BACKGROUND AND PURPOSE: Knee osteoarthritis (OA) is a common source of pain in older adults. Although OA-induced pain can be relieved with analgesics and anti-inflammatory drugs, the current opioid epidemic is fostering the exploration of nonpharmacologic strategies for pain mitigation. Amongs these, transcranial direct current stimulation (tDCS) and mindfulnessbased meditation (MBM) hold potential for pain-relief efficacy due to their neuromodulatory effects of the central nervous system, which is known to play a fundamental role in pain perception and processing. METHODS: In this double-blind study, we used functional near-infrared spectroscopy (fNIRS) to investigate the effects of tDCS combined with MBM on underlying pain processing mechanisms at the central nervous level in older adults with knee OA. Nineteen subjects were randomly assigned to two groups undergoing a 10-day active tDCS and MBM regimen and a sham tDCS and MBM regimen, respectively. RESULTS: Our results showed that the neuromodulatory intervention significantly relieved pain only in the group receiving active treatment. We also found that only the active treatment group showed a significant increase in oxyhemoglobin activation of the superior motor and somatosensory cortices colocated to the placement of the tDCS anodal electrode. To our knowledge, this is the first study in which the combined effect of tDCS and MBM is investigated using fNIRS. CONCLUSION: In conclusion, fNIRS can be effectively used to investigate neural mechanisms of pain at the cortical level in association with nonpharmacological, self-administered treatments.
Functional Near InfraRed Spectroscopy (fNIRS) connectivity analysis is often performed using the measured oxy-haemoglobin (HbO 2 ) signal, while the deoxy-haemoglobin (HHb) is largely ignored. The in-common information of the connectivity networks of both HbO 2 and HHb is not regularly reported, or worse, assumed to be similar. Here we describe a methodology that allows the estimation of the symmetry between the functional connectivity (FC) networks of HbO 2 and HHb and propose a differential symmetry index (DSI) indicative of the in-common physiological information. Our hypothesis is that the symmetry between FC networks associated with HbO 2 and HHb is above what should be expected from random networks. FC analysis was done in fNIRS data collected from six freely-moving healthy volunteers over 16 locations on the prefrontal cortex during a real-world task in an out-of-the-lab environment. In addition, systemic data including breathing rate (BR) and heart rate (HR) were also synchronously collected and used within the FC analysis. FC networks for HbO 2 and HHb were established independently using a Bayesian networks analysis. The DSI between both haemoglobin (Hb) networks with and without systemic influence was calculated. The relationship between the symmetry of HbO 2 and HHb networks, including the segregational and integrational characteristics of the networks (modularity and global efficiency respectively) were further described. Consideration of systemic information increases the path lengths of the connectivity networks by 3%. Sparse networks exhibited higher asymmetry than dense networks. Importantly, our experimental connectivity networks symmetry between HbO 2 and HHb departs from random (t-test: t(509) = 26.39, p < 0.0001). The DSI distribution suggests a threshold of 0.2 to decide whether both HbO 2 and HHb FC networks ought to be studied. For sparse FC networks, analysis of both haemoglobin species is strongly recommended. Our DSI can provide a quantifiable guideline for deciding whether to proceed with single or both Hb networks in FC analysis.
Accurate estimation of brain haemodynamics parameters such as cerebral blood flow and volume as well as oxygen consumption i.e. metabolic rate of oxygen, with funcional near infrared spectroscopy (fNIRS) requires precise characterization of light propagation through head tissues. An anatomically realistic forward model of the human adult head with unprecedented detailed specification of the 5 scalp sublayers to account for blood irrigation in the connective tissue layer is introduced. The full model consists of 9 layers, accounts for optical properties ranging from 750nm to 950nm and has a voxel size of 0.5mm. The whole model is validated comparing the predicted remitted spectra, using Monte Carlo simulations of radiation propagation with 10 8 photons, against continuous wave (CW) broadband fNIRS experimental data. As the true oxy-and deoxy-hemoglobin concentrations during acquisition are unknown, a genetic algorithm searched for the vector of parameters that generates a modelled spectrum that optimally fits the experimental spectrum. Differences between experimental and model predicted spectra was quantified using the Root mean square error (RMSE). RMSE was 0.071 ± 0.004, 0.108 ± 0.018 and 0.235 ± 0.015 at 1, 2 and 3cm interoptode distance respectively. The parameter vector of absolute concentrations of haemoglobin species in scalp and cortex retrieved with the genetic algorithm was within histologically plausible ranges. The new model capability to estimate the contribution of the scalp blood flow shall permit incorporating this information to the regularization of the inverse problem for a cleaner reconstruction of brain hemodynamics.
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