Neurophotonics最新文献

Multiphoton microscopy (MPM) has become a preferred technique for intravital imaging deep in living tissues with subcellular detail, where resolution and working depths are typically optimized utilizing high numerical aperture, water-immersion objectives with long focusing distances. However, this approach requires the maintenance of water between the specimen and the objective lens, which can be challenging or impossible for many intravital preparations with complex tissues and spatial arrangements. We introduce the novel use of cohesive hyaluronan gel (HG) as an immersion medium that can be used in place of water within existing optical setups to enable multiphoton imaging with equivalent quality and far superior stability. We characterize and compare imaging performance, longevity, and feasibility of preparations in various configurations. This combination of HG with MPM is highly accessible and opens the doors to new intravital imaging applications.

Significance: Neurovascular coupling (NVC) is key to research as hemodynamics can reflect neuronal activation and is often used in studies regarding the resting state network (RSN). However, several circumstances, including diseases that reduce blood vessel elasticity, can diminish NVC. In these cases, hemodynamic proxies might not accurately reflect the neuronal RSN.

Aim: We aim to investigate in resting state if (1) NVC differs over brain regions, (2) NVC remains intact with a mild rigidification of the carotid artery, (3) hemodynamic-based RSN reflects neuronal-based RSN, and (4) RSN differs with a mildly rigidified artery.

Approach: We rigidified the right common carotid artery of mice ( $n=15$ ) by applying a ${\mathrm{CaCl}}_2$ -soaked cloth to it (NaCl for Sham, $n=17$ ). With simultaneous GCaMP and intrinsic optical imaging, we compared neuronal activation to hemodynamic changes over the entire cortex.

Results: NVC parameters did not differ between the CaCl and Sham groups. Likewise, GCaMP and hemodynamic RSN showed similar connections in both groups. However, the parameters of NVC differed over brain regions. Retrosplenial regions had a slower response and a higher HbR peak than sensory and visual regions, and the motor cortex showed less HbO influx than sensory and visual regions.

Conclusions: NVC in a resting state differs over brain regions but is not altered by mild rigidification of the carotid artery.

Significance: Cerebral blood flow (CBF) imaging is crucial for diagnosing cerebrovascular diseases. However, existing large neuroimaging techniques with high cost, low sampling rate, and poor mobility make them unsuitable for continuous and longitudinal CBF monitoring at the bedside.

Aim: We aimed to develop a low-cost, portable, programmable scanning diffuse speckle contrast imaging (PS-DSCI) technology for fast, high-density, and depth-sensitive imaging of CBF in rodents.

Approach: The PS-DSCI employed a programmable digital micromirror device (DMD) for remote line-shaped laser (785 nm) scanning on tissue surface and synchronized a 2D camera for capturing boundary diffuse laser speckle contrasts. New algorithms were developed to address deformations of line-shaped scanning, thus minimizing CBF reconstruction artifacts. The PS-DSCI was examined in head-simulating phantoms and adult mice.

Results: The PS-DSCI enables resolving intralipid particle flow contrasts at different tissue depths. In vivo experiments in adult mice demonstrated the capability of PS-DSCI to image global/regional CBF variations induced by 8% ${\mathrm{CO}}_2$ inhalation and transient carotid artery ligations.

Conclusions: Compared with conventional point scanning, line scanning in PS-DSCI significantly increases spatiotemporal resolution. The high sampling rate of PS-DSCI is crucial for capturing rapid CBF changes while high spatial resolution is important for visualizing brain vasculature.

Significance: Diverse behaviors rely on coordinated activity and multi-regional functional connectivity within astrocyte-neuronal networks. However, current techniques for simultaneously measuring astrocytic and neuronal activities across multiple brain regions during behaviors remain limited.

Aim: We propose a multi-fiber solution that can simultaneously record activities of astrocyte-neuronal networks across multiple regions during behaviors.

Approach: We employed cell-specific dual-color genetically encoded calcium indicators (GECIs) and multi-fiber photometry to simultaneously measure astrocytic and neuronal Ca²⁺ transients across multiple brain regions in freely behaving animals.

Results: Our findings demonstrate that both movements and sensory stimuli induce synchronized and highly correlated Ca²⁺ transients in astrocytes and neurons of freely behaving mice. In addition, we recorded astrocytic and neuronal Ca²⁺ transients from multiple brain regions during mouse behaviors. Our observations reveal heightened synchronization of astrocytic and neuronal Ca²⁺ transients across different brain regions during movements or sensory stimuli, indicating enhanced functional connectivity within brain-wide astrocyte-neuronal networks.

Conclusions: Multi-fiber photometry, combined with cell-specific dual-color GECIs, represents a powerful approach for investigating astrocytic and neuronal activities across different brain regions during behaviors. This technique serves as a versatile tool for analyzing the multi-regional functional connectivity map of astrocyte-neuronal networks associated with specific behaviors.

Significance: The advances and miniaturization in functional near-infrared spectroscopy (fNIRS) instrumentation offer the potential to move the classical laboratory-based cognitive neuroscience investigations into more naturalistic settings. Wearable and mobile fNIRS devices also provide a novel child-friendly means to image functional brain activity in freely moving toddlers and preschoolers. Measuring brain activity in more ecologically valid settings with fNIRS presents additional challenges, such as the increased impact of physiological interferences. One of the most popular methods for minimizing such interferences is to regress out short separation channels from the long separation channels [i.e., superficial signal regression (SSR)]. Although this has been extensively investigated in adults, little is known about the impact of systemic changes on the fNIRS signals recorded in children in either classical or novel naturalistic experiments.

Aim: We aim to investigate if extracerebral physiological changes occur in toddlers and preschoolers and whether SSR can help minimize these interferences.

Approach: We collected fNIRS data from 3- to 7-year-olds during a conventional computerized static task and in a dynamic naturalistic task in an immersive virtual reality (VR) cave automatic virtual environment.

Results: Our results show that superficial signal contamination data are present in young children as in adults. Importantly, we find that SSR helps in improving the localization of functional brain activity, both in the computerized task and, to a larger extent, in the dynamic VR task.

Conclusions: Following these results, we formulate suggestions to advance the field of developmental neuroimaging with fNIRS, particularly in ecological settings.

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.

Significance: The accurate assessment and classification of residual consciousness are crucial for optimizing therapeutic interventions in patients with disorders of consciousness (DOCs). However, there remains an absence of effective and definitive diagnostic methods for DOC in clinical practice.

Aim: The primary objective was to investigate the feasibility of utilizing resting state functional near-infrared spectroscopy (rs-fNIRS) for evaluating residual consciousness. The secondary objective was to explore the distinguishing characteristics that are more effective in differentiating between the unresponsive wakefulness syndrome (UWS) and the minimally conscious state (MCS) and to identify the machine learning model that offers superior classification accuracy.

Approach: We utilized rs-fNIRS to evaluate the residual consciousness in patients with DOC. Specifically, rs-fNIRS was used to construct functional brain networks, and graph theory analysis was conducted to quantify the topological differences within these brain networks between MCS and UWS. After that, two classifiers were used to distinguish MCS from UWS.

Results: The graph theory results showed that the MCS group ( $n=8$ ) exhibited significantly higher global efficiency ( $E_g$ ) and smaller characteristic path length ( $L_p$ ) than the UWS group ( $n=10$ ). The functional connectivity results showed that the correlation within the left occipital cortex (L_OC) was significantly lower in the MCS group than in the UWS group. By using the indicators with significant differences as features for further classification, the accuracy for $K$ -nearest neighbors and linear discriminant analysis classifiers was improved by 0.89 and 0.83, respectively.

Conclusions: The resting state functional connectivity and graph theory analysis based on fNIRS has the potential to enhance the classification accuracy, providing valuable insights into the diagnosis of patients with DOC.

Significance: Reference cerebral near-infrared spectroscopy (NIRS) data on the pediatric population are scarce, and in most cases, only cerebral oxygen saturation ( ${\mathrm{SO}}_2$ ) measured by continuous wave spatially resolved spectroscopy NIRS is reported. Absolute data for baseline optical and hemodynamic parameters are missing.

Aim: We aimed at collecting baseline cerebral optical parameters [absorption coefficient, ${\mu }_a$ ; reduced scattering coefficient, ${\mu }_s^{\text{'}}$ ; differential pathlength factor (DPF)] and hemodynamic parameters [oxy-hemoglobin content ( ${\mathrm{HbO}}_2$ ), deoxyhemoglobin content (HHb), total hemoglobin content (tHB), ${\mathrm{SO}}_2$ ] in a large cohort of pediatric patients. The objectives are to establish reference optical values in this population and evaluate the reproducibility of a commercial time domain (TD) NIRS tissue oximeter.

Approach: TD NIRS measurements were performed in the prefrontal cortex at 686 and 830 nm with a 2.5-cm source-detector distance and 1-Hz acquisition rate. Five independent measurements (after probe replacement) were taken for every subject. TD NIRS data were fitted to a photon diffusion model to estimate the optical parameters. From the absorption coefficients, the hemodynamic parameters were derived by Beer's law. Auxological and physiological information was also collected to explore the potential correlations with NIRS data.

Results: We measured 305 patients in the age range of 2 to 18 years. Absolute values for baseline optical and hemodynamic parameters were shown as a function of age and auxological variables. From the analysis of the repositioning after probe replacement, the time-domain near-infrared spectroscopy device exhibited an average precision (intended as coefficient of variation) of $<5\%$ for ${\mu }_s^{\text{'}}$ , DPF, ${\mathrm{HbO}}_2$ , HHb, and tHb, whereas precision was $<2\%$ for ${\mathrm{SO}}_2$ .

Conclusions: We provided baseline values for optical and hemodynamic parameters in a large cohort of healthy pediatric subjects with good precision, providing a foundation for future investigations into clinically relevant deviations in these parameters.

Significance: The increasing sample sizes and channel densities in functional near-infrared spectroscopy (fNIRS) necessitate precise and scalable identification of signals that do not permit reliable analysis to exclude them. Despite the relevance of detecting these "bad channels," little is known about the behavior of fNIRS detection methods, and the potential of unsupervised and semi-supervised machine learning remains unexplored.

Aim: We developed three novel machine learning-based detectors, unsupervised, semi-supervised, and hybrid NiReject, and compared them with existing approaches.

Approach: We conducted a systematic literature search and demonstrated the influence of bad channel detection. Based on 29,924 signals from two independently rated datasets and a simulated scenario space of diverse phenomena, we evaluated the NiReject models, six of the most established detection methods in fNIRS, and 11 prominent methods from other domains.

Results: Although the results indicated that a lack of proper detection can strongly bias findings, detection methods were reported in only 32% of the included studies. Semi-supervised models, specifically semi-supervised NiReject, outperformed both established thresholding-based and unsupervised detectors. Hybrid NiReject, utilizing a human feedback loop, addressed the practical challenges of semi-supervised methods while maintaining precise detection and low rating effort.

Conclusions: This work contributes toward more automated and reliable fNIRS signal quality control by comprehensively evaluating existing and introducing novel machine learning-based techniques and outlining practical considerations for bad channel detection.

Significance: Extending the photoacoustic microscopy (PAM) into the mid-infrared (MIR) molecular fingerprint region constitutes a promising route toward label-free imaging of biological molecular structures. Realizing this objective requires a high-energy nanosecond MIR laser source. However, existing MIR laser technologies are limited to either low pulse energy or free-space structure that is sensitive to environmental conditions. Fiber lasers are promising technologies for PAM for their potential to offer both high pulse energy and robust performance, which however have not yet been used for PAM because it is still at the infant research stage.

Aim: We aim to employ the emerging gas-filled anti-resonant hollow-core fiber (ARHCF) laser technology for MIR-PAM for the purpose of imaging myelin-rich regions in a mouse brain.

Approach: This laser source is developed with a high-pulse-energy nanosecond laser at $3.4\mu m$ , targeting the main absorption band of myelin sheaths, the primary chemical component of axons in the central nervous system. The laser mechanism relies on two-order gas-induced vibrational stimulated Raman scattering for non-linear wavelength conversion, starting from a 1060-nm pump laser to $3.4\mu m$ through the two-stage gas-filled ARHCFs.

Results: The developed fiber Raman laser was used for the first time for MIR-PAM of mouse brain regions containing structures rich in myelin. The high peak power of $\sim 1.38\mathrm{kW}$ and robust performance of the generated MIR Raman pulse addressed the challenge faced by the commonly used MIR lasers.

Conclusions: We pioneered the potential use of high-energy and nanosecond gas-filled ARHCF laser source to MIR-PAM, with a first attempt to report this kind of fiber laser source for PAM of lipid-rich myelin regions in a mouse brain. We also open up possibilities for expanding into a versatile multiwavelength laser source covering multiple biomarkers and being employed to image other materials such as plastics.