A deep learning pipeline for mapping in situ network-level neurovascular coupling in multi-photon fluorescence microscopy

abstract

Functional hyperemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the activity of the surrounding neurons.

Although functional hyperemia has been extensively studied at the level of both tissue and individual vessels, vascular network-level coordination remains largely unknown.

To bridge this gap, we developed a deep learning-based pipeline that uses two-photon fluorescence microscopy images of cerebral microcirculation to enable automated reconstruction and quantification of the geometric changes across the microvascular network, comprising hundreds of interconnected blood vessels, pre and post-activation of the neighboring neurons.

The pipeline’s utility was demonstrated in the Thy1-ChR2 optogenetic mouse model, where we observed network-wide vessel radius changes to depend on the photostimulation intensity, with both dilations and constrictions occurring across the cortical depth, at an average of 16.1±14.3 μm (mean ± SD) away from the most proximal neuron for dilations; and at 21.9±14.6 μm away for constrictions.

We observed a significant heterogeneity of the vascular radius changes within vessels, with radius adjustment varying by an average of 24 ± 28% of the resting diameter, likely reflecting the heterogeneity of the distribution of contractile cells on the vessel walls.

A graph theory-based network analysis revealed that the assortativity of adjacent blood vessel responses rose by 152 ± 65% at 4.3 mW/mm 2 of blue photostimulation vs . the control, with a 4% median increase in the efficiency of the capillary networks during this level of blue photostimulation in relation to the baseline.

Interrogating individual vessels is thus not sufficient to predict how the blood flow is modulated in the network.

Our pipeline, enables tracking of the microvascular network geometry over time, relating caliber adjustments to vessel wall-associated cells’ state, and mapping network-level flow distribution impairments in experimental models of disease.

introduction

Introduction To support healthy brain functioning, the cerebrovascular network undergoes continual adjustments in vessel calibers ( Iadecola, 2017 ; Kisler et al., 2017 ; Kenney et al., 2016 ).

Neurovascular coupling refers to the change in blood flow following changes in the level of neuronal activity: under physiological conditions, a generous buffer of nutrients is granted to activated parenchyma via the capillary network ( Iadecola, 2017 ; Phillips et al., 2016 ).

This buffer is maintained through finely tuned regulation of flow through changes in the vessel caliber, mediated via contractile cells in the vessel walls.

In the absence of such tuning, pockets of tissue could experience inadequate access to metabolites ( Secomb et al., 2000 ).

Alterations in smooth muscle cells, pericytes, and astrocytes may lead to compromises in vessels’ dilatory capacity and thus deficits in neurovascular coupling ( Hartmann et al., 2021 ; Hall et al., 2014 ; Mester et al., 2021 ; Adams et al., 2018 ).

In various brain pathologies, including Alzheimer’s disease, stroke, and trauma, regional blood flow regulation gets impaired through vessel loss and/or dysfunction of the vessels’ dilatory capacity, resulting in regions of ischemia/hypoxia ( Carroll et al., 2020 ; Yang et al., 2022 ).

Previous studies have examined either individual vessels or the tissue level responses, with little attention having been paid to the vascular network, though network dysfunction frequently is associated with accelerated disease progression and long-term symptomatology ( Kenney et al., 2016 ; Mayer et al., 2011 ; Park et al., 2009 ; Petkus et al., 2016 ; Ramos-Cejudo et al., 2018 ; Franzmeier et al., 2019 ; Rabi, 2019 ; Boehme et al., 2021 ; Koliatsos and Rao, 2020 ; Ware et al., 2020 ).

While there is copious data on the functioning of individual vessels, interrogation of the microvascular network remains a challenge, in terms of both data acquisition and analysis ( Hartmann et al., 2021 ; Hall et al., 2014 ; Hill et al., 2015 ; Lindvere et al., 2010 ; O’Herron et al., 2022 ).

To date, studies on the brain vasculature have been done by sparsely imaging individual blood vessels at the cellular scale ( Hartmann et al., 2021 ; Hall et al., 2014 ; Hill et al., 2015 ; Alarcon-Martinez et al., 2020 ; Sakadžić et al., 2011 ; Guo et al., 2023 ; Giblin et al., 2023 ; McDowell et al., 2021 ; Kim et al., 2023 ; Mester et al., 2019 ; Kim et al., 2012 ; Kleinfeld et al., 1998 ), thereby severely undersampling the microvascular network; or by evaluating the averaged flow over many vessels at the mesoscopic scale, thus failing to discern the flow through individual vessels.

A critical gap in the field is the characterization of flow across hundreds of individual vessels, while imaging the network structure that links them together to determine how the vascular response is coordinated across the network.

This gap is particularly significant as studies investigating blood flow across several vessels at a time (imaged by varying the line acquisition pattern Hartmann et al., 2021 ; Alarcon-Martinez et al., 2020 ) have shown highly heterogeneous responses among capillaries.

Neuronal function impairments arise wherever local metabolite supply becomes inadequate, notwithstanding the physiological level of flow across the network as a whole, making mapping of vessel changes across the network of particular importance.

To address the limitations of previous work, we developed a novel deep learning (DL)-based pipeline for mapping changes to the geometry of the brain vascular network following neuronal activation, from a time series of volumetric two-photon fluorescence microscopy (2PFM) data.

Neuronal activation was elicited by photostimulation of pyramidal neurons expressing Channelrhodopsin-2 (ChR2; Boyden et al., 2005 ) in the Thy1-ChR2-YFP mouse model ( Arenkiel et al., 2007 ).

Our DL pipeline enabled automatic and accurate segmentation, registration, and network analysis of large 2PFM datasets across time.

We applied our pipeline in a dataset of 17 Thy1-ChR2-YFP mice to map photostimulation-induced changes across the microvascular network - at the level of individual vessels and at the level of vertices spaced every micrometer along the vessels - in relation to the distance to the closest pyramidal neurons expressing the optogenetic actuator and across the cortical depth.

Our findings demonstrate the utility of our pipeline for studying in situ microvascular morphology and function to address various neuroscientific hypotheses.

results

Results Application of our computational pipeline resulted in robust segmentation of the vasculature and neurons from 4D in situ 2PFM images and rendering of the microvasculature as a graph.

The vessel-wise and vertex-wise calibers were tracked across stimulation conditions and related to the cortical depth and the distance to the closest YFP-expressing neuron, mapping the network-level vascular responses to ChR2 activation and revealing the coordination of the microvascular responses following neuronal activation.

Segmentation model results and comparisons We compared an ensemble of UNETR models, an ensemble of U-Net models, and an ilastik random forest model on a test dataset of nine images (507x507x250 μm each) from six mice.

Examples of segmentation masks produced by each of the models are shown in Figure 3 and Appendix 1—figure 4 .⟦>zach claim=73b97665-ce32-4361-81d0-40fb149cea2b: @{Examples of segmentation masks produced by each of the models are shown in Figure 3 and Appendix 1—figure 4 .} novas3d-outperforms-ilastik⟧

When evaluating model performance, we paid close attention to the smoothness of the surface of the segmentation masks due to the sensitivity of the centerline extraction algorithms to irregularities in the surface of the masks: smoother vascular segmentation masks resulted in fewer falsely identified vessel branches.

Ilastik tended to over-segment vessels, that is the model returned numerous false positives, having a high recall (0.89±0.19) but low precision (0.37±0.33; Figure 4 , Supplementary file 3, table 3 ).⟦>zach claim=73b97665-ce32-4361-81d0-40fb149cea2b: @{Ilastik tended to over-segment vessels, that is the model returned numerous false positives, having a high recall (0.89±0.19) but low precision (0.37±0.33; Figure 4 , Supplementary file 3, table 3 ).} novas3d-outperforms-ilastik⟧

When comparing the UNETR and U-Net models, we focused on the surface-based mean surface distance and HD95 distance metrics.

Since we observed no significant differences in these metrics between the two models, we selected the UNETR model as our final model because it produced more consistent segmentations on visual inspection and showed significantly better performance than ilastik on HD95 for both vessel and neuron segmentation (p < 0.05).⟦>zach claim=db0a9448-8fe8-4336-b220-0a71aa19d870: @{Since we observed no significant differences in these metrics between the two models, we selected the UNETR model as our final model because it produced more consistent segmentations on visual inspection and showed significantly better performance than ilastik on HD95 for both vessel and neuron segmentation (p < 0.05).} unetr-outperforms-ilastik-hd95⟧

Figure 3. Model performance metrics.⟦>zach claim=73b97665-ce32-4361-81d0-40fb149cea2b: @{Figure 3. Model performance metrics.} novas3d-outperforms-ilastik⟧

The Dice, precision, recall, mean surface distance, and HD95 distance for the vascular ( A ) and neuron ( B ) channels.⟦>zach claim=73b97665-ce32-4361-81d0-40fb149cea2b: @{The Dice, precision, recall, mean surface distance, and HD95 distance for the vascular ( A ) and neuron ( B ) channels.} novas3d-outperforms-ilastik — These are the segmentation metrics on which the claim asserts the deep-learning pipeline beats the ilastik baseline for both channels.⟧

Each model was evaluated on the same test dataset composed of nine images (250 x 507 × 507 μm each) from six mice.

A Wilcoxon signed-rank test was used to compare the model’s performance on each performance metric for images from the test dataset. * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.⟦>zach claim=db0a9448-8fe8-4336-b220-0a71aa19d870: @{A Wilcoxon signed-rank test was used to compare the model’s performance on each performance metric for images from the test dataset. * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.} unetr-outperforms-ilastik-hd95⟧

Figure 4. Visual model comparison.⟦>zach claim=73b97665-ce32-4361-81d0-40fb149cea2b: @{Figure 4. Visual model comparison.} novas3d-outperforms-ilastik⟧

( A ) Raw images of the vascular channel with the neuron channel subtracted to facilitate vessel visualization.⟦>zach claim=no-assertion: @{( A ) Raw images of the vascular channel with the neuron channel subtracted to facilitate vessel visualization.} A description of how the displayed raw images were prepared for visualisation, not a result.⟧

The first and last stacks in each row span from the cortical surface to 250 μm below the surface, while the middle stack spans from 250 μm below the surface to 500 μm below the surface.

All images were from the test dataset, which was unseen during model training.

( B ) Ground truth segmentation masks for the vasculature were generated by a rater who utilized ilastik-assisted manual segmentation.⟦>zach claim=no-assertion: @{( B ) Ground truth segmentation masks for the vasculature were generated by a rater who utilized ilastik-assisted manual segmentation.} A statement of how the ground-truth masks were produced - dataset preparation rather than a finding.⟧

( C ) Ilastik predictions generated via a random forest model.⟦>zach claim=no-assertion: @{( C ) Ilastik predictions generated via a random forest model.} A bare panel label naming which model produced the displayed masks.⟧

( D ) Binary segmentation masks generated by an ensemble of 3D UNet models.⟦>zach claim=no-assertion: @{( D ) Binary segmentation masks generated by an ensemble of 3D UNet models.} A bare panel label naming which model produced the displayed masks.⟧

( E ) Binary segmentation masks generated by an ensemble of 3D UNETR models.⟦>zach claim=no-assertion: @{( E ) Binary segmentation masks generated by an ensemble of 3D UNETR models.} A bare panel label naming which model produced the displayed masks.⟧

Vessel extraction improvements via image registration Rigid registration across all time points from the same field of view improved the ability to trace vascular paths from in situ 2PFM data.

Firstly, registration decreased the mean squared error (MSE) between acquisitions from 1306±747–0.008±0.003 Signal Units.

The number of images acquired per field of view ranged from a total of 2–10 depending on how many repeats were able to be acquired.

Registration enabled the computation of the union of segmentation masks from all time points.

This increased the number of vessel segments identified in each field of view from 241±174 based on a single time point to 412±281 vessel segments per field of view (507 x 507 × 250 μm, n=107 fields of view).⟦>zach claim=2d67695f-3ee3-45e9-b4bc-ff547691c4bc: @{This increased the number of vessel segments identified in each field of view from 241±174 based on a single time point to 412±281 vessel segments per field of view (507 x 507 × 250 μm, n=107 fields of view).} registration-doubles-vessel-count⟧

Taking the union of segmentation masks of an image stack across all time points substantially decreased the incidence of gaps in capillaries, likely arising due to ‘transient RBC plugs’.

The pipeline’s ability to reconstruct the cortical vascular network was thus significantly improved by registering data obtained at different time points.

Validation of pipeline sensitivity to geometric changes To evaluate the ability of our computational pipeline to detect vessel caliber changes of various magnitudes, we simulated a range of vascular caliber changes and injected various levels of Gaussian noise.

Across > 100,000 simulations, the fit of the estimated radius following rescaling against the simulated radius had an R 2 value of 0.68. Figure 5B presents a heatmap of the estimated radius post-scaling vs. simulated radius, across different vertices of vessel centerlines, highlighting the ability of our pipeline to estimate vascular radii accurately.⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{Across > 100,000 simulations, the fit of the estimated radius following rescaling against the simulated radius had an R 2 value of 0.68. Figure 5B presents a heatmap of the estimated radius post-scaling vs. simulated radius, across different vertices of vessel centerlines, highlighting the ability of our pipeline to estimate vascular radii accurately.} radius-estimation-r2-0p68 — This is the R-squared of 0.68 against simulated radii across more than 100,000 simulations that the claim states.⟧

The addition of Gaussian noise revealed the robustness of the pipeline: radius estimates remained stable with increasing noise levels, until the addition of noise with a standard deviation of over 200 SU (with the intensity of the images ranging from 0 to 1023 SU).

Figure 5. Estimation of simulated radii changes.⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{Figure 5. Estimation of simulated radii changes.} radius-estimation-r2-0p68⟧

( A ) An image in the plane orthogonal to the local tangent to a capillary with the detected boundary (in blue) and with the estimated radius of 2.28 μm.⟦>zach claim=no-assertion: @{( A ) An image in the plane orthogonal to the local tangent to a capillary with the detected boundary (in blue) and with the estimated radius of 2.28 μm.} An illustrative cross-section showing the method's detected boundary on one example capillary, not a reported result.⟧

On the right, this image was resized (upsampling, via bicubic interpolation, by 1.10 times) to simulate dilation.

( B ) The plot shows correspondence between the estimated radius following scaling and the simulated level of scaling.⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{( B ) The plot shows correspondence between the estimated radius following scaling and the simulated level of scaling.} radius-estimation-r2-0p68 — The claim that the estimator recovers known simulated radii is what this correspondence plot displays.⟧

( C ) An image in the plane orthogonal to the local tangent of a capillary with the detected boundary (in blue) and with the estimated radius of 3.65 μm.⟦>zach claim=no-assertion: @{( C ) An image in the plane orthogonal to the local tangent of a capillary with the detected boundary (in blue) and with the estimated radius of 3.65 μm.} An illustrative cross-section showing the method's detected boundary on one example capillary, not a reported result.⟧

On the right, Gaussian noise with a sigma of 205.36 SU was added to the image.

( D ) The estimated % change in the vessel’s radius after the addition of varying levels of Gaussian noise, demonstrating the robustness of the radius estimated to noise.⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{( D ) The estimated % change in the vessel’s radius after the addition of varying levels of Gaussian noise, demonstrating the robustness of the radius estimated to noise.} radius-estimation-r2-0p68 — The claim's assertion that the estimate stays stable under added Gaussian noise is exactly what this panel shows.⟧

Our boundary detection algorithm successfully estimated the radius of precisely specified fluorescent beads.

The bead images had a signal-to-noise ratio of 6.79±0.16 (about 35% higher than our in vivo images): to match their SNR to that of in vivo vessel data, following deconvolution, we added Gaussian noise with a standard deviation of 85 SU to the images, bringing the SNR down to 5.05±0.15. The data processing pipeline was kept unaltered except for the bead segmentation, performed via image thresholding instead of our deep learning model (trained on vessel data).

The bead boundary was computed following the same algorithm used on vessel data: that is by the average of the minimum intensity gradients computed along 36 radial spokes emanating from the centerline vertex in the orthogonal plane.

To demonstrate an averaging-induced decrease in the uncertainty of the bead radius estimates on a scale that is finer than the nominal resolution of the imaging configuration, we tested four averaging levels in 289 beads.

Three of these averaging levels were lower than that used on the vessels, and one matched that used on the vessels (36 spokes per orthogonal plane and a minimum of 10 orthogonal planes per vessel).

As the amount of averaging increased, the uncertainty on the diameter of the beads decreased, and our estimate of the bead’s diameter converged upon the manufacturer’s Coulter counter-based specifications (7.32±0.27 μm), as tabulated below in Table 1 .⟦>zach claim=gap: @{As the amount of averaging increased, the uncertainty on the diameter of the beads decreased, and our estimate of the bead’s diameter converged upon the manufacturer’s Coulter counter-based specifications (7.32±0.27 μm), as tabulated below in Table 1 .} No claim covers the microsphere phantom validation - that averaging more orthogonal planes and spokes converges the diameter estimate onto the manufacturer's 7.32 um specification; the radius claim rests on simulations only.⟧

Table 1. Bead diameter estimates.⟦>zach claim=no-assertion: @{Table 1. Bead diameter estimates.} A bare table title.⟧

Number of orthogonal planes Number of spokes per plane Mean diameter estimate (μm) 1 3 7.54±0.68 2 4 7.44±0.51 4 12 7.34±0.38 10 36 7.34±0.32 Vascular morphology and heterogeneity within and among vessels Segmentation coupled with graph extraction enabled a detailed characterization of the microvascular network properties.

The morphological properties of extracted networks are listed in Table 2 , with the probability densities of the vessel length, baseline vessel radius, mean vessel segment depth, and vessel branch point depth shown in Appendix 1—figure 5 .⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{The morphological properties of extracted networks are listed in Table 2 , with the probability densities of the vessel length, baseline vessel radius, mean vessel segment depth, and vessel branch point depth shown in Appendix 1—figure 5 .} radius-estimation-r2-0p68⟧

On the extracted graphs, the vascular radius and distance to labeled neurons were sampled every 1–1.73 μm, enabling detailed analysis of the relationship between the vessel radius change and the proximity to the YFP-expressing neurons.

The radius was tracked across different time points, permitting the analysis of the stimulation-induced change in the vascular caliber.

To highlight the ability of the pipeline to detect vessels that significantly change their radius after stimulation, Figure 6A shows the standard deviation of the average radii on each vessel segment during baseline frames for three mice.⟦>zach claim=083ef4c3-9e93-4927-9867-3c785b2c03c0: @{To highlight the ability of the pipeline to detect vessels that significantly change their radius after stimulation, Figure 6A shows the standard deviation of the average radii on each vessel segment during baseline frames for three mice.} baseline-intra-vessel-radius-varies-24pct — The claim about baseline variability in vessel radius accounts for this display of per-segment baseline standard deviations.⟧

There was a large difference in this standard deviation across various blood vessels, showcasing the model’s ability to reveal baseline variations within each subject.

We examined the average change in the vascular radius of each vessel segment after vs. before photostimulation ( Figure 6B ), with even finer spatial patterns detected by analyzing the vertex-wise radius changes ( Figure 6C ).⟦>zach claim=de368a2f-dfdf-4cb5-9c5f-24e3e0cf3aae: @{We examined the average change in the vascular radius of each vessel segment after vs. before photostimulation ( Figure 6B ), with even finer spatial patterns detected by analyzing the vertex-wise radius changes ( Figure 6C ).} vessel-radius-heterogeneity-stimulation — The claim that radius adjustments during stimulation vary within vessels accounts for this segment-wise and vertex-wise comparison.⟧

The vascular diameter changes were related to the distance from the vessel’s surface to the closest labeled pyramidal neuron at each vertex of the centerline ( Figure 6D ).⟦>zach claim=c1381a0c-47be-4721-9022-ddec7d45b3fd: @{The vascular diameter changes were related to the distance from the vessel’s surface to the closest labeled pyramidal neuron at each vertex of the centerline ( Figure 6D ).} dilations-nearer-neurons-than-constrictions — The claim relates the direction of the radius change to distance from the nearest labelled pyramidal neuron, which is the relation shown here.⟧

The vertex-wise radii estimation allowed the assessment of the variations in radii changes within individual blood vessels ( Figure 7 ).⟦>zach claim=083ef4c3-9e93-4927-9867-3c785b2c03c0: @{The vertex-wise radii estimation allowed the assessment of the variations in radii changes within individual blood vessels ( Figure 7 ).} baseline-intra-vessel-radius-varies-24pct⟧

Notably, capillary radius varied along the vessel length across the baseline frames by 24±28% of the mean resting radius.

Consequently, point measurements in vessel calibers - that are widely reported in the literature - do not permit accurate estimation of the microvessel volume changes.

Together, the within- and across-vessel radii estimations illustrate the pipeline’s ability to capture spatial variations in the vascular reactivity and relate it to other morphological features (e.g. the distance to the closest labeled neuron).

Table 2. S1FL vascular network morphological properties.⟦>zach claim=no-assertion: @{Table 2. S1FL vascular network morphological properties.} A bare table title.⟧

Metric Mean ±SD N=17 Mice (9 M/8 F) Number of individual vessels per volume 368±239 32 FOVs Vessel density 5705±3705 mm –3 32 FOVs Number of vascular junctions per volume 207±154 32 FOVs Vascular junction density 3215±2385 mm –3 32 FOVs Number of terminal vessels per volume 128±52 32 FOVs Individual vessel length 70.7±61.1 μm 12555 vessel segments Cumulative vessel length density 0.40±0.22 m/mm 3 32 FOVs Baseline vessel radius 2.19±1.66 μm Range: 0.66–15.88 μm 12555 vessel segments Baseline intra-vessel radius standard deviation 0.53±0.47 μm 12555 vessel segments Baseline vascular volume density 0.010±0.007 mm 3 /mm 3 32 FOVs Number of pyramidal neurons per volume 313±202 neuronal somas 32 FOVs Pyramidal neuron density 4872±3145 neuronal somas/mm 3 32 FOVs Figure 6. Vascular graph examples.⟦>zach claim=1a2b3c4d-5e6f-7a8b-9c0d-1e2f3a4b5c6d: @{Metric Mean ±SD N=17 Mice (9 M/8 F) Number of individual vessels per volume 368±239 32 FOVs Vessel density 5705±3705 mm –3 32 FOVs Number of vascular junctions per volume 207±154 32 FOVs Vascular junction density 3215±2385 mm –3 32 FOVs Number of terminal vessels per volume 128±52 32 FOVs Individual vessel length 70.7±61.1 μm 12555 vessel segments Cumulative vessel length density 0.40±0.22 m/mm 3 32 FOVs Baseline vessel radius 2.19±1.66 μm Range: 0.66–15.88 μm 12555 vessel segments Baseline intra-vessel radius standard deviation 0.53±0.47 μm 12555 vessel segments Baseline vascular volume density 0.010±0.007 mm 3 /mm 3 32 FOVs Number of pyramidal neurons per volume 313±202 neuronal somas 32 FOVs Pyramidal neuron density 4872±3145 neuronal somas/mm 3 32 FOVs Figure 6. Vascular graph examples.} scope-pipeline-and-application-paper⟧

( A ) Baseline variability in vessel diameter estimated by the standard deviation of each vessel’s mean radius across baseline time frames.⟦>zach claim=083ef4c3-9e93-4927-9867-3c785b2c03c0: @{( A ) Baseline variability in vessel diameter estimated by the standard deviation of each vessel’s mean radius across baseline time frames.} baseline-intra-vessel-radius-varies-24pct — The claim about baseline variability in vessel radius is the quantity this table panel tabulates.⟧

( B ) Mean change in the vessel radius induced by optogenetic stimulation.⟦>zach claim=de368a2f-dfdf-4cb5-9c5f-24e3e0cf3aae: @{( B ) Mean change in the vessel radius induced by optogenetic stimulation.} vessel-radius-heterogeneity-stimulation — The stimulation-evoked radius changes tabulated here are the ones the claim quantifies.⟧

( C ) Mean change in the vertexwise radius, allowing the visualization of heterogeneity of radius changes within each vessel.⟦>zach claim=de368a2f-dfdf-4cb5-9c5f-24e3e0cf3aae: @{( C ) Mean change in the vertexwise radius, allowing the visualization of heterogeneity of radius changes within each vessel.} vessel-radius-heterogeneity-stimulation — Within-vessel heterogeneity of the radius change is precisely what the claim asserts.⟧

( D ) Distance from each vertex to the closest pyramidal neuron.⟦>zach claim=c1381a0c-47be-4721-9022-ddec7d45b3fd: @{( D ) Distance from each vertex to the closest pyramidal neuron.} dilations-nearer-neurons-than-constrictions — The claim states the vertex-to-nearest-neuron distances for dilating and constricting vessels that this panel tabulates.⟧

Each row corresponds to the vascular graph of a different mouse.

Figure 7. Vertex-wise radii along vessel lengths of a sample artery, capillary, and venule at baseline vs. post-stimulation.⟦>zach claim=083ef4c3-9e93-4927-9867-3c785b2c03c0: @{Figure 7. Vertex-wise radii along vessel lengths of a sample artery, capillary, and venule at baseline vs. post-stimulation.} baseline-intra-vessel-radius-varies-24pct⟧

( A ) MIP of an artery, vein, and capillary segments before (left) and after (right) optogenetic stimulation with 458 nm light at 1.1 mW/mm 2 .⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{( A ) MIP of an artery, vein, and capillary segments before (left) and after (right) optogenetic stimulation with 458 nm light at 1.1 mW/mm 2 .} artery-dilates-venule-unchanged-at-low-power⟧

The artery and capillary dilated by 1.33±0.86 μm and 0.42±0.39 μm, respectively (for both p<1e-4, Mann-Whitney U test), whereas there was no significant change in the venular caliber upon photostimulation (p=0.22, Mann-Whitney U test).⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{The artery and capillary dilated by 1.33±0.86 μm and 0.42±0.39 μm, respectively (for both p<1e-4, Mann-Whitney U test), whereas there was no significant change in the venular caliber upon photostimulation (p=0.22, Mann-Whitney U test).} artery-dilates-venule-unchanged-at-low-power⟧

( B ) Estimates of the vertex-wise radius obtained along each of the three vessels’ centrelines, before and after stimulation.⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{( B ) Estimates of the vertex-wise radius obtained along each of the three vessels’ centrelines, before and after stimulation.} artery-dilates-venule-unchanged-at-low-power — The claim reports the radius responses of the sample artery, capillary and venule whose before-and-after centreline profiles this panel shows.⟧

( C ) Vertex-wise radii changes in response to optogenetic stimulation.⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{( C ) Vertex-wise radii changes in response to optogenetic stimulation.} artery-dilates-venule-unchanged-at-low-power — These vertex-wise changes for the three sample vessels are the responses the claim quantifies.⟧

( D ).⟦>zach claim=no-assertion: @{( D ).} A bare panel label with no text.⟧

The vertex-wise distance from the vascular surface to the closest YFP-expressing neuron.

Vascular reactivity to optogenetic stimulation The ability of the pipeline to reveal novel spatial relationships between the vascular network reactivity and labeled neurons was demonstrated by examining the relationship between photostimulation-induced microvascular radii responses and (1) the closest YFP-labeled pyramidal neurons within 80 μm, and (2) the cortical depth, at the vertex-wise level and across different photostimulations ( Figure 8 ).⟦>zach claim=a68ebd2f-95bf-43a9-b0e0-f9ce7a3fa5bd: @{Vascular reactivity to optogenetic stimulation The ability of the pipeline to reveal novel spatial relationships between the vascular network reactivity and labeled neurons was demonstrated by examining the relationship between photostimulation-induced microvascular radii responses and (1) the closest YFP-labeled pyramidal neurons within 80 μm, and (2) the cortical depth, at the vertex-wise level and across different photostimulations ( Figure 8 ).} blue-light-dilations-exceed-green-control⟧

Vessels were coarsely segregated into small (average radius <5 μm) and large (average radius >5 μm) vessels, as we expected them to respond differently due to their differential wall-associated cell composition ( Hartmann et al., 2021 ; Bisht et al., 2021 ; Wu et al., 2022 ; Kirabali et al., 2019 ; Ren et al., 2021 ; Steinman et al., 2017 ; Berthiaume et al., 2018 ; Katz et al., 2023 ; Drouin-Ouellet et al., 2015 ).

Only vessels longer than 20 μm (i.e. vessels whose radius was computed by averaging over many cross-sectional planes) that significantly responded following optogenetic stimulation were analyzed: a vessel was deemed a responder if its radius changed by more than twice the baseline standard deviation in the vessel’s radius.

The morphometric properties of the responders, under different stimulation conditions, are listed in Table 3 .⟦>zach claim=no-assertion: @{The morphometric properties of the responders, under different stimulation conditions, are listed in Table 3 .} A pure cross-reference to Table 3.⟧

The average magnitude of significant microvascular radius changes across all stimulation conditions was 1.04±1.11 μm (62 ± 47%).

The variability in the radius change within the vessel was higher in the dilating vessels, 0.77±0.61 μm (66 ± 72%) than in the constricting vessels, 0.69±0.49 μm (46 ± 18%), for 458 nm, 4.3 m W m m 2 \begin{document}$4.3\frac{mW}{mm^{2}}$\end{document} photostimulation (p<1e-4; with no statistically significant changes detected for either 458 nm, 1.1 m W m m 2 \begin{document}$1.1\frac{mW}{mm^{2}}$\end{document} photostimulation or 552 nm, 4.3 m W m m 2 \begin{document}$4.3\frac{mW}{mm^{2}}$\end{document} photostimulation).⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{The variability in the radius change within the vessel was higher in the dilating vessels, 0.77±0.61 μm (66 ± 72%) than in the constricting vessels, 0.69±0.49 μm (46 ± 18%), for 458 nm, 4.3 m W m m 2 \begin{document}}$4.3\frac{mW}}{mm^{2}}}}$\end{document}} photostimulation (p<1e-4; with no statistically significant changes detected for either 458 nm, 1.1 m W m m 2 \begin{document}}$1.1\frac{mW}}{mm^{2}}}}$\end{document}} photostimulation or 552 nm, 4.3 m W m m 2 \begin{document}}$4.3\frac{mW}}{mm^{2}}}}$\end{document}} photostimulation).} artery-dilates-venule-unchanged-at-low-power⟧

Excluding vessels that did not change their radius by over twice the baseline standard deviation removed almost all large vessels from this analysis.

(Notwithstanding, Appendix 1—figure 5 depicts unfiltered large vessel constrictions and dilations).⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{(Notwithstanding, Appendix 1—figure 5 depicts unfiltered large vessel constrictions and dilations).} radius-estimation-r2-0p68⟧

We ran mixed effects models (at the vessel level) separately on constricting and dilating vessels to investigate the effect of optogenetic stimulation power on the vessel radius changes.

Each of the models was run separately on small and large vessels.

Table 3. Details of responder (Δ R >2 * σR baseline ) vessels.⟦>zach claim=no-assertion: @{Table 3. Details of responder (Δ R >2 * σR baseline ) vessels.} A bare table title, which restates the responder threshold rather than reporting a result.⟧

Stimulation condition Total number of vessel estimates Average minimum distance to the closest neuron (μm) Number of dilators Minimum distance from dilators to the closest neuron (μm) Average vessel depth of dilators (μm) Diameter change (μm) Number of constrictors Minimum distance from constrictors to the closest neuron (μm) Average vessel depth of constrictors (μm) Diameter change (μm) All vessels Dilators Constrictors Capillaries 552 nm 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} 5036 21.2±16.2 144 (2.9%) 25.5±19.0 186±114 0.58±0.92 49 (1.0%) 26.5±19.5 247±122 –0.37±0.30 458 nm 1.1 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} 10136 18.7±14.5 317 (3.1%) 16.8±13.5 196±138 0.90±0.93 255 (2.5%) 22.7±16.3 254±126 –1.39±1.51 458 nm 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} 12537 20.6±15.4 575 (4.6%) 16.1±14.3 237±146 0.90±0.77 874 (7.0%) 21.9±14.6 274±103 –1.19±1.13 Large vessels 552 nm 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} 225 43.1±19.5 1 (0.4%) 75.4 82 13.98 0 (0%) NA NA NA 458 nm 1.1 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} 545 38.4±19.5 1 (0.2%) 26.1 402 1.97 1 (0.2%) 19.0 179 –3.65 458 nm 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} 569 38.4±20.1 2 (0.35%) 53.1±6.3 84±34 2.47±2.93 6 (1.1%) 43.1±16.3 290±125 –6.07±2.45 Figure 8. Optogenetic activation-induced changes in vessel-wise microvascular radii.⟦>zach claim=a68ebd2f-95bf-43a9-b0e0-f9ce7a3fa5bd: @{Stimulation condition Total number of vessel estimates Average minimum distance to the closest neuron (μm) Number of dilators Minimum distance from dilators to the closest neuron (μm) Average vessel depth of dilators (μm) Diameter change (μm) Number of constrictors Minimum distance from constrictors to the closest neuron (μm) Average vessel depth of constrictors (μm) Diameter change (μm) All vessels Dilators Constrictors Capillaries 552 nm 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} 5036 21.2±16.2 144 (2.9%) 25.5±19.0 186±114 0.58±0.92 49 (1.0%) 26.5±19.5 247±122 –0.37±0.30 458 nm 1.1 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} 10136 18.7±14.5 317 (3.1%) 16.8±13.5 196±138 0.90±0.93 255 (2.5%) 22.7±16.3 254±126 –1.39±1.51 458 nm 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} 12537 20.6±15.4 575 (4.6%) 16.1±14.3 237±146 0.90±0.77 874 (7.0%) 21.9±14.6 274±103 –1.19±1.13 Large vessels 552 nm 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} 225 43.1±19.5 1 (0.4%) 75.4 82 13.98 0 (0%) NA NA NA 458 nm 1.1 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} 545 38.4±19.5 1 (0.2%) 26.1 402 1.97 1 (0.2%) 19.0 179 –3.65 458 nm 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} 569 38.4±20.1 2 (0.35%) 53.1±6.3 84±34 2.47±2.93 6 (1.1%) 43.1±16.3 290±125 –6.07±2.45 Figure 8. Optogenetic activation-induced changes in vessel-wise microvascular radii.} blue-light-dilations-exceed-green-control⟧

Capillary responses included both dilatations, shown in (A), and constrictions, shown in ( B ), with changes in the magnitude of the capillary response with increased photostimulation power. * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.⟦>zach claim=db0a9448-8fe8-4336-b220-0a71aa19d870: @{Capillary responses included both dilatations, shown in (A), and constrictions, shown in ( B ), with changes in the magnitude of the capillary response with increased photostimulation power. * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.} unetr-outperforms-ilastik-hd95⟧

( C ) Probability density function of constrictions and dilations for the 4.3 mW/mm 2 photostimulation.⟦>zach claim=gap: @{( C ) Probability density function of constrictions and dilations for the 4.3 mW/mm 2 photostimulation.} The control claim compares only dilation magnitudes between blue and green light; the distribution of constriction magnitudes shown alongside them is stated by no claim.⟧

( D ) Changes to capillary radii are displayed in relation to the closest pyramidal neurons.⟦>zach claim=c1381a0c-47be-4721-9022-ddec7d45b3fd: @{( D ) Changes to capillary radii are displayed in relation to the closest pyramidal neurons.} dilations-nearer-neurons-than-constrictions — The claim states how capillary radius changes are distributed relative to the closest pyramidal neurons, which is what this panel displays.⟧

The proportion of vessels constricting increased with the higher intensity of blue light stimulation, and constrictions tended to occur further away from pyramidal neurons than did dilations.

( E ) Mean cortical depth of responding capillaries showed a tendency for dilators to be closer to the surface and for constrictors to be deeper in the tissue.⟦>zach claim=a3ca491f-f3b6-456f-b69c-6f9449183c4a: @{( E ) Mean cortical depth of responding capillaries showed a tendency for dilators to be closer to the surface and for constrictors to be deeper in the tissue.} constrictions-deeper-than-dilations — The claim asserts that constrictors sit deeper and dilators nearer the surface, exactly the tendency this panel reports.⟧

Vessels further away from labeled neurons constrict while those closer to the activated neurons dilate We examined the relationship between vascular radius changes and the distance to the closest labeled pyramidal neuron, as microvascular response is thought to result from neuronal activation-elicited generation of vasoactive molecules that diffuse to the neighboring vessels.

For the control condition (552 nm, 4.3 m W m m 2 \begin{document}$4.3\frac{mW}{mm^{2}}$\end{document} photostimulation), 2.9% of small capillaries dilated while 1.0% of small capillaries constricted; in larger vessels, barely any responded (0.4% dilated).

For this control condition, there was no significant difference in the distance from constrictors or dilators to the closest pyramidal neuron.

For the 458 nm photostimulation, capillary constrictors were on average farther away than were dilators from the labeled pyramidal neuron: dilations occurred 16.8±13.5 μm away from labeled neurons while constrictions occurred 22.7±16.3 μm for 1.1 m W m m 2 \begin{document}$1.1\frac{mW}{mm^{2}}$\end{document} photostimulation (p=1.5e-3) whereas the 4.3 m W m m 2 \begin{document}$4.3\frac{mW}{mm^{2}}$\end{document} photostimulation had dilations occur 16.1±14.3 μm away and 21.9±14.6 μm for constrictors (p<1e-4).⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{For the 458 nm photostimulation, capillary constrictors were on average farther away than were dilators from the labeled pyramidal neuron: dilations occurred 16.8±13.5 μm away from labeled neurons while constrictions occurred 22.7±16.3 μm for 1.1 m W m m 2 \begin{document}}$1.1\frac{mW}}{mm^{2}}}}$\end{document}} photostimulation (p=1.5e-3) whereas the 4.3 m W m m 2 \begin{document}}$4.3\frac{mW}}{mm^{2}}}}$\end{document}} photostimulation had dilations occur 16.1±14.3 μm away and 21.9±14.6 μm for constrictors (p<1e-4).} artery-dilates-venule-unchanged-at-low-power⟧

There was no significant shift between the distance from vessels to neurons for the 1.1 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} and 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} stimulations with 458 nm light.

Dilations in capillaries following 458 nm photostimulation were larger than those following the 552 nm control photostimulation: 0.90±0.93 μm dilatations occurred with 1.1 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} and 0.90±0.78 μm with 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} at 458 nm; vs. 0.58±0.92 μm with 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} at 552 nm (p<1e-4).⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{Dilations in capillaries following 458 nm photostimulation were larger than those following the 552 nm control photostimulation: 0.90±0.93 μm dilatations occurred with 1.1 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} and 0.90±0.78 μm with 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} at 458 nm; vs. 0.58±0.92 μm with 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} at 552 nm (p<1e-4).} artery-dilates-venule-unchanged-at-low-power⟧

For constrictions, 458 nm photostimulations led to –1.39±1.51 μm radius changes with 1.1 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} and –1.20±1.13 μm radius changes with 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} (p=4.4e-3), whereas 552 nm photostimulation induced –0.37±0.30 μm radius changes with 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} of power, which was smaller than the 458 nm induced responses (p=0.02).⟦>zach claim=gap: @{For constrictions, 458 nm photostimulations led to –1.39±1.51 μm radius changes with 1.1 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} and –1.20±1.13 μm radius changes with 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} (p=4.4e-3), whereas 552 nm photostimulation induced –0.37±0.30 μm radius changes with 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} of power, which was smaller than the 458 nm induced responses (p=0.02).} The tree's light-control claim covers dilations only; the constriction magnitudes under 458 nm and their excess over the 552 nm control are reported by no claim.⟧

Vascular radius changes at increasing cortical depths Vascular responses were next segregated by the cortical depth of the vessel (i.e. the average vessel distance from the cortical surface; DeFelipe et al., 2002 ).

Dilators tended to be located closer to the cortical surface across all stimulation conditions.

Constricting vessels were located at an average 58±187 μm deeper than dilators for 458 nm stimulation at 1.1 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} (p=0.02), and 37±179 μm deeper for 458 nm photostimulation at 4.3 m W m m 2 \begin{document}$\frac{mW}{mm^{2}}$\end{document} (p<1e-4; with no change in the mean depth of either constricting or dilating vessels with changes in the photostimulation power).⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{Constricting vessels were located at an average 58±187 μm deeper than dilators for 458 nm stimulation at 1.1 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} (p=0.02), and 37±179 μm deeper for 458 nm photostimulation at 4.3 m W m m 2 \begin{document}}$\frac{mW}}{mm^{2}}}}$\end{document}} (p<1e-4; with no change in the mean depth of either constricting or dilating vessels with changes in the photostimulation power).} artery-dilates-venule-unchanged-at-low-power⟧

Vascular network coordination following optogenetic stimulation We examined the coordination of changes in the microvascular network as a whole via assortativity of radius changes and network efficiency changes.

The vessel responses were observed to be assortative, that is capillaries mirrored the responses in their neighbors.

The increases in stimulation power were accompanied by increases in the assortativity of capillary responses: increasing stimulation level resulted in heightened coordination between adjacent capillaries ( Figure 9 ).⟦>zach claim=ad12a413-e736-4cb5-908f-e331c3a21478: @{The increases in stimulation power were accompanied by increases in the assortativity of capillary responses: increasing stimulation level resulted in heightened coordination between adjacent capillaries ( Figure 9 ).} capillary-efficiency-increases-4pct⟧

Figure 9. Microvascular network coordination following optogenetic stimulation.⟦>zach claim=ad12a413-e736-4cb5-908f-e331c3a21478: @{Figure 9. Microvascular network coordination following optogenetic stimulation.} capillary-efficiency-increases-4pct⟧

( A ) Graph representation of a vascular network of 425 vascular segments from a single image stack.⟦>zach claim=no-assertion: @{( A ) Graph representation of a vascular network of 425 vascular segments from a single image stack.} An illustrative rendering of one image stack's vascular graph, showing the representation rather than asserting a finding.⟧

Vessel segments are depicted as nodes of the graph; vascular segments that are joined at junctions are connected by edges.

Nodes are colored by the change in the mean vessel-wise radius following photostimulation with 458 nm light at 4.3 mW/mm 2 .

( B ) Assortativity of photostimulation-induced changes in mean capillary radius increased with increasing photostimulation power.⟦>zach claim=e93b7cce-2578-4075-8e9b-cc9910fc5fd4: @{( B ) Assortativity of photostimulation-induced changes in mean capillary radius increased with increasing photostimulation power.} network-assortativity-increases-stimulation⟧

( C ) Photostimulation-induced changes in the efficiency of the capillary network.⟦>zach claim=ad12a413-e736-4cb5-908f-e331c3a21478: @{( C ) Photostimulation-induced changes in the efficiency of the capillary network.} capillary-efficiency-increases-4pct⟧

The capillary network efficiency changed by a median –0.16 PΩ –1 (IQR: –0.39–0.10 PΩ –1 ) in response to green light; –0.14 PΩ –1 (IQR: –0.55–0.27 PΩ –1 ) in response to lower intensity blue light; and 0.22 PΩ –1 (IQR = –0.43;1.47 PΩ –1 ) in response to higher intensity blue light.

There was a significant increase (p=0.03) in the capillary network efficiency post 458 nm light at 4.3 mW/mm 2 , when compared to that following the control green illumination.⟦>zach claim=ad12a413-e736-4cb5-908f-e331c3a21478: @{There was a significant increase (p=0.03) in the capillary network efficiency post 458 nm light at 4.3 mW/mm 2 , when compared to that following the control green illumination.} capillary-efficiency-increases-4pct — The claim that capillary network efficiency rises during stimulation is this significant increase over the green-light control.⟧

The measurements came from 72 paired acquisitions of 32 image stacks acquired in 17 mice (9 M/8 F). * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.⟦>zach claim=db0a9448-8fe8-4336-b220-0a71aa19d870: @{The measurements came from 72 paired acquisitions of 32 image stacks acquired in 17 mice (9 M/8 F). * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.} unetr-outperforms-ilastik-hd95⟧

The efficiency increased only at the strongest blue photostimulation, that is only at this level of stimulation did the resistivity along the average of all of the shortest paths between junctions in the vascular network decrease, resulting in attenuated resistance to flow through the network.

The distribution of changes to the efficiency was highly skewed (with a coefficient of skewness of –1.06 for green illumination, 2.92 for lower intensity blue photostimulation, and 4.87 for higher intensity blue photostimulation).

The median increase in the efficiency induced by the higher intensity blue photostimulation, of 4% (IQR: –8% to 38%), was significantly higher than the median –6% (IQR=−9–4%) efficiency change following the control green illumination.

discussion

Discussion Recent studies have demonstrated temporal propagation and coordination in cerebrovascular responses to neuronal activation, whereby arteries dilated after capillary exposure to increased potassium ion concentration ( Dabertrand et al., 2021 ); opposing geometric changes have been reported by some studies in capillaries connected by intercapillary tunneling nanotubes ( Alarcon-Martinez et al., 2020 ).

However, how the effects of these and other mechanisms influence in situ reactivity of the 3D brain capillary network remains unknown.

Here, we developed a pipeline for extracting graphs of brain microvascular networks from in situ 2PFM and examining coordination within and across capillaries.

Capillary networks and their geometrical changes were imaged via 2PFM during periods of baseline alternated with photostimulation of ChR2 in pyramidal neurons of transgenic mice, and the microvascular mesh was evaluated every 1–1.73 μm.

The vascular morphology was then analyzed vertex- and vessel-wise across the entire network.

All vessels exhibited significant heterogeneity in caliber changes along their length.

Neuronal activation induced both dilations and constrictions of vessels, and the incidence of constrictions increased with increasing cortical depth.

As the stimulation power increased, the tendency for vessels to change their radius by an amount similar to their neighbors increased.

Only the highest photostimulation intensity elicited an increase in the network efficiency.

Our findings reveal an intricate level of coordination among brain microvessels and provide a computational analysis platform for interrogating a host of hypotheses on cerebral microvascular reactivity.

Vascular segmentation and network extraction Intensity thresholding-based image processing pipelines have been used to examine vascular networks and quantify vascular morphology, but they have not gained widespread use due to difficulties in adapting them to highly heterogeneous levels of noise across samples ( Steinman et al., 2017 ; Tsai et al., 2009 ; Rennie et al., 2011 ; Lindvere et al., 2013 ).

Deep learning-based methods for analyzing vascular morphology from 3D microscopy images have become prevalent as they provide robust segmentation results over a wide range of signal-to-noise ratios.

Recent work has demonstrated steady improvements in segmentation models’ performance with respect to similarity-based metrics (i.e. Dice scores; Damseh et al., 2018 ; Goodarzi Ardakani et al., 2022 ; Livne et al., 2019 ; Mookiah et al., 2021 ; Tetteh et al., 2020 ; Poon et al., 2023 ), although surface-based metrics may be better predictors of how amenable segmentation outputs will be to subsequent morphological analysis of the microvascular network.

Our final UNETR model was selected based on a combination of performance metrics including mean surface distance, Hausdorff 95% distance, and rater evaluation, to maximize the smoothness of the surface of generated segmentation masks and reduce false positive branch points during centerline extraction; thereby leading to higher fidelity rendering of microvascular networks.

Graph generation was greatly facilitated by computing the union of the vascular segmentation masks across all time points as it enabled tracing of capillaries that had stalls at the individual time points (since the accompanying loss of the fluorescent label otherwise resulted in graph discontinuities).

We tested the ability of the pipeline to detect changes to simulated changes to images and the pipeline’s sensitivity to perturbations to extracted vascular morphology.

Using resizing, we confirmed that the boundary detection algorithm was able to detect prescribed changes ( Figure 5 ).⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{Using resizing, we confirmed that the boundary detection algorithm was able to detect prescribed changes ( Figure 5 ).} radius-estimation-r2-0p68⟧

The average radius estimates for vessels were then shown to vary by 0.64 ± 3.44% upon changes in centerline position, demonstrating that the radius estimates have a low sensitivity to small (2–3 μm) perturbations in centerline placement.

Visual examples of vessels repeatedly dilating or constricting are shown in Appendix 1—figures 7 and 8 .⟦>zach claim=083ef4c3-9e93-4927-9867-3c785b2c03c0: @{Visual examples of vessels repeatedly dilating or constricting are shown in Appendix 1—figures 7 and 8 .} baseline-intra-vessel-radius-varies-24pct⟧

Network morphological properties at baseline were in line with prior work.

The vascular length density measured from fixed tissue ranged from 0.44 to 1.10 m/mm 3 ( Tsai et al., 2009 ; Todorov et al., 2020 ; Boero et al., 1999 ; Lugo-Hernandez et al., 2017 ; Zhang et al., 2018a ; Miettinen et al., 2021 ).

Our reported vascular density in the forelimb region of the primary somatosensory cortex was 0.40±0.22 m/mm 3 , with the low end of the range value expected due to fluorescence absorption by hemoglobin in the large pial vessels leading to signal dropout (or shadowing) in the underlying tissue.

Our reported average capillary radius of 2.19±1.66 μm was also in line with other studies, where the mean capillary radius ranged from 1.75 to 2.2 μm as measured with confocal microscopy or 2PFM ( Hall et al., 2014 ; Tsai et al., 2009 ).

Next in situ changes to vessel calibers upon neuronal activation Caliber changes at individual vertices along vessel centerlines exhibited significant heterogeneity.

Such heterogeneity is expected due to non-uniformly distributed alpha smooth muscle actin-containing cells along vessel walls, as well as differential activations leading to heterogeneous metabolic demand within the tissue ( Iadecola, 2017 ; Hartmann et al., 2021 ; Kovacs-Oller et al., 2020 ; Abdelazim et al., 2022 ; Quelhas et al., 2020 ; Wang et al., 2015 ).

Many previous studies assumed vessel caliber changes to be uniform, compromising the accuracy of the estimates.

As expected, the control 552 nm stimulation led to minimal changes in vessel calibers.

To probe for off-target effects, non-transgenic mice were also tested with the same optical setup and photostimulation, with no changes to vascular diameters observed at any of the photostimulation powers utilized ( Appendix 1—figure 9 ).⟦>zach claim=ad12a413-e736-4cb5-908f-e331c3a21478: @{To probe for off-target effects, non-transgenic mice were also tested with the same optical setup and photostimulation, with no changes to vascular diameters observed at any of the photostimulation powers utilized ( Appendix 1—figure 9 ).} capillary-efficiency-increases-4pct⟧

In transgenic mice, we detected an average capillary dilation in significantly responding vessels of 70 ± 83% with low-intensity 458 nm stimulation, and 67 ± 61% with higher intensity 458 nm stimulation.

Across photostimulation conditions, the capillary dilations ranged from 2% to 805%.

These caliber changes were higher than those previously reported, which varied from 2% to 20% depending on the capillary branch order ( Hartmann et al., 2021 ; Hall et al., 2014 ; O’Herron et al., 2022 ; Del Franco et al., 2022 ; Stefanovic et al., 2008 ).

Far less data are available on constrictions.

In the current work, constrictions averaged 47 ± 20% for lower-intensity blue light stimulation and 47 ± 17% for higher-intensity blue light stimulation, with a constriction range of 5% to 97% of the baseline radius.

These are higher than the previously reported constrictions of 20% ( Hartmann et al., 2021 ; O’Herron et al., 2022 ), likely due to our identifying as responding vessels only those whose caliber changed by at least twice their baseline caliber variation.

It is also worth noting that vessels’ response directions were consistent on repeated trials.

Of the vessels whose radius change exceeded twice the baseline variability across time, 31.7% dilated on some trials while constricting on others; 41.1% dilated on each trial; and 27.2% constricted on each trial.

(Note that some trials use 1.1 vs 4.3 mW/mm 2 and some have opposite scanning directions). 458 nm photostimulation resulted in a mix of constrictions and dilations with 44.1% of significantly responding vessels within 10 μm of a labelled pyramidal neuron constricting and 55.1% dilating, while 53.3% of vessels further than 30 μm constricted and 46.7% dilated.

The cutoff distances from the closest labeled neuron were based on estimates of cerebral metabolic rate of oxygen consumption that showed a steep gradient in oxygen consumption with distance from arteries, CMRO2 being halved by 30 μm away ( Mächler et al., 2022 ).

The stronger blue light stimulation led to an increased rate of constrictions, double that of the low-powered blue light stimulation.

For larger vessels, both 458 nm stimulation powers led to a similar dilation level that diminished with increasing distance from labeled pyramidal neurons.

This tendency for vessels close to neurons to dilate and further away ones to constrict would be expected in flow redirection into regions of high level of neuronal activity.

Stimulation power dependence in blood flow changes has previously been reported in optogenetic mouse models with diffuse stimulation via LED probes, and following transcranial alternating current stimulation ( Lee et al., 2021 ; Turner et al., 2021 ).

However, neither of the previously employed methods was able to discern the spatial relationship between the vascular caliber changes, or relate these changes to the distribution of the stimulated neurons.

As the blue light stimulation power increased, the mean depth of both constricting and dilating vessels increased, likely resulting from higher intensity light reaching pyramidal neurons deeper in the tissue ( Johnson et al., 2021 ; Al Juboori et al., 2013 ).

The blue light would be expected to excite a lower number of neurons farther from the cortical surface at lower powers.

Our results underscore that the hemodynamic response following targeted neuronal activation is not uniformly distributed across the microvascular network: accurate neurovascular coupling assessment thus requires network-based analysis.

Vascular network reactivity To study the microvascular network response as a whole, we examined the assortativity between capillary radius changes and network efficiency changes following optogenetic stimulation.

These two graph theory metrics were selected as they both leverage the knowledge of the vascular network structure.

Assortativity sheds light on how the vascular network coordinates its responses, while efficiency provides insight into the extent to which those changes facilitate flow through the network.

The assortativity revealed that as the stimulation power increased, the tendency of vessels to match their changes to those of their neighbors increased.

Previously characterized assortative mechanisms include endothelial cell cation conduction via Kir2.1 channels to synchronize vascular responses ( Dabertrand et al., 2021 ), and spatial adjacency of pericytes on in vitro retinal preparation leading to assortative changes in neighboring capillaries ( Kovacs-Oller et al., 2020 ).

Disassortative (causing opposite changes) mechanisms of capillary coordination have also previously been observed in situ and may result from intercapillary nanotubules’ signaling causing connected pericytes to undergo opposing changes ( Alarcon-Martinez et al., 2020 ).

While not ruling out the presence of disassortative control mechanisms, our results suggest that assortative mechanisms dominate capillary responses to neuronal activation in the somatosensory cortex.

The network efficiency here can be thought of as paralleling mean transit time, i.e., the time it takes blood to traverse the capillary network from the arteries to the veins.

In situ studies of mean transit time have revealed a high heterogeneity of plasma traversal of the capillary bed during stimulation, with stimulation reducing plasma transit times by 11% to 20% from its resting levels ( Stefanovic et al., 2008 ; Gutiérrez-Jiménez et al., 2016 ), and simulations suggesting that capillary network geometry and locations of caliber changes exert a substantial influence on these responses ( Lücker et al., 2018 ).

The efficiency of the vascular network here increased significantly only with the strongest 458 nm stimulation.

Small dilatations may thus not increase flow in the cortex.

The differences in efficiency are likely due to the patterns of localized dilations and constrictions within the vascular network.

Efficiency calculations are sensitive to bottlenecks when traversing meshes and certain locations constricting or dilating can have profound impacts on the shortest paths between nodes and the path’s resistivity.

The highest-powered 458 nm stimulation increasing efficiency may have resulted from increased assortativity causing dilation in key locations within the microvascular network, leading to a significant reduction in shortest path resistivity.

Comparison with commercial and open-source vascular analysis pipelines To compare our results with those achievable on these data with other pipelines for segmentation and graph network extraction, we compared segmentation results qualitatively with Imaris version 9.2.1 (Bitplane) and vascular graph extraction with VesselVio ( Bumgarner and Nelson, 2022 ).

For the Imaris comparison, three small volumes were annotated by hand to label vessels.

Example slices of the segmentation results are shown in Appendix 1—figure 10 .⟦>zach claim=gap: @{Example slices of the segmentation results are shown in Appendix 1—figure 10 .}⟧

Imaris tended to either over- or under-segment vessels, disregard fine details of the vascular boundaries, and produce jagged edges in the vascular segmentation masks.

In addition to these issues with segmentation mask quality, manual segmentation of a single volume took days for a rater to annotate.

To compare to VesselVio, binary segmentation masks (one before and one after photostimulation) generated with our deep learning models were loaded into VesselVio for graph extraction, as VesselVio does not have its own method for generating segmentation masks.

This also facilitates a direct comparison of the benefits of our graph extraction pipeline to VesselVio.

Visualizations of the two graphs are shown in Appendix 1—figure 11 .⟦>zach claim=gap: @{Visualizations of the two graphs are shown in Appendix 1—figure 11 .}⟧

Vesselvio produced many hairs at both time points, and the total number of segments varied considerably between the two sequential stacks: while the baseline scan resulted in 546 vessel segments, the second scan had 642 vessel segments.

These discrepancies are difficult to resolve in post-processing and preclude a direct comparison of individual vessel segments across time.

As the segmentation masks we used in graph extraction derive from the union of multiple time points, we could better trace the vasculature and identify more connections in our extracted graph.

Furthermore, VesselVio relies on the distance transform of the user-supplied segmentation mask to estimate vascular radii; consequently, these estimates are highly susceptible to variations in the input segmentation masks.

We repeatedly saw slight variations between boundary placements of all of the models we utilized (ilastik, UNet, and UNETR) and those produced by raters.

Our pipeline mitigates this segmentation method bias by using intensity gradient-based boundary detection from centerlines in the image (as opposed to using the distance transform of the segmentation mask, as in VesselVio).

Pipeline limitations and adaptability The segmentation model was trained only on vascular and neuronal labels, limiting its generalizability to segmenting alternative cells in the current state.

However, it can easily be fine-tuned or retrained to label other brain cells (e.g. pericytes, astrocytes, or endothelial cells).

Our vascular segmentation model generalized well to C57BL/6J mouse and Fischer rat data, as well as to Thy1-ChR2 light-sheet fluorescence microscopy images gathered on an UltraMicroscope Blaze lightsheet fluorescence microscope (Miltenyi Biotech) ( Appendix 1—figures 12 and 13 and Supplementary file 3, table 3 ).⟦>zach claim=gap: @{Our vascular segmentation model generalized well to C57BL/6J mouse and Fischer rat data, as well as to Thy1-ChR2 light-sheet fluorescence microscopy images gathered on an UltraMicroscope Blaze lightsheet fluorescence microscope (Miltenyi Biotech) ( Appendix 1—figures 12 and 13 and Supplementary file 3, table 3 ).}⟧

However, the segmentation model performed poorly when significant bleeding occurred in the cranial window, compromising the vascular contrast.

Our imaging protocol, in turn, was challenged by the desire to resolve individual vessel responses yet capture the entire network within the span of the microvascular response to stimulation: we prioritized network assessment and thereby compromised our temporal sampling (every 42 s), so that our ensuing classification of vessels as dilators or constrictors was based on their caliber at this, rather delayed timepoint.

Accordingly, we are unable to comment on finer temporal scale network behavior or the kinetics of the microvascular network response; but the present analysis pipeline can readily be applied to 2PFM data obtained with finer temporal (e.g. via a Piezo objective positioner) or spatial resolution, and/or different size fields of view.

The temporal evolution of the response in individual vessels, however, has been reported on using line scanning acquisitions to measure red blood cell velocity and flux and in some cases vessels ( Hartmann et al., 2021 ; Adams et al., 2018 ; O’Herron et al., 2022 ; Mester et al., 2019 ; Kleinfeld et al., 1998 ; Stefanovic et al., 2008 ).

It is worth noting that the cases where vascular responses are drawn out following optogenetic stimulation use raster scanning over small regions of interest, and that optogenetic stimulations utilizing fiber optic probes shining light over large areas led to fast vascular responses.

Our study utilized raster scanning over small regions of interest.

Nevertheless, long-drawn-out vascular responses following optogenetic stimulation remain controversial and still need further study at higher temporal sampling, which our pipeline can readily adapt to, to be demonstrated conclusively.

Additionally, alternative definitions of responding vessels may be useful depending on the end goal of a study (e.g. selecting a threshold for the radius change based on a percentage change from the baseline level: Appendix 1—figure 14 for capillary changes above 10% of the baseline radius).⟦>zach claim=gap: @{Additionally, alternative definitions of responding vessels may be useful depending on the end goal of a study (e.g. selecting a threshold for the radius change based on a percentage change from the baseline level: Appendix 1—figure 14 for capillary changes above 10% of the baseline radius).}⟧

Finally, microvascular networks in different brain areas may show distinct spatiotemporal profiles of response to neuronal activation.

Future work is required to test the generalizability of present findings across different brain regions.

Conclusion We developed a novel deep learning-based computational pipeline for analysis of a time series of 3D 2PFM images and investigation of spatial patterns in microvascular network reactivity to neuronal activation.

The microvascular network was represented as a graph, allowing for the evaluation of network geometry changes over time.

We tracked the size of blood vessels throughout the network and related vessel radius changes to the distance from the stimulated neurons and the cortical depth.

Neuronal activation induced both dilatations and constrictions of capillaries, and the magnitude of these responses increased with increased photostimulation levels while showing significant heterogeneity within and between vessels.

In the analysis presented, vertex-wise measurements were aggregated for vessel-wise analysis, resulting in highly robust estimates of vessels’ calibers and allowing ready comparisons to literature.

Notwithstanding, the pipeline also affords vertex-wise analysis and thus registration of microvascular reactivity with other local morphological features, at an unprecedented spatial scale.

With increasing distance of the vessel from the most proximal activated neuron, dilatation magnitude decreased and the incidence of constrictions increased.

At the highest stimulation level investigated, the incidence of vessel constrictions also increased with cortical depth.

With increasing activation levels, capillaries displayed diameter changes that were similar to their immediate neighbors, while vascular network efficiency increased only under the strongest stimulation.

Our computational analysis pipeline permits probing microvascular network reactivity and sheds light on the heterogeneity and coordination of vessel caliber changes across the microvascular network.

The pipeline will be made available to the research community to propel future studies of neurovascular coupling and network reactivity.

captions

=== Figure 1 === Figure 1. Photostimulation setup.⟦>zach claim=6fb7137d-c89c-4651-9271-014bd6823995: @{=== Figure 1 === Figure 1. Photostimulation setup.} dl-model-scope-single-pipeline⟧

The excitation and stimulation light pass through a FV30-NDM690 dichroic mirror with two notch filters, at 458 nm and 552 nm, to excite TexasRed, EYFP, and ChR2 within the mouse.

The emitted light passes through the objective, is reflected off the FV30-NDM690 dichroic mirror, and passes through a 650 nm barrier filter before reaching a 570 nm long pass filter (LPF) separating emitted light from EYFP and TexasRed, which respectively pass through 495–540 nm and 575–630 nm barrier filters to be collected via GaAsP detectors. === Figure 2 === Figure 2. Computational analysis pipeline.⟦>zach claim=no-assertion: @{The emitted light passes through the objective, is reflected off the FV30-NDM690 dichroic mirror, and passes through a 650 nm barrier filter before reaching a 570 nm long pass filter (LPF) separating emitted light from EYFP and TexasRed, which respectively pass through 495–540 nm and 575–630 nm barrier filters to be collected via GaAsP detectors. === Figure 2 === Figure 2. Computational analysis pipeline.} A description of the microscope's emission path and filters - instrument setup, not a finding.⟧

( A ) The stacks of 2PFM slices were registered using ANTS rigid registration and aligned to the reference time point.

( B ) Images were upsampled using bicubic interpolation to an isotropic resolution of 0.99 x 0.99 × 0.99 μm.

( C ) An ensemble of UNETR deep learning models with dropout generated segmentation masks at each time point, producing probability maps.

( D ) The mean and standard deviation of the probability of each pixel being vasculature were computed and used to create binary vascular segmentation masks.

( E ) The union over the vascular segmentation masks for all time points was computed, and background pixel clusters within vessel masks were removed.

( F ) The vascular segmentation mask was thinned down to centerlines and rendered as a graph, where edges were vessel segments connecting branch points (nodes).

This skeleton was overlaid on the vasculature channel from which the neuron channel was subtracted.

( G ) The plane orthogonal to the tangent to the vessel’s travel direction was computed every micrometer along the centerline.

( H, I ) 1D signal intensity profiles at each centerline vertex were computed in the orthogonal plane every 10°.

( J ) The boundary for each profile was placed at the minimum of the signal gradient for that signal intensity profile.

( K ) The raw intensity image with the detected boundary points, where outlier boundary points (in green) were defined as points over 2 standard deviations from the mean were excluded.

( L ) Visualization of the changes in vertex-wise radii on a sample vascular network.

[panels detected: a, b, c, d, e, f, g, h, i, j, k, l] === Figure 3 === Figure 3. Model performance metrics.⟦>zach claim=73b97665-ce32-4361-81d0-40fb149cea2b: @{[panels detected: a, b, c, d, e, f, g, h, i, j, k, l] === Figure 3 === Figure 3. Model performance metrics.} novas3d-outperforms-ilastik⟧

The Dice, precision, recall, mean surface distance, and HD95 distance for the vascular ( A ) and neuron ( B ) channels.

Each model was evaluated on the same test dataset composed of nine images (250 x 507 × 507 μm each) from six mice.

A Wilcoxon signed-rank test was used to compare the model’s performance on each performance metric for images from the test dataset. * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.⟦>zach claim=db0a9448-8fe8-4336-b220-0a71aa19d870: @{A Wilcoxon signed-rank test was used to compare the model’s performance on each performance metric for images from the test dataset. * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.} unetr-outperforms-ilastik-hd95⟧

[panels detected: a, b] === Figure 4 === Figure 4. Visual model comparison.⟦>zach claim=73b97665-ce32-4361-81d0-40fb149cea2b: @{[panels detected: a, b] === Figure 4 === Figure 4. Visual model comparison.} novas3d-outperforms-ilastik⟧

( A ) Raw images of the vascular channel with the neuron channel subtracted to facilitate vessel visualization.

The first and last stacks in each row span from the cortical surface to 250 μm below the surface, while the middle stack spans from 250 μm below the surface to 500 μm below the surface.

All images were from the test dataset, which was unseen during model training.

( B ) Ground truth segmentation masks for the vasculature were generated by a rater who utilized ilastik-assisted manual segmentation.

( C ) Ilastik predictions generated via a random forest model.

( D ) Binary segmentation masks generated by an ensemble of 3D UNet models.

( E ) Binary segmentation masks generated by an ensemble of 3D UNETR models.

[panels detected: a, b, c, d, e] === Figure 5 === Figure 5. Estimation of simulated radii changes.⟦>zach claim=9639b9cb-4a39-4046-ba35-db6f8f88dfbb: @{[panels detected: a, b, c, d, e] === Figure 5 === Figure 5. Estimation of simulated radii changes.} radius-estimation-r2-0p68⟧

( A ) An image in the plane orthogonal to the local tangent to a capillary with the detected boundary (in blue) and with the estimated radius of 2.28 μm.

On the right, this image was resized (upsampling, via bicubic interpolation, by 1.10 times) to simulate dilation.

( B ) The plot shows correspondence between the estimated radius following scaling and the simulated level of scaling.

( C ) An image in the plane orthogonal to the local tangent of a capillary with the detected boundary (in blue) and with the estimated radius of 3.65 μm.

On the right, Gaussian noise with a sigma of 205.36 SU was added to the image.

( D ) The estimated % change in the vessel’s radius after the addition of varying levels of Gaussian noise, demonstrating the robustness of the radius estimated to noise.

[panels detected: a, b, c, d] === Figure 6 === Figure 6. Vascular graph examples.⟦>zach claim=de368a2f-dfdf-4cb5-9c5f-24e3e0cf3aae: @{[panels detected: a, b, c, d] === Figure 6 === Figure 6. Vascular graph examples.} vessel-radius-heterogeneity-stimulation⟧

( A ) Baseline variability in vessel diameter estimated by the standard deviation of each vessel’s mean radius across baseline time frames.

( B ) Mean change in the vessel radius induced by optogenetic stimulation.

( C ) Mean change in the vertexwise radius, allowing the visualization of heterogeneity of radius changes within each vessel.

( D ) Distance from each vertex to the closest pyramidal neuron.

Each row corresponds to the vascular graph of a different mouse.

[panels detected: a, b, c, d] === Figure 7 === Figure 7. Vertex-wise radii along vessel lengths of a sample artery, capillary, and venule at baseline vs. post-stimulation.⟦>zach claim=083ef4c3-9e93-4927-9867-3c785b2c03c0: @{[panels detected: a, b, c, d] === Figure 7 === Figure 7. Vertex-wise radii along vessel lengths of a sample artery, capillary, and venule at baseline vs. post-stimulation.} baseline-intra-vessel-radius-varies-24pct⟧

( A ) MIP of an artery, vein, and capillary segments before (left) and after (right) optogenetic stimulation with 458 nm light at 1.1 mW/mm 2 .

The artery and capillary dilated by 1.33±0.86 μm and 0.42±0.39 μm, respectively (for both p<1e-4, Mann-Whitney U test), whereas there was no significant change in the venular caliber upon photostimulation (p=0.22, Mann-Whitney U test).⟦>zach claim=af8bc967-3346-4e25-ac9e-8ad3536313fd: @{The artery and capillary dilated by 1.33±0.86 μm and 0.42±0.39 μm, respectively (for both p<1e-4, Mann-Whitney U test), whereas there was no significant change in the venular caliber upon photostimulation (p=0.22, Mann-Whitney U test).} artery-dilates-venule-unchanged-at-low-power⟧

( B ) Estimates of the vertex-wise radius obtained along each of the three vessels’ centrelines, before and after stimulation.

( C ) Vertex-wise radii changes in response to optogenetic stimulation.

( D ).

The vertex-wise distance from the vascular surface to the closest YFP-expressing neuron.

[panels detected: a, b, c, d] === Figure 8 === Figure 8. Optogenetic activation-induced changes in vessel-wise microvascular radii.⟦>zach claim=a68ebd2f-95bf-43a9-b0e0-f9ce7a3fa5bd: @{[panels detected: a, b, c, d] === Figure 8 === Figure 8. Optogenetic activation-induced changes in vessel-wise microvascular radii.} blue-light-dilations-exceed-green-control⟧

Capillary responses included both dilatations, shown in (A), and constrictions, shown in ( B ), with changes in the magnitude of the capillary response with increased photostimulation power. * p<0.05, ** p<0.005, and *** p<0.0005. p-values were not adjusted.⟦>zach claim=db0a9448-8fe8-4336-b220-0a71aa19d870: @{Capillary responses included both dilatations, shown in (A), and constrictions, shown in ( B ), with changes in the magn