We have come a long way since Jöbsis first demonstrated that noninvasive cerebral monitoring was possible in humans within the near-infrared light window (approximately 700 to 1000 nm) in the late 1970s.¹ What started as a tool to quantify blood oxygenation in the brain has exploded into a whole field of study that aims to exploit this window to reveal a battery of information related to human brain physiology, including brain metabolism, blood flow, autoregulation, perfusion pressure, water concentration, light scattering, exogenous contrast agents, and more. In this text, we use the term “diffuse optics” to broadly describe any technique that employs a near-infrared light source and a photodetector to capture multiply scattered photons in the diffusion limit (i.e., in the limit wherein the absorption coefficient, ${\mu }_a$, is much less than the reduced scattering coefficient, ${\mu }_s^{\prime }$, and the source-detector separation is much greater than the photon mean free path). The term “diffuse” has historical connotations in the field and reflects how near-infrared photons propagate through biological tissue. However, it should be noted that this term is in no way associated with how the field/community is organized or conducts its science. On the contrary, the breadth and depth of the progress made in the field over the past decade could only be accomplished by a cohesive, focused, and tight-knit community of researchers!

Functional near infrared spectroscopy (fNIRS) is a rapidly developing neuroimaging modality for exploring cortical brain behaviour. Despite recent advances, the quality of fNIRS experimentation may be compromised in several ways: firstly, by altering the optical properties of the tissues encountered in the path of light; secondly, through adulteration of the recovered biological signals (noise) and finally, by modulating neural activity. Currently, there is no systematic way to guide the researcher regarding these factors when planning fNIRS studies. Conclusions extracted from fNIRS data will only be robust if appropriate methodology and analysis in accordance with the research question under investigation are employed. In order to address these issues and facilitate the quality control process, a taxonomy of factors influencing fNIRS data have been established. For each factor, a detailed description is provided and previous solutions are reviewed. Finally, a series of evidence-based recommendations are made with the aim of improving consistency and quality of fNIRS research.

Virtual reality platforms capable of assisting rehabilitation must provide support for rehabilitation principles: promote repetition, task oriented training, appropriate feedback, and a motivating environment. As such, development of these platforms is a complex process which has not yet reached maturity. This paper presents our efforts to contribute to this field, presenting Gesture Therapy, a virtual reality-based platform for rehabilitation of the upper limb. We describe the system architecture and main features of the platform and provide preliminary evidence of the feasibility of the platform in its current status.

Evidence of superiority of robot training for the hand over classical therapies in stroke patients remains controversial. During the subacute stage, hand training is likely to be the most useful. To establish whether robot active assisted therapies provides any additional motor recovery for the hand when administered during the subacute stage (<4 months from event) in a Mexican adult population diagnosed with stroke. Compared to classical occupational therapy, robot based therapies for hand recovery will show significant differences at subacute stages. A randomized clinical trial. A between subjects randomized controlled trial was carried out on subacute stroke patients (n = 17) comparing robot active assisted therapy (RT) with a classical occupational therapy (OT). Both groups received 40 sessions ensuring at least 300 repetitions per session. Treatment duration was (mean ± std) 2.18 ± 1.25 months for the control group and 2.44 ± 0.88 months for the study group. The primary outcome was motor dexterity changes assessed with the Fugl-Meyer (FMA) and the Motricity Index (MI). Both groups (OT: n = 8; RT: n = 9) exhibited significant improvements over time (Non-parametric Cliff's delta-within effect sizes: dwOT-FMA = 0.5, dwOT-MI = 0.5, dwRT-FMA = 1, dwRT-MI = 1). Regarding differences between the therapies; the Fugl-Meyer score indicated a significant advantage for the hand training with the robot (FMA hand: WRS: W = 8, p <0.01), whilst the Motricity index suggested a greater improvement (size effect) in hand prehension for RT with respect to OT but failed to reach significance (MI prehension: W = 17.5, p = 0.080). No harm occurred. Robotic therapies may be useful during the subacute stages of stroke - both endpoints (FM hand and MI prehension) showed the expected trend with bigger effect size for the robotic intervention. Additional benefit of the robotic therapy over the control therapy was only significant when the difference was measured with FM, demanding further investigation with larger samples. Implications of this study are important for decision making during therapy administration and resource allocation.