Reliability evaluation on weighted graph metrics of fNIRS brain networks

Advances in the etiology and neuroimaging of children with attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children, characterized by age-inappropriate inattention, hyperactivity, and impulsivity, which can cause extensive damage to children's academic, occupational, and social skills. This review will present current advancements in the field of attention deficit hyperactivity disorder, including genetics, environmental factors, epigenetics, and neuroimaging features. Simultaneously, we will discuss the highlights of promising directions for further study.

Altered Functional Connectivity of Temporoparietal Lobe in Obstructive Sleep Apnea: A Resting-State fNIRS Study

Obstructive Sleep Apnea (OSA), a sleep disorder with high prevalence, is normally accompanied by affective, autonomic, and cognitive abnormalities, and is deemed to be linked to functional brain alterations. To investigate alterations in brain functional connectivity properties in patients with OSA, a comparative analysis of global and local topological properties of brain networks was conducted between patients with OSA and healthy controls (HCs), utilizing functional near-infrared spectroscopy (fNIRS) imaging. A total of 148 patients with OSA and 150 healthy individuals were involved. Firstly, quantitative alterations in blood oxygen concentration, changes in functional connectivity, and variations in graph theory-based network topological characteristics were assessed. Then, with Mann-Whitney statistics, this study compared whether there are significant differences in the above characteristics between patients with OSA and HCs. Lastly, the study further examined the correlation between the altered characteristics and the apnea hypopnea index (AHI) using linear regression. Results revealed a higher mean and standard deviation of hemoglobin concentration in the superior temporal gyrus among patients with OSA compared to HCs. Resting-state functional connectivity (RSFC) exhibited a slight increase between the superior temporal gyrus and other specific areas in patients with OSA. Notably, neither patients with OSA nor HCs demonstrated significant small-world network properties. Patients with OSA displayed an elevated clustering coefficient (p < 0.05) and local efficiency (p < 0.05). Additionally, patients with OSA exhibited a tendency towards increased nodal betweenness centrality (p < 0.05) and degree centrality (p < 0.05) in the right supramarginal gyrus, as well as a trend towards higher betweenness centrality (p < 0.05) in the right precentral gyrus. The results of multiple linear regressions indicate that the influence of the AHI on RSFC between the right precentral gyrus and right superior temporal gyrus (p < 0.05), as well as between the right precentral gyrus and right supramarginal gyrus (p < 0.05), are statistically significant. These findings suggest that OSA may compromise functional brain connectivity and network topological properties in affected individuals, serving as a potential neurological mechanism underlying the observed abnormalities in brain function associated with OSA.

Features of the speech processing network in post- and prelingually deaf cochlear implant users

The onset of hearing loss can lead to altered brain structure and functions. However, hearing restoration may also result in distinct cortical reorganization. A differential pattern of functional remodeling was observed between post- and prelingual cochlear implant users, but it remains unclear how these speech processing networks are reorganized after cochlear implantation. To explore the impact of language acquisition and hearing restoration on speech perception in cochlear implant users, we conducted assessments of brain activation, functional connectivity, and graph theory-based analysis using functional near-infrared spectroscopy. We examined the effects of speech-in-noise stimuli on three groups: postlingual cochlear implant users (n = 12), prelingual cochlear implant users (n = 10), and age-matched individuals with hearing controls (HC) (n = 22). The activation of auditory-related areas in cochlear implant users showed a lower response compared with the HC group. Wernicke's area and Broca's area demonstrated differences network attributes in speech processing networks in post- and prelingual cochlear implant users. In addition, cochlear implant users maintain a high efficiency of the speech processing network to process speech information. Taken together, our results characterize the speech processing networks, in varying noise environments, in post- and prelingual cochlear implant users and provide new insights for theories of how implantation modes impact remodeling of the speech processing functional networks.

Test-retest reliability of fNIRS in resting-state cortical activity and brain network assessment in stroke patients

Resting-state functional near infrared spectroscopy (fNIRS) scanning has attracted considerable attention in stroke rehabilitation research in recent years. The aim of this study was to quantify the reliability of fNIRS in cortical activity intensity and brain network metrics among resting-state stroke patients, and to comprehensively evaluate the effects of frequency selection, scanning duration, analysis and preprocessing strategies on test-retest reliability. Nineteen patients with stroke underwent two resting fNIRS scanning sessions with an interval of 24 hours. The haemoglobin signals were preprocessed by principal component analysis, common average reference and haemodynamic modality separation (HMS) algorithm respectively. The cortical activity, functional connectivity level, local network metrics (degree, betweenness and local efficiency) and global network metrics were calculated at 25 frequency scales × 16 time windows. The test-retest reliability of each fNIRS metric was quantified by the intraclass correlation coefficient. The results show that (1) the high-frequency band has higher ICC values than the low-frequency band, and the fNIRS metric is more reliable than at the individual channel level when averaged within the brain region channel, (2) the ICC values of the low-frequency band above the 4-minute scan time are generally higher than 0.5, the local efficiency and global network metrics reach high and excellent reliability levels after 4 min (0.5 < ICC < 0.9), with moderate or even poor reliability for degree and betweenness (ICC < 0.5), (3) HMS algorithm performs best in improving the low-frequency band ICC values. The results indicate that a scanning duration of more than 4 minutes can lead to high reliability of most fNIRS metrics when assessing low-frequency resting brain function in stroke patients. It is recommended to use the global correction method of HMS, and the reporting of degree, betweenness and single channel level should be performed with caution. This paper provides the first comprehensive reference for resting-state experimental design and analysis strategies for fNIRS in stroke rehabilitation.

Intrinsic brain activity is increasingly complex and develops asymmetrically during childhood and early adolescence

A large body of evidence suggests that brain signal complexity (BSC) may be an important indicator of healthy brain functioning or alternately, a harbinger of disease and dysfunction. However, despite recent progress our current understanding of how BSC emerges and evolves in large-scale networks, and the factors that shape these dynamics, remains limited. Here, we utilized resting-state functional near-infrared spectroscopy (rs-fNIRS) to capture and characterize the nature and time course of BSC dynamics within large-scale functional networks in 107 healthy participants ranging from 6-13 years of age. Age-dependent increases in spontaneous BSC were observed predominantly in higher-order association areas including the default mode (DMN) and attentional (ATN) networks. Our results also revealed asymmetrical developmental patterns in BSC that were specific to the dorsal and ventral ATN networks, with the former showing a left-lateralized and the latter demonstrating a right-lateralized increase in BSC. These age-dependent laterality shifts appeared to be more pronounced in females compared to males. Lastly, using a machine-learning model, we showed that BSC is a reliable predictor of chronological age. Higher-order association networks such as the DMN and dorsal ATN demonstrated the most robust prognostic power for predicting ages of previously unseen individuals. Taken together, our findings offer new insights into the spatiotemporal patterns of BSC dynamics in large-scale intrinsic networks that evolve over the course of childhood and adolescence, suggesting that a network-based measure of BSC represents a promising approach for tracking normative brain development and may potentially aid in the early detection of atypical developmental trajectories.

The interaction between changes of muscle activation and cortical network dynamics during isometric elbow contraction: a sEMG and fNIRS study

Objective: The relationship between muscle activation during motor tasks and cerebral cortical activity remains poorly understood. The aim of this study was to investigate the correlation between brain network connectivity and the non-linear characteristics of muscle activation changes during different levels of isometric contractions. Methods: Twenty-one healthy subjects were recruited and were asked to perform isometric elbow contractions in both dominant and non-dominant sides. Blood oxygen concentrations in brain from functional Near-infrared Spectroscopy (fNIRS) and surface electromyography (sEMG) signals in the biceps brachii (BIC) and triceps brachii (TRI) muscles were recorded simultaneously and compared during 80% and 20% of maximum voluntary contraction (MVC). Functional connectivity, effective connectivity, and graph theory indicators were used to measure information interaction in brain activity during motor tasks. The non-linear characteristics of sEMG signals, fuzzy approximate entropy (fApEn), were used to evaluate the signal complexity changes in motor tasks. Pearson correlation analysis was used to examine the correlation between brain network characteristic values and sEMG parameters under different task conditions. Results: The effective connectivity between brain regions in motor tasks in dominant side was significantly higher than that in non-dominant side under different contractions (p < 0.05). The results of graph theory analysis showed that the clustering coefficient and node-local efficiency of the contralateral motor cortex were significantly varied under different contractions (p < 0.01). fApEn and co-contraction index (CCI) of sEMG under 80% MVC condition were significantly higher than that under 20% MVC condition (p < 0.05). There was a significant positive correlation between the fApEn and the blood oxygen value in the contralateral brain regions in both dominant or non-dominant sides (p < 0.001). The node-local efficiency of the contralateral motor cortex in the dominant side was positively correlated with the fApEn of the EMG signals (p < 0.05). Conclusion: In this study, the mapping relationship between brain network related indicators and non-linear characteristic of sEMG in different motor tasks was verified. These findings provide evidence for further exploration of the interaction between the brain activity and the execution of motor tasks, and the parameters might be useful in evaluation of rehabilitation intervention.

Impact of thresholding on the consistency and sensitivity of diffusion MRI-based brain networks in patients with cerebral small vessel disease

INTRODUCTION

Thresholding of low-weight connections of diffusion MRI-based brain networks has been proposed to remove false-positive connections. It has been previously established that this yields more reproducible scan-rescan network architecture in healthy subjects. In patients with brain disease, network measures are applied to assess inter-individual variation and changes over time. Our aim was to investigate whether thresholding also achieves improved consistency in network architecture in patients, while maintaining sensitivity to disease effects for these applications.

METHODS

We applied fixed-density and absolute thresholding on brain networks in patients with cerebral small vessel disease (SVD, n = 86; ≈24 months follow-up), as a clinically relevant exemplar condition. In parallel, we applied the same methods in healthy young subjects (n = 44; scan-rescan interval ≈4 months) as a frame of reference. Consistency of network architecture was assessed with dice similarity of edges and intraclass correlation coefficient (ICC) of edge-weights and hub-scores. Sensitivity to disease effects in patients was assessed by evaluating interindividual variation, changes over time, and differences between those with high and low white matter hyperintensity burden, using correlation analyses and mixed ANOVA.

RESULTS

Compared to unthresholded networks, both thresholding methods generated more consistent architecture over time in patients (unthresholded: dice = .70; ICC: .70-.78; thresholded: dice = .77; ICC: .73-.83). However, absolute thresholding created fragmented nodes. Similar observations were made in the reference group. Regarding sensitivity to disease effects in patients, fixed-density thresholds that were optimal in terms of consistency (densities: .10-.30) preserved interindividual variation in global efficiency and node strength as well as the sensitivity to detect effects of time and group. Absolute thresholding produced larger fluctuations of interindividual variation.

CONCLUSIONS

Our results indicate that thresholding of low-weight connections, particularly when using fixed-density thresholding, results in more consistent network architecture in patients with longer rescan intervals, while preserving sensitivity to disease effects.

Effect of Shift Work on Cognitive Function in Chinese Coal Mine Workers: A Resting-State fNIRS Study

Aim: Pilot study to examine the impact of shift work on cognitive function in Chinese coal mine workers. Background: Shift work is commonly used in modern industries such as the coal industry, and there is growing concern over the impact that shift work has on miners’ work performance and personal well-being. Method: A total of 54 miners working three shifts (17 in morning shift, 18 in afternoon, and 19 in night shift) participated in this exploratory study. A resting-state fNIRS functional connectivity method was conducted to assess the cognitive ability before and after the work shift. Results: Results showed significant differences in cognitive ability between before and after the work shifts among the three-shift workers. The brain functional connectivity was reduced ranking as the night, afternoon, and morning shifts. Decreased brain functional connectivity at the end of the working shift was found compared with before in the morning and afternoon shifts. Opposite results were obtained during the night shift. The resting-state functional brain networks in the prefrontal cortex of all groups exhibited small-world properties. Significant differences in betweenness centrality and nodal local efficiency were found in the prefrontal cortex in the morning and night shifts. Conclusions: The current findings provide new insights regarding the effect of shift work on the cognitive ability of Chinese coal mine workers from the view of brain science.

Identifying Individuals by fNIRS-Based Brain Functional Network Fingerprints

Individual identification based on brain functional network (BFN) has attracted a lot of research interest in recent years, since it provides a novel biometric for identity authentication, as well as a feasible way of exploring the brain at an individual level. Previous studies have shown that an individual can be identified by its BFN fingerprint estimated from functional magnetic resonance imaging, electroencephalogram, or magnetoencephalography data. Functional near-infrared spectroscopy (fNIRS) is an emerging imaging technique that, by measuring the changes in blood oxygen concentration, can respond to cerebral activities; in this paper, we investigate whether fNIRS-based BFN could be used as a “fingerprint” to identify individuals. In particular, Pearson's correlation is first used to calculate BFN based on the preprocessed fNIRS signals, and then the nearest neighbor scheme is used to match the estimated BFNs between different individuals. Through the experiments on an open-access fNIRS dataset, we have two main findings: (1) under the cases of cross-task (i.e., resting, right-handed, left-handed finger tapping, and foot tapping), the BFN fingerprints generally work well for the individual identification, and, more interestingly, (2) the accuracy under cross-task is well above the accuracy under cross-view (i.e., oxyhemoglobin and de-oxyhemoglobin). These findings indicate that fNIRS-based BFN fingerprint is a potential biometric for identifying individual.

Is There a Difference in Brain Functional Connectivity between Chinese Coal Mine Workers Who Have Engaged in Unsafe Behavior and Those Who Have Not?

(1) Background: As a world-recognized high-risk occupation, coal mine workers need various cognitive functions to process the surrounding information to cope with a large number of perceived hazards or risks. Therefore, it is necessary to explore the connection between coal mine workers’ neural activity and unsafe behavior from the perspective of cognitive neuroscience. This study explored the functional brain connectivity of coal mine workers who have engaged in unsafe behaviors (EUB) and those who have not (NUB). (2) Methods: Based on functional near-infrared spectroscopy (fNIRS), a total of 106 workers from the Hongliulin coal mine of Shaanxi North Mining Group, one of the largest modern coal mines in China, completed the test. Pearson’s Correlation Coefficient (COR) analysis, brain network analysis, and two-sample t-test were used to investigate the difference in brain functional connectivity between the two groups. (3) Results: The results showed that there were significant differences in functional brain connectivity between EUB and NUB among the frontopolar area (p = 0.002325), orbitofrontal area (p = 0.02102), and pars triangularis Broca’s area (p = 0.02888). Small-world properties existed in the brain networks of both groups, and the dorsolateral prefrontal cortex had significant differences in clustering coefficient (p = 0.0004), nodal efficiency (p = 0.0384), and nodal local efficiency (p = 0.0004). (4) Conclusions: This study is the first application of fNIRS to the field of coal mine safety. The fNIRS brain functional connectivity analysis is a feasible method to investigate the neuropsychological mechanism of unsafe behavior in coal mine workers in the view of brain science.

NIRS measures in pain and analgesia: Fundamentals, features, and function

Current pain assessment techniques based only on clinical evaluation and self-reports are not objective and may lead to inadequate treatment. Having a functional biomarker will add to the clinical fidelity, diagnosis, and perhaps improve treatment efficacy in patients. While many approaches have been deployed in pain biomarker discovery, functional near-infrared spectroscopy (fNIRS) is a technology that allows for non-invasive measurement of cortical hemodynamics. The utility of fNIRS is especially attractive given its ability to detect specific changes in the somatosensory and high-order cortices as well as its ability to measure (1) brain function similar to functional magnetic resonance imaging, (2) graded responses to noxious and innocuous stimuli, (3) analgesia, and (4) nociception under anesthesia. In this review, we evaluate the utility of fNIRS in nociception/pain with particular focus on its sensitivity and specificity, methodological advantages and limitations, and the current and potential applications in various pain conditions. Everything considered, fNIRS technology could enhance our ability to evaluate evoked and persistent pain across different age groups and clinical populations.

Applications of Resting-State fNIRS in the Developing Brain: A Review From the Connectome Perspective

Early brain development from infancy through childhood is closely related to the development of cognition and behavior in later life. Human brain connectome is a novel framework for describing topological organization of the developing brain. Resting-state functional near-infrared spectroscopy (fNIRS), with a natural scanning environment, low cost, and high portability, is considered as an emerging imaging technique and has shown valuable potential in exploring brain network architecture and its changes during the development. Here, we review the recent advances involving typical and atypical development of the brain connectome from neonates to children using resting-state fNIRS imaging. This review highlights that the combination of brain connectome and resting-state fNIRS imaging offers a promising framework for understanding human brain development.