Insufficient O delivery to, and uptake by skeletal muscle can produce mobility limitations for patients with chronic diseases. Near-infrared spectroscopy (NIRS) can be used to noninvasively quantify the balance between skeletal muscle O delivery and utilization during contraction. However, it is not clear how the oxygenated or deoxygenated NIRS signal should be used to assess muscle O changes. This issue is related to the fact that the contributions of hemoglobin (Hb) and myoglobin (Mb) cannot be distinguished. This conundrum can be resolved by quantitative analysis of experimental data by computer simulations with a mechanistic, mathematical model. Model simulations distinguish dynamic responses of the oxygenated (HbO, MbO) and deoxygenated (HHb, HMb) contributions to the NIRS signal components (HbMbO, HHbMb). Simulations of muscle O uptake and NIRS kinetics correspond closely to published experimental data (Hernández et al., J Appl Physiol 108: 1169-1176, 2010). Simulated muscle O uptake and oxygenation kinetics with different blood flows indicate (1) faster O delivery is responsible for slower muscle oxygenation kinetics; (2) Hb and Mb contributions to the HbMbO are similar (40-60%); and (3) Hb and Mb contributions to the HHbMb are significantly different, 80% and 20%, respectively. The effect of slow blood flow kinetics on oxygenated Hb and Mb contributions is minimal. However, the effect on the imbalance between O delivery and utilization rates causes significant overshoots and undershoots of deoxygenated Hb and Mb contributions. Model analysis in combination with NIRS measurements and information on hemodynamic and microvascular distribution can help to determine the use of NIRS signal in evaluating the factors limiting exercise tolerance in health and disease states.
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http://dx.doi.org/10.1007/978-3-030-48238-1_58 | DOI Listing |
Sci Rep
January 2025
Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Optical techniques, such as functional near-infrared spectroscopy (fNIRS), contain high potential for the development of non-invasive wearable systems for evaluating cerebral vascular condition in aging, due to their portability and ability to monitor real-time changes in cerebral hemodynamics. In this study, thirty-six healthy adults were measured by single channel fNIRS to explore differences between two age groups using machine learning (ML). The subjects, measured during functional magnetic resonance imaging (fMRI) at Oulu University Hospital, were divided into young (age ≤ 32) and elderly (age ≥ 57) groups.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Electrical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates.
Accurately identifying and discriminating between different brain states is a major emphasis of functional brain imaging research. Various machine learning techniques play an important role in this regard. However, when working with a small number of study participants, the lack of sufficient data and achieving meaningful classification results remain a challenge.
View Article and Find Full Text PDFNeuroimage
January 2025
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China. Electronic address:
Functional near-infrared spectroscopy (fNIRS) is a widely-used transcranial brain imaging technique in neuroscience research. Nevertheless, the lack of anatomical information from recordings poses challenges for designing appropriate optode montages and for localizing fNIRS signals to underlying anatomical regions. The photon measurement density function (PMDF) is often employed to address these issues, as it accurately measures the sensitivity of an fNIRS channel to perturbations of absorption coefficients at any brain location.
View Article and Find Full Text PDFComput Methods Programs Biomed
January 2025
College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai, 201318, PR China; Shanghai Yangpu Mental Health Center, Shanghai, 200093, PR China. Electronic address:
Background And Objective: The hybrid brain computer interfaces (BCI) combining electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) have attracted extensive attention for overcoming the decoding limitations of the single-modality BCI. With the deepening application of deep learning approaches in BCI systems, its significant performance improvement has become apparent. However, the scarcity of brain signal data limits the performance of deep learning models.
View Article and Find Full Text PDFIBRO Neurosci Rep
June 2025
Rehabilitation Clinic, Beijing Rehabilitation Hospital, Capital Medical University, Beijing, China.
Objective: Unilateral spatial neglect (USN) following right hemisphere stroke is more pronounced, severe, and persistent than in the left hemisphere. However, the pathophysiological mechanisms underlying USN remain largely unknown. This study aims to investigate the relationship between the fractional amplitude of low-frequency fluctuations (fALFF) in the right hemisphere of patients with post-stroke USN and the severity of neglect using resting-state functional near-infrared spectroscopy (fNIRS) technology.
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