Neurophotonics Editor in Chief Anna Devor reflects on need to cherish and cultivate diversity within the global neuroscience and neurophotonics community by creating inclusive environments to welcome young students in sharing the joy of science.

Significance: Stimulated emission depletion (STED) microscopy enables nanoscale imaging of live samples, but it requires a specific spatial beam shaping that is highly sensitive to optical aberrations, limiting its depth penetration. Therefore, there is a need for methods to reduce optical aberrations and improve the spatial resolution of STED microscopy inside thick biological tissue.

Aim: The aim of our work was to develop and validate a method based on adaptive optics to achieve an a priori correction of spherical aberrations as a function of imaging depth.

Approach: We first measured the aberrations in a phantom sample of gold and fluorescent nanoparticles suspended in an agarose gel with a refractive index closely matching living brain tissue. We then used a spatial light modulator to apply corrective phase shifts and validate this calibration approach by imaging neurons in living brain slices.

Results: After quantifying the spatial resolution in depth in phantom samples, we demonstrated that the corrections can substantially increase image quality in living brain slices. Specifically, we could measure structures as small as 80 nm at a depth of 90  μm inside the biological tissue and obtain a 60% signal increase after correction.

Conclusion: We propose a simple and robust approach to calibrate and compensate the distortions of the STED beam profile introduced by spherical aberrations with increasing imaging depth and demonstrated that this method offers significant improvements in microscopy performance for nanoscale cellular imaging in live tissue.

Significance: As one part of the central nervous system, the retina manifests neurovascular defects in Alzheimer’s disease (AD). Quantitative imaging of retinal neurovascular abnormalities may promise a new method for early diagnosis and treatment assessment of AD. Previous imaging studies of transgenic AD mouse models have been limited to the central part of the retina. Given that the pathological hallmarks of AD frequently appear in different peripheral quadrants, a comprehensive regional investigation is needed for a better understanding of the retinal degeneration associated with AD-like pathology.

Aim: We aim to demonstrate concurrent optical coherence tomography (OCT) and OCT angiography (OCTA) of retinal neuronal and vascular abnormalities in the 5XFAD mouse model and to investigate region-specific retinal degeneration.

Approach: A custom-built OCT system was used for retinal imaging. Retinal thickness, vessel width, and vessel density were quantitatively measured. The artery and vein (AV) were classified for differential AV analysis, and trilaminar vascular plexuses were segmented for depth-resolved density measurement.

Results: It was observed that inner and outer retinal thicknesses were explicitly reduced in the dorsal and temporal quadrants, respectively, in 5XFAD mice. A significant arterial narrowing in 5XFAD mice was also observed. Moreover, overall capillary density consistently showed a decreasing trend in 5XFAD mice, but regional specificity was not identified.

Conclusions: Quadrant- and layer-specific neurovascular degeneration was observed in 5XFAD mice. Concurrent OCT and OCTA promise a noninvasive method for quantitative monitoring of AD progression and treatment assessment.

Significance: Diffuse optical tomography (DOT) uses near-infrared light spectroscopy (NIRS) to measure changes in cerebral hemoglobin concentration. Anatomical interpretations of NIRS data require accurate descriptions of the cranio-cerebral relations and DOT sensitivity to the underlying cortical structures. Such information is limited for pediatric populations because they undergo rapid head and brain development.

Aim: We aim to investigate age-related differences in scalp-to-cortex distance and mapping between scalp locations and cortical regions of interest (ROIs) among infants (2 weeks to 24 months with narrow age bins), children (4 and 12 years), and adults (20 to 24 years).

Approach: We used spatial scalp projection and photon propagation simulation methods with age-matched realistic head models based on MRIs.

Results: There were age-group differences in the scalp-to-cortex distances in infancy. The developmental increase was magnified in children and adults. There were systematic age-related differences in the probabilistic mappings between scalp locations and cortical ROIs.

Conclusions: Our findings have important implications in the design of sensor placement and making anatomical interpretations in NIRS and fNIRS research. Age-appropriate, realistic head models should be used to provide anatomical guidance for standalone DOT data in infants.

Significance: Diffuse correlation spectroscopy (DCS) measures cerebral blood flow non-invasively. Variations in blood flow can be used to detect neuronal activities, but its peak has a latency of a few seconds, which is slow for real-time monitoring. Neuronal cells also deform during activation, which, in principle, can be utilized to detect neuronal activity on fast timescales (within 100 ms) using DCS.

Aims: We aim to characterize DCS signal variation quantified as the change of the decay time of the speckle intensity autocorrelation function during neuronal activation on both fast (within 100 ms) and slow (100 ms to seconds) timescales.

Approach: We extensively modeled the variations in the DCS signal that are expected to arise from neuronal activation using Monte Carlo simulations, including the impacts of neuronal cell motion, vessel wall dilation, and blood flow changes.

Results: We found that neuronal cell motion induces a DCS signal variation of ∼10^−  5. We also estimated the contrast and number of channels required to detect hemodynamic signals at different time delays.

Conclusions: From this extensive analysis, we do not expect to detect neuronal cell motion using DCS in the near future based on current technology trends. However, multi-channel DCS will be able to detect hemodynamic response with sub-second latency, which is interesting for brain–computer interfaces.

Significance: Time domain diffuse correlation spectroscopy (TD-DCS) can offer increased sensitivity to cerebral hemodynamics and reduced contamination from extracerebral layers by differentiating photons based on their travel time in tissue. We have developed rigorous simulation and evaluation procedures to determine the optimal time gate parameters for monitoring cerebral perfusion considering instrumentation characteristics and realistic measurement noise.

Aim: We simulate TD-DCS cerebral perfusion monitoring performance for different instrument response functions (IRFs) in the presence of realistic experimental noise and evaluate metrics of sensitivity to brain blood flow, signal-to-noise ratio (SNR), and ability to reject the influence of extracerebral blood flow across a variety of time gates to determine optimal operating parameters.

Approach: Light propagation was modeled on an MRI-derived human head geometry using Monte Carlo simulations for 765- and 1064-nm excitation wavelengths. We use a virtual probe with a source–detector separation of 1 cm placed in the pre-frontal region. Performance metrics described above were evaluated to determine optimal time gate(s) for different IRFs. Validation of simulation noise estimates was done with experiments conducted on an intralipid-based liquid phantom.

Results: We find that TD-DCS performance strongly depends on the system IRF. Among Gaussian pulse shapes, ∼300  ps pulse length appears to offer the best performance, at wide gates (500 ps and larger) with start times 400 and 600 ps after the peak of the TPSF at 765 and 1064 nm, respectively, for a 1-s integration time at photon detection rates seen experimentally (600 kcps at 765 nm and 4 Mcps at 1064 nm).

Conclusions: Our work shows that optimal time gates satisfy competing requirements for sufficient sensitivity and sufficient SNR. The achievable performance is further impacted by system IRF with ∼300  ps quasi-Gaussian pulse obtained using electro-optic laser shaping providing the best results.

Significance: The ability of diffuse correlation spectroscopy (DCS) to measure cerebral blood flow (CBF) in humans is hindered by the low signal-to-noise ratio (SNR) of the method. This limits the high acquisition rates needed to resolve dynamic flow changes and to optimally filter out large pulsatile oscillations and prevents the use of large source-detector separations (≥3  cm), which are needed to achieve adequate brain sensitivity in most adult subjects.

Aim: To substantially improve SNR, we have built a DCS device that operates at 1064 nm and uses superconducting nanowire single-photon detectors (SNSPD).

Approach: We compared the performances of the SNSPD-DCS in humans with respect to a typical DCS system operating at 850 nm and using silicon single-photon avalanche diode detectors.

Results: At a 25-mm separation, we detected 13  ±  6 times more photons and achieved an SNR gain of 16  ±  8 on the forehead of 11 subjects using the SNSPD-DCS as compared to typical DCS. At this separation, the SNSPD-DCS is able to detect a clean pulsatile flow signal at 20 Hz in all subjects. With the SNSPD-DCS, we also performed measurements at 35 mm, showing a lower scalp sensitivity of 31  ±  6  %   with respect to the 48  ±  8  %   scalp sensitivity at 25 mm for both the 850 and 1064 nm systems. Furthermore, we demonstrated blood flow responses to breath holding and hyperventilation tasks.

Conclusions: While current commercial SNSPDs are expensive, bulky, and loud, they may allow for more robust measures of non-invasive cerebral perfusion in an intensive care setting.

Significance: The recently reported solvent-based optical clearing method FDISCO can preserve various fluorescent signals very well. However, the strict low-temperature storage condition of FDISCO is not conducive to long-time or repetitive imaging usually conducted at room temperature. Therefore, it is important to solve the contradiction between fluorescence preservation and imaging condition.

Aim: We develop a modified FDISCO clearing method, termed FDISCO+, to change the preservation condition from low temperature to room temperature.

Approach: Two alternative antioxidants were screened out to effectively inhibit the peroxide generation in the clearing agent at room temperature, enabling robust fluorescence preservation of cleared samples.

Results: FDISCO+ achieves comparable fluorescence preservation with the original FDISCO protocol and allows long-time storage at room temperature, making it easier for researchers to image and preserve the samples.

Conclusions: FDISCO+ is expected to be widely used due to its loose operation requirements.

Significance: Clinical use of fNIRS-derived features has always suffered low sensitivity and specificity due to signal contamination from background systemic physiological fluctuations. We provide an algorithm to extract cognition-related features by eliminating the effect of background signal contamination, hence improving the classification accuracy.

Aim: The aim in this study is to investigate the classification accuracy of an fNIRS-derived biomarker based on global efficiency (GE). To this end, fNIRS data were collected during a computerized Stroop task from healthy controls and patients with migraine, obsessive compulsive disorder, and schizophrenia.

Approach: Functional connectivity (FC) maps were computed from [HbO] time series data for neutral (N), congruent (C), and incongruent (I) stimuli using the partial correlation approach. Reconstruction of FC matrices with optimal choice of principal components yielded two independent networks: cognitive mode network (CM) and default mode network (DM).

Results: GE values computed for each FC matrix after applying principal component analysis (PCA) yielded strong statistical significance leading to a higher specificity and accuracy. A new index, neurocognitive ratio (NCR), was computed by multiplying the cognitive quotients (CQ) and ratio of GE of CM to GE of DM. When mean values of NCR (  NCR¯  ) over all stimuli were computed, they showed high sensitivity (100%), specificity (95.5%), and accuracy (96.3%) for all subjects groups.

Conclusions:   NCR¯   can reliable be used as a biomarker to improve the classification of healthy to neuropsychiatric patients.