Neurophotonics最新文献

Significance: In vivo one-photon fluorescence imaging of calcium and voltage indicators expressed in neurons enables noninvasive recordings of neural activity with submillisecond precision. However, data acquisition speed is limited by the frame rate of cameras.

Aim: We developed a compressive streak fluorescence microscope to record fluorescence in individual neurons at high speeds ( $\ge 200$ frames per second) exceeding the nominal frame rate of the camera by trading off spatial pixels for temporal resolution.

Approach: Our microscope leverages a digital micromirror device for targeted illumination, a galvo mirror for temporal scanning, and a ridge regression algorithm for fast computational reconstruction of fluorescence traces with high temporal resolution.

Results: In simulations, the ridge regression algorithm reconstructs traces of high temporal resolution with limited signal loss. Validation experiments with fluorescent beads and experiments in larval zebrafish demonstrate accurate reconstruction with a data compression ratio of 10 and accurate recordings of neural activity with 200- to 400-Hz sampling speeds.

Conclusions: Our compressive microscopy enables new experimental capabilities to monitor activity at a sampling speed that outpaces the nominal frame rate of the camera.

Significance: The highly scattering nature of near-infrared light in human tissue makes it challenging to collect photons using source-detector separations larger than several centimeters. The limits of detectability of light transmitted through the head remain unknown. Detecting photons in the extreme case through an entire adult head explores the limits of photon transport in the brain.

Aim: We explore the physical limits of photon transport in the head in the extreme case wherein the source and detector are diametrically opposite.

Approach: Simulations uncover possible migration pathways of photons from source to detector. We compare simulations with time-resolved photon counting experiments that measure pulsed light transmitted through the head.

Results: We observe good agreement between the peak delay time and width of the time-correlated histograms in experiments and simulations. Analysis of the photon migration pathways indicates sensitivity to regions of the brain well beyond accepted limits. Source repositioning can isolate sensitivity to targeted regions of the brain, including under the cerebrum.

Conclusions: We overcome attenuation of $\sim {10}^{18}$ and detect photons transmitted through an entire adult human head for a subject with fair skin and no hair. Photons measured in this regime explore regions of the brain currently inaccessible with noninvasive optical brain imaging.

Significance: Fiber photometry is a powerful tool for neuroscience. However, measured biosensor signals are contaminated by various artifacts (photobleaching and movement-related noise) that undermine analysis and interpretation. Currently, no universal pipeline exists to deal with these artifacts.

Aim: We aim to evaluate approaches for obtaining artifact-corrected neural dynamic signals from fiber photometry data and provide recommendations for photometry analysis pipelines.

Approach: Using simulated and real photometry data, we tested the effects of three key analytical decisions: choice of regression for fitting isosbestic control signals onto experimental signals [ordinary least squares (OLS) versus iteratively reweighted least squares (IRLS)], low-pass filtering, and dF/F versus dF calculations.

Results: IRLS surpassed OLS regression for fitting isosbestic control signals to experimental signals. We also demonstrate the efficacy of low-pass filtering signals and baseline normalization via dF/F calculations.

Conclusions: We conclude that artifact-correcting experimental signals via low-pass filter, IRLS regression, and dF/F calculations is a superior approach to commonly used alternatives. We suggest these as a new standard for preprocessing signals across photometry analysis pipelines.

Significance: Diffuse correlation spectroscopy (DCS) allows label-free, non-invasive investigation of microvascular dynamics deep within tissue, such as cerebral blood flow (CBF). However, the signal-to-noise ratio (SNR) in DCS limits its effective cerebral sensitivity in adults, in which the depth to the brain, through the scalp and skull, is substantially larger than in infants.

Aim: Therefore, we aim to increase its SNR and, ultimately, its sensitivity to CBF through new DCS techniques.

Approach: We present an in vivo demonstration of parallelized DCS (PDCS) to measure cerebral and muscular blood flow in healthy adults. Our setup employs an innovative array with hundreds of thousands single photon avalanche diodes (SPAD) in a $500\times 500$ grid to boost SNR by averaging all independent pixel measurements. We tested this device on different total pixel counts and frame rates. A secondary, smaller array was used for reference measurements from shallower tissue at lower source-detector-separation (SDS).

Results: The new system can measure pulsatile blood flow in cerebral and muscular tissue, at up to 4 cm SDS, while maintaining a similar measurement noise as compared with a previously published $32\times 32$ PDCS system at 1.5 cm SDS. Data from a cohort of 15 adults provide strong experimental evidence for functional CBF activity during a cognitive memory task and allowed analysis of pulse markers. Additional control experiments on muscular blood flow in the forearm with a different technical configuration provide converging evidence for the efficacy of this technique.

Conclusions: Our results outline successful PDCS measurements with large SPAD arrays to enable detect CBF in human adults. The ongoing development of SPAD camera technology is expected to result in larger and faster detectors in the future. In combination with new data processing techniques, tailored for the sparse signal of binary photon detection events in SPADs, this could lead to even greater SNR increase and ultimately greater depth sensitivity of PDCS.

Significance: The recently developed two-photon (2P) fiberscope offers attractive opportunities in neuroscience by enabling high-resolution neural imaging in freely behaving rodents. However, like other miniature 2P devices, it involves a tether (for fiber and scanner drive wires), which inevitably limits the animal's movement, especially its rotation.

Aim: We aim to develop a platform for 2P fiberscopes (and other tethered miniature devices), enabling rotational resistance-free neuroimaging in freely rotating/walking rodents.

Approach: We introduced a proactive optoelectrical commutator (pOEC) capable of real-time sensing and compensation for a tiny torque buildup in the tether (with a preselected threshold), preemptively eliminating the rotational resistance when the mouse physically rotates the fiberscope.

Results: Experimental results demonstrated that the pOEC effectively compensates for torque buildup in the fiberscope, thereby maintaining stable 2P imaging performance. In addition, the system minimizes the rotational resistance imposed by the head-mounted tether, enabling near-zero rotational burden during 2P neural imaging in freely behaving mice. Investigations of neural activity further revealed that a considerable proportion of motor cortex neurons exhibited statistically significant changes in their firing patterns when the mouse was restricted by tether-induced rotational resistance or completely immobilized via head fixation.

Conclusions: The results indicated that rotational restriction induced visible impacts on neuronal activities. Our platform offers a promising opportunity for studying dynamic neural circuit functions under nearly natural conditions with minimized impacts by the rotational restriction.

Significance: Compact tools capable of delivering multicolor optogenetic stimulation to deep tissue targets with sufficient span, spatiotemporal resolution, and optical power remain challenging to realize. Here, we demonstrate foundry-fabricated nanophotonic neural probes for blue and red photostimulation and electrophysiological recording, which use a combination of spatial multiplexing and on-shank wavelength demultiplexing to increase the number of on-shank emitters.

Aim: We demonstrate silicon (Si) photonic neural probes with 26 photonic channels and 26 recording sites, which were fabricated on 200-mm diameter wafers at a commercial Si photonics foundry. Each photonic channel consists of an on-shank demultiplexer and separate grating coupler emitters for blue and red light, for a total of 52 emitters.

Approach: We evaluate neural probe functionality through bench measurements and in vivo experiments by photostimulating through 16 of the available 26 emitter pairs.

Results: We report neural probe electrode impedances, optical transmission, and beam profiles. We validated a packaged neural probe in optogenetic experiments with mice sensitive to blue or red photostimulation.

Conclusions: Our foundry-fabricated nanophotonic neural probe demonstrates dense dual-color emitter integration on a single shank for targeted photostimulation. Given its two emission wavelengths, high emitter density, and long site span, this probe will facilitate experiments involving bidirectional circuit manipulations across both shallow and deep structures simultaneously.

Significance: Learning can be context-dependent, with better outcomes under some circumstances than others. Adult functional magnetic resonance imaging studies have shown that learning outcomes vary as a function of participants' brain states-patterns of intrinsic neural activity-prior to the learning task. Whether this is also the case in young infants is currently unknown. We report the first functional near-infrared spectroscopy (fNIRS) study that shows prior brain state-dependent learning in a language task in 6.5-month-old infants. Babies whose functional connectivity was lower in the right hemisphere, but not in the left, during a 2-min period prior to the task learned better a grammatical regularity in an artificial grammar learning task.

Aim: Adult neuroimaging studies have shown that variability in brain states immediately before specific learning tasks is correlated with variability in learning outcomes. Whether the developing infant brain also shows similar state-based learning is currently unknown.

Approach: We have explored whether 6.5-month-old infants' ability to learn artificial grammar was related to their brain state during a 2-min baseline period of rest prior to the grammar task. We have asked if functional connectivity, a global metric of the cortical brain state, as measured by fNIRS, is correlated with learning a non-adjacent regularity in the artificial grammar task.

Results: We have found that the overall level of functional connectivity in the 2-min period immediately prior to the learning experience is negatively correlated with the fNIRS measure of learning in the right hemisphere but not in the left.

Conclusions: We show for the first time that the cortical state of an infant immediately prior to a learning experience determines how well that infant learns and that this can account for some of the variability in learning outcomes.

Significance: Intensity-based two-photon microscopy is a cornerstone of neuroscience research but lacks the ability to measure concentrations, a pivotal task for longitudinal studies and quantitative comparisons. Fluorescence lifetime imaging (FLIM) based on time-correlated single photon counting (TCSPC) can overcome those limits but suffers from "pile-up" distortions at high photon count rates, severely limiting acquisition speed.

Aim: We introduce the "laser period blind time" (LPBT) method to correct pile-up distortions in photon counting electronics, enabling reliable low-cost TCSPC-FLIM at high count rates.

Approach: Using a realistic simulation of the TCSPC data collection, we evaluated the LPBT method's performance in silico. The correction was then implemented on low-cost hardware based on a field programable gate array and validated using in vitro, ex vivo, and in vivo measurements.

Results: The LBPT approach achieves $<3\%$ error in lifetime measurements at count rates more than 10 times higher than traditional limits, allowing robust FLIM imaging of subsecond metabolite dynamics with subcellular resolution.

Conclusions: We enable high-precision, cost-effective FLIM imaging at acquisition speeds comparable with state-of-the-art commercial systems, facilitating the adoption of FLIM in neuroscience and other fields of research needing robust quantitative live imaging solutions.

Significance: A shared understanding of terminology is essential for clear scientific communication and minimizing misconceptions. This is particularly challenging in rapidly expanding, interdisciplinary domains that utilize functional near-infrared spectroscopy (fNIRS), where researchers come from diverse backgrounds and apply their expertise in fields such as engineering, neuroscience, and psychology.

Aim: The fNIRS Glossary Project was established to develop a community-sourced glossary covering key fNIRS terms, including those related to the continuous-wave (CW), frequency-domain (FD), and time-domain (TD) NIRS techniques.

Approach: The glossary was collaboratively developed by a diverse group of 76 fNIRS researchers, representing a wide range of career stages (from PhD students to experts) and disciplines. This collaborative process, structured across five phases, ensured the glossary's depth and comprehensiveness.

Results: The glossary features over 300 terms categorized into six key domains: analysis, experimental design, hardware, neuroscience, mathematics, and physics. It also includes abbreviations, symbols, synonyms, references, alternative definitions, and figures where relevant.

Conclusions: The fNIRS glossary provides a community-sourced resource that facilitates education and effective scientific communication within the fNIRS community and related fields. By lowering barriers to learning and engaging with fNIRS, the glossary is poised to benefit a broad spectrum of researchers, including those with limited access to educational resources.

Significance: Understanding the brain's complex functions requires multimodal approaches that combine data from various neuroimaging techniques. Functional near-infrared spectroscopy (fNIRS) offers valuable insights into hemodynamic responses, complementing other modalities such as electroencephalography (EEG), magnetoencephalography (MEG), and magnetic resonance imaging. However, there is a lack of comprehensive and accessible toolboxes able to integrate fNIRS advanced analyses with other modalities. NIRSTORM addresses this gap by offering a unified platform for multimodal neuroimaging analysis.

Aim: NIRSTORM aims to provide a user-friendly and comprehensive environment for multimodal analysis while supporting the entire fNIRS analysis pipeline, from experiment planning to the reconstruction of hemodynamic fluctuations on the cortex.

Approach: Developed in MATLAB^®, NIRSTORM operates as a Brainstorm plugin, enhancing Brainstorm's capabilities for analyzing fNIRS data. Brainstorm is a widely used, GUI-based software originally designed for statistical analysis and source imaging of EEG and MEG data.

Results: NIRSTORM supports conventional fNIRS preprocessing and statistical analyses while introducing new advanced features such as optimal montage for planning optode placement and maximum entropy on the mean (MEM) for reconstructing hemodynamic fluctuations on the cortical surface.

Conclusion: As an open-access and user-friendly plugin, NIRSTORM extends Brainstorm's functionality to fNIRS, bridging the gap between EEG/MEG and hemodynamic analyses.