Sérgio Luiz Novi

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.

In neuroimaging research, efforts to enhance replication and reproducibility have increased the focus on improving transparency, particularly in the complex data analysis processes. We conducted a multi-lab collaborative study involving 38 international teams that analyzed two functional Near-Infrared Spectroscopy (fNIRS) datasets. These teams tested seven group-level and forty individual-level hypotheses, and they submitted detailed reports on their analysis pipelines and testing outcomes. The results showed significant variability in hypothesis testing outcomes due to differences in analytical approaches. There was greater consensus in group-level analyses compared to individual-level analyses. Factors such as the pruning method, hemodynamic response function model and estimation, and statistical analysis space partly account for the variability in hypothesis testing outcomes. Additionally, we have found higher similarity in hypothesis testing outcomes across the researchers who reported higher confidence in their analysis skills. This study underscores the importance of complying with best practices in fNIRS analysis methodologies and the need for standardized analysis protocols to improve reliability and credibility.

In neuroimaging research, efforts to enhance replication and reproducibility have increased the focus on improving transparency, particularly in the complex data analysis processes. We conducted a multi-lab collaborative study involving 38 international teams that analyzed two functional Near-Infrared Spectroscopy (fNIRS) datasets. These teams tested seven group-level and forty individual-level hypotheses, and they submitted detailed reports on their analysis pipelines and testing outcomes. The results showed significant variability in hypothesis testing outcomes due to differences in analytical approaches. There was greater consensus in group-level analyses compared to individual-level analyses. Factors such as the pruning method, hemodynamic response function model and estimation, and statistical analysis space partly account for the variability in hypothesis testing outcomes. Additionally, we have found higher similarity in hypothesis testing outcomes across the researchers who reported higher confidence in their analysis skills. This study underscores the importance of complying with best practices in fNIRS analysis methodologies and the need for standardized analysis protocols to improve reliability and credibility.

Resting-state functional connectivity (rsFC) is an attractive biomarker of brain function that can vary with brain injury. The simplicity of resting-state protocols coupled with the main features of functional near-infrared spectroscopy (fNIRS), such as portability and versatility, can facilitate the monitoring of unresponsive patients in acute settings at the bedside. However, accurately mapping rsFC networks is challenging due to signal contamination from non-neural components, such as scalp hemodynamics and systemic physiology. Physiological noise may be mitigated through the use of short channels which may be able to provide sufficient information to eliminate the need for additional measurement devices, decreasing the complexity of the experimental setup. To this end, we examined the extent to which systemic physiology is embedded in the short-channel data and improved short-channel regression to account for temporal heterogeneity in the scalp hemodynamics. Our findings indicate that using temporal shifts in the short-channel data increases the agreement, by 70% on average, between short-channel regression and regression that includes short channels and physiological recordings. Overall, this method decreases the need for additional physiological recordings when mapping rsFC networks, providing a viable alternative when such measurements are not available or feasible.

The neural correlates of narrative stimuli have facilitated research into the higher-order cognitive processes evoked when participants engage with complex, 'real-world' stimuli. These neural correlates have been successfully applied to measure higher-order cognitive processes in groups where behavioural evidence alone is inadequate, such as in clinical and developmental populations. While this research has been primarily conducted in fMRI and EEG, whether functional near-infrared spectroscopy (fNIRS) can reliably detect these neural correlates at an individual level, which is required for effective use in these populations, has yet to be established. This study replicated widespread inter-subject correlations (ISCs) in the frontal, parietal and temporal cortices in fNIRS in healthy participants when they watched part of the movie Bang! You're Dead and listened to an audio clip from the movie Taken. Conversely, these ISCs were primarily restricted to temporal cortices when participants viewed scrambled versions of those clips. Next, to assess whether these results were reliable at the single-participant level, two follow-up analyses were conducted. First, the consistency analysis compared each participant's ISCs against group results that excluded that individual. This approach found that 24/26 participants in Bang! You're Dead and 20/26 participants in Taken were statistically similar to the group. Second, the sensitivity analysis measured whether machine learning algorithms could decode between intact conditions and their scrambled counterparts. This approach yielded balanced accuracy scores of 81% in Bang! You're Dead and 79% in Taken. Overall, the neural correlates of narrative stimuli, as assessed by fNIRS, are reproducible across participants, supporting its broad application to clinical and developmental populations.

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.

The study of functional reorganization following stroke has been steadily growing supported by advances in neuroimaging techniques, such as functional magnetic resonance imaging (fMRI). Concomitantly, graph theory has been increasingly employed in neuroscience to model the brain's functional connectivity (FC) and to investigate it in a variety of contexts. The aims of this study were: 1) to investigate the reorganization of network topology in the ipsilesional (IL) and contralesional (CL) hemispheres of stroke patients with (motor stroke group) and without (control stroke group) motor impairment, and 2) to predict motor recovery through the relationship between local topological variations of the functional network and increased motor function. We modeled the brain's FC as a graph using fMRI data, and we characterized its interactions with the following graph metrics: degree, clustering coefficient, characteristic path length, and betweenness centrality (BC). For both patient groups, BC yielded the largest variations between the two analyzed time points, especially in the motor stroke group. This group presented significant correlations (P<0.05) between average BC changes and the improvements in upper-extremity Fugl-Meyer (UE-FM) scores at the primary sensorimotor cortex and the supplementary motor area for the CL hemisphere. These regions participate in processes related to the selection, planning, and execution of movement. Generally, higher increases in average BC over these areas were related to larger improvements in UE-FM assessment. Although the sample was small, these results suggest the possibility of using BC as an indication of brain plasticity mechanisms following stroke.

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.