Functional Near-Infrared Spectroscopy for the Classification of Motor-Related Brain Activity on the Sensor-Level

Brain Activity in Visual-Motor Illusions With Enhanced Joint Motion Intensity

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.

How robot-assisted gait training affects gait ability, balance and kinematic parameters after stroke: a systematic review and meta-analysis

INTRODUCTION

Gait ability is often cited by stroke survivors. Robot-assisted gait training (RAGT) can help stroke patients with lower limb motor impairment regain motor coordination.

EVIDENCE ACQUISITION

PubMed, Cochrane Library, Embase were systematically searched until September 2023, to identify randomized controlled trials presenting: stroke survivors as participants; RAGT as intervention; conventional rehabilitation as a comparator; gait assessment, through scales or quantitative parameters, as outcome measures.

EVIDENCE SYNTHESIS

Twenty-seven publications involving 1167 patients met the inclusion criteria. Meta-analysis showed no significant differences in speed, cadence, spatial symmetry, and changes in joint mobility angles between the RAGT group and the control group. In addition, RAGT was associated with changes in affected side step length (SMD=0.02, 95% CI: 0.01, 0.03; P<0.0001), temporal symmetry (SMD=-0.38, 95% CI: -0.6, -0.16; P=0.0006], Six-Minute Walk Test (SMD=25.14, 95% CI: 10.19, 40.09; P=0.0010] and Functional Ambulation Categories (SMD=0.32, 95% CI: 0.01, 0.63; P=0.04). According to the PEDro scale, 19 (70.4%) studies were of high quality and eight were of moderate quality (29.6%).

CONCLUSIONS

Taken together, the review synthesis showed that RAGT might have a potential role in the recovery of walking dysfunction after stroke. However, its superiority over conventional rehabilitation requires further research. Additionally, it may provide unexpected benefits that the effects of RAGT with different types or treatment protocols were further compared.

Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning

Recent research in the field of cognitive motor action decoding focuses on data acquired from Functional Near-Infrared Spectroscopy (fNIRS) and its analysis. This research aims to classify two different motor activities, namely, mental drawing (MD) and spatial navigation (SN), using fNIRS data from non-motor baseline data and other motor activities. Accurate activity detection in non-stationary signals like fNIRS is challenging and requires complex feature descriptors. As a novel framework, a new feature generation by fusion of wavelet feature, Hilbert, symlet, and Hjorth parameters is proposed for improving the accuracy of the classification. This new fused feature has statistical descriptor elements, time-localization in the frequency domain, edge feature, texture features, and phase information to detect and locate the activity accurately. Three types of independent component analysis, including FastICA, Picard, and Infomax were implemented for preprocessing which removes noises and motion artifacts. Two independent binary classifiers are designed to handle the complexity of classification in which one is responsible for mental drawing (MD) detection and the other one is spatial navigation (SN). Four different types of algorithms including nearest neighbors (KNN), Linear Discriminant Analysis (LDA), light gradient-boosting machine (LGBM), and Extreme Gradient Boosting (XGBOOST) were implemented. It has been identified that the LGBM classifier gives high accuracies-98% for mental drawing and 97% for spatial navigation. Comparison with existing research proves that the proposed method gives the highest classification accuracies. Statistical validation of the proposed new feature generation by the Kruskal-Wallis H-test and Mann-Whitney U non-parametric test proves the reliability of the proposed mechanism.

Effects of Functional Electrical Stimulation on Attention and Brain Activity in Healthy Participants Using Near-Infrared Spectroscopy: An Interventional Study

Background Involuntary limb activation using functional electrical stimulation (FES) can improve unilateral spatial neglect. However, the impact of FES on brain activity related to spatial attention remains unclear. Thus, in this study, we aimed to examine the effects of FES on spatial attention. Methodology In this interventional study, 13 healthy right-handed participants were asked to perform the Posner task for six minutes both before and after either FES or sham stimulation during each set, resulting in a total of two sets. FES was applied to the left forearm extensor muscles, with a frequency of 25 Hz, a pulse width of 100 μs, and the intensity adjusted to reach the motor threshold. Both the energization and pause times were set to five seconds. The Posner task was used to measure reaction time to a target appearing on a computer screen. Brain activity, indicated by oxygenated hemoglobin values, was measured using near-infrared spectroscopy with 24 probes according to the International 10-20 system method. Results In the left hemisphere, oxygenated hemoglobin values in the premotor and supplementary motor areas, primary somatosensory cortex, and somatosensory association areas were significantly higher after FES than after sham stimulation. In the right hemisphere, oxygenated hemoglobin values were significantly increased in the premotor, primary, and supplementary motor areas; in the supramarginal gyrus; and in the somatosensory association areas after FES. Reaction times in the Posner task did not differ significantly between the FES and sham conditions. Conclusions Collectively, these results suggest that FES of the upper limbs can activate the ventral pathway of the visual attention network and improve stimulus-driven attention. Activation of stimulus-driven attentional function could potentially contribute to symptom improvement in patients with unilateral spatial neglect.

Activation changes in patients with post-stroke cognitive impairment receiving intermittent theta burst stimulation: A functional near-infrared spectroscopy study

BACKGROUND

Intermittent theta burst stimulation (iTBS) has demonstrated efficacy in patients with cognitive impairment. However, activation patterns and mechanisms of iTBS for post-stroke cognitive impairment (PSCI) remain insufficiently understood.

OBJECTIVE

To investigate the activation patterns and potential benefits of using iTBS in patients with PSCI.

METHODS

A total of forty-four patients with PSCI were enrolled and divided into an iTBS group (iTBS and cognitive training) or a control group (cognitive training alone). Outcomes were assessed based on the activation in functional near-infrared spectroscopy (fNIRS), as well as Loewenstein Occupational Therapy Cognitive Assessment (LOTCA) and the modified Barthel Index (MBI).

RESULTS

Thirty-eight patients completed the interventions and assessments. Increased cortical activation was observed in the iTBS group after the interventions, including the right superior temporal gyrus (STG), left frontopolar cortex (FPC) and left orbitofrontal cortex (OFC). Both groups showed significant improvements in LOTCA and MBI after the interventions (p < 0.05). Furthermore, the iTBS group augmented superior improvement in the total score of MBI and LOTCA compared to the control group, especially in visuomotor organization and thinking operations (p < 0.05).

CONCLUSION

iTBS altered activation patterns and improved cognitive function in patients with PSCI. The activation induced by iTBS may contribute to the improvement of cognitive function.

Motor Imagery: How to Assess, Improve Its Performance, and Apply It for Psychosis Diagnostics

With this review, we summarize the state-of-the-art of scientific studies in the field of motor imagery (MI) and motor execution (ME). We composed the brain map and description that correlate different brain areas with the type of movements it is responsible for. That gives a more complete and systematic picture of human brain functionality in the case of ME and MI. We systematized the most popular methods for assessing the quality of MI performance and discussed their advantages and disadvantages. We also reviewed the main directions for the use of transcranial magnetic stimulation (TMS) in MI research and considered the principal effects of TMS on MI performance. In addition, we discuss the main applications of MI, emphasizing its use in the diagnostics of various neurodegenerative disorders and psychoses. Finally, we discuss the research gap and possible improvements for further research in the field.

Comparison of Functional Connectivity during Visual-Motor Illusion, Observation, and Motor Execution

This study investigated the functional connectivity during visual-motor illusion and compared it with observation and motor execution using functional near-infrared spectroscopy (fNIRS). Thirty subjects were randomly assigned to: illusion, observation, and motor execution group. Illusion group watched own finger joint movement video image and induced kinesthetic illusion, while the other group only performed observation or motor execution. Continuous brain activity was measured using fNIRS and functional connectivity was analyzed. The illusion group perceived (using 7-point Likert scale) a higher degree of kinesthetic illusion and sense of body ownership than the observation group. Visual-motor illusion was associated with stronger functional connectivity between the left premotor cortex and the left parietal area compared with observation and motor execution only, suggesting that these areas respond to visual-motor illusion.

Shedding light on neuroscience: Two decades of functional near-infrared spectroscopy applications and advances from a bibliometric perspective

Functional near-infrared spectroscopy (fNIRS) is a noninvasive optical brain-imaging technique that detects changes in hemoglobin concentration in the cerebral cortex. fNIRS devices are safe, silent, portable, robust against motion artifacts, and have good temporal resolution. fNIRS is reliable and trustworthy, as well as an alternative and a complement to other brain-imaging modalities, such as electroencephalography or functional magnetic resonance imaging. Given these advantages, fNIRS has become a well-established tool for neuroscience research, used not only for healthy cortical activity but also as a biomarker during clinical assessment in individuals with schizophrenia, major depressive disorder, bipolar disease, epilepsy, Alzheimer's disease, vascular dementia, and cancer screening. Owing to its wide applicability, studies on fNIRS have increased exponentially over the last two decades. In this study, scientific publications indexed in the Web of Science databases were collected and a bibliometric-type methodology was developed. For this purpose, a comprehensive science mapping analysis, including top-ranked authors, journals, institutions, countries, and co-occurring keywords network, was conducted. From a total of 2310 eligible documents, 6028 authors and 531 journals published fNIRS-related papers, Fallgatter published the highest number of articles and was the most cited author. University of Tübingen in Germany has produced the most trending papers since 2000. USA was the most prolific country with the most active institutions, followed by China, Japan, Germany, and South Korea. The results also revealed global trends in emerging areas of research, such as neurodevelopment, aging, and cognitive and emotional assessment.

Age-Related Distinctions in EEG Signals during Execution of Motor Tasks Characterized in Terms of Long-Range Correlations

The problem of revealing age-related distinctions in multichannel electroencephalograms (EEGs) during the execution of motor tasks in young and elderly adults is addressed herein. Based on the detrended fluctuation analysis (DFA), differences in long-range correlations are considered, emphasizing changes in the scaling exponent α. Stronger responses in elderly subjects are confirmed, including the range and rate of increase in α. Unlike elderly subjects, young adults demonstrated about 2.5 times more pronounced differences between motor task responses with the dominant and non-dominant hand. Knowledge of age-related changes in brain electrical activity is important for understanding consequences of healthy aging and distinguishing them from pathological changes associated with brain diseases. Besides diagnosing age-related effects, the potential of DFA can also be used in the field of brain–computer interfaces.