SignificanceFunctional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results.AimWe aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses.ApproachWe evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms.ResultsAlthough most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern.ConclusionsThe findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.
SignificanceDecline in cognitive ability is a significant issue associated with healthy aging. Transcranial photobiomodulation (tPBM) is an emerging non-invasive neuromodulation technique and has shown promise to overcome this challenge.AimThis study aimed to investigate the effects of seven-day repeated tPBM, compared to those of single tPBM and baseline, on improving N-back working memory in healthy older adults and to evaluate the persistent efficacy of repeated tPBM.ApproachIn a sham-controlled and within-subject design, 61 healthy older adults were recruited to participate in a longitudinal study involving an experimental baseline, seven days of tPBM treatment (12 min daily, 1064-nm laser, 250 mW / cm2) in the left dorsolateral prefrontal cortex and three weeks of follow-ups. Behavioral performance in the N-back (N = 1,2 , 3) was recorded poststimulation during the baseline, the first and seventh days of the tPBM session, and the three weekly follow-ups. A control group with 25 participants was included in this study to rule out the practice and placebo effects. The accuracy rate and response time were used in the statistical analysis.ResultsRepeated and single tPBM significantly improved accuracy rate in 1- and 3-back tasks and decreased response time in 3-back compared to the baseline. Moreover, the repeated tPBM resulted in a significantly higher improvement in accuracy rate than the single tPBM. These improvements in accuracy rate and response time lasted at least three weeks following repeated tPBM. In contrast, the control group showed no significant improvement in behavioral performance.ConclusionsThis study demonstrated that seven-day repeated tPBM improved the working memory of healthy older adults more efficiently, with the beneficial effect lasting at least three weeks. These findings provide fundamental evidence that repeated tPBM may be a potential intervention for older individuals with memory decline.
KEYWORDS: Brain, Visualization, Near infrared spectroscopy, Neuroimaging, Neurophotonics, Control systems, Data acquisition, Lutetium, Lithium, Visual process modeling
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.
Communication within the brain is highly dynamic. Alzheimer’s disease (AD) exhibits dynamic progression corresponding to a decline in memory and cognition. However, little is known of whether brain dynamics are disrupted in AD and its prodromal stage, mild cognitive impairment (MCI). For our study, we acquired high sampling rate functional near-infrared spectroscopy imaging data at rest from the entire cortex of 23 patients with AD dementia, 25 patients with amnestic mild cognitive impairment (aMCI), and 30 age-matched healthy controls (HCs). Sliding-window correlation and k-means clustering analyses were used to construct dynamic functional connectivity (FC) maps for each participant. We discovered that the brain’s dynamic FC variability strength (Q) significantly increased in both aMCI and AD group as compared to HCs. Using the Q value as a measurement, the classification performance exhibited a good power in differentiating aMCI [area under the curve (AUC = 82.5 % )] or AD (AUC = 86.4 % ) from HCs. Furthermore, we identified two abnormal brain FC states in the AD group, of which the occurrence frequency (F) exhibited a significant decrease for the first-level FC state (state 1) and a significant increase for the second-level FC state (state 2). We also found that the abnormal F in these two states significantly correlated with the cognitive impairment in patients. These findings provide the first evidence to demonstrate the disruptions of dynamic brain connectivity in aMCI and AD and extend the traditional static (i.e., time-averaged) FC findings in the disease (i.e., disconnection syndrome) and thus provide insights into understanding the pathophysiological mechanisms occurring in aMCI and AD.
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.
Functional near-infrared spectroscopy (fNIRS) detects hemodynamic responses in the cerebral cortex by transcranial spectroscopy. However, measurements recorded by fNIRS not only consist of the desired hemodynamic response but also consist of a number of physiological noises. Because of these noises, accurately detecting the regions that have an activated hemodynamic response while performing a task is a challenge when analyzing functional activity by fNIRS. In order to better detect the activation, we designed a multiscale analysis based on wavelet coherence. In this method, the experimental paradigm was expressed as a binary signal obtained while either performing or not performing a task. We convolved the signal with the canonical hemodynamic response function to predict a possible response. The wavelet coherence was used to investigate the relationship between the response and the data obtained by fNIRS at each channel. Subsequently, the coherence within a region of interest in the time-frequency domain was summed to evaluate the activation level at each channel. Experiments on both simulated and experimental data demonstrated that the method was effective for detecting activated channels hidden in fNIRS data.
Functional near infrared spectroscopy (fNIRS) is an optical technique measuring hemoglobin oxygenation and
deoxygenation concentrations of the brain cortex with higher temporal resolution than current alternative techniques. The
high temporal resolution enables collecting abundant brain functional information. However, the information collected
by fNIRS is correlated and mixed with a variety of physiological signals. Due to the mixture effect, activation detection
is one of challenges in fNIRS based studies of the brain functional activities. To achieve a better detection of activated
brain regions from the complicated information measures, we present a multi-scale analysis method based on a wavelet
coherence measure. In particular, the paradigm of an experiment is used as the reference signal. The coherence of the
signal with data measured by fNIRS at each channel is calculated and summed up to evaluate the activation level.
Experiments on simulated and real data have demonstrated that the proposed method is efficient and effective to detect
activated brain regions covered by the fNIRS probe.
Functional near-infrared spectroscopy (fNIRS) is recently utilized as a new approach to assess resting-state functional connectivity (RSFC) in the human brain. For any new technique or new methodology, it is necessary to be able to replicate similar experiments using different instruments in order to establish its liability and reproducibility. We apply two different diffuse optical tomographic (DOT) systems (i.e., DYNOT and CW5), with various probe arrangements to evaluate RSFC in the sensorimotor cortex by utilizing a previously published experimental protocol and seed-based correlation analysis. Our results exhibit similar spatial patterns and strengths in RSFC between the bilateral motor cortexes. The consistent observations are obtained from both DYNOT and CW5 systems, and are also in good agreement with the previous fNIRS study. Overall, we demonstrate that the fNIRS-based RSFC is reproducible by various DOT imaging systems among different research groups, enhancing the confidence of neuroscience researchers and clinicians to utilize fNIRS for future applications.
The goal for this study is to examine cerebral autoregulation in response to a repeated sit-stand maneuver using
both diffuse functional Near Infrared spectroscopy (fNIRS) and Transcranial Doppler sonography (TCD). While
fNIRS can provide transient changes in hemodynamic response to such a physical action, TCD is a noninvasive
transcranial method to detect the flow velocities in the basal or middle cerebral arteries (MCA). The initial
phase of this study was to measure fNIRS signals from the forehead of subjects during the repeated sit-stand
protocol and to understand the corresponding meaning of the detected signals. Also, we acquired preliminary
data from simultaneous measurements of fNIRS and TCD during the sit-stand protocol so as to explore the
technical difficulty of such an approach. Specifically, ten healthy adult subjects were enrolled to perform the
planned protocol, and the fNIRS array probes with 4 sources and 10 detectors were placed on the subject's
forehead to detect hemodynamic signal changes from the prefrontal cortex. The fNIRS results show that the
oscillations of hemoglobin concentration were spatially global and temporally dynamic across the entire region
of subject's forehead. The oscillation patterns in both hemoglobin concentrations and blood flow velocity
seemed to follow one another; changes in oxy-hemoglobin concentration were much larger than those in deoxyhemoglobin
concentration. These preliminary findings provide us with evidence that fNIRS is an appropriate
means readily for studying cerebral hemodynamics and autoregulation during sit-stand maneuvers.
One of the major challenges in diffuse optical tomography (DOT) is attributed to the severe decay of sensitivity along
depth. In conventional reconstruction method using regularized inversion, it yields significant depth distortion in the
reconstructed image as a cortical activation is always projected into the skull. Recently we developed a depth
compensation algorithm (DCA) to minimize the depth localization error in DOT, which introduces a depth-variant
weight matrix to counterbalance the severe sensitivity decay of A-matrix. The DCA algorithm has been previously
validated in both laboratory phantom experiments and an in vivo human study. In this study, we first present a
comprehensive analysis on how DCA alters the depth localization and spatial resolution in DOT. It reveals that DCA
greatly improves the transverse resolution in sub-cortical region. Second, we present a quantification approach for DCA.
By forming a spatial prior directly from the reconstructed image, this approach greatly improves the quantification
accuracy in DOT.
Stroke, due to ischemia or hemorrhage, is the neurological deficit of cerebrovasculature and is the third leading cause of
death in the United States. More than 80 percent of stroke patients are ischemic stroke due to blockage of artery in the
brain by thrombosis or arterial embolism. Hence, development of an imaging technique to image or monitor the cerebral
ischemia and effect of anti-stoke therapy is more than necessary. Near infrared (NIR) optical tomographic technique has
a great potential to be utilized as a non-invasive image tool (due to its low cost and portability) to image the embedded
abnormal tissue, such as a dysfunctional area caused by ischemia. Moreover, NIR tomographic techniques have been
successively demonstrated in the studies of cerebro-vascular hemodynamics and brain injury. As compared to a fiberbased
diffuse optical tomographic system, a CCD-camera-based system is more suitable for pre-clinical animal studies
due to its simpler setup and lower cost. In this study, we have utilized the CCD-camera-based technique to image the
embedded inclusions based on tissue-phantom experimental data. Then, we are able to obtain good reconstructed
images by two recently developed algorithms: (1) depth compensation algorithm (DCA) and (2) globally convergent
method (GCM). In this study, we will demonstrate the volumetric tomographic reconstructed results taken from tissuephantom;
the latter has a great potential to determine and monitor the effect of anti-stroke therapies.
A depth compensation algorithm (DCA) can effectively improve the depth localization of diffuse optical tomography (DOT) by compensating the exponentially decreased sensitivity in the deep tissue. In this study, DCA is investigated based on computer simulations, tissue phantom experiments, and human brain imaging. The simulations show that DCA can largely improve the spatial resolution of DOT in addition to the depth localization, and DCA is also effective for multispectral DOT with a wide range of optical properties in the background tissue. The laboratory phantom experiment demonstrates that DCA can effectively differentiate two embedded objects at different depths in the medium. DCA is further validated by human brain imaging using a finger-tapping task. To our knowledge, this is the first demonstration to show that DCA is capable of accurately localizing cortical activations in the human brain in three dimensions.
Diffuse optical tomography (DOT) is to reconstruct the images of internal optical parameters distribution from boundary
measurements. Due to the amount of available boundary measurements is less than the number of unknown optical
parameters to be recovered, this inverse problem usually shows the ill-posed characteristics. This will result in the
problem of low reconstruction image quality. In this paper, an adaptive regularization method based on the objective
function values is proposed, which reduces the ill-posed characteristics in the inverse problem by selecting an
appropriate regularization value at teach iteration. Results from computer simulations indicated that using this
regularization technique, DOT imaging quality is improved effectively. Furthermore, using the regularization technique,
the sensitivity to noise of the reconstructed images can be decreased greatly.
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