Rickson Coelho Mesquita

Reductions in musculoskeletal mass and function can occur with ageing and disuse. While resistance exercise training is known to minimise these detriments, it is not always feasible. Vortex wave stimulation (VWS) may mitigate skeletal muscle decline in such scenarios. The aim of the present study was to explore the acute physiological and metabolic effects of VWS in healthy older adults. Fourteen participants consumed deuterium oxide (D ₂ O) stable oral isotope tracer for 7 days. Serial skeletal muscle biopsies were obtained to measure integrated rates of myofibrillar (iMyoPS) and sarcoplasmic (iSarcPS) muscle protein synthesis and regulatory signalling over ∼48 h before (habitual) and after two consecutive‐day bouts of VWS. Myoelectrical activity and muscle oxygenation during VWS, and pre‐post changes in peripheral blood flow and concentrations of inflammatory and bone turnover markers were also measured. There was an increase in iMyoPS (0.23% day ⁻¹ , P = 0.025), but not iSarcPS (0.07% day ⁻¹ , P = 0.582) above habitual rates following VWS. There was no difference in the expression of anabolic signalling proteins, nor peripheral blood flow following VWS. Myoelectrical activity increased during VWS (9.47 µV, P = 0.010), while muscle oxygenation decreased at the gastrocnemius ( P = 0.001) and quadriceps ( P = 0.017). There was a reduction in P1NP (8.27 µg L ⁻¹ ) and lactate (0.84 mmol L ⁻¹ ) concentration post‐VWS (both P < 0.001), while there was no difference in other biomarkers. This is the first study to demonstrate that VWS elicits acute physiological responses congruent with musculoskeletal adaptive remodelling, which warrants further exploration. image Key points: Resistance exercise is known to mitigate skeletal muscle atrophy associated with ageing and/or disuse but is not always feasible. We investigated, for the first time, the effects of a novel mechanical stimulus – vortex wave stimulation (VWS) – on integrated rates of myofibrillar and sarcoplasmic muscle protein synthesis, alongside other musculoskeletal and physiological outcomes, in healthy older adults. We demonstrated that VWS elevated myofibrillar, but not sarcoplasmic muscle protein synthesis compared to baseline, while increasing myoelectrical activity and reducing muscle oxygen saturation, lactate and bone turnover markers. These findings suggest VWS elicits acute physiological responses congruent with musculoskeletal adaptive remodelling.

Resting-state functional connectivity (rsFC) provides insight into the intrinsic organization of brain networks and is increasingly recognized as a sensitive marker of age-related neural changes. Functional near-infrared spectroscopy (fNIRS) offers a portable and cost-effective approach to measuring rsFC, including in naturalistic settings. However, its sensitivity to age-related alterations in network topology remains poorly characterized. Here, we applied graph-based analysis to resting-state fNIRS data from 57 healthy participants, including 26 young adults (YA, 18–30 years) and 31 older adults (OA, 50–77 years). We observed that older adults exhibited a marked attenuation of low-frequency oscillation (LFO) power across all hemoglobin contrasts, corresponding to a 5–6-fold reduction in spectral power. In addition, network analysis revealed altered topological organization under matched sparsity conditions, characterized by reduced degree heterogeneity and increased segregation in older adults, with the strongest differences observed in the default mode (DMN), auditory, and frontoparietal control (FPC) networks. Network visualizations further indicated a shift toward more right-lateralized and posterior hub organization in older adults. Together, the coexistence of reduced oscillatory power and increased connectivity suggests that fNIRS-derived rsFC reflects combined neural and non-neural hemodynamic influences, including increased coherence arising from age-related vascular and systemic physiological processes. Overall, our findings demonstrate that fNIRS is sensitive to age-related changes in large-scale hemodynamic network organization. At the same time, sensitivity to non-neural hemodynamics highlights the need for cautious interpretation, but it may provide complementary, clinically relevant signatures of aging-related changes.

Resting-state functional connectivity (rsFC) provides insight into the intrinsic organization of brain networks and is increasingly recognized as a sensitive marker of age-related neural changes. Functional near-infrared spectroscopy (fNIRS) provides a portable and cost-effective approach to measuring rsFC, including in naturalistic environments. However, the sensitivity of fNIRS to age-related alterations in rsFC topology remains poorly characterized. Here, we applied graph-based analysis to fNIRS data acquired during rest from 57 healthy participants divided into two groups: 26 young adults (18-30 years) and 31 older adults (50-77 years). Despite observing significantly attenuated low-frequency oscillation (LFO) power in older adults (5-6 fold reduction, p < 0.001), network analysis revealed counterintuitively higher connectivity in this group. Specifically, older adults exhibited 50-86% greater degree density, 16-43% higher clustering coefficients, and 31-75% greater global efficiency compared to young adults. These patterns were consistent across all canonical resting-state networks (sensorimotor, auditory, visual, frontoparietal, default mode) and all hemoglobin contrasts. While network decay rates were slower in older adults, the coexistence of reduced oscillatory and increased connectivity may reflect vascular rather than purely neural contributions. Overall, our findings validate fNIRS as a sensitive tool for detecting age-related changes in network organization, while highlighting the importance of disentangling neural and vascular signal components in aging research.

SignificanceAccurate and efficient photon modeling plays an essential role in the rapidly developing field of diffuse optical imaging, whereby the use of model-based analysis and image reconstruction can provide both educational and research benefits.AimNIRFASTerFF is a cross-platform (Linux, macOS, and Windows) Python package for finite element method (FEM)-based light propagation modeling, supporting continuous-wave, frequency-domain, and time-resolved data for both exogenous and endogenous optical imaging applications. It also enables modeling of the autocorrelation function (G1) for diffuse correlation spectroscopy. Validation is performed through comparison with the original NIRFAST and gold-standard Monte Carlo simulations.ApproachNIRFASTerFF incorporates highly parallelized FEM solvers for efficient computation on both CPU and GPU, leveraging OpenMP and CUDA acceleration. To support image reconstruction tasks, voxel-based interpolation of the optical fluence is implemented, providing a flexible and accurate representation of the forward solution suitable for inverse problem formulations.ResultsCompared with its predecessor, NIRFASTer, the optimized algorithms provide a performance boost of 25% to 45% on GPU and up to 20% on CPU, and the results show good agreement with both Monte Carlo and analytical solutions.ConclusionThe NIRFASTerFF package provides a fast and license-free tool for photon modeling and can further streamline Python-based data processing in diffuse optical imaging, benefiting the biophotonics community.

Neurophotonics Associate Editors Edgar Guevara and Rickson C. Mesquita, along with Felipe Orihuela-Espina, share their perceptions of the evolution of functional near-infrared spectroscopy in Latin America based on a retrospective literature review and the narratives of three other scientists from the region.