hyperscanning” refers to the simultaneous recording of multiple individuals’ brain activity. As a methodology, hyperscanning allows the investigation of brain-to-brain synchrony. Despite being a promising technique, there is a limited number of publicly available functional Near-infrared Spectroscopy (fNIRS) hyperscanning recordings. In this paper, we report a dataset of fNIRS recordings from the prefrontal cortical (PFC) activity of 33 mother-child dyads and 29 father-child dyads. Data was recorded while the parent-child dyads participated in an experiment with two sessions: a passive video attention task and a free play session. Dyadic metadata, parental psychological traits, behavioural annotations of the play sessions and information about the video stimuli complementing the dataset of fNIRS signals are described. The dataset presented here can be used to design, implement, and test novel fNIRS analysis techniques, new hyperscanning analysis tools, as well as investigate the PFC activity in participants of different ages when they engage in passive viewing tasks and active interactive tasks.
... The General Linear Model is the standard approach for analysing and interpreting hemodynamic responses [54], [62]. Among the range of possibilities this approach offers, the wellknown analysis of variance (ANOVA) is a common technique to determine localised brain activation based on changes in simultaneous HbO and HbR concentrations in repeated measures designs [63]. Although it is common in the related literature to report only HbO, HbR or HbT -i.e. the combination of both-the hemodynamic is a bi-dimensional response and both chromophores, HbO and HbR, usually correlate negatively during brain stimulation. ...
Trust in automation is crucial for the safe and appropriate adoption of automated driving technology. Current research methods to measure trust mainly rely on subjective scales, with several intrinsic limitations. This empirical experiment proposes a novel method to measure trust objectively, using functional near-infrared spectroscopy (fNIRS). Through manipulating participants' expectations regarding driving automation credibility, we have induced and successfully measured opposing levels of trust in automation. Most notably, our results evidence two separate yet interrelated cortical mechanisms for trust and distrust. Trust is demonstrably linked to decreased monitoring and working memory, whereas distrust is event-related and strongly tied to affective (or emotional) mechanisms. This paper evidence that trust in automation and situation awareness are strongly interrelated during driving automation usage. Our findings are crucial for developing future driver state monitoring technology that mitigates the impact of inappropriate reliance, or over trust, in automated driving systems. Index Terms-fNIRS, highly automated driving, trust in automation.
... Functional near-infrared spectroscopy (fNIRS) is a noninvasive brain imaging technique that uses near-infrared light (typically of wavelengths between 650 and 1,000 nm) to monitor hemodynamics changes in the cortical layer. Compared to electroencephalography (EEG), fNIRS enables to measure brainactivity related hemodynamics in terms of cerebral oxygenation and is less susceptible to electric noises (Huppert et al., 2009;Tak and Ye, 2014;Naseer and Hong, 2015;Chiarelli et al., 2017;Afkhami et al., 2019;Ghafoor et al., 2019;Khan et al., 2021). In addition, fNIRS can be integrated into a portable, wearable, and ergonomic device at low costs and operational expenses, making it a superior candidate for a user-friendly brain-computer interface system compared to other modalities, such as functional magnetic resonance imaging (fMRI) and magnetoencephalography (MEG) (Hu et al., 2010;Piper et al., 2014;Scholkmann et al., 2014a;Pinti et al., 2015;Wyser et al., 2017;Zhao and Cooper, 2018;Hong and Zafar, 2018;Zhao H. B. et al., 2020Zhao H. B. et al., , 2021Ghafoor et al., 2021;Huang and Hong, 2021). ...
With the emergence of an increasing number of functional near-infrared spectroscopy (fNIRS) devices, the significant deterioration in measurement caused by motion artifacts has become an essential research topic for fNIRS applications. However, a high requirement for mathematics and programming limits the number of related researches. Therefore, here we provide the first comprehensive review for motion artifact removal in fNIRS aiming to (i) summarize the latest achievements, (ii) present the significant solutions and evaluation metrics from the perspective of application and reproduction, and (iii) predict future topics in the field. The present review synthesizes information from fifty-one journal articles (screened according to three criteria). Three hardware-based solutions and nine algorithmic solutions are summarized, and their application requirements (compatible signal types, the availability for online applications, and limitations) and extensions are discussed. Five metrics for noise suppression and two metrics for signal distortion were synthesized to evaluate the motion artifact removal methods. Moreover, we highlight three deficiencies in the existing research: (i) The balance between the use of auxiliary hardware and that of an algorithmic solution is not clarified; (ii) few studies mention the filtering delay of the solutions, and (iii) the robustness and stability of the solution under extreme application conditions are not discussed.
... Due to the highly diffuse nature of light propagation in scattering biological tissue, the DOT image reconstruction problem is highly ill-posed and the resultant image offers relatively poor spatial resolution [60] with a limited penetration depth [73]. Fantini et al. state that the spatial resolution of this technique is on the order of 1 cm, with a maximum depth penetration of 2 -3 cm [5]. ...
Diffuse correlation spectroscopy (DCS) is a non-invasive optical modality which can be used to measure cerebral blood flow (CBF) in real-time. It has important potential applications in clinical monitoring, as well as in neuroscience and the development of a non-invasive brain-computer interface. However, a trade-off exists between the signal-to-noise ratio (SNR) and imaging depth, and thus CBF sensitivity, of this technique. Additionally, as DCS is a diffuse optical technique, it is limited by a lack of inherent depth discrimination within the illuminated region of each source-detector pair, and the CBF signal is therefore also prone to contamination by the extracerebral tissues which the light traverses. Placing a particular emphasis on scalability, affordability, and robustness to ambient light, in this work I demonstrate a novel approach which fuses the fields of digital holography and DCS: holographic Fourier domain DCS (FD-DCS). The mathematical formalism of FD-DCS is derived and validated, followed by the construction and validation (for both in vitro and in vivo experiments) of a holographic FD-DCS instrument. By undertaking a systematic SNR performance assessment and developing a novel multispeckle denoising algorithm, I demonstrate the highest SNR gain reported in the DCS literature to date, achieved using scalable and low-cost camera-based detection. With a view to generating a forward model for holographic FD-DCS, in this thesis I propose a novel framework to simulate statistically accurate time-integrated dynamic speckle patterns in biomedical optics. The solution that I propose to this previously unsolved problem is based on the Karhunen-Loève expansion of the electric field, and I validate this technique against novel expressions for speckle contrast for different forms of homogeneous field. I also show that this method can readily be extended to cases with spatially varying sample properties, and that it can also be used to model optical and acoustic parameters.
... Capitalizing on solid theoretical grounds, regression analysis has traditionally dominated fNIRS group-level analysis using one-or two-level models (e.g., weighted linear regression and mixed-effects models). [247][248][249][250] Traditionally, two-level models account for both within subject variance (first level) and between-subject variance (second level). A common variant of the two-level model is to count the frequency of a particular response (or any other feature) observed across volunteers in the first level (i.e., within-subject), which is essentially a change of variables to the traditional approach. ...
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.
Despite extensive research, the differential roles of the prefrontal cortex (PFC) in implicit and explicit facial emotion processing remain elusive. Functional near-infrared spectroscopy (fNIRS) is a neuroimaging technique that can measure changes in both oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations. Currently, how HbO and HbR change during facial emotion processing remains unclear. Here, fNIRS was used to examine and compare PFC activation during implicit and explicit facial emotion processing. Forty young adults performed a facial-matching task that required either emotion discrimination (explicit task) or age discrimination (implicit task), and the activation of their PFCs was measured by fNIRS. Participants attempted the task on two occasions to determine whether their activation patterns were maintained over time. The PFC displayed increases in HbO and/or decreases in HbR during the implicit and explicit facial emotion tasks. Importantly, there were significantly greater changes in PFC HbO during the explicit task, whereas no significant difference in HbR changes between conditions was found. Between sessions, HbO changes were reduced across tasks, but the difference in HbO changes between the implicit and explicit tasks remained unchanged. The test-retest reliability of the behavioral measures was excellent, whereas that of fNIRS measures was mostly poor to fair. Thus, the PFC plays a specific role in recognizing facial expressions, and its differential involvement in implicit and explicit facial emotion processing can be consistently captured at the group level by changes in HbO. This study demonstrates the potential of fNIRS for elucidating the neural mechanisms underlying facial emotion recognition.
Many feature selection methods have been evaluated in functional near-infrared spectroscopy (fNIRS)-related studies. The local interpretable model-agnostic explanation (LIME) algorithm is a feature selection method for fNIRS datasets that has not yet been validated; the demand for its validation is increasing. To this end, we assessed the feature selection performance of LIME for fNIRS datasets in terms of classification accuracy. A comparative analysis was conducted for the benchmark (classification accuracy obtained without applying any feature selection method), LIME, two filter-based methods (minimum-redundancy maximum-relevance and t-test), and one wrapper-based method (sequential forward selection). To ensure the fairness and reliability of the performance evaluation, several open-access fNIRS datasets were used. The analysis revealed that LIME greatly outperformed the other feature selection methods in most cases and could achieve a statistically significantly better classification accuracy than that of the benchmark methods. These findings implied the effectiveness of LIME as a feature selection approach for fNIRS datasets.
Hybrid brain computer interfaces (BCI) utilizing the high temporal resolution of electroencephalography (EEG) and the high spatial resolution of near-infrared spectroscopy (fNIRS) are preferred over single-modal BCIs. However, due to the large dimensionality of the multi-class statistical features commonly used in fNIRS signals, it is easy to cause overfitting of the EEG-fNIRS hybrid BCI classifier. Therefore, a low-dimensional feature extraction method for fNIRS based on the EEG-informed fNIRS general linear model (GLM) analysis is proposed in this paper. First, a regression coefficient matrix is obtained by using the EEG-informed fNIRS GLM with a time window added, and the common spatial pattern (CSP) features of this regression coefficient matrix are extracted as the fNIRS features. Lastly, the fNIRS features were combined with the CSP features extracted from the optimal narrow band of EEG as hybrid features, and the support vector machine (SVM) method is used to classify the samples with hybrid features. The proposed method was tested on a publicly available motor imagery dataset. The classification accuracy using fNIRS signals alone reached 68.79% (oxygenated hemoglobin) and 68.62% (deoxygenated hemoglobin), and the classification accuracy of combining EEG-fNIRS features reached 79.48%, which was higher than other existing methods using the same dataset. By using this fNIRS feature extraction method, the problem of poor performance of CSP on fNIRS signals is solved, which not only enriches the processing methods of fNIRS signals, but also improves the classification accuracy of hybrid EEG-fNIRS BCI in motor imagery tasks.
Objective:
Speech imagery can be used as a reliable, natural, and user-friendly activation task for the development of brain-computer interface (BCI), which empowers individuals with severe disabilities to interact with their environment. The functional near-infrared spectroscopy (fNIRS) is advanced as one of the most suitable brain imaging methods for developing BCI systems owing to its advantages of being non-invasive, portable, insensitive to motion artifacts, and having relatively high spatial resolution.
Approach:
To improve the classification performance of speech imagery BCI based on fNIRS, a novel paradigm was developed in this work by simplifying the articulation movements in speech imagery to make the articulation movement differences clearer between different words imagery tasks. A speech imagery BCI was proposed to directly answer questions by covertly rehearsing the word '' or '' ('yes' or 'no' in English), and an unconstrained rest task also was contained in this BCI. The articulation movements of speech imagery were simplified by retaining only the movements of the jaw and lips of vowels in Chinese Pinyin for words '' and ''.
Main results:
Compared with conventional speech imagery, simplifying the articulation movements in speech imagery could generate more different brain activities among different tasks, which led to more differentiable temporal features and significantly higher classification performance. The average 3-class classification accuracies of the proposed paradigm across all 20 participants reached 69.6 % and 60.2% which were about 10.8% and 5.6% significantly higher than those of the conventional speech imagery paradigm operated in the 0-10 s and 0-2.5 s time windows, respectively.
Significance:
These results suggested that simplifying the articulation movements in speech imagery is promising for improving the classification performance of intuitive BCIs based on speech imagery.
Background
Previous studies have shown that cognitive impairment is common after stroke. Transcranial direct current stimulation (tDCS) is a promising tool for rehabilitating cognitive impairment. This study aimed to investigate the effects of tDCS on the rehabilitation of cognitive impairment in patients with stroke.
Methods
Twenty-two mild–moderate post-stroke patients with cognitive impairments were treated with 14 tDCS sessions. A total of 14 healthy individuals were included in the control group. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Cortical activation was assessed using functional near-infrared spectroscopy (fNIRS) during the verbal fluency task (VFT).
Results
The cognitive function of patients with stroke, as assessed by the MMSE and MoCA scores, was lower than that of healthy individuals but improved after tDCS. The cortical activation of patients with stroke was lower than that of healthy individuals in the left superior temporal cortex (lSTC), right superior temporal cortex (rSTC), right dorsolateral prefrontal cortex (rDLPFC), right ventrolateral prefrontal cortex (rVLPFC), and left ventrolateral prefrontal cortex (lVLPFC) cortical regions. Cortical activation increased in the lSTC cortex after tDCS. The functional connectivity (FC) between the cerebral hemispheres of patients with stroke was lower than that of healthy individuals but increased after tDCS.
Conclusion
The cognitive and brain functions of patients with mild-to-moderate stroke were damaged but recovered to a degree after tDCS. Increased cortical activation and increased FC between the bilateral cerebral hemispheres measured by fNIRS are promising biomarkers to assess the effectiveness of tDCS in stroke.
In the last decade, fNIRS has provided a non-invasive method to investigate neural activation in developmental populations. Despite its increasing use in developmental cognitive neuroscience, there is little consistency or consensus on how to pre-process and analyse infant fNIRS data. With this registered report, we investigated the feasibility of applying more advanced statistical analyses to infant fNIRS data and compared the most commonly used baseline-corrected averaging, General Linear Model (GLM)-based univariate, and Multivariate Pattern Analysis (MVPA) approaches, to show how the conclusions one would draw based on these different analysis approaches converge or differ. The different analysis methods were tested using a face inversion paradigm where changes in brain activation in response to upright and inverted face stimuli were measured in thirty 4-to-6-month-old infants. By including more standard approaches together with recent machine learning techniques, we aim to inform the fNIRS community on alternative ways to analyse infant fNIRS datasets.
Objective
By analyzing the cortical activation and functional connectivity of the prefrontal cortex (PFC) during dual-task obstacle negotiation in the older adults, cognitive resources allocation and neural regulatory mechanisms of aging brain were shed light on in complex walking conditions.
Methods
Twenty-eight healthy right-handed subjects participated in the study, including 15 men and 13 women (age: 68.6 ± 4.1 years, height: 162.96 ± 6.05 cm, weight: 63.63 ± 9.64 kg). There were four tasks: Normal Walk (NW), Obstacle Negotiation during Normal Walk (NW + ON), Walk while performing Cognitive Task (WCT), and Obstacle Negotiation during Walk while performing Cognitive Task (WCT + ON). Participants wore functional near-infrared spectroscopy (fNIRS) to collect hemodynamic signals from various regions of interest (ROIs) in the PFC, while the three-dimensional motion capture system was used to test the gait velocity. Cognitive task data was recorded.
Results
In WCT + ON, the HbO2 concentration change value (△HbO2) of the left dorsolateral prefrontal cortex was significantly greater than that in the other three tasks (p < 0.05), and the△HbO2 of the right dorsolateral prefrontal cortex was significantly greater than that in NW + ON (p < 0.05). The gait velocities in the four tasks were significantly different (p < 0.05) (NW > WCT > NW + ON > WCT + ON). There was no significant difference in cognitive performance between in the WCT and WCT + ON (p > 0.05). In WCT + ON, the left and right dorsolateral prefrontal areas had strong functional connectivity and the left frontal pole was most widely connected to the other ROIs. Compared to that in NW, the functional connectivity of the left prefrontal lobe was significantly enhanced in WCT + ON (p < 0.05).
Conclusions
As walking difficulty increased, the PFC activation in the older adults changed from right-sided to bilateral activation, indicating that the left PFC cognitive resources compensated for the right PFC in dual-task obstacle negotiation. The cognitive resources recruitment in dual-task obstacle negotiation might be achieved by synchronization and coordination of associated brain areas in the PFC, primarily to maintain dynamic postural balance when walking.
Epilepsy is a disorder of the brain denoted by frequent seizures. The symptoms of seizure include confusion, abnormal staring, and rapid, sudden, and uncontrollable hand movements. Epileptic seizure detection methods involve neurological exams, blood tests, neuropsychological tests, and neuroimaging modalities. Among these, neuroimaging modalities have received considerable attention from specialist physicians. One method to facilitate the accurate and fast diagnosis of epileptic seizures is to employ computer-aided diagnosis systems (CADS) based on deep learning (DL) and neuroimaging modalities. This paper has studied a comprehensive overview of DL methods employed for epileptic seizures detection and prediction using neuroimaging modalities. First, DL-based CADS for epileptic seizures detection and prediction using neuroimaging modalities are discussed. Also, descriptions of various datasets, preprocessing algorithms, and DL models which have been used for epileptic seizures detection and prediction have been included. Then, research on rehabilitation tools has been presented, which contains brain-computer interface (BCI), cloud computing, internet of things (IoT), hardware implementation of DL techniques on field-programmable gate array (FPGA), etc. In the discussion section, a comparison has been carried out between research on epileptic seizure detection and prediction. The challenges in epileptic seizures detection and prediction using neuroimaging modalities and DL models have been described. In addition, possible directions for future works in this field, specifically for solving challenges in datasets, DL, rehabilitation, and hardware models, have been proposed. The final section is dedicated to the conclusion which summarizes the significant findings of the paper.
Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we systematically compare the utility of a variety of published NIRS motion correction techniques using a simulated functional activation signal added to 20 real NIRS datasets which contain motion artifacts. Principle component analysis, spline interpolation, wavelet analysis, and Kalman filtering approaches are compared to one another and to standard approaches using the accuracy of the recovered, simulated hemodynamic response function (HRF). Each of the four motion correction techniques we tested yields a significant reduction in the mean-squared error (MSE) and significant increase in the contrast-to-noise ratio (CNR) of the recovered HRF when compared to no correction and compared to a process of rejecting motion-contaminated trials. Spline interpolation produces the largest average reduction in MSE (55%) while wavelet analysis produces the highest average increase in CNR (39%). On the basis of this analysis, we recommend the routine application of motion correction techniques (particularly spline interpolation or wavelet analysis) to minimize the impact of motion artifacts on functional NIRS data.
The human brain is a highly complex system that can be represented as a structurally interconnected and functionally synchronized network, which assures both the segregation and integration of information processing. Recent studies have demonstrated that a variety of neuroimaging and neurophysiological techniques such as functional magnetic resonance imaging (MRI), diffusion MRI and electroencephalography/magnetoencephalography can be employed to explore the topological organization of human brain networks. However, little is known about whether functional near infrared spectroscopy (fNIRS), a relatively new optical imaging technology, can be used to map functional connectome of the human brain and reveal meaningful and reproducible topological characteristics.
We utilized resting-state fNIRS (R-fNIRS) to investigate the topological organization of human brain functional networks in 15 healthy adults. Brain networks were constructed by thresholding the temporal correlation matrices of 46 channels and analyzed using graph-theory approaches. We found that the functional brain network derived from R-fNIRS data had efficient small-world properties, significant hierarchical modular structure and highly connected hubs. These results were highly reproducible both across participants and over time and were consistent with previous findings based on other functional imaging techniques.
Our results confirmed the feasibility and validity of using graph-theory approaches in conjunction with optical imaging techniques to explore the topological organization of human brain networks. These results may expand a methodological framework for utilizing fNIRS to study functional network changes that occur in association with development, aging and neurological and psychiatric disorders.
Near infrared spectroscopy (NIRS) and functional magnetic resonance imaging (fMRI) both allow non-invasive monitoring of cerebral cortical oxygenation responses to various stimuli. To compare these methods in elderly subjects and to determine the effect of age on cortical oxygenation responses, we determined motor-task-related changes in deoxyhemoglobin concentration ([HHb]) over the left motor cortex in six healthy young subjects (age 35 ± 9 years, mean ± SD) and five healthy elderly subjects (age 73 ± 3 years) by NIRS and blood-oxygen-level-dependent (BOLD) fMRI simultaneously. The motor-task consisted of seven cycles of 20-sec periods of contralateral finger-tapping at a rate as fast as possible alternated with 40-sec periods of rest. Time-locked averages over the seven cycles were used for further analysis. Task-related decreases in [HHb] over the motor cortex were measured by NIRS, with maximum changes of −0.83 ± 0.38 μmol/L (P < 0.01) for the young and −0.32 ± 0.17 μmol/L (P < 0.05) for the elderly subjects. The BOLD-fMRI signal increased over the cortex volume under investigation with NIRS, with maximum changes of 2.11 ± 0.72% (P < 0.01) for the young and 1.75 ± 0.71% (P < 0.01) for the elderly subjects. NIRS and BOLD-fMRI measurements showed good correlation in the young (r = −0.70, r2 = 0.48, P < 0.001) and elderly subjects (r = −0.82, r2 = 0.67, P < 0.001). Additionally, NIRS measurements demonstrated age-dependent decreases in task-related cerebral oxygenation responses (P < 0.05), whereas fMRI measurements demonstrated smaller areas of cortical activation in the elderly subjects (P < 0.05). These findings demonstrate that NIRS and fMRI similarly assess cortical oxygenation changes in young subjects and also in elderly subjects. In addition, cortical oxygenation responses to brain activation alter with aging. Hum. Brain Mapping 16:14–23, 2002. © 2002 Wiley-Liss, Inc.
Background:
This study assesses the utility of a hybrid optical instrument for noninvasive transcranial monitoring in the neurointensive care unit. The instrument is based on diffuse correlation spectroscopy (DCS) for measurement of cerebral blood flow (CBF), and near-infrared spectroscopy (NIRS) for measurement of oxy- and deoxy-hemoglobin concentration. DCS/NIRS measurements of CBF and oxygenation from frontal lobes are compared with concurrent xenon-enhanced computed tomography (XeCT) in patients during induced blood pressure changes and carbon dioxide arterial partial pressure variation.
Methods:
Seven neurocritical care patients were included in the study. Relative CBF measured by DCS (rCBF(DCS)), and changes in oxy-hemoglobin (DeltaHbO(2)), deoxy-hemoglobin (DeltaHb), and total hemoglobin concentration (DeltaTHC), measured by NIRS, were continuously monitored throughout XeCT during a baseline scan and a scan after intervention. CBF from XeCT regions-of-interest (ROIs) under the optical probes were used to calculate relative XeCT CBF (rCBF(XeCT)) and were then compared to rCBF(DCS). Spearman's rank coefficients were employed to test for associations between rCBF(DCS) and rCBF(XeCT), as well as between rCBF from both modalities and NIRS parameters.
Results:
rCBF(DCS) and rCBF(XeCT) showed good correlation (r (s) = 0.73, P = 0.010) across the patient cohort. Moderate correlations between rCBF(DCS) and DeltaHbO(2)/DeltaTHC were also observed. Both NIRS and DCS distinguished the effects of xenon inhalation on CBF, which varied among the patients.
Conclusions:
DCS measurements of CBF and NIRS measurements of tissue blood oxygenation were successfully obtained in neurocritical care patients. The potential for DCS to provide continuous, noninvasive bedside monitoring for the purpose of CBF management and individualized care is demonstrated.
Near infrared spectroscopy (NIRS) is rapidly gaining popularity for functional brain imaging. It is well suited to studies of patients or children; however, in these populations particularly, motion artifacts can present a problem. Here, we propose the use of imaging channels with negligible distance between light source and detector to detect subject motion, without the need for an additional motion sensor. Datasets containing deliberate motion artifacts were obtained from three subjects. Motion artifacts could be detected in the signal from the co-located channels with a minimum sensitivity of 0.75 and specificity of 0.98. Five techniques for removing motion artifact from the functional signals were compared, namely two-input recursive least squares (RLS) adaptive filtering, wavelet-based filtering, independent component analysis (ICA), and two-channel and multiple-channel regression. In most datasets, the median change in SNR across all channels was the greatest using ICA or multiple-channel regression. RLS adaptive filtering produced the smallest increase in SNR. Where sharp spikes were present, wavelet filtering produced the largest SNR increase. ICA and multiple-channel regression are promising ways to reduce motion artifact in functional NIRS without requiring time-consuming manual techniques.
The common approach to the multiplicity problem calls for controlling the familywise error rate (FWER). This approach, though, has faults, and we point out a few. A different approach to problems of multiple significance testing is presented. It calls for controlling the expected proportion of falsely rejected hypotheses – the false discovery rate. This error rate is equivalent to the FWER when all hypotheses are true but is smaller otherwise. Therefore, in problems where the control of the false discovery rate rather than that of the FWER is desired, there is potential for a gain in power. A simple sequential Bonferroni-type procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows that the gain in power is substantial. The use of the new procedure and the appropriateness of the criterion are illustrated with examples.
In this paper, we formulate diffuse optical tomography (DOT) problems as a source localization problem and propose a MUltiple SIgnal Classification (MUSIC) algorithm for functional brain imaging application. By providing MUSIC spectra for major chromophores such as oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR), we are able to investigate the spatial distribution of brain activities. Moreover, the false discovery rate (FDR) algorithm can be applied to control the family-wise error in the MUSIC spectra. The minimum distance between the center of mass in DOT and the Montreal Neurological Institute (MNI) coordinates of target regions in experiments was between approximately 6 and 18 mm, and the displacement of the center of mass in DOT and fMRI ranged between 12 and 28 mm, which demonstrate the legitimacy of the DOT-based imaging. The proposed brain mapping method revealed its potential as an alternative algorithm to monitor the brain activation.
In medical near-infrared spectroscopy (NIRS), movements of the subject often cause large step changes in the baselines of the measured light attenuation signals. This prevents comparison of hemoglobin concentration levels before and after movement. We present an accelerometer-based motion artifact removal (ABAMAR) algorithm for correcting such baseline motion artifacts (BMAs). ABAMAR can be easily adapted to various long-term monitoring applications of NIRS. We applied ABAMAR to NIRS data collected in 23 all-night sleep measurements and containing BMAs from involuntary movements during sleep. For reference, three NIRS researchers independently identified BMAs from the data. To determine whether the use of an accelerometer improves BMA detection accuracy, we compared ABAMAR to motion detection based on peaks in the moving standard deviation (SD) of NIRS data. The number of BMAs identified by ABAMAR was similar to the number detected by the humans, and 79% of the artifacts identified by ABAMAR were confirmed by at least two humans. While the moving SD of NIRS data could also be used for motion detection, on average 2 out of the 10 largest SD peaks in NIRS data each night occurred without the presence of movement. Thus, using an accelerometer improves BMA detection accuracy in NIRS.
The blood-oxygen level dependent (BOLD) signals measured by functional magnetic resonance imaging (fMRI) are contaminated with noise from various physiological processes, such as spontaneous low-frequency oscillations (LFOs), respiration, and cardiac pulsation. These processes are coupled to the BOLD signal by different mechanisms, and represent variations with very different frequency content; however, because of the low sampling rate of fMRI, these signals are generally not separable by frequency, as the cardiac and respiratory waveforms alias into the LFO band. In this study, we investigated the spatial and temporal characteristics of the individual noise processes by conducting concurrent near-infrared spectroscopy (NIRS) and fMRI studies on six subjects during a resting state acquisition. Three time series corresponding to LFO, respiration, and cardiac pulsation were extracted by frequency from the NIRS signal (which has sufficient temporal resolution to critically sample the cardiac waveform) and used as regressors in a BOLD fMRI analysis. Our results suggest that LFO and cardiac signals modulate the BOLD signal independently through the circulatory system. The spatiotemporal evolution of the LFO signal in the BOLD data correlates with the global cerebral blood flow. Near-infrared spectroscopy can be used to partition these contributing factors and independently determine their contribution to the BOLD signal.
A critical issue in human development is that of whether the language-related areas in the left frontal and temporal regions work as a functional network in preverbal infants. Here, we used 94-channel near-infrared spectroscopy to reveal the functional networks in the brains of sleeping 3-month-old infants with and without presenting speech sounds. During the first 3 min, we measured spontaneous brain activation (period 1). After period 1, we provided stimuli by playing Japanese sentences for 3 min (period 2). Finally, we measured brain activation for 3 min without providing the stimulus (period 3), as in period 1. We found that not only the bilateral temporal and temporoparietal regions but also the prefrontal and occipital regions showed oxygenated hemoglobin signal increases and deoxygenated hemoglobin signal decreases when speech sounds were presented to infants. By calculating time-lagged cross-correlations and coherences of oxy-Hb signals between channels, we tested the functional connectivity for the three periods. The oxy-Hb signals in neighboring channels, as well as their homologous channels in the contralateral hemisphere, showed high correlation coefficients in period 1. Similar correlations were observed in period 2; however, the number of channels showing high correlations was higher in the ipsilateral hemisphere, especially in the anterior-posterior direction. The functional connectivity in period 3 showed a close relationship between the frontal and temporal regions, which was less prominent in period 1, indicating that these regions form the functional networks and work as a hysteresis system that has memory of the previous inputs. We propose a hypothesis that the spatiotemporally large-scale brain networks, including the frontal and temporal regions, underlie speech processing in infants and they might play important roles in language acquisition during infancy.
Functional near-infrared optical topography (OT) is used to non-invasively measure the changes in oxygenated and deoxygenated haemoglobin (Δ[HbO2], Δ[HHb]) and hence investigate the brain haemodynamic changes, which occur in response to functional activation at specific regions of the cerebral cortex. However, when analysing functional OT data the task-related systemic changes should be taken into account.Here we used an independent component analysis (ICA) method on the OT [HbO2] signal, to determine the task-related independent components and then compared them with the systemic measurements (blood pressure, heart rate, scalp blood flow) to assess whether the components are due to systemic noise or neuronal activation. This analysis can therefore extract the true OT haemodynamic neuronal response and hence discriminate between regional activated cortical areas and global haemodynamic changes.
Near-infrared spectroscopy (NIRS) is a developing technology for low-cost noninvasive functional brain imaging. With multichannel optical instruments, it becomes possible to measure not only local changes in hemoglobin concentrations but also temporal correlations of those changes in different brain regions which gives an optical analog of functional connectivity traditionally measured by fMRI. We recorded hemodynamic activity during the Go-NoGo task from 11 right-handed subjects with probes placed bilaterally over prefrontal areas. Subjects were detecting animals as targets in natural scenes pressing a mouse button. Data were low-pass filtered<1 Hz and cardiac∕respiration∕superficial layers artifacts were removed using Independent Component Analysis. Fisher's transformed correlations of poststimulus responses (30 s) were averaged over groups of channels unilaterally in each hemisphere (intrahemispheric connectivity) and the corresponding channels between hemispheres (interhemispheric connectivity). The hemodynamic response showed task-related activation (an increase∕decrease in oxygenated∕deoxygenated hemoglobin, respectively) greater in the right versus left hemisphere. Intra- and interhemispheric functional connectivity was also significantly stronger during the task compared to baseline. Functional connectivity between the inferior and the middle frontal regions was significantly stronger in the right hemisphere. Our results demonstrate that optical methods can be used to detect transient changes in functional connectivity during rapid cognitive processes.
Resting state connectivity aims to identify spontaneous cerebral hemodynamic fluctuations that reflect neuronal activity at rest. In this study, we investigated the spatial-temporal correlation of hemoglobin concentration signals over the whole head during the resting state. By choosing a source-detector pair as a seed, we calculated the correlation value between its time course and the time course of all other source-detector combinations, and projected them onto a topographic map. In all subjects, we found robust spatial interactions in agreement with previous fMRI and NIRS findings. Strong correlations between the two opposite hemispheres were seen for both sensorimotor and visual cortices. Correlations in the prefrontal cortex were more heterogeneous and dependent on the hemodynamic contrast. HbT provided robust, well defined maps, suggesting that this contrast may be used to better localize functional connectivity. The effects of global systemic physiology were also investigated, particularly low frequency blood pressure oscillations which give rise to broad regions of high correlation and mislead interpretation of the results. These results confirm the feasibility of using functional connectivity with optical methods during the resting state, and validate its use to investigate cortical interactions across the whole head.
Confirmatory clinical trials often classify clinical response variables into primary and secondary endpoints. The presence of two or more primary endpoints in a clinical trial usually means that some adjustments of the observed p-values for multiplicity of tests may be required for the control of the type I error rate. In this paper, we discuss statistical concerns associated with some commonly used multiple endpoint adjustment procedures. We also present limited Monte Carlo simulation results to demonstrate the performance of selected p-value-based methods in protecting the type I error rate. © 1997 by John Wiley & Sons, Ltd.
In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected. This book describes the ideas and procedures that underlie the analysis of signals produced by the brain. The aim is to understand how the brain works, in terms of its functional architecture and dynamics. This book provides the background and methodology for the analysis of all types of brain imaging data, from functional magnetic resonance imaging to magnetoencephalography. Critically,Statistical Parametric Mappingprovides a widely accepted conceptual framework which allows treatment of all these different modalities. This rests on an understanding of the brain's functional anatomy and the way that measured signals are caused experimentally. The book takes the reader from the basic concepts underlying the analysis of neuroimaging data to cutting edge approaches that would be difficult to find in any other source. Critically, the material is presented in an incremental way so that the reader can understand the precedents for each new development. This book will be particularly useful to neuroscientists engaged in any form of brain mapping; who have to contend with the real-world problems of data analysis and understanding the techniques they are using. It is primarily a scientific treatment and a didactic introduction to the analysis of brain imaging data. It can be used as both a textbook for students and scientists starting to use the techniques, as well as a reference for practicing neuroscientists. The book also serves as a companion to the software packages that have been developed for brain imaging data analysis. * An essential reference and companion for users of the SPM software * Provides a complete description of the concepts and procedures entailed by the analysis of brain images * Offers full didactic treatment of the basic mathematics behind the analysis of brain imaging data * Stands as a compendium of all the advances in neuroimaging data analysis over the past decade * Adopts an easy to understand and incremental approach that takes the reader from basic statistics to state of the art approaches such as Variational Bayes * Structured treatment of data analysis issues that links different modalities and models * Includes a series of appendices and tutorial-style chapters that makes even the most sophisticated approaches accessible.
This book provides an up-to-date account of the theory and applications of linear models. It can be used as a text for courses in statistics at the graduate level as well as an accompanying text for other courses in which linear models play a part. The authors present a unified theory of inference from linear models with minimal assumptions, not only through least squares theory, but also using alternative methods of estimation and testing based on convex loss functions and general estimating equations. Some of the highlights include: - a special emphasis on sensitivity analysis and model selection; - a chapter devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models; - a chapter devoted to incomplete data sets; - an extensive appendix on matrix theory, useful to researchers in econometrics, engineering, and optimization theory; - a chapter devoted to the analysis of categorical data based on a unified presentation of generalized linear models including GEE- methods for correlated response; - a chapter devoted to incomplete data sets including regression diagnostics to identify Non-MCAR-processes The material covered will be invaluable not only to graduate students, but also to research workers and consultants in statistics. Helge Toutenburg is Professor for Statistics at the University of Muenchen. He has written about 15 books on linear models, statistical methods in quality engineering, and the analysis of designed experiments. His main interest is in the application of statistics to the fields of medicine and engineering.
The relatively good transparency of biological materials in the near
infrared region of the spectrum permits sufficient photon transmission
through organs in situ for the monitoring of cellular events.
Observations by infrared transillumination in the exposed heart and in
the brain in cephalo without surgical intervention show that oxygen
sufficiency for cytochrome a,a3, function, changes in tissue
blood volume, and the average hemoglobin-oxyhemoglobin equilibrium can
be recorded effectively and in continuous fashion for research and
clinical purposes. The copper atom associated with heme a3
did not respond to anoxia and may be reduced under normoxic conditions,
whereas the heme-a copper was at least partially reducible.
Methods for constructing simultaneous confidence intervals for all possible linear contrasts among several means of normally distributed variables have been given by Scheffé and Tukey. In this paper the possibility is considered of picking in advance a number (say m) of linear contrasts among k means, and then estimating these m linear contrasts by confidence intervals based on a Student t statistic, in such a way that the overall confidence level for the m intervals is greater than or equal to a preassigned value. It is found that for some values of k, and for m not too large, intervals obtained in this way are shorter than those using the F distribution or the Studentized range. When this is so, the experimenter may be willing to select the linear combinations in advance which he wishes to estimate in order to have m shorter intervals instead of an infinite number of longer intervals.
Introduction
Sire evaluation can logically be formulated as a process of prediction of future progeny of a sire produced by matings with specified females and making their records in some specified environment. Sewall Wright (1931) over 40 years ago suggested three types of prediction that might be of interest: (1) progeny of a particular mating, (2) future daughters in the same herd but out of a new sample of dams, (3) daughters out of a random sample of dams of the breed. Dr. Lush as early as 1931 had elucidated the principles of sire evaluation, Lush (1931 Lush (1933).
As was pointed out by Lehman (1961), two types of selection problems have been studied by statisticians. These are Model I and Model II selection, analagous to the corresponding models of analysis of variance. In Model I the candidates for selection are fixed, for example, choices are to be made among treatments, a random sample of observations having been taken on each fixed treatment. No really unified theory has been developed for this type of selection. In contrast, Model II selection involves candidates that are regarded as a random sample from some specified population. Model II represents the classical selection problem in animal breeding and had essentially been solved by Wright and Lush early in the 1930's. Smith's (1936) application to plant breeding and, in particular, Hazel's (1943) application to animal breeding formalized the techniques.
A third type of selection that has apparently been overlooked by both statisticians and animal breeders might
Recent developments promise to increase greatly the popularity of maximum likelihood (ml) as a technique for estimating variance components. Patterson and Thompson (1971) proposed a restricted maximum likelihood (reml) approach which takes into account the loss in degrees of freedom resulting from estimating fixed effects. Miller (1973) developed a satisfactory asymptotic theory for ml estimators of variance components. There are many iterative algorithms that can be considered for computing the ml or reml estimates. The computations on each iteration of these algorithms are those associated with computing estimates of fixed and random effects for given values of the variance components.
Near-Infrared Spectroscopy (NIRS) allows the recovery of the evoked hemodynamic response to brain activation. In adult human populations, the NIRS signal is strongly contaminated by systemic interference occurring in the superficial layers of the head. An approach to overcome this difficulty is to use additional NIRS measurements with short optode separations to measure the systemic hemodynamic fluctuations occurring in the superficial layers. These measurements can then be used as regressors in the post-experiment analysis to remove the systemic contamination and isolate the brain signal. In our previous work, we showed that the systemic interference measured in NIRS is heterogeneous across the surface of the scalp. As a consequence, the short separation measurement used in the regression procedure must be located close to the standard NIRS channel from which the evoked hemodynamic response of the brain is to be recovered. Here, we demonstrate that using two short separation measurements, one at the source optode and one at the detector optode, further increases the performance of the short separation regression method compared to using a single short separation measurement. While a single short separation channel produces an average reduction in noise of 33% for HbO, using a short separation channel at both source and detector reduces noise by 59% compared to the standard method using a general linear model (GLM) without short separation. For HbR, noise reduction of 3% is achieved using a single short separation and this number goes to 47% when two short separations are used. Our work emphasizes the importance of integrating short separation measurements both at the source and at the detector optode of the standard channels from which the hemodynamic response is to be recovered. While the implementation of short separation sources presents some difficulties experimentally, the improvement in noise reduction is significant enough to justify the practical challenges.
Although near-infrared spectroscopy (NIRS) was developed as a tool for clinical monitoring of tissue oxygenation, it also has potential for neuroimaging. A wide range of different NIRS instruments have been developed, and instruments for continuous intensity measurements with fixed spacing [continuous wave (CW)-type instruments], which are most readily available commercially, allow us to see dynamic changes in regional cerebral blood flow in real time. However, quantification, which is necessary for imaging of brain functions, is impossible with these CW-type instruments. Over the past 20 years, many different approaches to quantification have been tried, and several multichannel time-resolved and frequency-domain instruments are now in common use for imaging. Although there are still many problems with this technique, such as incomplete knowledge of how light propagates through the head, NIRS will not only open a window on brain physiology for subjects who have rarely been examined until now, but also provide a new direction for functional mapping studies.
This article discusses general modeling of multisubject and/or multisession FMRI data. In particular, we show that a two-level mixed-effects model (where parameters of interest at the group level are estimated from parameter and variance estimates from the single-session level) can be made equivalent to a single complete mixed-effects model (where parameters of interest at the group level are estimated directly from all of the original single sessions' time series data) if the (co-)variance at the second level is set equal to the sum of the (co-)variances in the single-level form, using the BLUE with known covariances. This result has significant implications for group studies in FMRI, since it shows that the group analysis requires only values of the parameter estimates and their (co-)variance from the first level, generalizing the well-established "summary statistics" approach in FMRI. The simple and generalized framework allows different prewhitening and different first-level regressors to be used for each subject. The framework incorporates multiple levels and cases such as repeated measures, paired or unpaired t tests and F tests at the group level; explicit examples of such models are given in the article. Using numerical simulations based on typical first-level covariance structures from real FMRI data we demonstrate that by taking into account lower-level covariances and heterogeneity a substantial increase in higher-level Z score is possible.
The brain network is usually constructed by estimating the connectivity matrix and thresholding it at an arbitrary level. The problem with this standard method is that we do not have any generally accepted criteria for determining a proper threshold. Thus, we propose a novel multiscale framework that models all brain networks generated over every possible threshold. Our approach is based on persistent homology and its various representations such as the Rips filtration, barcodes and dendrograms. This new persistent homological framework enables us to quantify various persistent topological features at different scales in a coherent manner. The barcode is used to quantify and visualize the evolutionary changes of topological features such as the Betti numbers over different scales. By incorporating additional geometric information to the barcode, we obtain a single linkage dendrogram that shows the overall evolution of the network. The difference between the two networks is then measured by the Gromov-Hausdorff distance over the dendrograms. As an illustration, we modeled and differentiated the FDGPET based functional brain networks of 24 attention-deficit hyperactivity disorder children, 26 autism spectrum disorder children and 11 pediatric control subjects.
We present a review of methods for the forward and inverse problems in optical tomography. We limit ourselves to the highly scattering case found in applications in medical imaging, and to the problem of absorption and scattering reconstruction. We discuss the derivation of the diffusion approximation and other simplifications of the full transport problem. We develop sensitivity relations in both the continuous and discrete case with special concentration on the use of the finite element method. A classification of algorithms is presented, and some suggestions for open problems to be addressed in future research are made.