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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.
... Considering the rising number of studies in this area, and the fact that fNIRS is a relatively recent neuroimaging tool, it is fundamental to acknowledge potential methodological criticisms and follow solid and reproducible experimental procedures. Recent works comparing different approaches outline the best practices in fNIRS use Di Lorenzo et al., 2019;Tak & Ye, 2014;Yücel et al., 2021) aiming to reduce methodological weaknesses (see e.g. Herold et al., 2018). ...
... The general linear model (GLM) is one of the most common approaches used in the fNIRS community, similar to fMRI data analyses (Poline and Brett, 2012). GLM can take advantage of the high temporal resolution of fNIRS and employ various regressors to improve the inference accuracy (Gagnon et al., 2011;Huppert, 2016;Pinti et al., 2019;Tachtsidis & Scholkmann, 2016;Tak & Ye, 2014). To enhance the robustness of the signal and analysis, it is advisable to aggregate channels in regions of interest . ...
... A similar analytical approach to the one used for hyperscanning could be employed to evaluate neural interactions among distinct brain regions, also referred to as functional and effective connectivity Duan et al., 2012;Lu et al., 2010;toolboxes: Xu et al., 2015;Ye et al., 2009). Functional connectivity refers to the statistical association between different signal time series (single channels or, more strongly recommended, regions of interest) and measures their correlation or coherence (Tak & Ye, 2014). It could be implemented as a seed-based approach in which a "seed" channel or region is selected as a reference point for all correlations. ...
Research investigating the neural processes related to music perception and production constitutes a well-established field within the cognitive neurosciences. While most neuroimaging tools have limitations in studying the complexity of musical experiences, functional Near-Infrared Spectroscopy (fNIRS) represents a promising, relatively new tool for studying music processes in both laboratory and ecological settings, which is also suitable for both typical and pathological populations across development. Here we systematically review fNIRS studies on music cognition, highlighting prospects and potentialities. We also include an overview of fNIRS basic theory, together with a brief comparison to characteristics of other neuroimaging tools. Fifty-nine studies meeting inclusion criteria (i.e., using fNIRS with music as the primary stimulus) are presented across five thematic sections. Critical discussion of methodology leads us to propose guidelines of good practices aiming for robust signal analyses and reproducibility. A continuously updated world map is proposed, including basic information from studies meeting the inclusion criteria. It provides an organized, accessible, and updatable reference database, which could serve as a catalyst for future collaborations within the community. In conclusion, fNIRS shows potential for investigating cognitive processes in music, particularly in ecological contexts and with special populations, aligning with current research priorities in music cognition.
... This trend can be explained as follows: although the excitation photons (i.e., photons coming from the source at λ x ) do not encounter the object in their trajectory from source to detector, the retrieved effective attenuation coefficient resembles the one from the surrounding liquid phantom, i.e., κ ¼ ð3μ aM μ s 0 Þ −1∕2 ; on the other hand, when the excitation photons begin to sense the vial (at a depth between 8.5 and 10.5 mm), more emission photons are produced, and hence, κ increases because the absorption coefficient tends to be the sum of μ aM and μ af . 24,25 Beyond 18.5 mm, the value of κ significantly decreases, although not to the baseline level, suggesting that deeper inclusions could still be detectable by the measurement system. Regarding the baseline level, the chosen optical properties for the host medium (μ aM ¼ 0.01 mm −1 and μ s 0 ¼ 1 mm −1 ) should give a value of κ ∼ 0.17 mm −1 . ...
... From this, it is clear that the tumor tissue uptake and clearance are slow in the kinetics curve and vary based on the tumor tissue sampled. 25 Fig. 7 Fluorescence depth sensing system was used to track ICG kinetics for 10 min in each mouse at multiple time points, which corresponded to different tumor sizes. Analysis showed that the system is able to resolve kinetic differences as a tumor becomes larger, with larger tumors showing delayed uptake and longer retention of ICG or the system autogain. ...
Significance: Fluorescence sensing within tissue is an effective tool for tissue characterization; however, the modality and geometry of the image acquisition can alter the observed signal.
Aim: We introduce a novel optical fiber-based system capable of measuring two fluorescent contrast agents through 2 cm of tissue with simple passive electronic switching between the excitation light, simultaneously acquiring fluorescence and excitation data. The goal was to quantify indocyanine green (ICG) and protoporphyrin IX (PpIX) within tissue, and the sampling method was compared with wide-field surface imaging to contrast the value of deep sensing versus surface imaging.
Approach: This was achieved by choosing filters for specific wavelengths that were mutually exclusive between ICG and PpIX and coupling these filters to two separate detectors, which allows for direct swapping of the excitation and emission channels by switching the on-time of each excitation laser between 780- and 633-nm wavelengths.
Results: This system was compared with two non-contact surface imaging systems for both ICG and PpIX, which revealed that the fluorescence depth sensing system was superior in its ability to resolve kinetics differences in deeper tissues that would normally be dominated by strong signals from skin and other surface tissues. Specifically, the system was tested using pancreatic adenocarcinoma tumors injected into murine models, which were imaged at several time points throughout tumor growth to its ∼6-mm diameter. This demonstrated the system’s capability to track longitudinal changes in ICG and PpIX kinetics that result from tumor growth and development, with larger tumors showing sluggish uptake and clearance of ICG, which was not observable with surface imaging. Similarly, PpIX was quantified, which showed slower kinetics over different time points, and was further compared with the wide-filed imager. These results were further validated through depth measurements in tissue phantoms and model-based interpretation.
Conclusion: This fluorescence depth sensing system can be used to sample the interior blood flow characteristics by ICG sensing of tissue as deep as 20 mm into the tissue with sensitivity to kinetics that are superior to surface imaging and may be combined with other imaging modalities such as ultrasound to provide guided deep fluorescence measurements.
... Before any further analysis processing, data filtering was applied to extract frequency components related to the hemodynamic signal. The HbO and HbR data were filtered with the canonical HRF filter to remove high-frequency noise and temporal correlations This was carried out according to the pre-staining method, which has been shown to be more effective in calculating activation maps than the bleaching method [37,38]. Finally, baseline correction was performed again by subtracting the new value of the concentration change at the starting time. ...
... The signal can be approximated as the convolution of a stimulus function and a hemodynamic response function (HRF). For model specification, the canonical HRF composed of two gamma functions was used [37]. ...
Objectives: The aim of this study was to identify the neural pattern activation during mirror therapy (MT) and explore any cortical reorganization and reducing asymmetry of hemispheric activity for upper limb rehabilitation in post-stroke patients. Methods: A box containing a mirror was placed between the arms of the patients to create the illusion of normal motion in the affected limb by reflecting the image of the unaffected limb in motion. We measured the cerebral hemodynamic response using near-infrared spectroscopy (NIRS). We enrolled ten right-handed stroke patients. They observed healthy hand movements in the mirror (MT condition) while performing various tasks (MT condition), and then repeated the same tasks with the mirror covered (N-MT condition). Results: Significant activation of some brain areas was observed in the right and left hemiparesis groups for the MT condition, while lower levels of activation were observed for the N-MT condition. The results showed significant differences in hemodynamic response based on oxygenated (HbO) concentrations between MT and N-MT conditions across all tasks in sensorimotor areas. These neural circuits were activated despite the motor areas being affected by the brain injury, indicating that the reflection of movement in the mirror helped to activate them. Conclusions: These results suggest that MT promotes cortical activations of sensory motor areas in affected and non-affected brain sides in subacute post-stroke patients, and it encourages the use of these tools in clinical practice.
... It works on the basis that the rays coming out of the transmitter diode are detected by the receiver diodes. The spectral absorptions of oxygenated hemoglobin and deoxygenated hemoglobin are different, and NIRS uses this difference 8,9 . The basis of the NIRS operating principle is the Beer-Lambert law. ...
... Chromophores have specific absorption rates depending on the oxygen concentration in the tissue. The amount of light absorbed by tissues depends on the chromophore concentration 8 . At least two different wavelengths must be used to compare chromophore concentrations in NIRS measurements. ...
Yoğun bakımlarda hasta takibinin en önemli parametrelerinden birisi monitörizasyondur. Beyin metabolik olarak en aktif organlarımızdan birisi olup hipoksi ve iskemiye karşı çok duyarlıdır. Dolayısıyla serebral oksijenizasyonunun takibi önemlidir. Serebral doku oksijenizasyon takibinde birçok yöntem olmakla birlikte yatak başı kullanımı, non-invaziv olması ve kullanım kolaylığı nedeniyle serebral oksimetreler sıklıkla kullanılmaktadır. Bu cihazlar yakın kızılötesi ışığın oksijenize hemoglobin ve deoksijenize hemoglobin tarafından farklı oranlarda absorbe edilmesi temeline dayanarak çalışmaktadır. Yoğun bakımlarda travmatik beyin hasarı olan (TBI, kanama, stroke) hastalarda serebral iskemi ya da hipoksiyi erken tespit edip ikincil hasarı önlemek amacıyla kullanılabilmektedir
... Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technology, which can study the local changes of cerebral blood flow and quantify the changes of oxygen and deoxyhemoglobin concentration related to tasks (Csipo et al., 2019;Tak & Ye, 2014). fNIRS is not sensitive to motion artifacts, is not limited by walking activities, and is portable (Herold et al., 2017;Tak & Ye, 2014). ...
... Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technology, which can study the local changes of cerebral blood flow and quantify the changes of oxygen and deoxyhemoglobin concentration related to tasks (Csipo et al., 2019;Tak & Ye, 2014). fNIRS is not sensitive to motion artifacts, is not limited by walking activities, and is portable (Herold et al., 2017;Tak & Ye, 2014). ...
Background
Hypertension increases the risk of cognitive impairment and related dementia, causing impaired executive function and unusual gait parameters. However, the mechanism of neural function illustrating this is unclear. Our research aimed to explore the differences of cerebral cortex activation, gait parameters, and working memory performance between healthy older adults (HA) and older hypertensive (HT) patients when performing cognitive and walking tasks.
Method
A total of 36 subjects, including 12 healthy older adults and 24 older hypertensive patients were asked to perform series conditions including single cognitive task (SC), single walking task (SW), and dual‐task (DT), wearing functional near‐infrared spectroscopy (fNIRS) equipment and Intelligent Device for Energy Expenditure and Activity equipment to record cortical hemodynamic reactions and various gait parameters.
Results
The left somatosensory cortex (L‐S1) and bilateral supplementary motor area (SMA) showed higher cortical activation (p < .05) than HA when HT performed DT. The intragroup comparison showed that HT had higher cortical activation (p < .05) when performing DT as SW. The cognitive performance of HT was significantly worse (p < .05) than HA when executing SC. The activation of the L‐S1, L‐M1, and bilateral SMA in HT were significantly higher during SW (p < .05).
Conclusion
Hypertension can lead to cognitive impairment in the elderly, including executive function and walking function decline. As a result of these functional declines, elderly patients with hypertension are unable to efficiently allocate brain resources to support more difficult cognitive interference tasks and need to meet more complex task demands by activating more brain regions.
... fNIRS detects these changes, providing an indirect measure of neural activity. This method is advantageous for its non-invasiveness, portability, and relative insensitivity to motion artifacts compared to other neuroimaging techniques, making it suitable for use in diverse settings, including those that simulate real-world teleoperation environments (Tak and Ye, 2014). ...
... Upon importing the raw fNIRS data, it was converted into optical density ( OD), a measure reflecting changes in light absorption due to variations in chromophore concentration in the brain tissue (Tak and Ye, 2014). An essential step in ensuring data quality involved the evaluation of the Scalp Coupling Index (SCI), an objective metric quantifying the quality of the optode-scalp connection (Pollonini et al., 2016). ...
Introduction
As robot teleoperation increasingly becomes integral in executing tasks in distant, hazardous, or inaccessible environments, operational delays remain a significant obstacle. These delays, inherent in signal transmission and processing, adversely affect operator performance, particularly in tasks requiring precision and timeliness. While current research has made strides in mitigating these delays through advanced control strategies and training methods, a crucial gap persists in understanding the neurofunctional impacts of these delays and the efficacy of countermeasures from a cognitive perspective.
Methods
This study addresses the gap by leveraging functional Near-Infrared Spectroscopy (fNIRS) to examine the neurofunctional implications of simulated haptic feedback on cognitive activity and motor coordination under delayed conditions. In a human-subject experiment (N = 41), sensory feedback was manipulated to observe its influences on various brain regions of interest (ROIs) during teleoperation tasks. The fNIRS data provided a detailed assessment of cerebral activity, particularly in ROIs implicated in time perception and the execution of precise movements.
Results
Our results reveal that the anchoring condition, which provided immediate simulated haptic feedback with a delayed visual cue, significantly optimized neural functions related to time perception and motor coordination. This condition also improved motor performance compared to the asynchronous condition, where visual and haptic feedback were misaligned.
Discussion
These findings provide empirical evidence about the neurofunctional basis of the enhanced motor performance with simulated synthetic force feedback in the presence of teleoperation delays. The study highlights the potential for immediate haptic feedback to mitigate the adverse effects of operational delays, thereby improving the efficacy of teleoperation in critical applications.
... The LMMs have been increasingly used in psychological research because they allow more adequate modelling of data that are nested within participants by simultaneously considering variability within as well as across participants and multiple variables 43 . These models are also well-suited for analysing fNIRS data since haemoglobin concentrations from multiple channels are collected for a given participant in different experimental conditions 44 . The models used here included two experimental factors (type of road scene and plausibility) as fixed effects, whereas the NIRS channels nested with participants were included as random intercepts. ...
... We used the containment method for estimating degrees of freedom as provided in S 102 or SAS 103 ; this method takes into account the hierarchical (nested) structure of the data. The partial eta squared ( η 2 p ), one of the indicators of effect sizes 104 , can be computed from the F-statistic and its associated degrees of freedom 45,46 (for discussions about relations between degrees of freedom and threshold values of test statics in general see 44). Depending on the degrees of freedom, when using LMMs with complex variance-covariance structures, the values of partial eta-squared ( η 2 p ) tend to be small and may not be interpretable using the usual ranges that are applicable for less complex models, e.g., η 2 p = 0.01 (small), η 2 p = 0.06 (medium), η 2 p = 0.14 (large). ...
Digital technologies, such as virtual or augmented reality, can potentially support neurocognitive functions of the aging populations worldwide and complement existing intervention methods. However, aging-related declines in the frontal-parietal network and dopaminergic modulation which progress gradually across the later periods of the adult lifespan may affect the processing of multisensory congruence and expectancy based contextual plausibility. We assessed hemodynamic brain responses while middle-aged and old adults experienced car-riding virtual-reality scenarios where the plausibility of vibrotactile stimulations was manipulated by delivering stimulus intensities that were either congruent or incongruent with the digitalized audio-visual contexts of the respective scenarios. Relative to previous findings observed in young adults, although highly plausible vibrotactile stimulations confirming with contextual expectations also elicited higher brain hemodynamic responses in middle-aged and old adults, this effect was limited to virtual scenarios with extreme expectancy violations. Moreover, individual differences in plausibility-related frontal activity did not correlate with plausibility violation costs in the sensorimotor cortex, indicating less systematic frontal context-based sensory filtering in older ages. These findings have practical implications for advancing digital technologies to support aging societies.
... We used an LMM because it is particularly useful for examining brain hemodynamic changes over time as noted in Brain Sci. 2024, 14, 503 5 of 13 this review of statistical analysis of fNIRS data [50]. LMMs incorporate random effects for individual participants while capturing variability between groups of participants [51,52]. ...
... False discovery rate (FDR) correction was applied to LMM results to correct for multiple testing familywise across the entire list of optodes (with q = 0.1) [56]. Bonferroni correction was applied during the post hoc analysis of individual contrast comparisons within an optode that survived the previous FDR correction [50]. ...
Autism spectrum disorder (ASD) is a neurodevelopmental disorder affecting individuals worldwide and characterized by deficits in social interaction along with the presence of restricted interest and repetitive behaviors. Despite decades of behavioral research, little is known about the brain mechanisms that influence social behaviors among children with ASD. This, in part, is due to limitations of traditional imaging techniques specifically targeting pediatric populations. As a portable and scalable optical brain monitoring technology, functional near infrared spectroscopy (fNIRS) provides a measure of cerebral hemodynamics related to sensory, motor, or cognitive function. Here, we utilized fNIRS to investigate the prefrontal cortex (PFC) activity of young children with ASD and with typical development while they watched social and nonsocial video clips. The PFC activity of ASD children was significantly higher for social stimuli at medial PFC, which is implicated in social cognition/processing. Moreover, this activity was also consistently correlated with clinical measures, and higher activation of the same brain area only during social video viewing was associated with more ASD symptoms. This is the first study to implement a neuroergonomics approach to investigate cognitive load in response to realistic, complex, and dynamic audiovisual social stimuli for young children with and without autism. Our results further confirm that new generation of portable fNIRS neuroimaging can be used for ecologically valid measurements of the brain function of toddlers and preschool children with ASD.
... The existing mainstream neuroimaging techniques used in combination with brain stimulation techniques include positron emission tomography (PET), functional magnetic resonance imaging (fMRI), functional near-infrared spectroscopy (fNIRS), and electroencephalography (EEG). However, fMRI and PET have limitations when applied routinely compared to EEG and fNIRS techniques [18]. For instance, fMRI is expensive, susceptible to magnetic interference and motion artifacts, lacks mobility, has a low temporal resolution, and precludes those who suffer from noise and claustrophobia [19], [20]. ...
... In the present study, the EEG power spectrum was calculated by the wavelet transform algorithm, Morlet, with a frequency step of 0.5 Hz. The divided EEG bands are delta (1-4 Hz), theta (4-8 Hz), alpha (8-12 Hz), low beta (12-18 Hz), high beta (18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (30-50 Hz) bands. After visual inspection, we selected the task period (i.e., from 5 s to 35 s) from each trial for analyzing the EEG signal's changes to ensure no interference from the tACS. ...
Transcranial electrical stimulation has demonstrated the potential to enhance cognitive functions such as working memory, learning capacity, and attentional allocation. Recently, it was shown that periodic stimulation within a specific duration could augment the human brain’s neuroplasticity. This study investigates the effects of repetitive transcranial alternating current stimulation (tACS; 1 mA, 5 Hz, 2 min duration) on cognitive function, functional connectivity, and topographic changes using both electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). Fifteen healthy subjects were recruited to measure brain activity in the pre-, during-, and post-stimulation sessions under tACS and sham stimulation conditions. Fourteen trials of working memory tasks and eight repetitions of tACS/sham stimulation with a 1-minute intersession interval were applied to the frontal cortex of the participants. The working memory score, EEG band-wise powers, EEG topography, concentration changes of oxygenated hemoglobin, and functional connectivity (FC) were individually analyzed to quantify the behavioral and neurophysiological effects of tACS. Our results indicate that tACS increases: i) behavioral scores (i.e., 15.08,
p
<0.001) and EEG band-wise powers (i.e., theta and beta bands) compared to the sham stimulation condition, ii) FC of both EEG-fNIRS signals, especially in the large-scale brain network communication and interhemispheric connections, and iii) the hemodynamic response in comparison to the pre-stimulation session and the sham condition. Conclusively, the repetitive theta-band tACS stimulation improves the working memory capacity regarding behavioral and neuroplasticity perspectives. Additionally, the proposed fNIRS biomarkers (mean, slope), EEG band-wise powers, and FC can be used as neuro-feedback indices for closed-loop brain stimulation.
... One potential challenge in these approaches is related to the nature of hemodynamic signals recorded using fNIRS. Long-separation fNIRS signals, which are typically captured source-detector pairs separated by approximately 3 cm [27], contain cerebral and extracerebral (i.e., scalp) hemodynamic components [28]. By contrast, short-separation fNIRS channels use a shorter (~8 mm) source-detector distance to independently measure scalp hemodynamics for the purpose of further accounting for or removing their influence from the long-separation channels [29]. ...
... Recent developments in signal analysis approaches including independent measurements (e.g. short-separation fNIRS channels, respiration measurements) of these nuisance signals allow experimenters to account for their contribution to the fNIRS signals recorded using long-separation channels [28]. These developments will allow researchers to further explore the deeply interconnected relationships between electrocortical signals measured using EEG and cortical hemodynamics measured using fNIRS by providing methods to account for the contribution of potential physiological and non-physiological confounds that make exploring these relationships challenging. ...
Multimodal neuroimaging using electroencephalography (EEG) and functional near-infrared spectroscopy
(fNIRS) provides complementary views of cortical processes, including those related to auditory processing.
However, current multimodal approaches often overlook potential insights that can be gained from nonlinear
interactions between electrical and hemodynamic signals. Here, we explore electro-vascular phase-amplitude
coupling (PAC) between low-frequency hemodynamic and high-frequency electrical oscillations during an
auditory task. We further apply a temporally embedded canonical correlation analysis (tCCA)-general linear
model (GLM)-based correction approach to reduce the possible effect of systemic physiology on fNIRS recordings.
Before correction, we observed significant PAC between fNIRS and broadband EEG in the frontal region
(p ≪ 0.05), β (p ≪ 0.05) and γ (p = 0.010) in the left temporal/temporoparietal (left auditory; LA) region, and γ
(p = 0.032) in the right temporal/temporoparietal (right auditory; RA) region across the entire dataset. Significant
differences in PAC across conditions (task versus silence) were observed in LA (p = 0.023) and RA (p =
0.049) γ sub-bands and in lower frequency (5–20 Hz) frontal activity (p = 0.005). After correction, significant
fNIRS-γ-band PAC was observed in the frontal (p = 0.021) and LA (p = 0.025) regions, while fNIRS-α (p = 0.003)
and fNIRS-β (p = 0.041) PAC were observed in RA. Decreased frontal γ-band (p = 0.008) and increased β-band (p
≪ 0.05) PAC were observed during the task. These outcomes represent the first characterization of electrovascular
PAC between fNIRS and EEG signals during an auditory task, providing insights into electro-vascular
coupling in auditory processing.
... Hemodynamic changes within the prefrontal cortex were assessed using a high-density functional near-infrared spectroscopy (fNIRS) device, NIRSIT (OBELAB) [31,32]. NIRSIT's curved panel has 24 laser diodes (sources) that emit light at two wavelengths (780 nm and 850 nm) and 32 photodetectors with a sampling rate of 8.138 Hz. ...
... The dMBLL is based on two assumptions: (1) the absorption of the tissue changes homogeneously and (2) the scattering loss is constant. An analytically treatable special case (semi-infinite, homogeneous medium with optical properties of the cerebral cortex) was utilized here to estimate its order of magnitude [31]. ...
This paper provides an approach that addresses the negative social awareness of games and improves psychological and mental healing effects. It has been perceived that games can lead to reduced physical activity and psychological withdrawal. However, exercise games can simultaneously provide positive aspects of gaming enjoyment and the sensations of physical activities. In this study, we aim to verify a preliminary experiment for treating game-addicted adolescents with exercise games using augmented-reality (AR) technology. In this work, 20 students (average age: 19.5, male: six; female: 14) carried out offline exercise protocols or played an experimental game called AR Earthman with HoloLens2 AR devices. Regarding the measurement tools, a survey and NIRSIT were carried out (game addiction, mood state, and motion recognition), and heart rate and motor awareness were monitored. The experimental results showed no difference in exercise effectiveness between offline and AR exercise. It was confirmed that exercise based on AR technology is effective in treating game-addicted students. The results of this study are as follows: AR exercise games increase a subject’s mental pleasure, and they become satisfied with the exercise’s positive effect. Rather than offline exercise, fun AR exercise games with gamification effects can be suggested as a more helpful method for teenagers. There are differences between game addiction and over-immersion in gaming, but the treatment methods are similar. Therefore, it was confirmed that applying the AR exercise protocol to students who are overly immersed in games could realize psychological and mental healing effects due to excessive immersion in games.
... Hence, dimensionality reduction plays an essential role in improving the performance of machine learning algorithms. A great deal of reduction techniques have been put forward [34], like independent component analysis [35,36], isometric mapping [37,38], locally linear embedding [39,40], etc. Those techniques are mainly for numerical features with far too little attention paid to categorical factors. ...
Dependence between various objects is an important issue which has attracted many researchers. This paper will focus on a new Gini dependence measure having been proposed recently, which measures association between a continuous random vector and a categorical variable. We extend the original Gini method from three perspectives: to various RKHS, to various distance methods and to multi-factor variable. As numerical simulations revealed, Laplacian kernel Gini correlation is statistically efficient both for K-sample tests and for ultrahigh dimensional grouped feature screening. Moreover, we make robustness analysis and discuss the situations appropriate for different distance based Gini measures, including high dimensional sparse data, data with outliers, etc. Real data application implies the estimation accuracy of the proposed Laplacian kernel Gini correlation and its effectiveness for high dimensional K-sample tests. Grouped factor selection reduces time complexity with accuracy of regression models roughly maintained. Our Gini dependence for multi-factor data makes it possible for researchers to comprehensively analyze the impacts of attributes on multiple categorical outcomes.
... To investigate the cognitive impact of latency and the effectiveness of mitigation methods, this study utilizes Functional Near-Infrared Spectroscopy (fNIRS) for analyzing brain activities and underlying cognitive behaviors (Tak and Ye, 2014). Compared to medical neuroimaging techniques relying on neurovascular coupling, such as functional magnetic resonance imaging (fMRI) (Ochsner et al., 2002;Logothetis, 2008) and positron emission tomography (PET) (Andreasen et al., 1996), fNIRS demonstrates better portability and flexibility in field research. ...
Introduction
Long-distance robot teleoperation faces high latencies that pose cognitive challenges to human operators. Latency between command, execution, and feedback in teleoperation can impair performance and affect operators’ mental state. The neural underpinnings of these effects are not well understood.
Methods
This study aims to understand the cognitive impact of latency in teleoperation and the related mitigation methods, using functional Near-Infrared Spectroscopy (fNIRS) to analyze functional connectivity. A human subject experiment (n = 41) of a simulated remote robot manipulation task was performed. Three conditions were tested: no latency, with visual and haptic latency, with visual latency and no haptic latency. fNIRS and performance data were recorded and analyzed.
Results
The presence of latency in teleoperation significantly increased functional connectivity within and between prefrontal and motor cortexes. Maintaining visual latency while providing real-time haptic feedback reduced the average functional connectivity in all cortical networks and showed a significantly different connectivity ratio within prefrontal and motor cortical networks. The performance results showed the worst performance in the all-delayed condition and best performance in no latency condition, which echoes the neural activity patterns.
Conclusion
The study provides neurological evidence that latency in teleoperation increases cognitive load, anxiety, and challenges in motion planning and control. Real-time haptic feedback, however, positively influences neural pathways related to cognition, decision-making, and sensorimotor processes. This research can inform the design of ergonomic teleoperation systems that mitigate the effects of latency.
... Data from 48 channels with a 3 cm source-detector distance were processed. Prior to analysis, task data were isolated to eliminate unnecessary time windows between tasks (33,34). The preprocessing pipeline began with signal quality control. ...
Background
Mild cognitive impairment (MCI) is a growing concern among older adults, with limited effective pharmacological treatments available. Despite the potential of herbal medicine and acupuncture in managing MCI, there is a lack of research on their long-term effects on cognitive function and brain activity in clinical practice settings. This study aimed to address this gap by exploring the effects of a community-based program integrating herbal medicine and acupuncture on cognitive function and neural responses in older individuals with MCI.
Methods
Nineteen individuals were enrolled from a pool of 250 individuals registered in the 2021 Busan Dementia Prevention & Care Program. Participants with MCI received herbal medicine, acupuncture, and pharmacopuncture treatments over a 6-month period. The Montreal Cognitive Assessment (MoCA) was administered at baseline and after 3 and 6 months to evaluate cognitive function. Functional near-infrared spectroscopy (fNIRS) was used to measure prefrontal cortex activity during cognitive task performance, including verbal fluency, Stroop color and word, and digit span backward tests.
Results
Seventeen participants (13 female; mean age, 69.5 years) with MCI completed the study. Following the 6-month intervention, they exhibited a significant increase in the MoCA total score over time [F(2.32) =10.59, p < 0.0001]. Additionally, the deoxygenated hemoglobin beta coefficient in the left frontopolar prefrontal cortex significantly decreased during the Stroop task after the intervention.
Conclusion
The Dementia Prevention & Care Program, which integrates herbal medicine and acupuncture, may enhance cognitive function in individuals with MCI. Moreover, the observed changes in prefrontal cortex activity after completion of the program suggest a need for further investigation of the underlying mechanisms.
... Channels with low amplitudes were excluded from group processing. The signals were then processed using the principal component analysis method to eliminate any systematic artifacts, following the methods described in previous literature (Tak & Ye, 2013). Motion artifacts exceeding a threshold of more than 15 standard deviations from the mean were identified and replaced with spline interpolation based on the preceding and subsequent segments of the signals. ...
We investigated the acute effects of high-intensity intermittent exercise (HIIE) on cortical hemodynamic changesand sex differences during recognition memory and visuospatial tasks. Forty-six healthy adults (18–30 years)were randomly assigned to HIIE (n = 23, including 11 males and 12 females) or control groups (n = 23, including10 males and 13 females). Functional near-infrared spectroscopy measured prefrontal cortex (PFC) activationduring Warrington's word and facial Recognition Memory Test (RMT), and Shipley-2 test before and after theintervention. HIIE resulted in improved word recognition memory scores, but no significant changes in facerecognition or visuospatial scores. PFC activation during tasks did not significantly differ following HIIE. Sexdifferences were observed, with males showing greater word recognition memory scores and associatedhemodynamics compared to females, but no sex differences in face recognition or visuospatial tasks in responseto HIIE. In summary, HIIE improved word recognition memory without affecting PFC activation. Moreover, sexdifferences in PFC activation during word recognition tasks were evident following HIIE. These findings contributeto our understanding of the acute effects of HIIE on cognitive performance and highlight the potential influence ofsex on cortical hemodynamics during word recognition memory tasks.
... We defined edges as connections between the regions, with Pearson's correlation coefficients above a certain threshold. These were based on the timeseries data of 16 regions of interest obtained from the NIRS analysis [18,19]. ...
... An analogous procedure of cap positioning was utilized in the present study, so cap placement probably contributed minimally to fNIRS data variability. fNIRS data is known to be intrinsically variable between participants and group averaging over many participants is necessary to reveal underlying neural dynamics [31,37,118]. This impacted statistical results: because the data was not normally distributed, outliers could not be excluded using parametric tools such as Grubb's [119] test. ...
This study aimed to investigate integration of alternating speech, a stimulus which classically produces a V-shaped speech intelligibility function with minimum at 2–6 Hz in typical-hearing (TH) listeners. We further studied how degraded speech impacts intelligibility across alternating rates (2, 4, 8, and 32 Hz) using vocoded speech, either in the right ear or bilaterally, to simulate single-sided deafness with a cochlear implant (SSD-CI) and bilateral CIs (BiCI), respectively. To assess potential cortical signatures of across-ear integration, we recorded activity in the bilateral auditory cortices (AC) and dorsolateral prefrontal cortices (DLPFC) during the task using functional near-infrared spectroscopy (fNIRS). For speech intelligibility, the V-shaped function was reproduced only in the BiCI condition; TH (with ceiling scores) and SSD-CI conditions had significantly higher scores across all alternating rates compared to the BiCI condition. For fNIRS, the AC and DLPFC exhibited significantly different activity across alternating rates in the TH condition, with altered activity patterns in both regions in the SSD-CI and BiCI conditions. Our results suggest that degraded speech inputs in one or both ears impact across-ear integration and that different listening strategies were employed for speech integration manifested as differences in cortical activity across conditions.
... BMC Medicine (2024) 22:386 level. We used restricted maximum likelihood (ReML) estimation for all LMM analyses [60]. Three steps of multilevel LMM were conducted. ...
Background
Long-term deterioration in the mental health of healthcare workers (HCWs) has been reported during and after the COVID-19 pandemic. Determining the impact of COVID-19 incidence and mortality rates on the mental health of HCWs is essential to prepare for potential new pandemics. This study aimed to investigate the association of COVID-19 incidence and mortality rates with depressive symptoms over 2 years among HCWs in 20 countries during and after the COVID-19 pandemic.
Methods
This was a multi-country serial cross-sectional study using data from the first and second survey waves of the COVID-19 HEalth caRe wOrkErS (HEROES) global study. The HEROES study prospectively collected data from HCWs at various health facilities. The target population included HCWs with both clinical and non-clinical roles. In most countries, healthcare centers were recruited based on convenience sampling.
As an independent variable, daily COVID-19 incidence and mortality rates were calculated using confirmed cases and deaths reported by Johns Hopkins University. These rates represent the average for the 7 days preceding the participants’ response date. The primary outcome was depressive symptoms, assessed by the Patient Health Questionnaire-9. A multilevel linear mixed model (LMM) was conducted to investigate the association of depressive symptoms with the average incidence and mortality rates.
Results
A total of 32,223 responses from the participants who responded to all measures used in this study on either the first or second survey, and on both the first and second surveys in 20 countries were included in the analysis. The mean age was 40.1 (SD = 11.1), and 23,619 responses (73.3%) were from females. The 9323 responses (28.9%) were nurses and 9119 (28.3%) were physicians. LMM showed that the incidence rate was significantly and positively associated with depressive symptoms (coefficient = 0.008, standard error 0.003, p = 0.003). The mortality rate was significantly and positively associated with depressive symptoms (coefficient = 0.049, se = 0.020, p = 0.017).
Conclusions
This is the first study to show an association between COVID-19 incidence and mortality rates with depressive symptoms among HCWs during the first 2 years of the outbreak in multiple countries. This study’s findings indicate that additional mental health support for HCWs was needed when the COVID-19 incidence and mortality rates increase during and after the early phase of the pandemic, and these findings may apply to future pandemics.
Trial registration
Clinicaltrials.gov, NCT04352634.
... For the ANOVA of fNIRS data, the 13 ROIs were initially analyzed and the p values were corrected by the false discovery rate (FDR) method with a corrected α level of .05 (Tak & Ye, 2014). Only the p values that survived the FDR correction were administered in the subsequent post hoc test. ...
Idea generation and elaboration are two cognitive processes for fostering figural creativity. Critically, however, studies investigating the effect of creativity anxiety on figural creativity have confounded these processes. Consequently, this study explored whether individuals with high creativity anxiety could effectively engage in state-augmented figural generation and elaboration. Using functional near-infrared spectroscopy (fNIRS), 32 high creativity-anxious (HCA) and 32 low creativity-anxious (LCA) participants were tested on a picture completion task under four conditions: “draw creatively” (cued generation), draw the picture that immediately springs to mind (uncued generation), improve the novelty of pregenerated drawings (cued elaboration), or “elaborate commonly” (uncued elaboration). In the cued generation condition, HCA and LCA individuals exhibited comparable behavioral performance. However, the latter demonstrated prefrontal deactivation. In the uncued generation condition, HCA individuals demonstrated less elaboration than LCA individuals and lower activation in the right-frontopolar cortex, left-frontopolar cortex, and left-temporoparietal junction. Additionally, they showed higher functional coupling between the default and executive networks. In the cued elaboration condition, HCA individuals only performed weaker functional connectivity in the left-frontopolar cortex_left-superior temporal gyrus and right dorsal lateral prefrontal cortex_left superior temporal gyrus than LCA individuals. In the uncued elaboration condition, HCA individuals reported less elaboration and functional coupling between the default and executive networks than LCA individuals. Overall, the findings suggest that HCA individuals tend to recruit more resources to perform comparably to LCA individuals when engaged in creative thinking tasks. However, HCA individuals demonstrate lower levels of creativity and brain deactivation in tasks where explicit creativity prompts are absent. Consequently, HCA individuals exhibit inefficient completion of figural idea generation and elaboration.
... The brain imaging method used in this study is functional near-infrared spectroscopy (fNIRS). fNIRS uses near-infrared light to measure the relative change in oxygenated and deoxygenated hemoglobin (oxy-Hb/deoxy-Hb) in the cortex as an indirect measure of neural activity [26,27]. The aim of this pilot study was to determine whether a set of questionnaires, cognitive tests and functional brain imaging could identify subgroups of long-term adult childhood ALL survivors who suffer from neurocognitive impairments in order to help understand the underlying neurocognitive mechanism. ...
Acute lymphoblastic leukemia (ALL) is the most common childhood malignancy. Due to the drastic increase in survivor rate over the last 50 years, long lasting treatment effect on moods and neurocognitive function has become a present issue. Most studies of late effects of treatment of ALL survivors investigate patients in their adolescents. This pilot study aims to identify measurements for evaluating late effect of childhood ALL survivors regarding neurocognitive and mood problems in adulthood. ALL survivors who received neurotoxic treatment with high-dose methotrexate and cranial radiotherapy (Chemo+CRT) (n=10) and ALL survivors only treated with high-dose methotrexate (Chemo) (n=10), plus age and sex match controls (n=20) where recruited to the study. The study protocol involved questionnaires, neurocognitive tests and optical brain imaging with functional near infrared spectroscopy (fNIRS) over the frontal and parietal cortex. The fNIRS results indicate a reduced involvement of the parietal cortex during conflict processing for the ALL survivors compared to controls. The study protocol shows promising results for identifying subgroups that suffers from neurocognitive and mood problems and we aim to expand upon it in a larger study. As our results indicate increased challenges among female ALL survivors, especially pathological fatigue, anxiety, and information processing, it is important to explore in future investigations
the interplay between the risk of hormonal interaction with chemotherapy during development and occupational and social pressure during adulthood.
... The average concentration changes of Δoxy-Hb, Δdeoxy-Hb and THI was calculated for each period [41]. In this study, we used the Kolmogorov-Smirnov test to test the normal distribution of the data, the normally distributed variables are compared by t-test, while non-normally distributed variables are compared by the rank sum test. ...
The trail making test (TMT) is a commonly used tool for evaluating executive functions, and the activation of cerebral oxygenation in the prefrontal cortex (PFC) during the test can reflect the participation of executive function. This study aimed to compare the differences in cerebral oxygenation in the PFC between the computer- and paper-based versions of the TMT and provide a theoretical basis for the optimization and clinical application of the computer-based version. A total of 32 healthy adult participants completed the computer- and paper-based TMT Types A and B. Cerebral oxygenation changes in the PFC were monitored during the experiment using near-infrared spectroscopy. Moreover, average changes in oxyhemoglobin (Δoxy-Hb) levels at the baseline and during activation periods in different types of testing were compared and analyzed. The number of correct connections in the computer-based version Type B was less than that in the paper-based version Type B (p < .001). The task time of the computer-based version was longer than that of the paper-based version (p < .001). The B/A ratio of the number of correct connections in the computer-based version was lower than that in the paper-based version (p < .001). The Δoxy-Hb in the PFC of the paper-based version was higher than that of the computer-based version (p < .001). Significant differences in oxygenation in the PFC were observed between the paper- and computer-based versions of TMT. After further improvement and correction in the subsequent development of the computer-based TMT, and taking into account the psychological feelings and preferences of the participants when performing different versions of the TMTs, the computer-based TMT is expected to play a good auxiliary role in clinical evaluation.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12868-024-00886-9.
... While fNIRS offers a portable, non-invasive means of monitoring brain activity it does have some limitations, which may contribute to bias. These are largely related to artefacts caused by motion or physiological noise (44,45). For example, blood flow changes in the extracerebral layers of the head are known to interfere with fNIRS signals. ...
Objective: To explore cognitive load in people with
transfemoral amputations fitted with socket or
bone-anchored prostheses by describing activity
in the left and right dorsolateral prefrontal cortices
during single- and dual-task walking.
Design: Cross-sectional pilot study.
Patients: 8 socket prosthesis users and 8 bone�anchored prosthesis users. All were fitted with
microprocessor-controlled prosthetic knees.
Methods: Participants answered self-report ques�tionnaires and performed gait tests during 1 single�task walking condition and 2 dual-task walking
conditions. While walking, activity in the dorsola�teral prefrontal cortex was measured using functio�nal near-infrared spectroscopy. Cognitive load was
investigated for each participant by exploring the
relative concentration of oxygenated haemoglobin
in the left and right dorsolateral prefrontal cortex.
Symmetry of brain activity was investigated by cal�culating a laterality index.
Results: Self-report measures and basic gait vari�ables did not show differences between the groups.
No obvious between-group differences were obser�ved in the relative concentration of oxygenated
haemoglobin for any walking condition. There was a
tendency towards more right-side brain activity for
participants using a socket prosthesis during dual�task conditions.
Conclusions: This pilot study did not identify
substantial differences in cognitive load or latera�lization between socket prosthesis users and bone�anchored prosthesis users.
... Fig. 6 illustrates the graphical comparison, highlighting the best-performing classifier, which is kNN with an average accuracy of 88.19 ± 2.55%. A student's t-test ( -value) was performed to determine the significant difference between the mean values of results of classification accuracies [52]. kNN performed significantly better than other classifiers, therefore, a student's t-test was performed to indicate statistical significance by comparing the results of kNN with LDA and Tree classifier. ...
To improve mobility and rehabilitation, precise and adaptive control mechanisms have been developed for lower limb exoskeletons. Brain-computer interface (BCI) provides advanced and intuitive control of assistive and rehabilitation exoskeletons to aid the user. Functional near-infrared spectroscopy (fNIRS) is a non-invasive, and portable brain imaging modality, gained momentum in rehabilitation studies in the last decade. This study provides a novel approach to control a lower limb exoskeleton with enhanced classification accuracy using fNIRS-based BCI, the k-nearest neighbors (kNN) classifier, and optimal feature combination. The brain signals were acquired using fNIRS for walking vs rest for twenty healthy participants, having ten trials for each participant. The statistical measures: mean, peak, variance, skewness, kurtosis, and slope are extracted as features. Optimal feature combination was analyzed and selected for enhanced classification accuracy. kNN was analyzed and selected as an optimal classifier with optimal ‘k’ (number of nearest neighboring data points that the kNN considers while classifying a new data point) using elbow method to improve classification performance. The proposed method achieves an average classification accuracy of 88.19 ± 2.55 %, in offline configuration. In order to control exoskeleton in online settings, simulated online classification was performed using one unknown trial, fed as real-time signal. Sliding window of 2.5 sec is used and achieved average classification accuracy of 97.5%. This research represents a major advancement in user-centric assistive technologies and advances the field of neuro-powered exoskeletons. It also lays the groundwork for future advancements in the integration of neuroimaging, machine learning, and rehabilitation.
... The raw data were then converted into optical density and corrected for motion artifacts using spike removal and TDDR [48]. Next, the optical density data were converted into changes in concentration using the modified Beer-Lambert law [49] and band-pass filtered ([0.01-0.5 Hz]) to remove high-frequency noise. Lastly, the signals were normalized, and general linear modeling (GLM) analysis was conducted at the individual level. ...
(1) Background: Hyperactivity may play a functional role in upregulating prefrontal cortical hypoarousal and executive functioning in ADHD. This study investigated the neurocognitive impact of movement during executive functioning on children with ADHD. (2) Methods: Twenty-four children with and without ADHD completed a Stroop task and self-efficacy ratings while remaining stationary (Stationary condition) and while desk cycling (Movement condition). Simultaneous functional near-infrared spectroscopy (fNIRS) recorded oxygenated and deoxygenated changes in hemoglobin within the left dorsolateral prefrontal cortex (DLPFC). (3) Results: Among children with ADHD, the Movement condition produced superior Stroop reaction time compared to the Stationary condition (p = 0.046, d = 1.00). Self-efficacy improved in the Movement condition (p = 0.033, d = 0.41), whereas it did not in the Stationary condition (p = 0.323). Seventy-eight percent of participants showed greater oxygenation in the left DLPFC during the Movement condition vs. the Stationary condition. Among children without ADHD, there were no differences in Stroop or self-efficacy outcomes between Stationary and Movement conditions (ps > 0.085, ts < 1.45); 60% of participants showed greater oxygenation in the left DLPFC during the Movement vs. the Stationary condition. (4) Conclusions: This work provides supportive evidence that hyperactivity in ADHD may be a compensatory mechanism to upregulate PFC hypoarousal to support executive functioning and self-efficacy.
... io) with a TensorFlow backend. Each deep learning model was trained to minimize the categorical cross-entropy loss function with a batch size of 32 and the Adamax optimizer [49], at a default learning rate of 0.001 to ensure fair comparison under uniform conditions. A fixed random seed of 0 was used to maintain consistency across the training sessions. ...
Background. Functional near-infrared spectroscopy (fNIRS) is being extensively explored as a potential primary screening tool for major depressive disorder (MDD) because of its portability, cost-effectiveness, and low susceptibility to motion artifacts. However, the fNIRS-based computer-aided diagnosis (CAD) of MDD using deep learning methods has rarely been studied. In this study, we propose a novel deep learning framework based on a convolutional neural network (CNN) for the fNIRS-based CAD of MDD with high accuracy. Materials and Methods. The fNIRS data of participants—48 patients with MDD and 68 healthy controls (HCs)—were obtained while they performed a Stroop task. The hemodynamic responses calculated from the preprocessed fNIRS data were used as inputs to the proposed CNN model with an ensemble CNN architecture, comprising three 1D depth-wise convolutional layers specifically designed to reflect interhemispheric asymmetry in hemodynamic responses between patients with MDD and HCs, which is known to be a distinct characteristic in previous MDD studies. The performance of the proposed model was evaluated using a leave-one-subject-out cross-validation strategy and compared with those of conventional machine learning and CNN models. Results. The proposed model exhibited a high accuracy, sensitivity, and specificity of 84.48%, 83.33%, and 85.29%, respectively. The accuracies of conventional machine learning algorithms—shrinkage linear discriminator analysis, regularized support vector machine, EEGNet, and ShallowConvNet—were 73.28%, 74.14%, 62.93%, and 62.07%, respectively. Conclusions. In conclusion, the proposed deep learning model can differentiate between the patients with MDD and HCs more accurately than the conventional models, demonstrating its applicability in fNIRS-based CAD systems.
... Statistical parametric mapping (SPM) analysis was conducted to create a cortical activation map of the cerebral hemodynamic responses to oxy-Hb. A general linear model with a canonical hemodynamic response function was used in the SPM analysis to model the hypothesized oxy-Hb response and to test for significant cortical activation during the task block compared with the resting block [28]. Each task was executed for 1 min, with four distinct tasks consecutively executed in each block. ...
Background
Stroke causes long-term disabilities, highlighting the need for innovative rehabilitation strategies for reducing residual impairments. This study explored the potential of functional near-infrared spectroscopy (fNIRS) for monitoring cortical activation during rehabilitation using digital therapeutics.
Methods
This cross-sectional study included 18 patients with chronic stroke, of whom 13 were men. The mean age of the patients was 67.0 ± 7.1 years. Motor function was evaluated through various tests, including the Fugl–Meyer assessment for upper extremity (FMA-UE), grip and pinch strength test, and box and block test. All the patients completed the digital rehabilitation program (MotoCog®, Cybermedic Co., Ltd., Republic of Korea) while being monitored using fNIRS (NIRScout®, NIRx Inc., Germany). Statistical parametric mapping (SPM) was employed to analyze the cortical activation patterns from the fNIRS data. Furthermore, the K-nearest neighbor (K-NN) algorithm was used to analyze task performance and fNIRS data to classify the severity of motor impairment.
Results
The participants showed diverse task performances in the digital rehabilitation program, demonstrating distinct patterns of cortical activation that correlated with different motor function levels. Significant activation was observed in the ipsilesional primary motor area (M1), primary somatosensory area (S1), and contralateral prefrontal cortex. The activation patterns varied according to the FMA-UE scores. Positive correlations were observed between the FMA-UE scores and SPM t-values in the ipsilesional M1, whereas negative correlations were observed in the ipsilesional S1, frontal lobe, and parietal lobe. The incorporation of cortical hemodynamic responses with task scores in a digital rehabilitation program substantially improves the accuracy of the K-NN algorithm in classifying upper limb functional levels in patients with stroke. The accuracy for tasks, such as the gas stove-operation task, increased from 44.4% using only task scores to 83.3% when these scores were combined with oxy-Hb t-values from the ipsilesional M1.
Conclusions
The results advocated the development of tailored digital rehabilitation strategies by combining the behavioral and cerebral hemodynamic data of patients with stroke. This approach aligns with the evolving paradigm of personalized rehabilitation in stroke recovery, highlighting the need for further extensive research to optimize rehabilitation outcomes.
... The analysis of fNIRS data was conducted using the GLM approach 73, 74 , a widely utilized statistical technique in neuroimaging studies [75][76][77] . Our experimental paradigm involved alternating periods of task engagement and rest, each lasting 1 minute and 30 seconds, respectively, repeated ten times over a 15-minute session. ...
As societal interactions increasingly involve both intentional and unintentional agents, understanding their effects on human cognition becomes paramount. This study investigates the neural correlates of interacting with intentional versus artificial agents in a simulated tennis game scenario. Employing functional near-infrared spectroscopy (fNIRS), brain activity in 50 male participants during gameplay against both types of opponents was analyzed. The used methodological approach ensures ecological validity by simulating real-world decision-making scenarios while participants undergo fNIRS scanning, avoiding the constraints of traditional neuroimaging methods. Six prefrontal cortex channels are focused on, leveraging the 10-20 system, to capture nuanced differences in brain activity. Wavelet analysis was utilized to dissect the data into frequency-specific differences, revealing subtle variations across different channels and frequency bands. Moreover, activity was quantified by comparing average data signals between rest and play modes across all points using Generalized Linear Model (GLM). The findings unveil significant differences in neural activation patterns, particularly in one specific channel and frequency range, suggesting distinct cognitive processing when interacting with intentional agents. These results align with previous neuroimaging studies and contribute to understanding the neural underpinnings of human-agent interactions in naturalistic settings. While acknowledging study limitations, including sample homogeneity and spatial accuracy constraints, the study's findings underscore the potential of fNIRS in exploring complex cognitive phenomena beyond laboratory confines.
... In contrast, neuroimaging can be used as a supplement to reveal links between neural activity and medium-and long-term, ecologically valid outcomes in laboratory settings 20,21 . Functional near-infrared spectroscopy (fNIRS) is an promising non-invasive imaging methodology which has garnered increasing popularity as a neuroimaging technique for cerebral function research in recent years [22][23][24] . Compared to functional magnetic resonance imaging (fMRI), this technology has several advantages, such as portability, extensive long-term data acquisition, and a high sample rate. ...
As the depth of coal mining increases, the temperature and humidity of the underground environment also rise, which can negatively impact the physiological health of miners, and may even pose a threat to their safety and lives. However, studies on the neurocognitive mechanisms underlying the relationship between temperature, humidity, and miners’ alertness are scant. This study investigates several research objectives: (A) the differences in reaction time and error rate in different temperature and humidity conditions, which factor has a greater impact; (B) the differences in the levels of Oxy-Hb in different conditions and which factor has a greater impact; (C) the differences of activation degree between different regions of interest; and (D) the differences in the shape of Oxy-Hb time course between different conditions between different regions of interests. The fNIRS was used to measure the activity in 100 participants’ prefrontal cortex in this study. The results showed that both temperature and humidity would lead to decreased alertness of miners, which would not only prolong the reaction time, increase the error rate, and increase the Oxy-Hb concentration, but also lead to increased activation of the prefrontal cortex and greater activation of the right side than that of the left side, the Oxy-Hb time course was different on both sides, and temperature has a greater effect on alertness than humidity.
... Functional near-infrared spectroscopy (fNIRS) is a noninvasive method to measure brain activity by measuring the absorption of the near-infrared light between 650 and 950 nm through the intact skull (31)(32)(33)(34)(35)(36). It is a relatively non-invasive and inexpensive functional neuroimaging technique that provides direct and quantitative measurement of cortical hemodynamic responses to cognitive tasks (31)(32)(33)(34). ...
Introduction
Inhibition control, as the core component of executive function, might play a crucial role in the understanding of attention deficit/hyperactivity disorder (ADHD) and specific learning disorders (SLD). Inhibition control deficits have been observed in children with ADHD or SLD. This study sought to test in a multi-modal fashion (i.e., behavior and plus brain imaging) whether inhibition control abilities would be further deteriorated in the ADHD children due to the comorbidity of SLD.
Method
A total number of 90 children (aged 6-12 years) were recruited, including 30 ADHD, 30 ADHD+SLD (children with the comorbidity of ADHD and SLD), and 30 typically developing (TD) children. For each participant, a 44-channel functional near infrared spectroscopy (fNIRS) equipment was first adopted to capture behavioral and cortical hemodynamic responses during a two-choice Oddball task (a relatively new inhibition control paradigm). Then, 50 metrics were extracted, including 6 behavioral metrics (i.e., OddballACC, baselineACC, totalACC, OddballRT, baselineRT, and totalRT) and 44 beta values in 44 channels based on general linear model. Finally, differences in those 50 metrics among the TD, ADHD, and ADHD+SLD children were analyzed.
Results
Findings showed that: (1) OddballACC (i.e., the response accuracy in deviant stimuli) is the most sensitive metric in identifying the differences between the ADHD and ADHD+SLD children; and (2) The ADHD+SLD children exhibited decreased behavioral response accuracy and brain activation level in some channels (e.g., channel CH35) than both the ADHD and TD children.
Discussion
Findings seem to support that inhibition control abilities would be further decreased in the ADHD children due to the comorbidity of SLD.
... a pre-coloring method which used a hRF (hemodynamic response function) lowpass filter was conducted to attenuate high frequency components and provide sufficient temporal smoothing (Worsley and Friston 1995;Ye et al. 2009). after that, a general linear model estimation (GlM) was utilised to obtain the temporal correlation estimations of Beta in the durations of walking period and 60 s rest, which represent the magnitude of hemodynamic response of hbO, which means the activation degree of cerebral cortex (Plichta et al. 2007;tak and Ye 2014). the beta values were baseline-corrected by subtracting the average values of rest duration from that of interaction task. ...
Although trust plays a vital role in human-robot interaction, there is currently a dearth of literature examining the effect of users' openness personality on trust in actual interaction. this study aims to investigate the interaction effects of users' openness and robot reliability on trust. We designed a voice-based walking task and collected subjective trust ratings, task metrics, eye-tracking data, and fNiRs signals from users with different openness to unravel the psychological intentions, task performance, visual behaviours, and cerebral activations underlying trust. the results showed significant interaction effects. Users with low openness exhibited lower subjective trust, more fixations, and higher activation of rtPJ in the highly reliable condition than those with high openness. the results suggested that users with low openness might be more cautious and suspicious about the highly reliable robot and allocate more visual attention and neural processing to monitor and infer robot status than users with high openness. PRACTITIONER SUMMARY the study could deepen practitioners' understanding of the effect of openness on trust in robots by examining the psychological intention, task performance, visual behaviours, and physiological activations. Moreover, the interaction effect could provide guidelines for designing robots adaptive to users' personalities, and the multimodal method would be practical for measuring trust in interaction.
... The 48 channels of the fNIRS divided the PFC into eight Regions of Interest (ROIs), four on either side of the brain as shown in Figure 3. The raw data obtained from the fNIRS software was preprocessed (Tak and Ye 2014). This included converting raw light density to Optical Density, then correcting for movements using the Temporal Derivative Distribution Repair method (Fishburn et al. 2019) and filtering out physiological artifacts such as heartbeat and blood pressure. ...
... The MATLAB-based NIRS_SPM toolkit was used to perform a general linear model (GLM) analysis to obtain the activation of brain areas. Specifically, GLM decomposes the observed signal as follows [27]: ...
Background
This study aimed to compare the balance ability and functional brain oxygenation in the prefrontal cortex (PFC) among older adults with mild cognitive impairment (MCI) under single and dual tasks, and also investigate their relationship. Neural regulatory mechanisms of the brain in the MCI were shed light on in balance control conditions.
Methods
21 older adults with MCI (female = 12, age: 71.19 ± 3.36 years) were recruited as the experimental group and 19 healthy older adults (female = 9, age: 70.16 ± 4.54 years) as the control group. Participants completed balance control of single task and dual task respectively. Functional near-infrared spectroscopy (fNIRS) and force measuring platform are used to collect hemodynamic signals of the PFC and center of pressure (COP) data during the balance task, respectively.
Results
The significant Group*Task interaction effect was found in maximal displacement of the COP in the medial-lateral (ML) direction (D-ml), 95% confidence ellipse area (95%AREA), root mean square (RMS), the RMS in the ML direction (RMS-ml), the RMS in the anterior-posterior (AP) direction (RMS-ap), sway path (SP), the sway path in the ML direction (SP-ml), and the sway path in the AP direction (SP-ap). The significant group effect was detected for five regions of interest (ROI), namely the left Brodmann area (BA) 45 (L45), the right BA45 (R45), the right BA10 (R10), the left BA46 (L46), and the right BA11 (R11). Under single task, maximal displacement of the COP in the AP direction (D-ap), RMS, and RMS-ap were significantly negatively correlated with R45, L45, and R11 respectively. Under dual task, both RMS and 95%AREA were correlated positively with L45, and both L10 and R10 were positively correlated with RMS-ap.
Conclusion
The MCI demonstrated worse balance control ability as compared to healthy older adults. The greater activation of PFC under dual tasks in MCI may be considered a compensatory strategy for maintaining the standing balance. The brain activation was negatively correlated with balance ability under single task, and positively under dual task.
Trial registration
ChiCTR2100044221, 12/03/2021.
... We used a general linear model (GLM) to systemically quantify the neural activation characteristics in each period of the task (Tak & Ye, 2014). We constructed a design matrix for each subject by using a boxcar function-based regressor convolved with the canonical hemodynamic response function (HRF) across the fixation, delay, and decision periods of the DMTS task. ...
The prefrontal cortex (PFC) has been extensively studied in relation to various cognitive abilities, including executive function, attention, and memory. Nevertheless, there is a gap in our scientific knowledge regarding the functionally dissociable neural dynamics across the PFC during a cognitive task and their individual differences in performance. Here, we explored this possibility using a delayed match‐to‐sample (DMTS) working memory (WM) task using NIRSIT, a high‐density, wireless, wearable functional near‐infrared spectroscopy (fNIRS) system. First, upon presentation of the sample stimulus, we observed an immediate signal increase in the ventral (orbitofrontal) region of the anterior PFC, followed by activity in the dorsolateral PFC. After the DMTS test stimulus appeared, the orbitofrontal cortex activated once again, while the rest of the PFC showed overall disengagement. Individuals with higher accuracy showed earlier and sustained activation of the PFC across the trial. Furthermore, higher network efficiency and functional connectivity in the PFC were correlated with individual WM performance. Our study sheds new light on the dynamics of PFC subregional activity during a cognitive task and its potential applicability in explaining individual differences in experimental, educational, or clinical populations.
Practitioner Points
Wearable functional near‐infrared spectroscopy (fNIRS) captured dissociable temporal dynamics across prefrontal subregions during a delayed match‐to‐sample task.
Anterior regions of the orbitofrontal cortex (OFC) activated first during the delay period, followed by the dorsolateral prefrontal cortex (PFC).
PFC disengaged overall after the delay, but the OFC reactivated to the test stimulus.
Earlier and sustained activation of PFC was associated with better accuracy.
Functional connectivity and network efficiency also varied with task performance.
... While wearing the fNIRS cap, students were asked to complete a word-tracing task to record baseline activation in their brains. This type of baseline recording is typical among neurocognitive studies [15], [16]. After the word tracing, participants were asked to rest for thirty seconds by staring at a crosshair. ...
... Therefore, it is necessary to preprocess the collected fNIRS data and conduct a statistical analysis. Relevant studies have reviewed these methods (Tak and Ye, 2014;Kamran et al., 2016;Chen W. L. et al., 2020;Dans et al., 2021). ...
Non-drug therapies of traditional Chinese medicine (TCM), including acupuncture, massage, tai chi chuan, and Baduanjin, have emerged as widespread interventions for the treatment of various diseases in clinical practice. In recent years, preliminary studies on the mechanisms of non-drug therapies of TCM have been mostly based on functional near-infrared spectroscopy (fNIRS) technology. FNIRS is an innovative, non-invasive tool to monitor hemodynamic changes in the cerebral cortex. Our review included clinical research conducted over the last 10 years, establishing fNIRS as a reliable and stable neuroimaging technique. This review explores new applications of this technology in the field of neuroscience. First, we summarize the working principles of fNIRS. We then present preventive research on the use of fNIRS in healthy individuals and therapeutic research on patients undergoing non-drug therapies of TCM. Finally, we emphasize the potential for encouraging future advancements in fNIRS studies to establish a theoretical framework for research in related fields.
... In this study, we used Matlab (R2013b) and the HomER2 processing program to preprocess the fNIRS data. The primary purpose of preprocessing was to remove noise in the original data and retain the blood oxygen change signal components caused by neural activity as much as possible (Tak and Ye, 2014). We first converted the original intensity data into optical density changes. ...
Purpose
Rapid eye movement sleep behavior disorder (RBD) affects 30%–40% of patients with Parkinson’s disease (PD) and has been linked to a higher risk of cognitive impairment, especially executive dysfunction. The aim of this study was to investigate the brain activation patterns in PD patients with RBD (PD-RBD+) compared to those without RBD (PD-RBD−) and healthy controls (HCs), and to analyze the correlation between changes in cerebral cortex activity and the severity of RBD.
Methods
We recruited 50 PD patients, including 30 PD-RBD+, 20 PD-RBD−, and 20 HCs. We used functional near infrared spectroscopy during a verbal fluency task (VFT-fNIRS) and clinical neuropsychological assessment to explore the correlation between PD-RBD+ and executive function and changes in neural activity.
Results
The VFT-fNIRS analysis revealed a significant reduction in activation among PD-RBD+ patients across multiple channels when compared to both the PD-RBD− and HC groups. Specifically, PD-RBD+ patients exhibited diminished activation in the bilateral dorsolateral prefrontal cortex (DLPFC) and the right ventrolateral prefrontal cortex (VLPFC) relative to their PD-RBD− counterparts. Furthermore, compared to the HC group, PD-RBD+ patients displayed reduced activation specifically in the right DLPFC. Significantly, a noteworthy negative correlation was identified between the average change in oxygenated hemoglobin concentration (ΔHbO2) in the right DLPFC of PD-RBD+ patients and the severity of their RBD.
Conclusion
Our study offers compelling evidence that RBD exacerbates cognitive impairment in PD, manifested as executive dysfunction, primarily attributed to reduced prefrontal activation. These aberrations in brain activation may potentially correlate with the severity of RBD.
... Brain imaging techniques such as functional magnetic resonance imaging (fMRI), electroencephalography (EEG), and functional near-infrared spectroscopy (fNIRS) have been used to measure changes in brain function, which could be evidence of cognitive training efficacy. Of brain imaging techniques, fNIRS non-invasively measures brain activity using changes in light absorption in the brain [21,22]. fNIRS has the advantages of portability, movement tolerability, and safety of use compared to other neuroimaging modalities [23]. ...
To date, budget management in virtual shopping training has not been given much importance. The main objective of this study was to investigate the effects of virtual shopping budget-management training on executive functions and brain activation. Sixteen participants were randomly assigned to the experimental group that received virtual shopping budget-management training or the waitlist control group for a total of 16 sessions. To examine the effects of virtual shopping budget-management training on brain activation, HbO2 was measured in the prefrontal cortex via functional near-infrared spectroscopy (fNIRS) during the Trail Making Test Part B (TMT-B) and Stroop test. Mann–Whitney and Wilcoxon signed-rank tests were used to compare outcomes between and within the two groups. The virtual shopping budget-management training showed no significant difference in all outcomes between both groups (p > 0.05). No significant differences were observed in HbO2 levels during both TMT-B (p > 0.05) and the Stroop test (p > 0.05). However, in the pre-post comparisons, there was a significant difference in the TMT-B (p < 0.05) and Stroop test (p < 0.05) in the experimental group. In this study, although we did not find a distinct advantage in training, it confirmed its potential for clinical benefits in healthy young adults through training.
Significance
The advancement of multichannel functional near-infrared spectroscopy (fNIRS) has enabled measurements across a wide range of brain regions. This increase in multiplicity necessitates the control of family-wise errors in statistical hypothesis testing. To address this issue, the effective multiplicity (Meff) method designed for channel-wise analysis, which considers the correlation between fNIRS channels, was developed. However, this method loses reliability when the sample size is smaller than the number of channels, leading to a rank deficiency in the eigenvalues of the correlation matrix and hindering the accuracy of Meff calculations.
Aim
We aimed to reevaluate the effectiveness of the Meff method for fNIRS data with a small sample size.
Approach
In experiment 1, we used resampling simulations to explore the relationship between sample size and Meff values. Based on these results, experiment 2 employed a typical exponential model to investigate whether valid Meff could be predicted from a small sample size.
Results
Experiment 1 revealed that the Meff values were underestimated when the sample size was smaller than the number of channels. However, an exponential pattern was observed. Subsequently, in experiment 2, we found that valid Meff values can be derived from sample sizes of 30 to 40 in datasets with 44 and 52 channels using a typical exponential model.
Conclusions
The findings from these two experiments indicate the potential for the effective application of Meff correction in fNIRS studies with sample sizes smaller than the number of channels.
Introduction: Breastfeeding is a fundamental biological function in mammals, allowing the progeny to develop in a physiological way. A physical and emotional dialog between mothers and offspring during breastfeeding has been described as part of the attachment relationship, and a synchronicity between maternal and neonatal brains can be hypothesized. This study aimed to assess if neonatal and maternal cortical areas activated during breastfeeding are functionally synchronized since the second day of life. Materials and Methods: Twenty mothers and their term newborns were enrolled. Cortical activation during breastfeeding was identified by multichannel near-infrared spectroscopy, which detects changes in haemoglobin concentration from multiple cortical regions. Functional activity was simultaneously detected (hyperscanning) in mothers and newborns' frontal and motor/primary somatosensory cortical areas during the first 5 minutes of breastfeeding. Cluster analysis and Student's t test were used to detect oxygenated haemoglobin increase, as cortical activation estimate. Wavelet transform coherence (WTC) analysis was used to identify a possible synchronization between maternal and neonatal activated cortical regions. Results: Mothers showed an activation of the central motor/primary somatosensory cortex, above the sagittal fissure. In newborns, the bilateral frontal cortex was activated. WTC analysis revealed two different cyclical synchronizations between mothers and infants' activated cortical regions. Conclusions: Such evidence may reflect a very early common sharing of experiences, possibly associated with reciprocal dynamic motor adjustments, hormonal coregulation, and somatic stimulations and sensations. The observed cyclical neural synchronization, between the mother and her newborn's cortex during breastfeeding, may play an important role in promoting their bonding.
Functional near-infrared spectroscopy (fNIRS) was used to explore the effects of sedentary behavior on the brain functional connectivity characteristics of college students in the resting state after recovering from Corona Virus Disease 2019 (COVID-19). Twenty-two college students with sedentary behavior and 22 college students with sedentary behavior and maintenance of exercise habits were included in the analysis; moreover, 8 min fNIRS resting-state data were collected. Based on the concentrations of oxyhemoglobin (HbO2) and deoxyhemoglobin (HbR) in the time series, the resting-state functional connection strength of the two groups of subjects, including the prefrontal cortex (PFC) and the lower limb supplementary motor area (LS), as well as the functional activity and functional connections of the primary motor cortex (M1) were calculated. The following findings were demonstrated. (1) Functional connection analysis based on HbO2 demonstrated that in the comparison of the mean functional connection strength of homologous regions of interest (ROIs) between the sedentary group and the exercise group, there was no significant difference in the mean functional strength of the ROIs between the two groups (p>0.05). In the comparison of the mean functional connection strengths of the two groups of heterologous ROIs, the functional connection strengths of the right PFC and the right LS (p=0.0097), the left LS (p=0.0127), and the right M1 (p=0.0305) in the sedentary group were significantly greater. The functional connection strength between the left PFC and the right LS (p=0.0312) and the left LS (p=0.0370) was significantly greater. Additionally, the functional connection strength between the right LS and the right M1 (p=0.0370) and the left LS (p=0.0438) was significantly greater. (2) Functional connection analysis based on HbR demonstrated that there was no significant difference in functional connection strength between the sedentary group and the exercise group (p>0.05) or between the sedentary group and the exercise group (p>0.05). Similarly, there was no significant difference in the mean functional connection strength of the homologous and heterologous ROIs of the two groups. Additionally, there was no significant difference in the mean ROIs functional strength between the two groups (p>0.05). Experimental results and graphical analysis based on functional connectivity indicate that in this experiment, college student participants who exhibited sedentary behaviors showed an increase in fNIRS signals. Increase in fNIRS signals among college students exhibiting sedentary behaviors may be linked to their status post-SARS-CoV-2 infection and the sedentary context, potentially contributing to the strengthened functional connectivity in the resting-state cortical brain network. Conversely, the fNIRS signals decreased for the participants with exercise behaviors, who maintained reasonable exercise routines under the same conditions as their sedentary counterparts. The results may suggest that exercise behaviors have the potential to mitigate and reduce the impacts of sedentary behavior on the resting-state cortical brain network.
In this article, we present a systematic and exhaustive review regarding the trends, datasets employed, as well as findings achieved in the last 11 years in neurological disorder prediction using machine learning models. In this work we present a comparison between the biomarkers used in ML field with the biomarkers that are obtained through other non‐ml‐based research fields. This will help in identifying the potential research gaps for ML domain. As the study of neurological disorders is a far‐reaching task due to the wide variety of diseases, hence the scope of this study is restricted to the three most prevalent neurological diseases, that is, Alzheimer's, Parkinson's, and Autism Spectrum Disorder (ASD). From our analysis, it has been found that over time deep learning techniques especially Convolutional Neural Networks have proved to be beneficial for the disease prediction task. For this reason, Magnetic Resonance Imaging have been a popular modality across all three considered diseases. It is also notable that the employment of a transfer learning approach and maintenance of a global data centre helps in dealing with data scarcity problems for model training. The manuscript also discusses the potential challenges and future scope in this field. To the best of our knowledge, unlike other studies, this work attempts to put forth a conclusion of every article discussed highlighting the salient aspects of the major studies for a particular problem.
Early detection of Major Depressive Disorder (MDD) remains a significant challenge in the field of mental health. This work proposed an end-to-end deep learning framework designed to extract features from 52 channels of functional near-infrared spectroscopy (fNIRS) and aid in the diagnosis of MDD. We began by investigating the performance of multiple deep learning models as the core of our framework. Next, we implemented our novel deep learning framework, which employs an optimized model for MDD classification. We compared the performance of deep learning models both with and without our framework. In addition, we leveraged a traditional machine learning model, Support Vector Machine (SVM), to analyze single fNIRS channels to gain a deeper understanding of the impact areas of MDD. Our proposed framework, with a convolutional neural network (CNN) as the core model, achieved a highest classification rate of 82.4% on the test set of a large dataset of 374 subjects (including 181 MDD subjects and 193 healthy controls). Moreover, our framework improved the performance of deep learning models by 3.7%. Furthermore, the results from our classification of single channels revealed the crucial role of the dorsolateral prefrontal cortex and primary somatosensory cortex in MDD identification. Our study suggests that this framework may provide a promising methodology to facilitate the diagnosis of MDD. With the potential to help detect MDD at an earlier stage, the implementation of deep learning approaches in clinical practice could prove invaluable for the treatment of MDD.
Background
This study aimed to compare the balance ability and functional brain oxygenation in the prefrontal cortex (PFC) among older adults with mild cognitive impairment (MCI) under single and dual tasks, and also investigate their relationship. Neural regulatory mechanisms of the brain in the MCI were shed light on in balance control conditions.
Methods
21 older adults with MCI were recruited as the experimental group and 19 healthy older adults as the control group. Participants completed balance control of single task and dual task respectively. Functional near-infrared spectroscopy (fNIRS) and force measuring platform are used to collect hemodynamic signals of the PFC and center of pressure (COP) data during the balance task, respectively.
Results
The significant Group*Task interaction effect was found in D-ml, 95%AREA, RMS, RMS-ml, RMS-ap, SP, SP-ml, and SP-ap. The significant group effect was detected for five regions of interest (ROI), namely L45, R45, R10, L46, and R11. Under single task, D-ap, RMS, and RMS-ap were significantly negatively correlated with R45, L45, and R11 respectively. Under dual task, both RMS and 95%AREA were correlated positively with L45, and both L10 and R10 were positively correlated with RMS-ap.
Conclusion
The MCI demonstrated worse balance control ability as compared to healthy older adults. The greater activation of PFC under dual tasks in MCI may be considered a compensatory strategy for maintaining the standing balance. The brain activation was negatively correlated with balance ability under single task, and positively under dual task.
Trial registration: ChiCTR2100044221, 12/03/2021.
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.
Frequency-domain diffusion imaging uses the magnitude and phase of modulated light propagating through a highly scattering medium to reconstruct an image of the spatially dependent scattering or absorption coefficients in the medium. An inversion algorithm is formulated in a Bayesian framework and an efficient optimization technique is presented for calculating the maximum a posteriori image. In this framework the data are modeled as a complex Gaussian random vector with shot-noise statistics, and the unknown image is modeled as a generalized Gaussian Markov random field. The shot-noise statistics provide correct weighting for the measurement, and the generalized Gaussian Markov random field prior enhances the reconstruction quality and retains edges in the reconstruction. A localized relaxation algorithm, the iterative-coordinate-descent algorithm, is employed as a computationally efficient optimization technique. Numerical results for two-dimensional images show that the Bayesian framework with the new optimization scheme outperforms conventional approaches in both speed and reconstruction quality.
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 18mm, and the displacement of the center of mass in DOT and fMRI ranged between 12 and 28mm, 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.
The model generally considered in analysis of covariance has all levels of classification factors and interactions fixed, and also covariate regression coefficients fixed. Mixed models are more appropriate in most applications. A summary of estimation and hypothesis testing for analysis of covariance in the mixed model, including the case of random regression coefficients, is presented. Higher-level covariate regressions (i.e., regressions in which, for all levels of a factor or interaction, all observations on the same level have a common covariate value) are discussed. Nonestimability problems that result from defining such covariates at the levels of fixed effects are illustrated. The case of nonhomogeneous covariate regressions in the mixed model is considered in the context of interpreting predicted future differences among levels of a given factor or interaction. Nonhomogeneous regressions complicate interpretations only when they are associated with the contrast(s) of interest among fixed effects in the model. The question of whether the regressions are homogeneous is itself often of substantive interest. Different random regression coefficients associated with the levels of a random effect are also examined.
Objective To test the hypothesis that intrapartum maternal oxygen administration increases fetal cerebral oxygenation during normal labour.
Design A prospective study comparing changes in fetal cerebral concentrations of oxyhaemo- globin, deoxyhaemoglobin and cerebral blood volume measured by near infrared spectroscopy, before, during and after maternal oxygen administration using a 60% Ventimask.
Setting Teaching hospital obstetric unit.
Subjects Ten term fetuses during uncomplicated labour.
Results Maternal oxygen administration for 15 min resulted in a significant increase in the mean concentration of fetal cerebral oxyhaemoglobin (0.78 μmol (SD 0.42) 100 g-1 brain tissue, P < 0.001) and a significant decrease in the mean concentration of deoxyhaemoglobin (0.80 μmol (SD 0.51) l00 g-1, P < 0.00l). These changes were associated with a significant increase in the calculated mean cerebral oxygen saturation from 43.9 % (SD 6.3) to 57.3 % (SD 5.6) (P < 0.001). The maximum rise in cerebral oxyhaemoglobin concentration occurred at a mean of 10.7 min (SD 3.9) following commencement of oxygen administration. On returning to air breathing these changes reversed. There were no changes in cerebral blood volume.
Conclusion Maternal oxygen administration during normal labour leads to a significant rise in fetal cerebral oxygenation.