Stroke core revealed by tissue scattering using spatial frequency domain imaging

Spatial nonstationarity of image noise in widefield optical imaging and its effects on cluster-based inference for resting-state functional connectivity

BACKGROUND

Resting-state functional connectivity (RSFC) analysis with widefield optical imaging (WOI) is a potentially powerful tool to develop imaging biomarkers in mouse models of disease before translating them to human neuroimaging with functional magnetic resonance imaging (fMRI). The delineation of such biomarkers depends on rigorous statistical analysis. However, statistical understanding of WOI data is limited. In particular, cluster-based analysis of neuroimaging data depends on assumptions of spatial stationarity (i.e., that the distribution of cluster sizes under the null is equal at all brain locations). Whether actual data deviate from this assumption has not previously been examined in WOI.

NEW METHOD

In this manuscript, we characterize the effects of spatial nonstationarity in WOI RSFC data and adapt a "two-pass" technique from fMRI to correct cluster sizes and mitigate spatial bias, both parametrically and nonparametrically. These methods are tested on multi-institutional data.

RESULTS AND COMPARISON WITH EXISTING METHODS

We find that spatial nonstationarity has a substantial effect on inference in WOI RSFC data with false positives much more likely at some brain regions than others. This pattern of bias varies between imaging systems, contrasts, and mouse ages, all of which could affect experimental reproducibility if not accounted for.

CONCLUSIONS

Both parametric and nonparametric corrections for nonstationarity result in significant improvements in spatial bias. The proposed methods are simple to implement and will improve the robustness of inference in optical neuroimaging data.

Measurements of slow tissue dynamics with short-separation speckle contrast optical spectroscopy

Laser speckle contrast imaging (LSCI) measures 2D maps of cerebral blood flow (CBF) in small animal brains such as mice. The contrast measured in LSCI also includes the static and slow-varying components that contain information about brain tissue dynamics. But these components are less studied as compared to the fast dynamics of CBF. In traditional wide-field LSCI, the contrast measured in the tissue is largely contaminated by neighboring blood vessels, which reduces the sensitivity to these static and slow components. Our goal is to enhance the sensitivity of the contrast to static and slow tissue dynamics and test models to quantify the characteristics of these components. To achieve this, we have developed a short-separation speckle contrast optical spectroscopy (ss-SCOS) system by implementing point illumination and point detection using multi-mode fiber arrays to enhance the static and slow components in speckle contrast measurements as compared to traditional wide-field LSCI (WF-LSCI). We observed larger fractions of the static and slow components when measured in the tissue using ss-SCOS than in traditional LSCI for the same animal and region of interest. We have also established models to obtain the fractions of the static and slow components and quantify the decorrelation time constants of the intensity auto-correlation function for both fast blood flow and slower tissue dynamics. Using ss-SCOS, we demonstrate the variations of fast and slow brain dynamics in animals before and post-stroke, as well as within an hour post-euthanasia. This technique establishes the foundation to measure brain tissue dynamics other than CBF, such as intracellular motility.

High throughput detection of capillary stalling events with Bessel beam two-photon microscopy

Significance

Brief disruptions in capillary flow, commonly referred to as capillary "stalling," have gained interest recently for their potential role in disrupting cerebral blood flow and oxygen delivery. Approaches to studying this phenomenon have been hindered by limited volumetric imaging rates and cumbersome manual analysis. The ability to precisely and efficiently quantify the dynamics of these events will be key in understanding their potential role in stroke and neurodegenerative diseases, such as Alzheimer's disease.

Aim

Our study aimed to demonstrate that the fast volumetric imaging rates offered by Bessel beam two-photon microscopy combined with improved data analysis throughput allows for faster and more precise measurement of capillary stall dynamics.

Results

We found that while our analysis approach was unable to achieve full automation, we were able to cut analysis time in half while also finding stalling events that were missed in traditional blind manual analysis. The resulting data showed that our Bessel beam system was captured more stalling events compared to optical coherence tomography, particularly shorter stalling events. We then compare differences in stall dynamics between a young and old group of mice as well as a demonstrate changes in stalling before and after photothrombotic model of stroke. Finally, we also demonstrate the ability to monitor arteriole dynamics alongside stall dynamics.

Conclusions

Bessel beam two-photon microscopy combined with high throughput analysis is a powerful tool for studying capillary stalling due to its ability to monitor hundreds of capillaries simultaneously at high frame rates.

Neurovascular coupling is preserved in chronic stroke recovery after targeted photothrombosis

Functional neuroimaging, which measures hemodynamic responses to brain activity, has great potential for monitoring recovery in stroke patients and guiding rehabilitation during recovery. However, hemodynamic responses after stroke are almost always altered relative to responses in healthy subjects and it is still unclear if these alterations reflect the underlying brain physiology or if the alterations are purely due to vascular injury. In other words, we do not know the effect of stroke on neurovascular coupling and are therefore limited in our ability to use functional neuroimaging to accurately interpret stroke pathophysiology. To address this challenge, we simultaneously captured neural activity, through fluorescence calcium imaging, and hemodynamics, through intrinsic optical signal imaging, during longitudinal stroke recovery. Our data suggest that neurovascular coupling was preserved in the chronic phase of recovery (2 weeks and 4 weeks post-stoke) and resembled pre-stroke neurovascular coupling. This indicates that functional neuroimaging faithfully represents the underlying neural activity in chronic stroke. Further, neurovascular coupling in the sub-acute phase of stroke recovery was predictive of long-term behavioral outcomes. Stroke also resulted in increases in global brain oscillations, which showed distinct patterns between neural activity and hemodynamics. Increased neural excitability in the contralesional hemisphere was associated with increased contralesional intrahemispheric connectivity. Additionally, sub-acute increases in hemodynamic oscillations were associated with improved sensorimotor outcomes. Collectively, these results support the use of hemodynamic measures of brain activity post-stroke for predicting functional and behavioral outcomes.