Neurophotonics最新文献

Significance: Microcirculation and neurovascular coupling are important parameters to study in neurological and neuro-ophthalmic conditions. As the retina shares many similarities with the cerebral cortex and is optically accessible, a special focus is directed to assessing the chorioretinal structure, microvasculature, and hemodynamics of mice, a vital animal model for vision and neuroscience research.

Aim: We aim to introduce an optical imaging tool enabling in vivo volumetric mouse retinal monitoring of vascular hemodynamics with high temporal resolution.

Approach: We translated the spatio-temporal optical coherence tomography (STOC-T) technique into the field of small animal imaging by designing a new optical system that could compensate for the mouse eye refractive error. We also developed post-processing algorithms, notably for the assessment of (i) localized hemodynamics from the analysis of pulse wave-induced Doppler artifact modulation and (ii) retinal tissue displacement from phase-sensitive measurements.

Results: We acquired high-quality, in vivo volumetric mouse retina images at a rate of 113 Hz over a lateral field of view of $\sim 500\mu m$ . We presented high-resolution en face images of the retinal and choroidal structure and microvasculature from various layers, after digital aberration correction. We were able to measure the pulse wave velocity in capillaries of the outer plexiform layer with a mean speed of 0.35 mm/s and identified venous and arterial pulsation frequency and phase delay. We quantified the modulation amplitudes of tissue displacement near major vessels (with peaks of 150 nm), potentially carrying information about the biomechanical properties of the retinal layers involved. Last, we identified the delays between retinal displacements due to the passing of venous and arterial pulse waves.

Conclusions: The developed STOC-T system provides insights into the hemodynamics of the mouse retina and choroid that could be beneficial in the study of neurovascular coupling and vasculature and flow speed anomalies in neurological and neuro-ophthalmic conditions.

Significance: A shortcoming of the routine clinical use of diffuse optics (DO) in the injured head has been that the results from commercial near-infrared spectroscopy-based devices are not reproducible, often give physiologically invalid values, and differ among systems. Besides the limitations due to the physics of continuous-wave light sources, one culprit is the head heterogeneity and the underlying morphological and functional abnormalities of the probed tissue.

Aim: The aim is to investigate the effect that different tissue alterations in the damaged head have on DO signals and provide guidelines to avoid data misinterpretation.

Approach: DO measurements and computed tomography scans were acquired on brain-injured patients. The relationship between the signals and the underlying tissue types was classified on a case-by-case basis.

Results: Examples and suggestions to establish quality control routines were provided. The findings suggested guidelines for carrying out DO measurements and speculations toward improved devices.

Conclusions: We advocate for the standardization of the DO measurements to secure a role for DO in neurocritical care. We suggest that blind measurements are unacceptably problematic due to confounding effects and care using a priori and a posteriori quality control routines that go beyond an assessment of the signal-to-noise ratio that is typically utilized.

Significance: Functional near-infrared spectroscopy (fNIRS) has been widely used to assess brain functional networks due to its superior ecological validity. Generally, fNIRS signals are sensitive to motion artifacts (MA), which can be removed by various MA correction algorithms. Yet, fNIRS signals may also undergo varying degrees of distortion due to MA correction, leading to notable alternation in functional connectivity (FC) analysis results.

Aim: We aimed to investigate the effect of different MA correction algorithms on the performance of brain FC and topology analyses.

Approach: We evaluated various MA correction algorithms on simulated and experimental datasets, including principal component analysis, spline interpolation, correlation-based signal improvement, Kalman filtering, wavelet filtering, and temporal derivative distribution repair (TDDR). The mean FC of each pre-defined network, receiver operating characteristic (ROC), and graph theory metrics were investigated to assess the performance of different algorithms.

Results: Although most algorithms did not differ significantly from each other, the TDDR and wavelet filtering turned out to be the most effective methods for FC and topological analysis, as evidenced by their superior denoising ability, the best ROC, and an enhanced ability to recover the original FC pattern.

Conclusions: The findings of our study elucidate the varying impact of MA correction algorithms on brain FC analysis, which could serve as a reference for choosing the most appropriate method for future FC research. As guidance, we recommend using TDDR or wavelet filtering to minimize the impact of MA correction in brain network analysis.

Significance: The ability to monitor cerebral blood flow (CBF) at the bedside is essential to managing critical-care patients with neurological emergencies. Diffuse correlation spectroscopy (DCS) is ideal because it is non-invasive, portable, and inexpensive. We investigated a near-infrared spectroscopy (NIRS) approach for converting DCS measurements into physiological units of blood flow.

Aim: Using magnetic resonance imaging perfusion as a reference, we investigated the accuracy of absolute CBF measurements from a bolus-tracking NIRS method that used transient hypoxia as a flow tracer and hypercapnia-induced increases in CBF measured by DCS.

Approach: Twelve participants (7 female, $28\pm 6$ years) completed a hypercapnia protocol with simultaneous CBF recordings from DCS and arterial spin labeling (ASL). Nine participants completed the transient hypoxia protocol while instrumented with time-resolved NIRS. The estimate of baseline CBF was subsequently used to calibrate hypercapnic DCS data.

Results: Moderately strong correlations at baseline ( $\text{slope}=0.79$ and $R^2=0.59$ ) and during hypercapnia ( $\text{slope}=0.90$ and $R^2=0.58$ ) were found between CBF values from calibrated DCS and ASL (range 34 to $85\mathrm{mL}/100g/\min$ ).

Conclusions: Results demonstrated the feasibility of an all-optics approach that can both quantify CBF and perform continuous perfusion monitoring.

Significance: Anemia is a common problem in preterm neonates, and red blood cell transfusion (RBCT) is used to improve oxygen delivery. However, RBCT is associated with complications, although an increase in cerebral oxygenation has been documented, and no universally accepted biomarker for the need for transfusion (i.e., the concentration of hemoglobin in the blood) has been defined.

Aim: We used a hybrid optical device (BabyLux device) that merges time-domain near-infrared spectroscopy (TD-NIRS) and diffuse correlation spectroscopy (DCS) to potentially obtain a better assessment of the cerebral effects of RBCT compared with previous studies using continuous wave (CW) spatially resolved NIRS.

Approach: Eighteen clinically stable preterm neonates were assessed before and after RBCT by the BabyLux device as five repetitions of 60 s measurement (with 1 s acquisition time), estimating the cerebral blood flow (CBF) as a blood flow index (BFI), the total hemoglobin concentration (tHb), and the cerebral tissue oxygen saturation ( $S_t{O}_2$ ). $S_t{O}_2$ was also continuously monitored by a commercial CW-NIRS device, as well as peripheral saturation, $S_p{O}_2$ . Tissue oxygen extraction (TOE) and cerebral metabolic rate of oxygen consumption ( ${\mathrm{tCMRO}}_2$ ) were computed, and the Wilcoxon signed-rank test for paired data was performed, comparing the data acquired before and after RBCT.

Results: The BabyLux data from four neonates did not meet quality criteria and were discarded. After the transfusion, tHb and $S_t{O}_2$ (measured both with TD-NIRS and CW-NIRS devices) significantly increased, causing a significant decrease in TOE. CW-NIRS showed a wider dispersion of $S_t{O}_2$ data compared with TD-NIRS. However, CBF did not decrease proportionally but the variation was high, as well as for ${\mathrm{tCMRO}}_2$ .

Conclusions: The results confirm previous CW-NIRS studies, but the wide variability of BFI makes the effects of RBCT on cerebral metabolism uncertain.

Significance: Assessment of pain and its clinical diagnosis rely on subjective methods which become even more complicated under analgesic drug administrations.

Aim: We aim to propose a deep learning (DL)-based transfer learning (TL) methodology for objective classification of functional near-infrared spectroscopy (fNIRS)-derived cortical oxygenated hemoglobin responses to painful and non-painful stimuli presented under different timings post-analgesic and placebo drug administration.

Approach: A publicly available fNIRS dataset obtained during painful/non-painful stimuli was used. Separate fNIRS scans were taken under the same protocol before drug (morphine and placebo) administration and at three different timings (30, 60, and 90 min) post-administration. Data from pre-drug fNIRS scans were utilized for constructing a base DL model. Knowledge generated from the pre-drug model was transferred to six distinct post-drug conditions by following a TL approach. The DeepSHAP method was utilized to unveil the contribution weights of nine regions of interest for each of the pre-drug and post-drug decoding models.

Results: Accuracy, sensitivity, specificity, and area under curve (AUC) metrics of the pre-drug model were above 90%, whereas each of the post-drug models demonstrated a performance above 90% for the same metrics. Post-placebo models had higher decoding accuracy than post-morphine models. Knowledge obtained from a pre-drug base model could be successfully utilized to build pain decoding models for six distinct brain states that were scanned at three different timings after either analgesic or placebo drug administration. The contribution of different cortical regions to classification performance varied across the post-drug models.

Conclusions: The proposed DL-based TL methodology may remove the necessity to build DL models for data collected at clinical or daily life conditions for which obtaining training data is not practical or building a new decoding model will have a computational cost. Unveiling the explanation power of different cortical regions may aid the design of more computationally efficient fNIRS-based brain-computer interface (BCI) system designs that target other application areas.

Significance: Voltage imaging is a powerful tool for studying the dynamics of neuronal activities in the brain. However, voltage imaging data are fundamentally corrupted by severe Poisson noise in the low-photon regime, which hinders the accurate extraction of neuronal activities. Self-supervised deep learning denoising methods have shown great potential in addressing the challenges in low-photon voltage imaging without the need for ground-truth but usually suffer from the trade-off between spatial and temporal performances.

Aim: We present DeepVID v2, a self-supervised denoising framework with decoupled spatial and temporal enhancement capability to significantly augment low-photon voltage imaging.

Approach: DeepVID v2 is built on our original DeepVID framework, which performs frame-based denoising by utilizing a sequence of frames around the central frame targeted for denoising to leverage temporal information and ensure consistency. Similar to DeepVID, the network further integrates multiple blind pixels in the central frame to enrich the learning of local spatial information. In addition, DeepVID v2 introduces a new spatial prior extraction branch to capture fine structural details to learn high spatial resolution information. Two variants of DeepVID v2 are introduced to meet specific denoising needs: an online version tailored for real-time inference with a limited number of frames and an offline version designed to leverage the full dataset, achieving optimal temporal and spatial performances.

Results: We demonstrate that DeepVID v2 is able to overcome the trade-off between spatial and temporal performances and achieve superior denoising capability in resolving both high-resolution spatial structures and rapid temporal neuronal activities. We further show that DeepVID v2 can generalize to different imaging conditions, including time-series measurements with various signal-to-noise ratios and extreme low-photon conditions.

Conclusions: Our results underscore DeepVID v2 as a promising tool for enhancing voltage imaging. This framework has the potential to generalize to other low-photon imaging modalities and greatly facilitate the study of neuronal activities in the brain.

Significance: Functional near-infrared spectroscopy (fNIRS) is a promising alternative to functional magnetic resonance imaging for measuring brain activity, but the presence of hair reduces data quality.

Aim: To improve research efficiency and promote wider subject inclusivity, we developed the "Mini Comb"-an attachment for commercial fNIRS head caps that can rapidly clear hair with a simple twisting motion.

Approach: To test the utility of the Mini Comb on different hair types, we measured the clearance achieved with eight unique sliding leg extrusions on 10 wigged mannequins of various hair characteristics. Following mannequin testing, we recruited a total of 15 participants to evaluate the performance of the Mini Comb as pertains to the time needed to create clearance and the signal quality captured.

Results: The Mini Comb achieves comparable signal-to-noise ratios (SNRs) as standard hair clearance procedures while reducing hair clearance time by nearly 50%. Importantly, group analysis revealed better SNR results when the Mini Comb sliding leg design is matched to hair type, suggesting that consideration of hair type is important when conducting fNIRS studies.

Conclusions: The Mini Comb thus opens the door for the deployment of fNIRS for more widespread, inclusive, and comprehensive neuroimaging studies.

The editorial discusses the practice of peer review for SPIE Neurophotonics.

The brain enables adaptive behavior via the dynamic coordination of diverse neuronal signals across spatial and temporal scales: from fast action potential patterns in microcircuits to slower patterns of distributed activity in brain-wide networks. Understanding principles of multiscale dynamics requires simultaneous monitoring of signals in multiple, distributed network nodes. Combining optical and electrical recordings of brain activity is promising for collecting data across multiple scales and can reveal aspects of coordinated dynamics invisible to standard, single-modality approaches. We review recent progress in combining opto- and electrophysiology, focusing on mouse studies that shed new light on the function of single neurons by embedding their activity in the context of brain-wide activity patterns. Optical and electrical readouts can be tailored to desired scales to tackle specific questions. For example, fast dynamics in single cells or local populations recorded with multi-electrode arrays can be related to simultaneously acquired optical signals that report activity in specified subpopulations of neurons, in non-neuronal cells, or in neuromodulatory pathways. Conversely, two-photon imaging can be used to densely monitor activity in local circuits while sampling electrical activity in distant brain areas at the same time. The refinement of combined approaches will continue to reveal previously inaccessible and under-appreciated aspects of coordinated brain activity.