Validation of red blood cell flux and velocity estimations based on optical coherence tomography intensity fluctuations

Near-lifespan longitudinal tracking of brain microvascular morphology, topology, and flow in male mice

In age-related neurodegenerative diseases, pathology often develops slowly across the lifespan. As one example, in diseases such as Alzheimer's, vascular decline is believed to onset decades ahead of symptomology. However, challenges inherent in current microscopic methods make longitudinal tracking of such vascular decline difficult. Here, we describe a suite of methods for measuring brain vascular dynamics and anatomy in mice for over seven months in the same field of view. This approach is enabled by advances in optical coherence tomography (OCT) and image processing algorithms including deep learning. These integrated methods enabled us to simultaneously monitor distinct vascular properties spanning morphology, topology, and function of the microvasculature across all scales: large pial vessels, penetrating cortical vessels, and capillaries. We have demonstrated this technical capability in wild-type and 3xTg male mice. The capability will allow comprehensive and longitudinal study of a broad range of progressive vascular diseases, and normal aging, in key model systems.

Deep Learning and Simulation for the Estimation of Red Blood Cell Flux With Optical Coherence Tomography

We present a deep learning and simulation-based method to measure cortical capillary red blood cell (RBC) flux using Optical Coherence Tomography (OCT). This method is more accurate than the traditional peak-counting method and avoids any user parametrization, such as a threshold choice. We used data that was simultaneously acquired using OCT and two-photon microscopy to uncover the distribution of parameters governing the height, width, and inter-peak time of peaks in OCT intensity associated with the passage of RBCs. This allowed us to simulate thousands of time-series examples for different flux values and signal-to-noise ratios, which we then used to train a 1D convolutional neural network (CNN). The trained CNN enabled robust measurement of RBC flux across the entire network of hundreds of capillaries.

Neurophotonic tools for microscopic measurements and manipulation: status report

Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.

Dissecting the microvascular contributions to diffuse correlation spectroscopy measurements of cerebral hemodynamics using optical coherence tomography angiography

Significance: Diffuse correlation spectroscopy (DCS) is an emerging noninvasive, diffuse optical modality that purportedly enables direct measurements of microvasculature blood flow. Functional optical coherence tomography angiography (OCT-A) can resolve blood flow in vessels as fine as capillaries and thus has the capability to validate key attributes of the DCS signal. Aim: To characterize activity in cortical vasculature within the spatial volume that is probed by DCS and to identify populations of blood vessels that are most representative of the DCS signals. Approach: We performed simultaneous measurements of somatosensory-evoked cerebral blood flow in mice in vivo using both DCS and OCT-A. Results: We resolved sensory-evoked blood flow in the somatosensory cortex with both modalities. Vessels with diameters smaller than 10    μ m featured higher peak flow rates during the initial poststimulus positive increase in flow, whereas larger vessels exhibited considerably larger magnitude of the subsequent undershoot. The simultaneously recorded DCS waveforms correlated most highly with flow in the smallest vessels, yet featured a more prominent undershoot. Conclusions: Our direct, multiscale, multimodal cross-validation measurements of functional blood flow support the assertion that the DCS signal preferentially represents flow in microvasculature. The significantly greater undershoot in DCS, however, suggests a more spatially complex relationship to flow in cortical vasculature during functional activation.