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 robust tool for measuring brain function.^{1 – 8} It probes the brain using the differential absorption of near-infrared (NIR) light by hemoglobin. Owing to features such as high temporal resolution, low cost, and portability, fNIRS is frequently chosen by researchers to investigate human populations ranging from neonates^{9 – 24} to patients with severe injuries.^{2,25,26 – 31} Moreover, fNIRS is useful during a variety of activities, even in unconstrained environments.^{32 – 34} In speech task applications, for example, fNIRS can potentially determine whether cerebral blood oxygenation changes in subjects with speech and language disabilities are different from those of normal subjects,^{8,35 – 37} and fNIRS could track motor learning during treatment of individuals with voice disorders.

Resting state cerebral dynamics has been a useful approach to explore the brain’s functional organization. In this study, we employed graph theory to deeply investigate resting state functional connectivity (rs-FC) as measured by near-infrared spectroscopy (NIRS). Our results suggest that network parameters are very similar across time and subjects. We also identified the most frequent connections between brain regions and the main hubs that participate in the spontaneous activity of brain hemodynamics. Similar to previous findings, we verified that symmetrically located brain areas are highly connected. Overall, our results introduce new insights in NIRS-based functional connectivity at rest.

SignificanceBrain fingerprinting refers to identifying participants based on their functional patterns. Despite its success with functional magnetic resonance imaging (fMRI), brain fingerprinting with functional near-infrared spectroscopy (fNIRS) still lacks adequate validation.AimWe investigated how fNIRS-specific acquisition features (limited spatial information and nonneural contributions) influence resting-state functional connectivity (rsFC) patterns at the intra-subject level and, therefore, brain fingerprinting.ApproachWe performed multiple simultaneous fNIRS and fMRI measurements in 29 healthy participants at rest. Data were preprocessed following the best practices, including the removal of motion artifacts and global physiology. The rsFC maps were extracted with the Pearson correlation coefficient. Brain fingerprinting was tested with pairwise metrics and a simple linear classifier.ResultsOur results show that average classification accuracy with fNIRS ranges from 75% to 98%, depending on the number of runs and brain regions used for classification. Under the right conditions, brain fingerprinting with fNIRS is close to the 99.9% accuracy found with fMRI. Overall, the classification accuracy is more impacted by the number of runs and the spatial coverage than the choice of the classification algorithm.ConclusionsThis work provides evidence that brain fingerprinting with fNIRS is robust and reliable for extracting unique individual features at the intra-subject level once relevant spatiotemporal constraints are correctly employed.

This study aimed to assess task-induced activation in motor cortex and its association with motor performance in full-term and preterm born infants at six months old. A cross-sectional study of 73 six-month-old infants was conducted (35 full-term and 38 preterm infants). Motor performance was assessed using the Bayley Scales of Infant Development third edition—Bayley-III. Brain hemodynamic activity during motor task was measured by functional near-infrared spectroscopy (fNIRS). Motor performance was similar in full-term and preterm infants. However, differences in hemodynamic response were identified. Full terms showed a more homogeneous unilateral and contralateral activated area, whereas in preterm-born the activation response was predominantly bilateral. The full-term group also exhibited a shorter latency for the hemodynamic response than the preterm group. Hemodynamic activity in the left sensorimotor region was positively associated with motor performance measured by Bayley-III. The results highlight the adequacy of fNIRS to assess differences in task-induced activation in sensorimotor cortex between groups. The association between motor performance and the hemodynamic activity require further investigation and suggest that fNIRS can become a suitable auxiliary tool to investigate aspects of neural basis on early development of motor abilities.