UNASSIGNED: Abnormal brain activation patterns in patients with knee osteoarthritis (KOA) at rest have been revealed, but it is unclear how brain activation patterns change during movement. This study aimed to investigate the alterations in brain activation patterns in KOA patients during knee isokinetic movement, and the correlation between cortical activity changes and pain severity and dysfunction.
UNASSIGNED: Eighteen patients with KOA and 18 healthy controls (HC) were recruited, and to performed the knee isokinetic test with three speeds. Functional near-infrared spectroscopy (fNIRS) was used to detect the cerebral cortex hemodynamics changes of primary somatosensory (S1), primary motor (M1) and somatosensory association cortex (SAC) in the region of interest (ROI) during movement. Then, we evaluated potential correlations between M1, S1 and SAC values and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and visual analog scale (VAS) scores.
UNASSIGNED: The results showed that peak torque of knee extension in KOA patients was significantly smaller than that in HC. For HC, unilateral knee movement activated bilateral ROIs. The contralateral activation was dominant, showing the phenomenon of high contralateral activation. For KOA patients, there were no statistical difference in the activation level between the left and right of the cerebral cortex, with both sides showing lower activation levels compared to HC. Further analysis found that the contralateral M1, S1, and SAC of the affected knee in KOA patients were significantly lower than those in HC, while no difference was found on the ipsilateral side. Moreover, during isokinetic movement at 180°/s, VAS score in KOA patients was negatively correlated with the activation level of the contralateral S1 and M1 values, and WOMAC was negatively correlated with the activation level of the contralateral M1 value.
UNASSIGNED: Contralateral activation of the sensorimotor cortex exists during unilateral knee movement, but in KOA patients, this contralateral cortical activation is suppressed. Furthermore, the clinical pain and dysfunction in KOA patients are associated with activation levels of specific brain regions. These findings can provide a better understanding of KOA brain science and are expected to contribute to the development of central intervention for the disease.

Background Visual-motor illusion (VMI) is a cognitive approach used to evoke kinesthetic sensations. Research suggests that VMI can modulate brain activity depending on the specific joint movement observed. This study aimed to identify differences in brain activity when observing video images of joint movements at different intensities of movement in VMI. Methodology The study included 14 healthy adult participants. Two types of video images were used: pure ankle dorsiflexion movements (Standard-VMI) and ankle dorsiflexion movements with added resistance (Power-VMI). The brain activity measurement protocol employed a block design with one set of 15 seconds rest, 30 seconds VMI task, and 30 seconds follow-up. Each participant performed the VMI task twice, alternating between Standard-VMI and Power-VMI. Brain activity was measured using functional near-infrared spectroscopy, focusing on motor-related regions. Subjective impressions were assessed using visual analog scales (VAS) for kinesthetic illusions. Results The results revealed that Power-VMI stimulated significantly greater brain activity in the premotor and supplementary motor cortex, supramarginal gyrus, and superior parietal lobule compared with Standard-VMI. Power-VMI resulted in higher VAS values for kinesthetic illusion than Standard-VMI. Additionally, a positive correlation was observed between brain activity in the superior parietal lobule and the degree of kinesthetic illusion. Conclusions These findings indicate that Power-VMI enhances both motor-related brain areas and motor-sensory illusions, potentially having a greater impact on improving motor function. This study provides valuable insights for developing VMI interventions for rehabilitation, particularly for individuals with paralysis or movement impairments.

In the field of brain-computer interfaces (BCIs) based on functional near-infrared spectroscopy (fNIRS), traditional subject-specific decoding methods suffer from the limitations of long calibration time and low cross-subject generalizability, which restricts the promotion and application of BCI systems in daily life and clinic. To address the above dilemma, this study proposes a novel deep transfer learning approach that combines the revised inception-residual network (rIRN) model and the model-based transfer learning (TL) strategy, referred to as TL-rIRN. This study performed cross-subject recognition experiments on mental arithmetic (MA) and mental singing (MS) tasks to validate the effectiveness and superiority of the TL-rIRN approach. The results show that the TL-rIRN significantly shortens the calibration time, reduces the training time of the target model and the consumption of computational resources, and dramatically enhances the cross-subject decoding performance compared to subject-specific decoding methods and other deep transfer learning methods. To sum up, this study provides a basis for the selection of cross-subject, cross-task, and real-time decoding algorithms for fNIRS-BCI systems, which has potential applications in constructing a convenient and universal BCI system.
在基于功能近红外光谱（fNIRS）的脑-机接口（BCI）领域中，传统的受试特定解码方法存在校准时间长和跨受试泛化性低等问题，从而限制了BCI系统在日常生活和临床领域中的推广和应用。为解决上述困境，本文提出了一种新颖的深度迁移学习方法，该方法联合了改进型启发式残差网络（rIRN）模型和基于模型的迁移学习（TL）策略，简称TL-rIRN。本文开展了跨受试识别心算和心唱任务的试验，以验证TL-rIRN方法的有效性和优越性。结果表明，相较于受试特定解码方法和其他深度迁移学习方法，TL-rIRN方法显著缩短了校准时间，减少了目标模型的训练时间和计算资源的消耗，并增强了跨受试解码性能。综上，本研究为fNIRS-BCI系统的跨受试、跨任务以及实时解码算法的选择提供了依据，在构建便捷通用型BCI系统方面具有潜在应用价值。.

Background: Patients with lower extremity burn injuries have decreased gait function. Gait dysfunctions are compensated by activation of executive areas such as the prefrontal cortex (PFC). Although robot-assisted gait training (RAGT) can improve gait function, the training mechanisms of RAGT are unknown. We aimed to determine the clinical effects of RAGT in patients with burns and investigate their underlying mechanisms. Methods: This single-blind, randomized controlled trial involved 54 patients with lower extremity burns. The RAGT group underwent RAGT using SUBAR® and conventional training. The control (CON) group underwent only conventional training. The primary outcome was cortical activity measured using a functional near-infrared spectroscopy device before and after 8 weeks of training to confirm the compensatory effect of gait dysfunction. The secondary outcomes were the functional ambulation category (FAC) to evaluate gait performance, 6-min walking test (6 MWT) distance to measure gait speed, isometric force and range of motion (ROM) of lower extremities to evaluate physical function, and the visual analog scale (VAS) score to evaluate subjective pain during gait. Results: PFC activation during the gait phase in the RAGT group decreased significantly compared with that of the CON. The VAS score decreased and FAC score improved after 8 weeks of training in both groups. The 6 MWT scores, isometric strengths (the left knee flexor and bilateral ankle plantar flexors), and the ROMs (the extensions of bilateral hip and bilateral knee) of the RAGT group were significantly improved compared with those of the CON. RAGT improved gait speed, lower extremity ROMs, and lower extremity muscles strengths in patients with burns. Conclusions: The improvement in gait speed and cerebral blood flow evaluation results suggests that the automatization of gait is related to the treatment mechanism during RAGT.

There is broad consensus that listening effort is an important outcome for measuring hearing performance. However, there remains debate on the best ways to measure listening effort. This study sought to measure neural correlates of listening effort using functional near-infrared spectroscopy (fNIRS) in experienced adult hearing aid users. The study evaluated impacts of amplification and signal-to-noise ratio (SNR) on cerebral blood oxygenation, with the expectation that easier listening conditions would be associated with less oxygenation in the prefrontal cortex. Thirty experienced adult hearing aid users repeated sentence-final words from low-context Revised Speech Perception in Noise Test sentences. Participants repeated words at a hard SNR (individual SNR-50) or easy SNR (individual SNR-50 + 10 dB), while wearing hearing aids fit to prescriptive targets or without wearing hearing aids. In addition to assessing listening accuracy and subjective listening effort, prefrontal blood oxygenation was measured using fNIRS. As expected, easier listening conditions (i.e., easy SNR, with hearing aids) led to better listening accuracy, lower subjective listening effort, and lower oxygenation across the entire prefrontal cortex compared to harder listening conditions. Listening accuracy and subjective listening effort were also significant predictors of oxygenation.

UNASSIGNED: To explore the activation patterns in the frontal cortex of patients with post-stroke cognitive impairment during the execution of working memory tasks.
UNASSIGNED: 15 patients with post-stroke cognitive impairment, 17 patients without cognitive impairment, and 15 healthy controls of similar age and sex were included. All participants under-went immediate recall task testing and near-infrared spectroscopy imaging to measure frontal cortex activation during the task.
UNASSIGNED: The healthy control group performed the best in the immediate recall task, followed by the post-stroke non-cognitive impairment group. The post-stroke cognitive impairment group had the poorest performance. The near-infrared spectroscopy results revealed that during the immediate recall task, the healthy control group primarily activated the left frontal lobe region. In contrast, post-stroke patients exhibited reduced activation in the left frontal lobe and increased activation in the right frontal cortex, particularly in the right frontopolar and orbitofrontal regions, with the post-stroke cognitive impairment group displaying the most pronounced changes.
UNASSIGNED: Patients with post-stroke cognitive impairment exhibit reduced activation in the left prefrontal cortex during the working memory tasks. They rely on compensatory activation in the right prefrontal cortex, particularly in the frontopolar and orbitofrontal cortex, to successfully complete the task.

Academic achievement is a critical measure of intellectual ability, prompting extensive research into cognitive tasks as potential predictors. Neuroimaging technologies, such as functional near-infrared spectroscopy (fNIRS), offer insights into brain hemodynamics, allowing understanding of the link between cognitive performance and academic achievement. Herein, we explored the association between cognitive tasks and academic achievement by analyzing prefrontal fNIRS signals. A novel quantum annealer (QA) feature selection algorithm was applied to fNIRS data to identify cognitive tasks correlated with CSAT scores. Twelve features (signal mean, median, variance, peak, number of peaks, sum of peaks, range, minimum, kurtosis, skewness, standard deviation, and root mean square) were extracted from fNIRS signals at two time windows (10- and 60-s) to compare results from various feature variable conditions. The feature selection results from the QA-based and XGBoost regressor algorithms were compared to validate the former\'s performance. In a two-step validation process using multiple linear regression models, model fitness (adjusted R2) and model prediction error (RMSE) values were calculated. The quantum annealer demonstrated comparable performance to classical machine learning models, and specific cognitive tasks, including verbal fluency, recognition, and the Corsi block tapping task, were correlated with academic achievement. Group analyses revealed stronger associations between Tower of London and N-back tasks with higher CSAT scores. Quantum annealing algorithms have significant potential in feature selection using fNIRS data, and represents a novel research approach. Future studies should explore predictors of academic achievement and cognitive ability.

UNASSIGNED: This case report investigates the application of intermittent Theta-Burst Stimulation (iTBS) in aphasia rehabilitation following a right hemisphere stroke.
UNASSIGNED: A 52-year-old Chinese male with Broca\'s aphasia post-stroke was treated with iTBS. His progress was evaluated using Functional Near-Infrared Spectroscopy (fNIRS) and behavioral assessments. Significant language function improvement was noted, with fNIRS showing increased activation in right hemisphere language-related cortical areas and altered functional connectivity patterns.
UNASSIGNED: The findings indicate that iTBS is effective in facilitating language recovery in right hemisphere stroke-induced aphasia, highlighting the importance of personalized neurorehabilitation strategies. Despite focusing on a single case, the study contributes to understanding neural plasticity mechanisms in right hemisphere stroke-induced aphasia.

UNASSIGNED: Removing motion artifacts (MAs) from functional near-infrared spectroscopy (fNIRS) signals is crucial in practical applications, but a standard procedure is not available yet. Artificial neural networks have found applications in diverse domains, such as voice and image processing, while their utility in signal processing remains limited.
UNASSIGNED: In this work, we introduce an innovative neural network-based approach for online fNIRS signals processing, tailored to individual subjects and requiring minimal prior experimental data. Specifically, this approach employs one-dimensional convolutional neural networks with a penalty network (1DCNNwP), incorporating a moving window and an input data augmentation procedure. In the training process, the neural network is fed with simulated data derived from the balloon model for simulation validation and semi-simulated data for experimental validation, respectively.
UNASSIGNED: Visual validation underscores 1DCNNwP\'s capacity to effectively suppress MAs. Quantitative analysis reveals a remarkable improvement in signal-to-noise ratio by over 11.08 dB, surpassing the existing methods, including the spline-interpolation, wavelet-based, temporal derivative distribution repair with a 1 s moving window, and spline Savitzky-Goaly methods. Contrast-to-noise ratio (CNR) analysis further demonstrated 1DCNNwP\'s ability to restore or enhance CNRs for motionless signals. In the experiments of eight subjects, our method significantly outperformed the other approaches (except offline TDDR, t < -3.82, p < 0.01). With an average signal processing time of 0.53 ms per sample, 1DCNNwP exhibited strong potential for real-time fNIRS data processing.
UNASSIGNED: This novel univariate approach for fNIRS signal processing presents a promising avenue that requires minimal prior experimental data and adapts seamlessly to varying experimental paradigms.

BACKGROUND: Fatigue is one of the most common neurological symptoms reported post coronavirus disease 2019 (COVID-19) infection. In order to establish effective early intervention strategies, more emphasis should be placed on the correlation between fatigue and cortical neurophysiological changes, especially in healthcare workers, who are at a heightened risk of COVID-19 infection.
METHODS: A prospective cohort study was conducted involving 29 COVID-19 medical workers and 24 healthy controls. The assessment included fatigue, sleep and health quality, psychological status, and physical capacity. Functional near-infrared spectroscopy (fNIRS) was employed to detect activation of brain regions. Bilateral primary motor cortex (M1) excitabilities were measured using single- and paired-pulse transcranial magnetic stimulation. Outcomes were assessed at 1, 3, and 6 months into the disease course.
RESULTS: At 1-month post-COVID-19 infection, 37.9% of patients experienced severe fatigue symptoms, dropping to 10.3% at 3 months. Interestingly, the remarkable decreased activation/excitability of bilateral prefrontal lobe (PFC) and M1 were closely linked to fatigue symptoms after COVID-19. Notably, greater increase in M1 region excitability correlated with more significant fatigue improvement. Re-infected patients exhibited lower levels of brain activation and excitability compared to single-infection patients.
CONCLUSIONS: Both single infection and reinfection of COVID-19 lead to decreased activation and excitability of the PFC and M1. The degree of excitability improvement in the M1 region correlates with a greater recovery in fatigue. Based on these findings, targeted interventions to enhance and regulate the excitability of M1 may represent a novel strategy for COVID-19 early rehabilitation.
BACKGROUND: The Ethics Review Committee of Xijing Hospital, No. KY20232051-F-1; www.chictr.org.cn , ChiCTR2300068444.