Speaker-listener neural coupling reveals a right-lateralized mechanism for non-native speech-in-noise comprehension.

While the increasingly globalized world has brought more and more demands for non-native language communication, the prevalence of background noise in everyday life poses a great challenge to non-native speech comprehension. The present study employed an interbrain approach based on functional near-infrared spectroscopy (fNIRS) to explore how people adapt to comprehend non-native speech information in noise. A group of Korean participants who acquired Chinese as their non-native language was invited to listen to Chinese narratives at 4 noise levels (no noise, 2 dB, -6 dB, and - 9 dB). These narratives were real-life stories spoken by native Chinese speakers. Processing of the non-native speech was associated with significant fNIRS-based listener-speaker neural couplings mainly over the right hemisphere at both the listener's and the speaker's sides. More importantly, the neural couplings from the listener's right superior temporal gyrus, the right middle temporal gyrus, as well as the right postcentral gyrus were found to be positively correlated with their individual comprehension performance at the strongest noise level (-9 dB). These results provide interbrain evidence in support of the right-lateralized mechanism for non-native speech processing and suggest that both an auditory-based and a sensorimotor-based mechanism contributed to the non-native speech-in-noise comprehension.

Spectral Analysis of Muscle Hemodynamic Responses in Post-Exercise Recovery Based on Near-Infrared Spectroscopy.

Spectral analysis of blood flow or blood volume oscillations can help to understand the regulatory mechanisms of microcirculation. This study aimed to explore the relationship between muscle hemodynamic response in the recovery period and exercise quantity. Fifteen healthy subjects were required to perform two sessions of submaximal plantarflexion exercise. The blood volume fluctuations in the gastrocnemius lateralis were recorded in three rest phases (before and after two exercise sessions) using near-infrared spectroscopy. Wavelet transform was used to analyze the total wavelet energy of the concerned frequency range (0.005-2 Hz), which were further divided into six frequency intervals corresponding to six vascular regulators. Wavelet amplitude and energy of each frequency interval were analyzed. Results showed that the total energy raised after each exercise session with a significant difference between rest phases 1 and 3. The wavelet amplitudes showed significant increases in frequency intervals I, III, IV, and V from phase 1 to 3 and in intervals III and IV from phase 2 to 3. The wavelet energy showed similar changes with the wavelet amplitude. The results demonstrate that local microvascular regulators contribute greatly to the blood volume oscillations, the activity levels of which are related to the exercise quantity.

Noncontact full-angle fluorescence molecular tomography system based on rotary mirrors.

We propose a novel noncontact fluorescence molecular tomography system that achieves full-angle capacity with the use of a new rotary-mirrors-based imaging head. In the imaging head, four plane mirrors are mounted on a rotating gantry to enable illumination and detection over 360°. In comparison with existing full-angle systems, our system does not require rotation of the specimen animal, a large and heavy light source (with scanning head), or a bulky camera (with filters and lens). The system design and implementation are described in detail. Both physical phantom and in vivo experiments are performed to verify the performance of the proposed system.

Functional near-infrared spectroscopy in non-invasive neuromodulation.

ABSTRACT: Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks, which have been widely applied in the field of central neurological diseases, such as stroke, Parkinson's disease, and mental disorders. Although significant advances have been made in neuromodulation technologies, the identification of optimal neurostimulation parameters including the cortical target, duration, and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits. Moreover, the neural mechanism underlying neuromodulation for improved behavioral performance remains poorly understood. Recently, advancements in neuroimaging have provided insight into neuromodulation techniques. Functional near-infrared spectroscopy, as a novel non-invasive optical brain imaging method, can detect brain activity by measuring cerebral hemodynamics with the advantages of portability, high motion tolerance, and anti-electromagnetic interference. Coupling functional near-infrared spectroscopy with neuromodulation technologies offers an opportunity to monitor the cortical response, provide real-time feedback, and establish a closed-loop strategy integrating evaluation, feedback, and intervention for neurostimulation, which provides a theoretical basis for development of individualized precise neurorehabilitation. We aimed to summarize the advantages of functional near-infrared spectroscopy and provide an overview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation, transcranial electrical stimulation, neurofeedback, and brain-computer interfaces. Furthermore, the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized. In conclusion, functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central neural reorganization to achieve better functional recovery from central nervous system diseases.

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.

D2-RecST: Dual-domain joint reconstruction strategy for fluorescence molecular tomography based on image domain and perception domain.

BACKGROUND AND OBJECTIVE: Fluorescence molecular tomography (FMT) is a promising molecular imaging modality for quantifying the three-dimensional (3D) distribution of fluorescent probes in small animals. Over the past few years, learning-based FMT reconstruction methods have achieved promising results. However, these methods typically attempt to minimize the mean-squared error (MSE) between the reconstructed image and the ground truth. Although signal-to-noise ratios (SNRs) are improved, they are susceptible to non-uniform artifacts and loss of structural detail, making it extremely challenging to obtain accurate and robust FMT reconstructions under noisy measurements. METHODS: We propose a novel dual-domain joint strategy based on the image domain and perception domain for accurate and robust FMT reconstruction. First, we formulate an explicit adversarial learning strategy in the image domain, which greatly facilitates training and optimization through two enhanced networks to improve anti-noise ability. Besides, we introduce a novel transfer learning strategy in the perceptual domain to optimize edge details by providing perceptual priors for fluorescent targets. Collectively, the proposed dual-domain joint reconstruction strategy can significantly eliminate the non-uniform artifacts and effectively preserve the structural edge details. RESULTS: Both numerical simulations and in vivo mouse experiments demonstrate that the proposed method markedly outperforms traditional and cutting-edge methods in terms of positioning accuracy, image contrast, robustness, and target morphological recovery. CONCLUSIONS: The proposed method achieves the best reconstruction performance and has great potential to facilitate precise localization and 3D visualization of tumors in in vivo animal experiments.

Fluorescence molecular tomography using a two-step three-dimensional shape-based reconstruction with graphics processing unit acceleration.

In fluorescence molecular tomography, the accurate and stable reconstruction of fluorescence-labeled targets remains a challenge for wide application of this imaging modality. Here we propose a two-step three-dimensional shape-based reconstruction method using graphics processing unit (GPU) acceleration. In this method, the fluorophore distribution is assumed as the sum of ellipsoids with piecewise-constant fluorescence intensities. The inverse problem is formulated as a constrained nonlinear least-squares problem with respect to shape parameters, leading to much less ill-posedness as the number of unknowns is greatly reduced. Considering that various shape parameters contribute differently to the boundary measurements, we use a two-step optimization algorithm to handle them in a distinctive way and also stabilize the reconstruction. Additionally, the GPU acceleration is employed for finite-element-method-based calculation of the objective function value and the Jacobian matrix, which reduces the total optimization time from around 10 min to less than 1 min. The numerical simulations show that our method can accurately reconstruct multiple targets of various shapes while the conventional voxel-based reconstruction cannot separate the nearby targets. Moreover, the two-step optimization can tolerate different initial values in the existence of noises, even when the number of targets is not known a priori. A physical phantom experiment further demonstrates the method's potential in practical applications.

Assessment of Brain Function in Patients With Cognitive Impairment Based on fNIRS and Gait Analysis.

Background: Early detection of mild cognitive impairment is crucial in the prevention of Alzheimer's disease (AD). This study aims to explore the changes in gait and brain co-functional connectivity between cognitively healthy and cognitively impaired groups under dual-task walking through the functional near-infrared spectroscopy (fNIRS) and gait analysis devices. Method: This study used fNIRS device and gait analysis devices to collect the data of 54 older adults. According to the Mini-mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) scales, the older adults were cognitively healthy (control group) and cognitively impaired (experimental group), of which 38 were in the control group and 16 were in the experimental group. The experiment was divided into a total of three sets of task experiments: a walking-only experiment, a dual-task walking-easy (DTW-easy) experiment, and a dual-task walking-difficult (DTW-difficult) experiment. Main Result: For the cognitively impaired and cognitively healthy populations, there were no significant differences in overall functional connectivity, region of interest (ROI) connection strength, and gait performance during single-task walking between the two groups.Whereas the performances of DTW differed significantly from the single-task walking in terms of between-group variability of functional connectivity strength change values, and ROI connection strength change values in relation to the dual-task cost of gait. Finally, the cognitively impaired group was significantly more affected by DTW-difficult tasks than the cognitively healthy group. Conclusion: This study provides a new approach to assist in the diagnosis of people with cognitive impairment and provides a new research pathway for the identification of cognitive impairment.

Early screening model for mild cognitive impairment based on resting-state functional connectivity: a functional near-infrared spectroscopy study.

Significance: As an early stage of Alzheimer's disease (AD), the diagnosis of amnestic mild cognitive impairment (aMCI) has important clinical value for timely intervention of AD. Functional near-infrared spectroscopy (fNIRS)-based resting-state brain connectivity analysis, which could provide an economic and quick screening strategy for aMCI, remains to be extensively investigated. Aim: This study aimed to verify the feasibility of fNIRS-based resting-state brain connectivity for evaluating brain function in patients with aMCI, and to determine an early screening model for auxiliary diagnosis. Approach: The resting-state fNIRS was utilized for exploring the changes in functional connectivity of 64 patients with aMCI. The region of interest (ROI)-based and channel-based connections with significant inter-group differences have been extracted through the two-sample t -tests and the receiver operating characteristic (ROC). These connections with specificity and sensitivity were then taken as features for classification. Results: Compared with healthy controls, connections of the MCI group were significantly reduced between the bilateral prefrontal, parietal, occipital, and right temporal lobes. Specifically, the long-range connections from prefrontal to occipital lobe, and from prefrontal to parietal lobe, exhibited stronger identifiability (area under the ROC curve > 0.65 , ** p < 0.01 ). Subsequently, the optimal classification accuracy of ROI-based connections was 71.59%. Furthermore, the most responsive connections were located between the right dorsolateral prefrontal lobe and the left occipital lobe, concomitant with the highest classification accuracy of 73.86%. Conclusion: Our findings indicate that fNIRS-based resting-state functional connectivity analysis could support MCI diagnosis. Notably, long-range connections involving the prefrontal and occipital lobes have the potential to be efficient biomarkers.

Decreased Hemodynamic Responses in Left Parietal Lobule and Left Inferior Parietal Lobule in Older Adults with Mild Cognitive Impairment: A Near-Infrared Spectroscopy Study.

BACKGROUND: The brain activation patterns of mild cognitive impairment (MCI) are still unclear and they involve multiple brain regions. Most previous studies have focused on abnormal activation in the frontal and temporal lobes, with few investigating the entire brain. OBJECTIVE: To identify and compare the changes in cerebral hemodynamics and abnormal activation patterns in the entire brain of MCI patients and healthy older adults. METHODS: Patients with MCI (n = 22) and healthy controls (HC, n = 34) matched by age, education levels, sex, and mental state were enrolled. They performed the same letter and category verbal fluency test (VFT) tasks while their behavioral performance and global cerebral hemodynamics were analyzed. RESULTS: The performance during the category VFT task was significantly better than that during the letter VFT task across all participants (HC: correct: p < 0.001; intrusions: p < 0.001; MCI: correct: p < 0.001; intrusions: p < 0.001). The number of correct words during the letter and category VFT tasks was significantly higher in the HC group than in the MCI group (p < 0.001). The deoxygenated-hemoglobin (HbR) concentrations in the left parietal lobule (p = 0.022) and left inferior parietal lobule (p = 0.034) were significantly different during the category VFT task. CONCLUSION: The differences between HC and MCI groups were greater in the category task. The HbR concentration was more sensitive for the category VFT task and concentration changes in the left parietal lobule and left inferior parietal lobule may be useful for clinical screening and application; thus, they deserve more attention.

Motion artifact correction for resting-state neonatal functional near-infrared spectroscopy through adaptive estimation of physiological oscillation denoising.

Significance: Functional near-infrared spectroscopy (fNIRS) for resting-state neonatal brain function evaluation provides assistance for pediatricians in diagnosis and monitoring treatment outcomes. Artifact contamination is an important challenge in the application of fNIRS in the neonatal population. Aim: Our study aims to develop a correction algorithm that can effectively remove different types of artifacts from neonatal data. Approach: In the study, we estimate the recognition threshold based on the amplitude characteristics of the signal and artifacts. After artifact recognition, Spline and Gaussian replacements are used separately to correct the artifacts. Various correction method recovery effects on simulated artifact and actual neonatal data are compared using the Pearson correlation ( R ) and root mean square error (RMSE). Simulated data connectivity recovery is used to compare various method performances. Results: The neonatal resting-state data corrected by our method showed better agreement with results by visual recognition and correction, and significant improvements ( R = 0.732 ± 0.155 , RMSE = 0.536 ± 0.339 ; paired t -test, ** p < 0.01 ). Moreover, the method showed a higher degree of recovery of connectivity in simulated data. Conclusions: The proposed algorithm corrects artifacts such as baseline shifts, spikes, and serial disturbances in neonatal fNIRS data quickly and more effectively. It can be used for preprocessing in clinical applications of neonatal fNIRS brain function detection.

Effective brain network analysis in unilateral and bilateral upper limb exercise training in subjects with stroke.

PURPOSE: Knowing the patterns of brain activation that occur and networks involved under different interventions is important for motor recovery in subjects with stroke. This study aimed to study the patterns of brain activation and networks in two interventions, affected upper limb side and bilateral exercise training, using concurrent functional near-infrared spectroscopy (fNIRS) imaging. METHODS: Thirty-two patients in the early subacute stage were randomly divided into two groups: unilateral and bilateral groups. The patients in the unilateral group underwent isokinetic muscle strength training on the affected upper limb side and patients in the bilateral group underwent bilateral upper limb training. Oxyhemoglobin and deoxyhemoglobin concentration changes (ΔHbO2 and ΔHbR, respectively) were recorded in the ipsilateral and contralateral prefrontal cortex (IPFC and CPFC, respectively) and ipsilateral and contralateral motor cortex (IMC and CMC, respectively) by fNIRS equipment in the resting state and training conditions. The phase information of a 0.01-0.08 Hz fNIRS signal was extracted by the wavelet transform method. Dynamic Bayesian inference was adopted to calculate the coupling strength and direction of effective connectivity. The network threshold was determined by surrogate signal method, the global (weighted clustering coefficient, global efficiency, and small-worldness) and local (degree, betweenness centrality, and local efficiency) network metrics were calculated. The degree of cerebral lateralization was also compared between the two groups. RESULTS: The results of covariance analysis showed that, compared with bilateral training, the coupling effect of CMC â†’ IMC was significantly enhanced (p = 0.03); also, the local efficiency of the IMC (p = 0.01), IPFC (p < 0.001), and CPFC (p = 0.006) and the hemispheric autonomy index of IPFC (p = 0.007) were significantly increased in unilateral training. In addition, there was a significant positive correlation between the coupling intensity of the inter-hemispheric motor area and the shifted local efficiency. CONCLUSIONS: The results indicated that unilateral upper limb training could more effectively promote the interaction and balance of bilateral motor hemispheres and help brain reorganization in the IMC and prefrontal cortex in stroke patients. The method provided in this study could be used to evaluate dynamic brain activation and network reorganization under different interventions, thus improving the strategy of rehabilitation intervention in a timely manner and resulting in better motor recovery.

Multi-branch attention prior based parameterized generative adversarial network for fast and accurate limited-projection reconstruction in fluorescence molecular tomography.

Limited-projection fluorescence molecular tomography (FMT) allows rapid reconstruction of the three-dimensional (3D) distribution of fluorescent targets within a shorter data acquisition time. However, the limited-projection FMT is severely ill-posed and ill-conditioned due to insufficient fluorescence measurements and the strong scattering properties of photons in biological tissues. Previously, regularization-based methods, combined with the sparse distribution of fluorescent sources, have been commonly used to alleviate the severe ill-posed nature of the limited-projection FMT. Due to the complex iterative computations, time-consuming solution procedures, and less stable reconstruction results, the limited-projection FMT remains an intractable challenge for achieving fast and accurate reconstructions. In this work, we completely discard the previous iterative solving-based reconstruction themes and propose multi-branch attention prior based parameterized generative adversarial network (MAP-PGAN) to achieve fast and accurate limited-projection FMT reconstruction. Firstly, the multi-branch attention can provide parameterized weighted sparse prior information for fluorescent sources, enabling MAP-PGAN to effectively mitigate the ill-posedness and significantly improve the reconstruction accuracy of limited-projection FMT. Secondly, since the end-to-end direct reconstruction strategy is adopted, the complex iterative computation process in traditional regularization algorithms can be avoided, thus greatly accelerating the 3D visualization process. The numerical simulation results show that the proposed MAP-PGAN method outperforms the state-of-the-art methods in terms of localization accuracy and morphological recovery. Meanwhile, the reconstruction time is only about 0.18s, which is about 100 to 1000 times faster than the conventional iteration-based regularization algorithms. The reconstruction results from the physical phantoms and in vivo experiments further demonstrate the feasibility and practicality of the MAP-PGAN method in achieving fast and accurate limited-projection FMT reconstruction.

Resting-state brain networks in neonatal hypoxic-ischemic brain damage: a functional near-infrared spectroscopy study.

Significance: There is an emerging need for convenient and continuous bedside monitoring of full-term newborns with hypoxic-ischemic brain damage (HIBD) to determine whether early intervention is required. Functional near-infrared spectroscopy (fNIRS)-based resting-state brain network analysis, which could provide an effective evaluation method, remains to be extensively studied. Aim: Our study aims to verify the feasibility of fNIRS-based resting-state brain networks for evaluating brain function in infants with HIBD to provide a new and effective means for clinical research in neonatal HIBD. Approach: Thirteen neonates with HIBD were scanned using fNIRS in the resting state. The brain network properties were explored to attempt to extract effective features as recognition indicators. Results: Compared with healthy controls, newborns with HIBD showed decreased brain functional connectivity. Specifically, there were severe losses of long-range functional connectivity of the contralateral parietal-temporal lobe, contralateral parietal-frontal lobe, and contralateral parietal lobe. The node degree showed a widespread decrease in the left frontal middle gyrus, left superior frontal gyrus dorsal, and right central posterior gyrus. However, newborns with HIBD showed a significantly higher local network efficiency (* p < 0.05 ). Subsequently, network indicators based on small-worldness, local efficiency, modularity, and normalized clustering coefficient were extracted for HIBD identification with the accuracy observed as 79.17%. Conclusions: Our findings indicate that fNIRS-based resting-state brain network analysis could support early HIBD diagnosis.

Time-evolving coupling functions for evaluating the interaction between cerebral oxyhemoglobin and arterial blood pressure with hypertension.

PURPOSES: This study aimed to investigate the network coupling between arterial blood pressure (ABP) and changes in cerebral oxyhemoglobin concentration (Δ [O2 Hb]/Δ [HHb]) oscillations based on dynamical Bayesian inference in hypertensive subjects. METHODS: Two groups of subjects, consisting of 30 healthy (Group Control, 55.1 ± 10.6 y), and 32 hypertensive individuals (Group AH, 58.9 ± 8.7 y), participated in this study. A functional near-infrared spectroscopy system was used to measure the Δ [O2 Hb] and Δ [HHb] signals in the bilateral prefrontal cortex (LPFC/RPFC), motor cortex (LMC/RMC), and occipital lobe (LOL/ROL) during the resting state (12 min). Based on continuous wavelet analysis and coupling functions, the directed coupling strength (CS) between ABP and cerebral hemoglobin was identified and analyzed in three frequency intervals (I: 0.6-2 Hz, II: 0.145-0.6 Hz, III: 0.01-0.08 Hz). The Pearson correlations between the CS and blood pressure parameters were calculated in the hypertension group. RESULTS: In interval I, Group AH exhibited a significantly higher CS for the coupling from ABP to Δ [O2 Hb] than Group Control in LMC, RMC, LOL, and ROL. In interval III, the CS from ABP to Δ [O2 Hb] in LPFC, RPFC, LMC, RMC, LOL, and ROL was significantly higher in Group AH than in Group Control. For the patients with hypertension, diastolic blood pressure was negatively and pulse pressure was positively related to the CS from ABP to Δ [O2 Hb] oscillations in interval III. CONCLUSIONS: The higher CS from ABP to Δ [O2 Hb] in interval I indicated that the components of cardiac activity in cerebral hemoglobin oscillations were more directly responsive to the changes in systematic ABP in patients with hypertension than in healthy subjects. Meanwhile, the higher CS from ABP to Δ [O2 Hb] in interval III indicated that the cerebral hemoglobin oscillations were susceptible to changes in blood pressure in hypertensive subjects. The results may serve as evidence of impairment in cerebral autoregulation after hypertension. The Pearson correlation results showed that diastolic blood pressure and pulse pressure might be regarded as predictors of cerebral autoregulation function in patients with hypertension, and may be useful for hypertension stratification. This study provides novel insights into the interaction mechanism between ABP and cerebral hemodynamics and could help in the development of new assessment techniques for cerebral vascular disease.

Prospects for intelligent rehabilitation techniques to treat motor dysfunction.

More than half of stroke patients live with different levels of motor dysfunction after receiving routine rehabilitation treatments. Therefore, new rehabilitation technologies are urgently needed as auxiliary treatments for motor rehabilitation. Based on routine rehabilitation treatments, a new intelligent rehabilitation platform has been developed for accurate evaluation of function and rehabilitation training. The emerging intelligent rehabilitation techniques can promote the development of motor function rehabilitation in terms of informatization, standardization, and intelligence. Traditional assessment methods are mostly subjective, depending on the experience and expertise of clinicians, and lack standardization and precision. It is therefore difficult to track functional changes during the rehabilitation process. Emerging intelligent rehabilitation techniques provide objective and accurate functional assessment for stroke patients that can promote improvement of clinical guidance for treatment. Artificial intelligence and neural networks play a critical role in intelligent rehabilitation. Multiple novel techniques, such as brain-computer interfaces, virtual reality, neural circuit-magnetic stimulation, and robot-assisted therapy, have been widely used in the clinic. This review summarizes the emerging intelligent rehabilitation techniques for the evaluation and treatment of motor dysfunction caused by nervous system diseases.

Principal component analysis of dynamic fluorescence diffuse optical tomography images.

Challenges remain in resolving drug distributions within small animals utilizing fluorescence diffuse optical tomography (FDOT). In this paper, we present a new method for detecting and visualizing organs with different kinetics utilizing principal component analysis (PCA). Indocynaine green (ICG) metabolic processes are simulated and imaged using FDOT. When applied to the time series of generated FDOT images, PCA provides a set of the principal components (PCs) which can represent spatial patterns associated with different kinetic behavior. Simulation and experiment studies are both performed to validate the performance of the proposed algorithm. The results suggest that we are able to extract and illustrate changes in ICG kinetic behavior between the heart and the lungs.

Analysis of fast full angle fluorescence diffuse optical tomography with beam-forming illumination.

Challenges remain in imaging fast biological activities through whole body using fluorescence diffuse optical tomography (FDOT). We propose and analyze three full angle FDOT systems with different beam-forming illuminations (BF-FDOT), including line illumination (L-FDOT), area illumination (A-FDOT), and multiple-points illumination (MP-FDOT). Singular value analysis and experimental validation are used to optimize the experimental parameters in terms of hardware design, data collection and utilization. Comparisons are made on the system performance between L-FDOT and the conventional point illumination based full angle FDOT system (P-FDOT) with both numerical simulation and phantom experiment. We demonstrate that at least three cycles of projections are needed for P-FDOT to achieve comparable whole body image quality with L-FDOT. We also compare these three BF-FDOT systems and further discuss how these optimized parameters can be employed to improve spatial and temporal performances within current computational capacities, and guide the design of the BF-FDOT systems.

Limb linkage rehabilitation training-related changes in cortical activation and effective connectivity after stroke: A functional near-infrared spectroscopy study.

Stroke remains the leading cause of long-term disability worldwide. Rehabilitation training is essential for motor function recovery following stroke. Specifically, limb linkage rehabilitation training can stimulate motor function in the upper and lower limbs simultaneously. This study aimed to investigate limb linkage rehabilitation task-related changes in cortical activation and effective connectivity (EC) within a functional brain network after stroke by using functional near-infrared spectroscopy (fNIRS) imaging. Thirteen stroke patients with either left hemiparesis (L-H group, n = 6) and or right hemiparesis (R-H group, n = 7) and 16 healthy individuals (control group) participated in this study. A multichannel fNIRS system was used to measure changes in cerebral oxygenated hemoglobin (delta HbO2) and deoxygenated hemoglobin (delta HHb) in the bilateral prefrontal cortices (PFCs), motor cortices (MCs), and occipital lobes (OLs) during (1) the resting state and (2) a motor rehabilitation task with upper and lower limb linkage (first 10 min [task_S1], last 10 min [task_S2]). The frequency-specific EC among the brain regions was calculated based on coupling functions and dynamic Bayesian inference in frequency intervals: high-frequency I (0.6-2 Hz) and II (0.145-0.6 Hz), low-frequency III (0.052-0.145 Hz), and very-low-frequency IV (0.021-0.052 Hz). The results showed that the stroke patients exhibited an asymmetric (greater activation in the contralesional versus ipsilesional motor region) cortical activation pattern versus healthy controls. Compared with the healthy controls, the stroke patients showed significantly lower EC (p < 0.025) in intervals I and II in the resting and task states. The EC from the MC and OL to the right PFC in interval IV was significantly higher in the R-H group than in the control group during the resting and task states (p < 0.025). Furthermore, the L-H group showed significantly higher EC from the MC and OL to the left PFC in intervals III and IV during the task states compared with the control group (p < 0.025). The significantly increased influence of the MC and OL on the contralesional PFC in low- and very-low-frequency bands suggested that plastic reorganization of cognitive resources severed to compensate for impairment in stroke patients during the motor rehabilitation task. This study can serve as a basis for understanding task-related reorganization of functional brain networks and developing novel assessment techniques for stroke rehabilitation.

Implementation of digital optical phase conjugation with embedded calibration and phase rectification.

Focused and controllable optical delivery beyond the optical diffusion limit in biological tissue has been desired for long yet considered challenging. Digital optical phase conjugation (DOPC) has been proven promising to tackle this challenge. Its broad applications, however, have been hindered by the system's complexity and rigorous requirements, such as the optical beam quality, the pixel match between the wavefront sensor and wavefront modulator, as well as the flatness of the modulator's active region. In this paper, we present a plain yet reliable DOPC setup with an embedded four-phase, non-iterative approach that can rapidly compensate for the wavefront modulator's surface curvature, together with a non-phase-shifting in-line holography method for optical phase conjugation in the absence of an electro-optic modulator (EOM). In experiment, with the proposed setup the peak-to-background ratio (PBR) of optical focusing through a standard ground glass in experiment can be improved from 460 up to 23,000, while the full width at half maximum (FWHM) of the focal spot can be reduced from 50 down to 10 µm. The focusing efficiency, as measured by the value of PBR, reaches nearly 56.5% of the theoretical value. Such a plain yet efficient implementation, if further engineered, may potentially boost DOPC suitable for broader applications.