Correction of global physiology in resting-state functional near-infrared spectroscopy

Effects of Physical Exercise Breaks on Executive Function in a Simulated Classroom Setting: Uncovering a Window into the Brain

Acknowledging the detrimental effects of prolonged sitting, this study examined the effects of an acute exercise break during prolonged sitting on executive function, cortical hemodynamics, and microvascular status. In this randomized crossover study, 71 college students completed three conditions: (i) uninterrupted sitting (SIT); (ii) SIT with a 15 min moderate-intensity cycling break (MIC); and (iii) SIT with a 15 min vigorous-intensity cycling break (VIC). Behavioral outcomes, retinal vessel diameters (central retinal artery equivalents [CRAE], retinal vein equivalents [CRVE], arteriovenous ratio [AVR]), cortical activation, and effective connectivity were evaluated. Linear mixed models identified significant positive effects of exercise conditions on behavioral reaction time (RT), error rate, and inverse efficiency score (β = -2.62, -0.19, -3.04: ps < 0.05). MIC and VIC conditions produced pre-to-post-intervention increases in CRAE and CRVE (β = 4.46, 6.34), frontal activation, and resting-state and task-state causal density (β = 0.37, 0.06) (ps < 0.05) compared to SIT; VIC was more beneficial for executive function and neurobiological parameters. The effect of AVR on average RT was mediated through task-based causal density (indirect effect: -0.82). Acutely interrupting prolonged sitting improves executive function, microvascular status, and cortical activation and connectivity, with causal density mediating the microvascular-executive function link.

Computer Vision-Driven Movement Annotations to Advance fNIRS Pre-Processing Algorithms

Functional near-infrared spectroscopy (fNIRS) is beneficial for studying brain activity in naturalistic settings due to its tolerance for movement. However, residual motion artifacts still compromise fNIRS data quality and might lead to spurious results. Although some motion artifact correction algorithms have been proposed in the literature, their development and accurate evaluation have been challenged by the lack of ground truth information. This is because ground truth information is time- and labor-intensive to manually annotate. This work investigates the feasibility and reliability of a deep learning computer vision (CV) approach for automated detection and annotation of head movements from video recordings. Fifteen participants performed controlled head movements across three main rotational axes (head up/down, head left/right, bend left/right) at two speeds (fast and slow), and in different ways (half, complete, repeated movement). Sessions were video recorded and head movement information was obtained using a CV approach. A 1-dimensional UNet model (1D-UNet) that detects head movements from head orientation signals extracted via a pre-trained model (SynergyNet) was implemented. Movements were manually annotated as a ground truth for model evaluation. The model's performance was evaluated using the Jaccard index. The model showed comparable performance between the training and test sets (J train = 0.954; J test = 0.865). Moreover, it demonstrated good and consistent performance at annotating movement across movement axes and speeds. However, performance varied by movement type, with the best results being obtained for repeated (J test = 0.941), followed by complete (J test = 0.872), and then half movements (J test = 0.826). This study suggests that the proposed CV approach provides accurate ground truth movement information. Future research can rely on this CV approach to evaluate and improve fNIRS motion artifact correction algorithms.

Comparing different motion correction approaches for resting-state functional connectivity analysis with functional near-infrared spectroscopy data

Significance

Motion artifacts are a notorious challenge in the functional near-infrared spectroscopy (fNIRS) field. However, little is known about how to deal with them in resting-state data.

Aim

We assessed the impact of motion artifact correction approaches on assessing functional connectivity, using semi-simulated datasets with different percentages and types of motion artifact contamination.

Approach

Thirty-five healthy adults underwent a 15-min resting-state acquisition. Semi-simulated datasets were generated by adding spike-like and/or baseline-shift motion artifacts to the real dataset. Fifteen pipelines, employing various correction approaches, were applied to each dataset, and the group correlation matrix was computed. Three metrics were used to test the performance of each approach.

Results

When motion artifact contamination was low, various correction approaches were effective. However, with increased contamination, only a few pipelines were reliable. For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing was optimal. Conversely, when both spike and baseline-shift artifacts were present, discarding contaminated frames before pre-processing yielded the best results.

Conclusions

This study emphasizes the need for customized motion correction approaches as the effectiveness varies with the specific type and amount of motion artifacts present.

Early childhood malnutrition impairs adult resting brain function using near-infrared spectroscopy

Introduction

Early childhood malnutrition affects 200+ million children under 5 years of age worldwide and is associated with persistent cognitive, behavioral and psychiatric impairments in adulthood. However, very few studies have investigated the long-term effects of childhood protein-energy malnutrition (PEM) on brain function using a functional hemodynamic brain imaging technique.

Objective and methods

This study aims to investigate functional brain network alterations using near infrared spectroscopy (NIRS) in adults, aged 45-51 years, from the Barbados Nutrition Study (BNS) who suffered from a single episode of malnutrition restricted to their first year of life (n = 26) and controls (n = 29). A total of 55 individuals from the BNS cohort underwent NIRS recording at rest.

Results and discussion

Using functional connectivity and permutation analysis, we found patterns of increased Pearson's correlation with a specific vulnerability of the frontal cortex in the PEM group (ps < 0.05). Using a graph theoretical approach, mixed ANCOVAs showed increased segregation (ps = 0.0303 and 0.0441) and decreased integration (p = 0.0498) in previously malnourished participants compared to healthy controls. These results can be interpreted as a compensatory mechanism to preserve cognitive functions, that could also be related to premature or pathological brain aging. To our knowledge, this study is the first NIRS neuroimaging study revealing brain function alterations in middle adulthood following early childhood malnutrition limited to the first year of life.

Investigating the Effect of Data Length on the Performance of Frequency-Domain fNIRS Functional Connectivity Measures

Resting-state functional connectivity is a promising tool for understanding and characterizing brain network architecture. However, obtaining uninterrupted long recording of resting-state data is challenging in many clinically relevant populations. Moreover, the interpretation of connectivity results may heavily depend on the data length and functional connectivity measure used. We compared the performance of three frequency-domain connectivity measures: magnitude-squared, wavelet and multitaper coherence; and the effect of data length ranging from 3 to 9 minutes. Performance was characterized by distinguishing two groups of channel pairs with known different connectivity strengths. While all methods considered improved the ability to distinguish the two groups with increasing data lengths, wavelet coherence performed best for the shortest time window of 3 minutes. Knowledge of which measure is more reliably used when shorter fNIRS recordings are available could make the utility of functional connectivity biomarkers more feasible in clinical populations of interest.

Age-dependent neurovascular coupling characteristics in children and adults during general anesthesia

General anesthesia is an indispensable procedure in clinical practice. Anesthetic drugs induce dramatic changes in neuronal activity and cerebral metabolism. However, the age-related changes in neurophysiology and hemodynamics during general anesthesia remain unclear. Therefore, the objective of this study was to explore the neurovascular coupling between neurophysiology and hemodynamics in children and adults during general anesthesia. We analyzed frontal electroencephalogram (EEG) and functional near-infrared spectroscopy (fNIRS) signals recorded from children (6-12 years old, n = 17) and adults (18-60 years old, n = 25) during propofol-induced and sevoflurane-maintained general anesthesia. The neurovascular coupling was evaluated in wakefulness, maintenance of a surgical state of anesthesia (MOSSA), and recovery by using correlation, coherence and Granger-causality (GC) between the EEG indices [EEG power in different bands and permutation entropy (PE)], and hemodynamic responses the oxyhemoglobin (Δ[HbO]) and deoxy-hemoglobin (Δ[Hb]) from fNIRS in the frequency band in 0.01-0.1 Hz. The PE and Δ[Hb] performed well in distinguishing the anesthesia state (p > 0.001). The correlation between PE and Δ[Hb] was higher than those of other indices in the two age groups. The coherence significantly increased during MOSSA (p < 0.05) compared with wakefulness, and the coherences between theta, alpha and gamma, and hemodynamic activities of children are significantly stronger than that of adults' bands. The GC from neuronal activities to hemodynamic responses decreased during MOSSA, and can better distinguish anesthesia state in adults. Propofol-induced and sevoflurane-maintained combination exhibited age-dependent neuronal activities, hemodynamics, and neurovascular coupling, which suggests the need for separate rules for children's and adults' brain states monitoring during general anesthesia.

Self-administered transcranial direct current stimulation treatment of knee osteoarthritis alters pain-related fNIRS connectivity networks

Significance

Knee osteoarthritis (OA) is a disease that causes chronic pain in the elderly population. Currently, OA is mainly treated pharmacologically with analgesics, although research has shown that neuromodulation via transcranial direct current stimulation (tDCS) may be beneficial in reducing pain in clinical settings. However, no studies have reported the effects of home-based self-administered tDCS on functional brain networks in older adults with knee OA.

Aim

We used functional near-infrared spectroscopy (fNIRS) to investigate the functional connectivity effects of tDCS on underlying pain processing mechanisms at the central nervous level in older adults with knee OA.

Approach

Pain-related brain connectivity networks were extracted using fNIRS at baseline and for three consecutive weeks of treatment from 120 subjects randomly assigned to two groups undergoing active tDCS and sham tDCS.

Results

Our results showed that the tDCS intervention significantly modulated pain-related connectivity correlation only in the group receiving active treatment. We also found that only the active treatment group showed a significantly reduced number and strength of functional connections evoked during nociception in the prefrontal cortex, primary motor (M1), and primary somatosensory (S1) cortices. To our knowledge, this is the first study in which the effect of tDCS on pain-related connectivity networks is investigated using fNIRS.

Conclusions

fNIRS-based functional connectivity can be effectively used to investigate neural circuits of pain at the cortical level in association with nonpharmacological, self-administered tDCS treatment.