Resting-state Functional Near-infrared Spectroscopy Research Articles

Correction of global physiology in resting-state functional near-infrared spectroscopy.

.Significance: Resting-state functional connectivity (RSFC) analyses of functional near-infrared spectroscopy (fNIRS) data reveal cortical connections and networks across the brain. Motion artifacts and systemic physiology in evoked fNIRS signals present unique analytical challenges, and methods that control for systemic physiological noise have been explored. Whether these same methods require modification when applied to resting-state fNIRS (RS-fNIRS) data remains unclear.Aim: We systematically examined the sensitivity and specificity of several RSFC analysis pipelines to identify the best methods for correcting global systemic physiological signals in RS-fNIRS data.Approach: Using numerically simulated RS-fNIRS data, we compared the rates of true and false positives for several connectivity analysis pipelines. Their performance was scored using receiver operating characteristic analysis. Pipelines included partial correlation and multivariate Granger causality, with and without short-separation measurements, and a modified multivariate causality model that included a non-traditional zeroth-lag cross term. We also examined the effects of pre-whitening and robust statistical estimators on performance.Results: Consistent with previous work on bivariate correlation models, our results demonstrate that robust statistics and pre-whitening are effective methods to correct for motion artifacts and autocorrelation in the fNIRS time series. Moreover, we found that pre-filtering using principal components extracted from short-separation fNIRS channels as part of a partial correlation model was most effective in reducing spurious correlations due to shared systemic physiology when the two signals of interest fluctuated synchronously. However, when there was a temporal lag between the signals, a multivariate Granger causality test incorporating the short-separation channels was better. Since it is unknown if such a lag exists in experimental data, we propose a modified version of Granger causality that includes the non-traditional zeroth-lag term as a compromising solution.Conclusions: A combination of pre-whitening, robust statistical methods, and partial correlation in the processing pipeline to reduce autocorrelation, motion artifacts, and global physiology are suggested for obtaining statistically valid connectivity metrics with RS-fNIRS. Further studies should validate the effectiveness of these methods using human data.

The Relationship between the Prefrontal Cortex and Limb Motor Function in Stroke: A Study Based on Resting-State Functional Near-Infrared Spectroscopy

The Relationship between the Prefrontal Cortex and Limb Motor Function in Stroke: A Study Based on Resting-State Functional Near-Infrared Spectroscopy

Antidepressant Monotherapy and Combination Therapy with Acupuncture in Depressed Patients: A Resting-State Functional Near-Infrared Spectroscopy (fNIRS) Study.

Depression is a common psychiatric illness affecting over 300 million people globally. Acupuncture has been reported to be a safe complementary treatment for depression. This study is aimed to investigate the efficacy and mechanism of combining acupuncture with antidepressants in treating depression compared to the sole use of antidepressants. Seventy depression patients were randomly assigned to the treatment group (n = 50) and control group (n = 20). The treatment group received acupuncture combined antidepressants treatment for 3weeks, while the control group took antidepressants monotherapy for 3weeks. Among the 70 patients, 40 participants (20 control; 20 treatment) were randomized for studying functional connectivity (FC) of the dorsolateral prefrontal cortex (DLPFC) measured by the functional near-infrared spectroscopy. The primary outcome was HAMD-17 and secondary outcomes were PHQ-9, and the relationships of resting-state FC (rsFC) with the depression severity. PHQ-9 and HAMD-17 scores in the treatment group were significantly lower than those in the control group at Week 3 (p = 0.01) with effect sizes of -0.4 and -0.61 respectively. The rsFC in F1, F3, AF3, AF7, FC3, FC5 (left DLPFC, 10-20 system), AF8, and F6 (right DLPFC) in the treatment group had significant temporal correlation (p < 0.05, FDR corrected) in DLPFC compared to the channels in the control group. No significant correlation was found between the changes of rsFC and depression severity. In conclusion, depressed patients receiving acupuncture combined with antidepressants have improvement of depressive symptoms and the stronger rsFC in the DLPFC compared to those using antidepressants alone.

Abnormal functional connectivity within the prefrontal cortex in interstitial cystitis/bladder pain syndrome (IC/BPS): A pilot study using resting state functional near-infrared spectroscopy (rs-fNIRS).

To investigate the abnormalities of functional connectivity (FC) within the prefrontal cortex (PFC) of patients with interstitial cystitis/bladder pain syndrome (IC/BPS) based on resting state functional near-infrared spectroscopy (rs-fNIRS) data using FC matrix analysis. Ten patients with IC/BPS (females, 9; mean age, 56.9 ± 12.432 years) and 15 age- and gender-matched healthy controls (HC) (females, 12; mean age, 55.067 ± 7.46 years) participated in this rs-fNIRS study. Two rs-fNIRS scans were performed (when the bladder was empty and when the desire to void was strong). The Pearson's correlation coefficient between the time series of the 22 channels was calculated to obtain a 22 × 22 FC matrix for each subject. A two-sample t-test (p < .05) was performed to compare group differences in the FC matrix between patients with IC/BPS and HC. FC was significantly decreased within the PFC in the IC/BPS group based on a two-sample t-test (p < .05) compared with HC. FC decreased in a wider range of brain regions during the strong desire to void state (4 brain regions and 28 edges) when compared with the empty bladder state (3 brain regions and 18 edges). FC abnormalities in IC/BPS patients may lead to frontal lobe disorders involved in processing sensory integration, motivation drive, emotional control, and decision-making whether to urinate, leading to urinary control dysfunction manifested as typical clinical IC/BPS symptoms. Our results may provide new insight into the pathogenesis of IC/BPS and new brain biomarkers for diagnosis.

Disrupted signal variability of spontaneous neural activity in children with attention-deficit/hyperactivity disorder.

Brain signal variability (BSV) has shown to be powerful in characterizing human brain development and neuropsychiatric disorders. Multiscale entropy (MSE) is a novel method for quantifying the variability of brain signal, and helps elucidate complex dynamic pathological mechanisms in children with attention-deficit/hyperactivity disorder (ADHD). Here, multiple-channel resting-state functional near-infrared spectroscopy (fNIRS) imaging data were acquired from 42 children with ADHD and 41 healthy controls (HCs) and then BSV was calculated for each participant based on the MSE analysis. Compared with HCs, ADHD group exhibited reduced BSV in both high-order and primary brain functional networks, e.g., the default mode, frontoparietal, attention and visual networks. Intriguingly, the BSV aberrations negatively correlated with ADHD symptoms in the frontoparietal network and negatively correlated with reaction time variability in the frontoparietal, default mode, somatomotor and attention networks. This study demonstrates a wide alternation in the moment-to-moment variability of spontaneous brain signal in children with ADHD, and highlights the potential for using MSE metric as a disease biomarker.

Assessment of flourishing levels of individuals by using resting-state fNIRS with different functional connectivity measures

Flourishing is an important criterion for assessing well-being, however, controversy remains, especially while assessing it with self-report measures. Therefore, to understand the underlying neural mechanisms of well-being, researchers often use neuroimaging techniques. However, previous neuroimaging studies using conventional statistical approaches provided answers in an average sense rather than individual answers. In this study, we used machine learning algorithms to classify highly flourishing from normally flourishing individuals using a publicly available resting-state functional near-infrared spectroscopy (rs-fNIRS) dataset collected from 43 participants to obtain an answer for individual level. We utilized both the Pearson’s correlation (CC) and the Dynamic Time Warping (DTW) algorithm to estimate functional connectivity matrices from the rs-fNIRS data on the temporo-parieto-occipital region and used them as input for machine learning algorithms. Our results showed that we were able to classify flourishing individuals with 90 % accuracy with AUC 0.90 and 0.93 using Nearest Neighbor and Radial Basis Kernel Support Vector Machine using oxyhemoglobin concentration change with Pearson’s correlation (CC – ΔHbO) and deoxy hemoglobin concentration change with dynamic time warping (DTW – ΔHb). This finding suggests that temporo-parieto-occipital region-based resting-state functional connectivity might be a potential biomarker to identify the levels of flourishing and using both connectivity measures might allow us to find different potential biomarkers.

Quantitative Assessment of Resting-State for Mild Cognitive Impairment Detection: A Functional Near-Infrared Spectroscopy and Deep Learning Approach.

Mild cognitive impairment (MCI) is considered a prodromal stage of Alzheimer's disease. Early diagnosis of MCI can allow for treatment to improve cognitive function and reduce modifiable risk factors. This study aims to investigate the feasibility of individual MCI detection from healthy control (HC) using a minimum duration of resting-state functional near-infrared spectroscopy (fNIRS) signals. In this study, nine different measurement durations (i.e., 30, 60, 90, 120, 150, 180, 210, 240, and 270 s) were evaluated for MCI detection via the graph theory analysis and traditional machine learning approach, such as linear discriminant analysis, support vector machine, and K-nearest neighbor algorithms. Moreover, feature representation- and classification-based transfer learning (TL) methods were applied to identify MCI from HC through the input of connectivity maps with 30 and 90 s duration. There was no significant difference among the nine various time windows in the machine learning and graph theory analysis. The feature representation-based TL showed improved accuracy in both 30 and 90 s cases (i.e., 30 s: 81.27% and 90 s: 76.73%). Notably, the classification-based TL method achieved the highest accuracy of 95.81% using the pre-trained convolutional neural network (CNN) model with the 30 s interval functional connectivity map input. The results indicate that a 30 s measurement of the resting-state with fNIRS could be used to detect MCI. Moreover, the combination of neuroimaging (e.g., functional connectivity maps) and deep learning methods (e.g., CNN and TL) can be considered as novel biomarkers for clinical computer-assisted MCI diagnosis.

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.

Disrupted functional brain connectivity networks in children with attention-deficit/hyperactivity disorder: evidence from resting-state functional near-infrared spectroscopy.

.Significance: Attention-deficit/hyperactivity disorder (ADHD) is the most common psychological disease in childhood. Currently, widely used neuroimaging techniques require complete body confinement and motionlessness and thus are extremely hard for brain scanning of ADHD children.Aim: We present resting-state functional near-infrared spectroscopy (fNIRS) as an imaging technique to record spontaneous brain activity in children with ADHD.Approach: The brain functional connectivity was calculated, and the graph theoretical analysis was further applied to investigate alterations in the global and regional properties of the brain network in the patients. In addition, the relationship between brain network features and core symptoms was examined.Results: ADHD patients exhibited significant decreases in both functional connectivity and global network efficiency. Meanwhile, the nodal efficiency in children with ADHD was also found to be altered, e.g., increase in the visual and dorsal attention networks and decrease in somatomotor and default mode networks, compared to the healthy controls. More importantly, the disrupted functional connectivity and nodal efficiency significantly correlated with dimensional ADHD scores.Conclusions: We clearly demonstrate the feasibility and potential of fNIRS-based connectome technique in ADHD or other neurological diseases in the future.

Assessment of the effect of data length on the reliability of resting-state fNIRS connectivity measures and graph metrics

The reliability assessment of connectivity measures and graph metrics is crucial for characterizing topological properties of resting-state brain networks that are intrinsic to the functioning of the brain and not biased by variability across subjects and data lengths. In this study, we investigated the effect of data length on the reliability and stability of four functional connectivity measures, Pearson’s Correlation (PC), percentage-Bend Correlation (BC), Mutual Information (MI) and Partial Correlation (PtC), and twelve graph theoretical metrics derived from resting state functional near-infrared spectroscopy (rsfNIRS) data using data lengths ranging from 0.5 to 4.5 min. We analyzed rsfNIRS data collected in two separate sessions from 13 healthy adult subjects using an optical probe covering the whole brain. Our results showed that PC and BC stabilized with data lengths longer than 1–2.5 min depending on concentration signals. The stabilization for MI occurred with medium to long-range data lengths (more than 2.5 min). PtC showed stability only for data lengths shorter than 2.5 min. The reliability of the majority of the PC, BC and MI-derived network metrics improved significantly by data lengths of at least 1.5 to 2.5 min, depending on functional connectivity (FC) measures and concentration signals. For the PC and BC and MI-based networks, degree, global efficiency, characteristic path length, clustering coefficient and transitivity, graph radius and diameter exhibited high reliability. For these networks, the betweenness, modularity and vulnerability metrics showed moderate to high reliability with increasing data length for oxyhemoglobin (HbO), deoxyhemoglobin (HbR) and/or total-hemoglobin (HbT) signals. The participation coefficient, however, showed no specific pattern of changes or improvement with increasing data length. The hierarchy measure also showed variable reliability trends with increasing data length. The PtC-derived network metrics exhibited moderate to high reliability only with short-range data lengths shorter than 2 min for HbO, HbR and/or HbT. Our results show that data length can significantly affect the results of the FC analysis as well as the topological properties of weighted functional brain networks. This suggests that caution should be taken when comparing results from studies on functional network organization when FC analysis is performed with different data lengths.

Functional near-infrared spectroscopy evidence for the development of topological asymmetry between hemispheric brain networks from childhood to adulthood.

Cerebral asymmetry is considered an important marker of the successful development of the human brain. Recent studies have demonstrated topological asymmetries between structurally hemispheric networks in the human brain. However, it remains largely unknown whether and how the functionally topological asymmetries evolve from childhood to adulthood, a critical period that constitutes the primary peak of human brain and cognitive development. Here, we adopted resting-state functional near-infrared spectroscopy imaging data to construct hemispheric functional networks and then applied graph theory analysis to quantify the topological characteristics of the hemispheric networks. We found that the adult group exhibited consistent leftward hemispheric asymmetries in both global and local network efficiency, and the degree of leftward asymmetry in local network efficiency was significantly increased with development from childhood to adulthood. At the nodal level, the degree of leftward asymmetry in nodal efficiency, mainly involving the frontal, parietal-occipital junction, and occipital regions, increased with development. These developmental patterns of topological asymmetries suggest that the protracted maturation of functional segregation in the left hemisphere could underlie language development from childhood to adulthood and provide insight into the development of human brain functional networks.

Effect of prewhitening in resting-state functional near-infrared spectroscopy data.

Near-infrared spectroscopy (NIRS) offers the potential to characterize resting-state functional connectivity (RSFC) in populations that are not easily assessed otherwise, such as young infants. In addition to the advantages of NIRS, one should also consider that the RS-NIRS signal requires specific data preprocessing and analysis. In particular, the RS-NIRS signal shows a colored frequency spectrum, which can be observed as temporal autocorrelation, thereby introducing spurious correlations. To address this issue, prewhitening of the RS-NIRS signal has been recently proposed as a necessary step to remove the signal temporal autocorrelation and therefore reduce false-discovery rates. However, the impact of this step on the analysis of experimental RS-NIRS data has not been thoroughly assessed prior to the present study. Here, the results of a standard preprocessing pipeline in a RS-NIRS dataset acquired in infants are compared with the results after incorporating two different prewhitening algorithms. Our results with a standard preprocessing replicated previous studies. Prewhitening altered RSFC patterns and disrupted the antiphase relationship between oxyhemoglobin and deoxyhemoglobin. We conclude that a better understanding of the effect of prewhitening on RS-NIRS data is still needed before directly considering its incorporation to the standard preprocessing pipeline.

The development of functional network organization in early childhood and early adolescence: A resting-state fNIRS study

Early childhood (7–8 years old) and early adolescence (11–12 years old) constitute two landmark developmental stages that comprise considerable changes in neural cognition. However, very limited information from functional neuroimaging studies exists on the functional topological configuration of the human brain during specific developmental periods. In the present study, we utilized continuous resting-state functional near-infrared spectroscopy (rs-fNIRS) imaging data to examine topological changes in network organization during development from early childhood and early adolescence to adulthood. Our results showed that the properties of small-worldness and modularity were not significantly different across development, demonstrating the developmental maturity of important functional brain organization in early childhood. Intriguingly, young children had a significantly lower global efficiency than early adolescents and adults, which revealed that the integration of the distributed networks strengthens across the developmental stages underlying cognitive development. Moreover, local efficiency of young children and adolescents was significantly lower than that of adults, while there was no difference between these two younger groups. This finding demonstrated that functional segregation remained relatively steady from early childhood to early adolescence, and the brain in these developmental periods possesses no optimal network configuration. Furthermore, we found heterogeneous developmental patterns in the regional nodal properties in various brain regions, such as linear increased nodal properties in the frontal cortex, indicating increasing cognitive capacity over development. Collectively, our results demonstrated that significant topological changes in functional network organization occurred during these two critical developmental stages, and provided a novel insight into elucidating subtle changes in brain functional networks across development.

The minimum resting-state fNIRS imaging duration for accurate and stable mapping of brain connectivity network in children

Resting-state functional near-infrared spectroscopy (fNIRS) is a potential technique for the study of brain functional connectivity (FC) and networks in children. However, the necessary fNIRS scanning duration required to map accurate and stable functional brain connectivity and graph theory metrics in the resting-state brain activity remains largely unknown. Here, we acquired resting-state fNIRS imaging data from 53 healthy children to provide the first empirical evidence for the minimum imaging time required to obtain accurate and stable FC and graph theory metrics of brain network activity (e.g., nodal efficiency and network global and local efficiency). Our results showed that FC was accurately and stably achieved after 7.0-min fNIRS imaging duration, whereas the necessary scanning time for accurate and stable network measures was a minimum of 2.5 min at low network thresholds. These quantitative results provide direct evidence for the choice of the resting-state fNIRS imaging time in children in brain FC and network topology study. The current study also demonstrates that these methods are feasible and cost-effective in the application of time-constrained infants and critically ill children.

Characterization of the relative contributions from systemic physiological noise to whole-brain resting-state functional near-infrared spectroscopy data using single-channel independent component analysis.

Functional near-infrared spectroscopy (fNIRS) is a noninvasive neuroimaging technique used to measure changes in oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) in the brain. In this study, we present a decomposition approach based on single-channel independent component analysis (scICA) to investigate the contribution of physiological noise to fNIRS signals during rest. Single-channel ICA is an underdetermined decomposition method, which separates a single time series into components containing nonredundant spectral information. Using scICA, fNIRS signals from a total of 17 subjects were decomposed into the constituent physiological components. The percentage contribution of the classes of physiology to the fNIRS signals including low-frequency (LF) fluctuations, respiration, and cardiac oscillations was estimated using spectral domain classification methods. Our results show that LF oscillations accounted for 40% to 55% of total power of both the oxy-Hb and deoxy-Hb signals. Respiration and its harmonics accounted for 10% to 30% of the power, and cardiac pulsations and cardio-respiratory components accounted for 10% to 30%. We describe this scICA method for decomposing fNIRS signals, which unlike other approaches to spatial covariance reduction is applicable to both single- or multiple-channel fNIRS signals and discuss how this approach allows functionally distinct sources of noise with disjoint spectral support to be separated from obscuring systemic physiology.

Dynamic functional connectivity revealed by resting-state functional near-infrared spectroscopy.

The brain is a complex network with time-varying functional connectivity (FC) and network organization. However, it remains largely unknown whether resting-state fNIRS measurements can be used to characterize dynamic characteristics of intrinsic brain organization. In this study, for the first time, we used the whole-cortical fNIRS time series and a sliding-window correlation approach to demonstrate that fNIRS measurement can be ultimately used to quantify the dynamic characteristics of resting-state brain connectivity. Our results reveal that the fNIRS-derived FC is time-varying, and the variability strength (Q) is correlated negatively with the time-averaged, static FC. Furthermore, the Q values also show significant differences in connectivity between different spatial locations (e.g., intrahemispheric and homotopic connections). The findings are reproducible across both sliding-window lengths and different brain scanning sessions, suggesting that the dynamic characteristics in fNIRS-derived cerebral functional correlation results from true cerebral fluctuation.

Resting-State Functional Brain Connectivity

Resting-state functional near-infrared spectroscopy (R-fNIRS) is an active area of interest and is currently attracting considerable attention as a new imaging tool for the study of resting-state brain function. Using variations in hemodynamic concentration signals, R-fNIRS measures the brain’s low-frequency spontaneous neural activity, combining the advantages of portability, low-cost, high temporal sampling rate and less physical burden to participants. The temporal synchronization of spontaneous neuronal activity in anatomically separated regions is referred to as resting-state functional connectivity (RSFC). In the past several years, an increasing body of R-fNIRS RSFC studies has led to many important findings about functional integration among local or whole-brain regions by measuring inter-regional temporal synchronization. Here, we summarize recent advances made in the R-fNIRS RSFC methodologies, from the detection of RSFC (e.g., seed-based correlation analysis, independent component analysis, whole-brain correlation analysis, and graph-theoretical topological analysis), to the assessment of RSFC performance (e.g., reliability, repeatability, and validity), to the application of RSFC in studying normal development and brain disorders. The literature reviewed here suggests that RSFC analyses based on R-fNIRS data are valid and reliable for the study of brain function in healthy and diseased populations, thus providing a promising imaging tool for cognitive science and clinics.

Is resting-state functional connectivity revealed by functional near-infrared spectroscopy test-retest reliable?

Recently, resting-state functional near-infrared spectroscopy (rs-fNIRS) research has experienced tremendous progress. Resting-state functional connectivity (RSFC) has been adopted as a pivotal biomarker in rs-fNIRS studies. However, it is yet to be clear if the RSFC derived from rs-fNIRS is reliable. This concern impedes extensive utilization of rs-fNIRS. We systematically address the issue of reliability. Sixteen subjects participate in two rs-fNIRS sessions held one week apart. RSFC in sensorimotor system is calculated using the seed-correlation approach. Then, test-retest reliability is evaluated at three different scales (map-, cluster-, and channelwise) for individual- and group-level RSFC derived from different types of fNIRS signals [oxygenated (HbO), deoxygenated (HbR), and total hemoglobin (HbT)]. The results show that, for HbO signals, individual-level RSFC generally has good-to-excellent map-/clusterwise reliability, while group-level RSFC has excellent reliability. For HbT signals, the results are similar. For HbR signals, the clusterwise reliability is comparable to that for HbO while the mapwise reliability is slightly lower (fair to good). Focusing on RSFC at a single channel, we report poor channelwise reliability for all three types of signals. We hereby propose that fNIRS-derived RSFC is a reliable biomarker if interpreted in map- and clusterwise manners. However, channelwise interpretation of individual RSFC should proceed with caution.

Test–retest assessment of independent component analysis-derived resting-state functional connectivity based on functional near-infrared spectroscopy

Recent studies of resting-state functional near-infrared spectroscopy (fNIRS) have emerged as a hot topic and revealed that resting-state functional connectivity (RSFC) is an inherent characteristic of the resting brain. However, it is currently unclear if fNIRS-based RSFC is test–retest reliable. In this study, we utilized independent component analysis (ICA) as an effective RSFC detection tool to address the reliability question. Sixteen subjects participated in two resting-state fNIRS recording sessions held 1week (6.88±1.09days) apart. Then, RSFC in the sensorimotor regions was extracted using ICA. Test–retest reliability was assessed for intra- and inter-sessions, at both individual and group levels, and for different hemoglobin concentration signals. Our results clearly demonstrated that map-wise reliability was excellent at the group level (with Pearson's r coefficients up to 0.88) and generally fair at the individual level. Cluster-wise reliability was better at the group level (having reproducibility indices of up to 0.97 for the size and up to 0.80 for the location of the detected RSFC) and was weaker but still fair at the individual level (0.56 and 0.46 for intra- and inter-session reliabilities, respectively). Cluster-wise intra-class correlation coefficients (ICCs) also exhibited fair-to-good reliability (with single-measure ICC up to 0.56), while channel-wise single-measure ICCs indicated lower reliability. We conclude that fNIRS-based, ICA-derived RSFC is an essential and reliable biomarker at the individual and group levels if interpreted in map- and cluster-wise manners. Our results also suggested that channel-wise individual-level RSFC results should be interpreted with caution if no optode co-registration procedure had been conducted and indicated that “cluster” should be treated as a minimal analytical unit in further RSFC studies using fNIRS.