CAA-related enlarged perivascular spaces are associated with
abnormal angioarchitecture in human brain tissue:
a key role for white matter atrophy?

Marion Giraud<fnref n="1" />, Dae Hee Yun<fnref n="2" />, Leon P. Munting<fnref n="3" />,
Kwanghun Chung<fnref n="2" />, Brian J. Bacskai<fnref n="3" />, Steven M. Greenberg<fnref n="3" />,<fnref n="4" />, Matthew P. Frosch<fnref n="3" />,<fnref n="5" />,
Alain Goriely<fnref n="6" />, Susanne J. van Veluw<fnref n="3" />,<fnref n="4" />*, Sylvie Lorthois<fnref n="1" />*

Cerebral Amyloid Angiopathy, a common age-related small vessel disease leading to hemorrhagic stroke, shares many characteristics with Alzheimer's disease: toxic amyloid deposits, microvascular alterations and enlarged perivascular spaces (EPVS). Together, PVS enlargement, reduced amyloid-β clearance and further accumulation form a vicious cycle underlying disease progression. Yet, the neuropathological correlates of EPVS, including the associated angioarchitecture, are poorly understood. We provide quantitative 3D reconstructions of human brain microvascular networks and their topographical associations with EPVS in large volumes of cleared human tissue spanning over the gray/white matter interface. We reveal the existence of six vessel/PVS morphotypes, including sinusoid and helical vessels, enclosed in increasingly enlarged PVS, and increasingly disconnected from their surrounding network. Based on the buckling of elongated structures, we discuss how they likely result from generic processes of mechanical origin, driven by white matter atrophy, thus advancing our understanding of the pathophysiological overlap between amyloid-related and cerebrovascular disease.

**Keywords:** Cerebral Amyloid Angiopathy, Microvascular network, Perivascular spaces, Quantitative microanatomy, Vascular Contribution to Cognitive Impairment and Dementia.

<footnote n="1">¹Institut de Mécanique des Fluides de Toulouse (IMFT), UMR 5502, Université de Toulouse, CNRS, INPT, Université Toulouse III - Paul Sabatier (UPS), Toulouse, France.</footnote>

<footnote n="2">²Institute for Medical Engineering and Science (IMES), Massachusetts Institute of Technology, Cambridge, MA, USA.</footnote>

<footnote n="3">³Department of Neurology, MassGeneral Institute for Neurodegenerative Disease, Massachusetts General Hospital / Harvard Medical School, Charlestown, MA, USA.</footnote>

<footnote n="4">⁴Department of Neurology, J. Philip Kistler Stroke Research Center, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA.</footnote>

<footnote n="5">⁵Neuropathology Service, C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital / Harvard Medical School, Boston, MA, USA.</footnote>

<footnote n="6">⁶Mathematical Institute, University of Oxford, Oxford, United Kingdom.</footnote>

<footnote>*Corresponding authors. **Sylvie Lorthois**, Institut de Mécanique des Fluides de Toulouse, Allées du Professeur Camille Soula, 31400 Toulouse, France (sylvie.lorthois@imft.fr)</footnote>

<footnote>**Susanne van Veluw**, Massachusetts General Hospital Stroke Research Center, MA, USA, now at British Heart Foundation - UK Dementia Research Institute Center for Vascular Dementia Research, The University of Edinburgh, Edinburgh, United Kingdom (susanne.vanveluw@ed.ac.uk).</footnote>
**Introduction**

Metabolic waste, including amyloid-β (Aβ) and its neurotoxic byproducts, are cleared from the brain by distinct but anatomically [1] and mechanically [2] coupled pathways: elimination by the bloodstream after crossing the blood-brain barrier and, elimination by cerebrospinal fluid (CSF) in perivascular spaces (PVS), i.e., micron-range porous gaps between the walls of small blood vessels and astrocyte end-feet in the brain [3, 4].

These pathways are involved in both Alzheimer's disease (AD), the leading cause of dementia, and Cerebral Amyloid Angiopathy (CAA), a common cerebral small vessel disease in older individuals with no disease-modifying treatment options [5]. The common feature of both neurological disorders is Aβ that accumulates in the cortical tissue in AD and in the walls of small cortical blood vessels in CAA [6, 7]. In fact, it is the hallmark of these diseases as detection of tissue or vascular Aβ at autopsy is required for a definitive diagnosis of either AD or CAA. In addition to Aβ build-up, both pathologies exhibit abnormal PVS enlargement, to such an extent that PVS become visible on magnetic resonance imaging (MRI) with sizes up to millimeters [8, 9]. It is likely that enlarged perivascular spaces (EPVS) are part of a self-reinforcing cycle of reduced perivascular clearance, increased vascular Aβ and further PVS enlargement leading to increased CAA and AD [10]. Both pathologies also exhibit microvascular alterations, such as increased vessel tortuosity, and microvascular lesions, such as microhemorrhages [11, 12].

Importantly, CAA is the most common vascular comorbidity in AD [6, 13], the leading cause of hemorrhagic stroke in the elderly, and a major vascular contributor to cognitive impairment and dementia [14]. However, the sequence of events between vascular Aβ build up and the hemorrhagic manifestations of CAA is poorly understood. In particular, intracerebral hemorrhage, which is radiologically detected in the cortex and surrounding white matter (WM) in CAA [15], is believed to be a direct consequence of Aβ-induced vessel wall weakening [16, 10]. However, observations from ex vivo MRI-targeted neuropathological investigations in autopsy material recently revealed that microhemorrhages in the cortex occur in vessels without vascular Aβ at the rupture site [16]. These findings suggest that blood vessels likely undergo an additional step of vascular remodeling between initial Aβ deposition and vessel rupture [17, 18]. This remodeling could induce large scale redistributions of blood flows and pressures that may lead to vessel rupture distant from the site of vascular Aβ. Indeed, scattered microanatomical data show that blood vessels within EPVS exhibit highly abnormal and tortuous shapes, not only in CAA and AD but also in ageing and other diseases [19, 20, 21, 22, 23, 24]. This suggests that PVS enlargement involves profound alterations in the adjoining vascular and parenchymal tissue components, which could drive vessel remodeling, not only at the local but also at the network scale.

To examine this hypothesis, we focus on CAA and provide the first quantitative three-dimensional (3D) reconstructions of CAA-affected and healthy microvascular networks colocalized with their surrounding EPVS in large volumes (15 to 45 mm³) of cleared human tissue spanning over the gray matter (GM)/WM interface. We use these unique data to examine the spatial relationships between EPVS and network-scale vascular alterations that may lead to distant microvascular lesions (e.g., microhemorrhages). In particular, we ask: (i) Where are EPVS located in the interconnected vessel network, in terms of hierarchical distance to the capillary bed? (ii) Is increased PVS enlargement associated with increased morphological alterations of the vessels within the EPVS? (iii) Are these local morphological alterations associated with larger-scale topological changes within the vessel network? Finally, we use the theory of mechanical buckling in slender structures to show that corresponding vessel/EPVS morphotypes likely result from a generic mechanical instability, thus explaining the overlap between (peri-)vascular alterations in ageing and neurodegenerative disease.
# Materials and Methods

Histopathology on thin paraffin sections is the gold standard for neuropathological investigations from autopsy material but hampers the appreciation of complex network properties within the interconnected 3D microvasculature and its topographical associations with EPVS. To overcome this limitation, we leveraged recent advances with optical clearing and labeling, enabling the vasculature and Aβ deposits in large human brain tissue samples from two CAA cases (CAA1 & CAA2) and one non-CAA control (CTRL) (Table 1 and Section *Cases*) to be imaged as a whole at cellular resolution by light-sheet microscopy (LSM), see more details in Sections *Tissue processing and clearing* and *Light-sheet microscopy*.

We selected six regions of interest (ROIs) spanning over the GM/WM interface and with volumes ranging from 16 to 44 mm³ (Section *Regions of Interest*), half of them from CAA cases (Table 1). By combining open, commercial and in-house software as described in Section *Image analysis for microvessel network*, we reconstructed the entire vascular network in each ROI. These networks are fully interconnected but, taken separately, arterioles and venules have a dichotomous, tree-like organization, while the capillary network has a net-like structure [25, 26, 27, 28, 29, 30]. Thus, we automatically extracted arterioles and venules by iteratively following the branches of individual trees, starting from their larger trunk, and stopping when encountering a loop, as described in Section *Vessel taxonomy and metrics*. We further used the diameter-modified Strahler taxonomy to produce a hierarchical ranking of vessels in these trees, assigning them an order from 0, for the terminal branches closest to the capillaries, to a maximal rank for the branches closest to the pial tree origin [31, 26]. In many instances, several vessel segments of the same order were connected in series: their combination was called a vessel element [26]. From the LSM images, we also reconstructed all the Aβ deposits, as described in Section *Vessel taxonomy and metrics* as well as all EPVS, as described in Section *Image analysis and metrics for EPVS*.

# Cases

Cases were selected from an ongoing brain donation program in clinically diagnosed patients with CAA within the stroke research center at the Massachusetts General Hospital (MGH) [19, 32, 17]. Study approval was received from the MGH institutional review board. The study was conducted in accordance with the Declaration of Helsinki and informed consent was obtained prior to autopsy. Brains were extracted and fixed in 10% formalin, after which they were split through a mid-sagittal cut. Brain samples including cortex and juxtamarginal white matter were collected after ~1-2 weeks of fixation (10% buffered formalin, 1 week at room temperature (RT)) and placed in water, as longer periods of formalin fixation negatively impact brain clearing. A total number of eight brain areas (measuring 10x20x5mm) were sampled from three CAA cases and one non-CAA control. Note that the non-CAA control case met criteria for probable CAA during life, but upon neuropathological examination did not reveal substantial CAA pathology to support a definite CAA diagnosis [19]. Samples were taken from single gyri, and care was taken to include the convexities, although occasionally the adjacent sulci were included as well (see Fig. 1 I-IIIa). Two samples from a single CAA brain had to be excluded from further analysis, because the tissue clearing process was unsuccessful. Thus, the final dataset consisted of six samples from three cases (Table 1). For all the included cases, whole hemisphere ex vivo MRI scans and stained sections (H&E, Aβ) were available from the corresponding areas in the contralateral hemisphere as summarized in Supplementary Methods SI1.1.
## Tissue processing and clearing

Preservation of human brain samples was carried out according to the previously published SHIELD protocol with longer incubation steps to accommodate the larger sample sizes [33, 34]: samples were incubated for 5 days at 4°C in SHIELD-OFF solution and 1 day at RT in SHIELD-ON solution before thorough washing in Phosphate Buffered Saline (PBS) for at least 1 day. To improve macromolecule permeability and optical access, samples were cleared using stochastic electrotransport (SmartClear Pro, LifeCanvas Technologies) in delipidation solution for 10-15 days to remove lipids. Cleared samples were each labeled with 100 µL of Thioflavin S (ThioS), labeling Aβ, and 100 µL of DyLight 649 conjugated Tomato Lectin (Vectorlabs), labeling vasculature, using eFLASH [35] where electric field is used to improve the transport of molecular probes for uniform labeling of large samples. They were additionally fixed (PBS with 4% formaldehyde, 40 mL per sample) overnight at RT for lectin retention, then thoroughly washed with PBS for at least 1 day. Labeled and fixed samples were incubated in EasyIndex (LifeCanvas Technologies) for refractive-index (RI) matching for two days at 37°C, 20 mL per sample. RI-matched samples were mounted in 2%(w/v) agarose dissolved EasyIndex.

## Light-sheet microscopy

Mounted samples were imaged with a 488 nm and 647 nm laser using a SmartSPIM (LifeCanvas Technologies) light-sheet microscope system at 2 or 4 µm z-step size. 4X 0.2NA custom objective was used with 1.8 µm x 1.8 µm resolution. Acquired images were preprocessed according to [36] with 1/ stripe artifact correction, 2/ volume stitching and 3/ illumination correction, as summarized in Supplementary Methods SI1.2. Representative optical sections from the 488nm (Aβ channel) and 647nm (vessel channel) excitation are displayed in Fig. 1 I-IIIb,c.

## Regions of Interest

Visual examination of the LSM data in both channels showed a variable surface density of LSM-visible dark spots and rings around vessels, that we operationally defined as EPVS for the purpose of this study (see Fig. 1 I-IIId, arrows) by analogy with the STRIVE criteria [37]. We thus chose Regions of Interest (ROIs) as to enclose all visible EPVS in a given sample, while avoiding its borders and areas with the largest heterogeneities of luminosity (see Fig. 1 I-IIIc,d). These resulted in ROI volumes ranging from 16 to 44 mm³. Cortical GM and WM in these ROIs were obtained by manual segmentation guided by overall differences in background intensity in both channels and in differences in vessel density. This segmentation was checked in CAA cases as it overlapped with the distribution of Aβ deposits in the Aβ channel, which are preferentially found in the GM, see Supplementary Methods SI1.3 and Supplementary Fig. S1.

## Image analysis for microvessel network

Depending on their radii, vessel projections in the image planes appeared as filled or hollow tubes in the vessel channel, with some vessels also showing up in the Aβ channel. To extract a graph description of the microvessel network in an entire ROI, i.e., for each vessel, the list of centerline coordinates and associated radii, we combined: further custom processing steps developed to handle heterogeneous luminosity, larger radius distribution than for rodent samples and double labeling, with segmentation methods implemented in the opensource code ClearMap [38] and thinning and skeletonization methods [26, 39], as implemented in the commercial software Avizo (Thermo Fisher Scientific). Details on the procedures are given in
Supplementary Methods SI1.4. Because of the large radius differences among vessels and of the presence of Aβ deposits, many artifacts were present in the obtained graph (Supplementary Fig. S2a). We corrected these artifacts by 1/ performing an independent skeletonization of the larger vessels (Supplementary Fig. S2b) and replacing the corresponding edges in the global skeleton, and 2/ by locating tissue Aβ deposits in the Aβ channel and specifically correcting for the associated artifacts, as described in Supplementary Methods SI1.5 and SI1.6, respectively. Remaining loop artifacts were corrected (Supplementary Fig. S2c) using a minimal spanning tree algorithm as described in Supplementary Methods SI1.5.

## Vessel taxonomy and metrics

Vessels were classified depending on their type: capillaries were identified as vessels belonging to the mesh-like part of the microvascular network, while arterioles or venules are those belonging to its tree-like structures [29, 30]. These tree-like structures, some of which are illustrated in Supplementary Fig. S3a for a control sample (CTRLp), were automatically extracted as described in Supplementary Methods SI1.7. After visually inspecting each resulting tree, these vessels were considered as arterioles or venules. Their topology was analyzed according to the diameter-defined Strahler taxonomy [31, 26]. This taxonomy produces a hierarchical ranking of vessel segments, assigning them an order from 0, for the terminal branches closest to the capillaries, to a maximal rank for the branches closest to the pial tree origin, as illustrated in Supplementary Fig. S3b, limiting the variability of hierarchical ranking in incomplete trees that cannot be traced back to this origin. In many instances, several vessel segments of the same order are connected in series: their combination is then called a vessel element (Supplementary Fig. S3b) [26].

We further characterized the morphology of these vessel elements based on the combination of three shape descriptors, as detailed in Supplementary Methods SI1.8. Briefly, we defined 1/ the mean deviation from the principal plane (MDP), which distinguishes curves embedded in a plane from 3D ones, 2/ the inflection count metrics (ICM), which multiplies the number of inflections plus one present in a 3D space curve by the distance metrics and is designed to characterize curves depending on their number of bends [40, 41]; and 3/ the maximal amplitude ratio (MAR) of these bends, i.e. portions of vessels between two inflection points, after projection in the principal plane, which characterizes the extension of bends out of the main axis. In addition, we estimated their lineic connectivity (LC), i.e., number of offshoots by unit length, by dividing the number of segments within a vessel element by the total arc length of this element.

We also visually assessed the amount of Aβ in their walls, thus giving a score from 0, corresponding to no vascular Aβ deposit (no signal in the Aβ channel) to score 3 for a large Aβ deposit around the vessel, with radius larger than twice the vessel radius (see Supplementary Methods SI1.9 and Supplementary Fig. S4). We further grouped scores 0-1 (no-CAA) and scores 2-3 (CAA positive). This classification was qualitatively checked by comparison with elongated Aβ deposits automatically identified from the Aβ channel (see Supplementary Methods SI1.3).

Finally, elements located within an EPVS, including non-continuous EPVS (see below), were detected from the coordinates and serial surfaces of each EPVS. For morphological comparisons between vessel elements within an EPVS with non-EPVS vessels, only the single central element of the EPVS was selected as the longest Strahler element inside its volume.

## Image analysis and metrics for EPVS

EPVS projections in the image planes appeared as dark areas in both the Aβ and vessel channels, as illustrated in Fig. 1 IVa,b for a CAA sample (CAA1o). Most EPVS were surrounding, or located next, to a vessel, with approximately elliptical (red boxes in Fig. 1 IV) or more elongated (blue boxes) outer contours. A few
of them did not contain any detectable vessel (black boxes in Fig. 1 IV). This results in a large variability of EPVS shapes, as highlighted by the hatched areas in Fig. 1 IVd, which are difficult to differentiate from the typical lightsheet artifacts of luminosity that are induced by some of the bright Aβ-filled vessels in the Aβ channel (gray boxes in Fig. 1 IV). Thus, to automatically segment these EPVS, we implemented a two-step procedure. Exploiting the strong local contrast between EPVS and the background in the Aβ channel (Fig. 1 IVa,c), we first extracted 3D sub-ROIs, each one approximately centered around a given EPVS, as described in Supplementary Methods SI1.10. We then focused on each of these sub-ROIs in turn, excluding those spanning over less than 20 optical sections to avoid the erroneous selection of artifactual structures, to automatically segment the EPVS by Maximum Entropy Thresholding (see Supplementary Methods SI1.11). For all individual EPVS, this yielded the shape of their projections in each optical section *k*, as illustrated by the hatched areas in Fig. 1 IVd. From these, we extracted a series of metrics characterizing the morphology of each EPVS, including the average surface *S*<sub>EPVS</sub> of its cross sections, its total volume *V*<sub>EPVS</sub> and its average equivalent outer radius *r*<sub>out</sub> and eccentricity *e*, as detailed in Supplementary Methods SI1.12. Finally, by a thorough visual inspection of the images in the excluded sub-ROIs (with *D* ≤ 20 slices) or in EPVS with *V*<sub>EPVS</sub> ≤ 10,000 voxels or elongations *e*<sup>*k*</sup> > 2.5, we identified the raw images with a small region in the vicinity of the vessel that appeared darker than the associated luminosity artifact (see Supplementary Methods SI1.10 and Supplementary Fig. S5). These were considered as EPVS that did not span the whole length of a vessel and qualitatively labeled as non-continuous EPVS, without further metric quantification.

## Statistical analysis

Due to the non-normal distribution of the data, only non-parametric statistical tests were used. Furthermore, due to the limited sample size, no testing was performed between the patient and control groups. However, the large sample volumes allowed for statistical analysis between the different types of vessels or EPVS found within each individual sample. Thus, we compared the tortuosity and connectivity between different vessel types, based on a Kruskal Wallis test (scipy.stats.kruskal in scipy python library), reporting the degrees of freedom and H statistic as H(df). If the test showed that there was a statistical difference between the different vessel types in the same sample, we identified which groups were significantly different from each other using post-hoc one-sided Bonferroni-corrected Wilcoxon signed rank tests (BWSRT) (scipy.stats.ranksums). We also used the post-hoc one-sided BWSRT to compare the morphometry of different vessel and EPVS morphotypes across all samples. The significance thresholds were chosen as: * *p* < 0.05, ** *p* < 0.01, *** *p* < 0.001. Multiple comparisons were accounted for in all multiple-group *p*-value calculations by multiplying by the number of comparisons. To assess the magnitude of observed differences, we computed Cliff's delta (δ, a non-parametric effect size measure) along with its 95% confidence intervals (CI).
## Results

Using the above pipeline, we provide the first quantitative 3D reconstructions of CAA-affected and healthy microvascular networks (Supplementary Fig. S6, raw data available as detailed in Data Availability Statement) in large volumes (15 to 45 mm³) of cleared human tissue spanning over the GM/WM interface (see e.g., Supplementary Movies S1,S2). From these networks, we automatically extracted a total of 698 arteriolar or venular trees (Fig. 2a and Supplementary Fig. S7). We further used the diameter-modified Strahler taxonomy to produce a hierarchical ranking of vessels in these trees, including 20,866 vessel elements, with Strahler orders between 0 and 4 (Table 2 and Fig. 2b). We also reconstructed all EPVS (in gray in Fig. 2c-f) as well as the Aβ deposits (in orange in Fig. 2e). A total of 57 EPVS were observed in CAA cases, with the large majority (44 of them) in sample CAA1o, while only 6 were found in the control sample (Table 2). In CAA1o, some EPVS were found with no detectable vessel inside (n=13), as shown in blue in Fig. 2e. These results are consistent with the known occipital predominance of vascular Aβ in CAA [42, 17].

### EPVS are localized around vessels with high to intermediate hierarchical position in vascular trees

We traced the connections of vessels surrounded by EPVS within the overall vascular network. No capillaries were found within EPVS. All EPVS-associated vessels were within the extracted tree-like structures. The longest Strahler element inside each EPVS volume (e.g., elements displayed in green inside EPVS contours in Fig. 2c-d) was always longer than 300 µm and of order between 2 and 4. Noteworthy, the maximal Strahler order in the healthy human cortex, i.e. at the connection with the pial vasculature, is 5 [26]. Thus, elements within EPVS were in an intermediate to higher position in the vascular hierarchy (i.e., penetrating to small but not terminal arterioles and venules).

For comparison, we identified a total of 1,351 elements with similar properties (order ≥ 2, length ≥ 300 µm). The large majority of them (1,202) was not associated with any detectable enlargement of their PVS (green cells in Table S1). Among these elements, those with vascular Aβ (i.e., with scores 2 or 3, see Section *Vessel taxonomy and metrics* and Supplementary Fig. S4) were labeled as positive CAA elements. As expected, all were found in the GM, and the proportion of CAA elements per tissue sample was 0%, 9% and 32% for CTRL, CAA2 and CAA1, respectively, consistent with the documented cortical CAA severity in each case (Table 1). Vessel elements belonging to a tree with at least one CAA element were labeled as connected to Aβ. As expected, none of them were found in the control samples. The corresponding numbers of elements for each sample are summarized in Table S1.

### Morphological alterations of vessels within EPVS are associated with PVS enlargement

By visually inspecting the topographical association between EPVS and the longest element inside, we identified various morphotypes of the vessel/PVS units, as illustrated in Fig. 2f. In some cases, the EPVS spanned the whole length of a vessel element that oscillated with sinusoid-like bends roughly contained within a single plane (Fig. 2f, upper panel). The EPVS outer border followed the envelope of these oscillations, resulting in a ribbon-like EPVS shape, with elongated cross-section (Fig. 1 IVc, blue boxes, Supplementary Fig. S8, upper panel). In other cases, the central EPVS vessel exhibited a 3D helical shape (Fig. 2f, lower panel), with a tube-like EPVS outer border (Fig. 1 IVc, red boxes, Supplementary Fig. S8,
lower panel). We labeled these EPVS-associated morphotypes as types 4 (T4) and 5 (T5), respectively (Fig. 3a). The segregation into two types was confirmed by a statistical analysis of the metrics characterizing vessel planarity (mean deviation from the principal plane, MDP) and EPVS shape (eccentricity), respectively. MDP was significantly different (*p* = 0.00038, δ = -0.67, CI [-0.98, -0.29], Bonferroni-corrected Wilcoxon signed rank test (BWSRT)) between T4 (median=5.10 µm; interdecile range=0.39 – 9.60 µm) and T5 elements (14.41 µm; 2.46 – 26.74 µm), confirming their planar and 3D nature, respectively (Fig. 3b). We also found a highly significant decrease of mean EPVS eccentricity with a large effect size (*p* = 1.76×10⁻⁶, δ = 0.88, CI [0.74–0.99], between T4 (0.84; 0.67 – 0.92) and T5 (0.71; 0.64 – 0.78), confirming the transition between ribbon-like and tube-like EPVS shapes (Fig. 3c).

By analogy with the Standards for Reporting Vascular changes on nEuroimaging (STRIVE) criteria, which define perivascular spaces as *fluid-filled spaces that follow the typical course of a vessel as it goes through grey or white matter, and with similar signal intensity to CSF* [37], we labeled EPVS with no detectable vessel inside as morphotype 6 (T6, Fig. 3a, Supplementary Fig. S9). These EPVS (Fig. 1 IVc, black boxes and Fig. 2e, blue contours) were mostly tube-like, with the exception of one, which was ribbon-like. They had significantly lower eccentricity than T4 (0.73; 0.59 – 0.76, *p* = 3.77×10⁻⁶, δ = 0.88, CI [0.72–1.00]) but were not significantly different from T5 (*p* = 0.079 δ = -0.33, CI [-0.80–0.14]) (Fig. 3c). In addition, the EPVS average surface *S*<sub>EPVS</sub> increased significantly from T4 (9276 µm²; 392 – 19,297 µm²) to T5 (13,960 µm²; 3778 – 26,391 µm²) and 6 (25,256; 2588 – 48,854 µm²), with *p* = 0.035, Cliff's δ = -0.34, CI [-0.64 to 0.01] and *p* = 0.0013, δ = -0.59, CI [-0.87 to -0.25], respectively. No significant EPVS surface difference was found between T5 and T6 (*p* = 0.057, δ = -0.37, CI [-0.78 to 0.06]), Fig. 3d.

Other morphotypes were observed, including abnormally tortuous vessels only intermittently associated with an EPVS, labeled as type 3 (T3). T3 vessels exhibited oscillations with sinusoid-like bends approximately contained in a single plane, similar to T4 vessels. Other sinusoid-like vessels did not exhibit any PVS enlargement, and were labeled as type 2 (T2). All other vessels had less than 2 inflection points. They did not exhibit any PVS enlargement and were labeled as type 1 (T1). Fig. 3a displays a schematic representation of all morphotypes.

## Morphological alterations of PVS and their associated vessels correlate with neuropathological findings

The occurrence of each morphotype in the WM and GM of all samples, as well as their relationship with CAA pathology, is presented in Table S1. For each case, the overall proportion of vessel elements of each type is displayed in Fig. 3e-g. Considering both GM and WM elements, the total proportion of abnormal morphotypes (T2 to T6) decreases from 26% for CAA1 to 14% for CAA2 to 13% for controls (Fig. 3e), and from 36% for CAA1 to 23% for CAA2 to 18% for controls if only considering WM elements (Fig. 3f). Among these elements, the strongly altered morphotypes, i.e., associated with continuous EPVS (T4 to T6), were predominantly found in the WM, with 100% of them in the WM for CAA2 and control, and more than 95% for CAA1. The ~5% observed in the GM were close to the GM/WM interface. Moreover, these morphotypes still amounted to 15% and 9% of elements in the WM of CAA1 and CAA2, respectively, but less than 2.5% in the WM of controls. These results are consistent with the documented cumulative CAA score and correlate with the overall pathological findings (see Table 1).

Finally, in the tissue samples from CAA1, all T4 to T6 elements could be tracked back to CAA positive vessels in the cortical portion of the same vascular tree, as determined by global automatic detection of elongated Aβ deposits in the Aβ channel (as illustrated in orange in Fig. 2d,e and Supplementary Fig. S9) or by local scoring of individual vessel elements (Table S2). This represents more than twice the proportion of
the T1 elements with vascular Aβ in the overlying cortex (44%). In contrast, in the samples from CAA2, more of the T1 elements were connected to CAA positive ones: 77% of T1 versus 27% of T4 to T6.

## Vessel elements within EPVS are more tortuous than normal vessels in the same sample

Taking advantage of the large number of vessel elements extracted, we then compared their number of bends and the amplitude of these bends among morphotypes in each sample, based on the inflection count metrics (ICM) and maximal amplitude ratio (MAR), respectively. The 3D nature of vessel elements was also compared based on the MDP. In all samples where more than five EPVS elements were found (CAA1o, CAA2f and CTRLf), Kruskal Wallis tests showed that at least one morphotype is significantly different from the others for these three metrics, with *p* < 0.01 except for ICM in CTRLf (*p* < 0.05, H(3)=10.04). Accordingly, for all these samples, significant increases of medians were observed for all shape descriptors based on Wilcoxon rank sum tests between the group of non-EPVS-elements (T1 to T3) and the group of EPVS-elements (T4 and T5). In CAA1f, where only two EPVS elements were found (two T4 elements), the Kruskal-Wallis test for differences between types was still significant for ICM (*p* = 0.015, H(3)=10.360) and MAR (*p* = 0.033, H(3)= 8.725). The difference in terms of MDP was non-significant, which is consistent with the planar nature of T1 to T4.

When comparing vessels of different types in the same sample, an overall increase of all shape descriptors with increasing morphological alterations, from T1 to T5, was observed, as illustrated in Fig. 4a-c for CAA1o. First, a global increase of the median between successive types was obtained, despite some noise (e.g., small decrease between T2 and T3 in CAA1o and CTRLf for MAR, see Fig. 4a-c, Supplementary Fig. S10), leading to ratios of the medians *m*(Tj)/*m*(Ti) with most values above one if *j* > *i* for all shape descriptors (Supplementary Fig. S11). In samples CAA1o, CAA2f and CTRLf, the number of elements of each type was sufficient to perform pairwise comparisons (BWSRT) between T1 and T2 to T4 (to T5 in CAA1o). For all these samples, a significant increase of the MAR median with large effect size (*p* < 0.01, *δ* <-0.5) was obtained between T1 and T4 or T5 (Supplementary Fig. S10a and S11). When comparing successive levels of alterations, significance could be lost due to multiple comparisons, but, noteworthy, T5 elements always exhibit significantly higher median of MDP than all other types (Supplementary Fig. S11), confirming the transition between sinusoidal and helical elements as schematized in Fig. 3a. In contrast, in samples which only included non-EPVS elements, either too few T2 or T3 elements were detected, or no significant differences were found between these types, see Supplementary Fig. S11 for CTRLp and CTRLt.

Altogether, these results show that vessel elements within EPVS are morphologically altered, with a higher number and amplitude of bends than normal vessels. Moreover, the most tortuous vessels (T5) also have 3D shapes, while other morphotypes are mostly contained in a single plane.

Importantly, all elements considered were of Strahler orders 2 to 4, ensuring that these differences in tortuosity did not originate from differences of hierarchical positions in the vascular trees. Moreover, Fig. 4 (right panel) and Supplementary Fig. S10b show that these differences in tortuosity neither originate from a differential position in the GM versus WM. Except in sample CTRLf, were the number of non-EPVS elements in the GM is very small (*n* = 8), BWSRT highlight no significant differences between the tortuosity of non-EPVS elements in the GM and in the WM, while they show highly significant increases (*p* < 10⁻⁶, *δ* >0.7) between non-EPVS and EPVS elements in the WM. Also, the shape descriptors for the non-EPVS GM elements were of same order of magnitude as those computed from a healthy reference human database of GM elements [26] (Fig. 4), with significant differences only for the MAR (Fig. 4 and Supplementary Fig. S10b).
## Vessel elements within EPVS are less connected to the surrounding microvasculature than normal vessels

Besides changes in tortuosity, 3D reconstructions of vascular trees (Fig. 2b) suggested decreased connectivity to the surrounding microvasculature (decreased numbers of offshoots) for trees with some elements in EPVS, compared to trees including only non-EPVS elements. This often led to smaller values of the highest Strahler number in the former (Fig. 2b). This qualitative observation was confirmed by the statistics of lineic connectivity by element type displayed in Fig. 4d for CAA1o and Supplementary Fig. S12a. In particular, Kruskal Wallis tests showed highly significant differences of at least one morphotype in all samples where more than five EPVS elements were found ($p = 2.9 \times 10^{-11}$, $p = 9.3 \times 10^{-8}$ and $p = 5.7 \times 10^{-5}$ and $H(4)=55.23$, $H(3)= 35.57$ and $H(3)=22.27$, in CAA1o, CAA2f and CTRLf, respectively). Accordingly, for all these samples, highly significant decreases of medians were observed based on Wilcoxon rank sum tests between the group of non-EPVS-elements (T1 to T3) and the group of EPVS-elements (T4 and T5), with large effect sizes $p = 2.1 \times 10^{-11}$, $p = 4.2 \times 10^{-8}$ and $p = 1.4 \times 10^{-6}$, $\delta = -0.67$, $\delta = -0.97$ and $\delta = -0.88$, and CI [-0.93, -0.33], [-0.99, -0.93] and [-0.99, -0.73] in CAA1o, CAA2f and CTRLf, respectively. In CAA1f, where only two EPVS elements were found (two T4 elements), the Kruskal-Wallis test for differences between types was still significant ($p = 0.038$, $H(3)=8.40$).

When comparing vessels of different types in the same sample, Fig. 4d, left, for CAA1o and Supplementary Fig. S12a suggest an overall decrease of lineic connectivity with increasing morphological alterations, from T1 to T5. In samples CAA1o, CAA2f and CTRLf, the number of elements of each type is sufficient to perform pairwise comparisons (BWSRT) between T1 and T2 to T4 (to T5 in CAA1o). For each of these samples, the significance level increases when comparing T1 to other types: non-significant between T1 and T2, always significant, even if moderately when comparing T1 and T3, and always highly significant when comparing T1 and T4 or T5 (Supplementary Fig. S12). When comparing successive levels of alterations, significance may be lost due to multiple comparisons (Supplementary Fig. S11). However, when a sufficient number of elements has been detected in the sample to perform the comparison, T2 always exhibits significant difference with T4 or T5 (Supplementary Fig. S11). In contrast, in samples which only included non-EPVS elements, either too few T2 or T3 elements were detected, or no significant differences could be detected between elements of T1 and T2, see Supplementary Fig. S11 for CTRLp and CTRLt. Altogether, these results suggest that vessel elements within EPVS are less connected to their surrounding microvasculature than normal vessels, with stronger morphological alterations related to further decreases in connectivity.

Noteworthy, along the same lines as before, these differences in connectivity do neither originate from differences of hierarchical positions in the vascular trees nor from a differential position in the GM versus WM (Fig. 4d, right, and Supplementary Fig. S12b), no significant difference between non-EPVS elements in the GM and in the WM vs. highly significant decrease ($p < 10^{-4}$) for EPVS elements in the WM with large effect sizes across all samples ($\delta<-0.5$). Thus, they imply large-scale topological reorganizations in CAA compared to normal vessel networks in the WM.
Discussion and outlook

Recent advances in tissue clearing methods combined with LSM have transformed the way we can visualize
the microvasculature of the brain, by enabling large volumes to be imaged at unprecedented spatial resolution.
High performance post-processing methods in cleared whole-brain samples have enabled extensive 3D
reconstructions of the vascular network in rodents [38, 43, 44]. Here, we adapted this strategy to brain
samples from patients with CAA and non-CAA controls to study the spatial relationships between the
brain microvasculature and EPVS.

Focusing on vessels with high to intermediate hierarchical position in vascular trees (i.e., penetrating to small but not terminal arterioles and venules), our results revealed the existence of six morphotypes of vessel/PVS units in our samples (Fig. 3a), characterized by 1/ increased tortuosity of vessels from healthy ones (T1) to sinusoid ones (T2-T4), with further transition to 3D coils (T5); 2/ increased PVS enlargement, from undistinguishable enlargement (T1-T2) and the appearance of locally-enlarged spaces inside vessel bends (T3) to continuous EPVS with increased cross-section and transition from planar ribbon-like (T4) to 3D tube-like (T5-T6) shapes; and 3/ progressive disconnection from surrounding vessel network (T1-T5), with ultimately total apparent disconnection for EPVS with no detectable vessel inside (T6). The strongly altered morphotypes, i.e., associated with continuous EPVS (T4-T6), were predominantly found in the WM, whereas those without detectable PVS enlargement (T1-T2), were distributed across the GM/WM interface (~40%/60%).

Noteworthy, while ex vivo samples and preparation procedures could lead to losses of microstructural integrity and/or distortions that may alter the shapes of anatomical structures, the SHIELD technique combined with stochastic electrotransport, used here for tissue clearing and labeling, both have been specifically developed to preserve tissue architectural integrity across scales [34, 35], as demonstrated by moderate and isotropic expansion in mouse brain samples and visualization of cyto- and myelo-architectural details from cellular to tissue scale in human brain samples [34]. Moreover, tortuous medullary arteries, with diameter ~50-200 µm have been recently observed within MRI- visible PVS in vivo in cognitively normal individuals [45], with positive associations between the number of EPVS or the proportion of those with an enclosed tortuous artery and age [45]. In this in vivo study, tortuous vessels not surrounded by an EPVS have also been found [45] suggesting that our observed morphotypes are not artifacts due to the ex vivo nature of samples nor tissue clearing. Furthermore, tortuous vessels could also be observed in retrospect within EPVS from ex vivo MRI in our same dataset (see Supplemental Movies in [19]). Similar morphotypes could also be detected on classical histopathological sections, where T4 and T5 respectively appear as roughly aligned dots within a thin cavity (Fig. 2g, upper panel) or as scattered dots within a larger cavity (Fig. 2g, lower panel). The existence of these two classes of abnormal vessels has also been described from their 2D projections as "tortuous" (Fig. 5a) versus "looped" [11], "glomerular loop formations" or "intracortical coiling" (Fig. 5b) [46, 25] and has been histologically observed within EPVS not only in CAA and AD but also in ageing and other diseases [19, 20, 21, 22, 23, 24]. This suggests that generic mechanisms may explain their formation across a large range of vessel scales and conditions.

What may be these mechanisms? On the one hand, brain atrophy, inflammation, hypertension, microvas-
cular obstruction or impaired perivascular flow, possibly induced by vascular Aβ in the overlying cortex in
CAA, have all been hypothesized to underlie PVS enlargement (see [8, 19] and references therein), while [45]
suggested it may be initiated by the deformation of inside vessels, which exert "outward pressure". On the
other hand, our observed vessel morphotypes typically match the shapes taken by rods or tubes embedded
in an elastic matrix and submitted to increasing compressive loads (buckling modes, which include in-plane sinusoidal shapes similar to T2-T4 and out-of-plane coiled shapes similar to T5 [47, 48, 49]), suggesting there may be a mechanical origin. To gain insight on such mechanisms, we performed a linear stability analysis of an idealized blood vessel in the presence of a symmetry-breaking mechanism, e.g., induced by non-axisymmetric vessel cross sections. We found that the smallest compressive force triggering unstable behavior is $F_{cr}^{min} = 2(K_{min}k)^{1/2}$, where $K_{min}$ is the minimal azimuthal bending stiffness and $k$ is the matrix stiffness. The associated shape is an in-plane sinusoidal shape (Fig. 5c) with a number $N$ of bends proportional to $(k/K_{min})^{1/4}$ (for more detail, see Supplementary Results SI2), so that increasing the stiffness of the matrix or decreasing that of the vessel increases the number of bends. Noting that the WM is stiffer than the GM in healthy human brains [50], the above mechanisms are consistent with the preferential localization of highly tortuous vessels in the WM found in human post-mortem brain tissue, independent of age [51], and in controls in the present study.

Applying larger forces triggers a second planar instability in the perpendicular transverse direction, which superimposes to the first one, eventually yielding helical shapes with elliptical sections in the ideal case ($K_1 \approx K_2$, Fig. 5d) or helical-like shapes (Fig. 5e), depending on the parameters (rod diameter and length, rod and matrix rigidity and their anisotropy), consistent with experimental observations in model systems [47]. As a result, with small variability of the compressive load or out-of-plane perturbations, all these shapes can be observed simultaneously, so that this universal instability mechanism of a compressed slender structure embedded in a matrix is fully consistent with the coexistence of T2 to T5 in CAA1o (Table S1).

When considering pressurized pipes, a similar behavior has been observed, with higher transmural pressures ($p_{lumen} - p_{PVS}$) generically leading to smaller critical compressive forces associated with a given mode [48, 49], once again consistent with the theory of linear elastic stability, so that hypertension would favor buckling in contrast to fluid stagnation in EPVS. Thus, the main mechanical ingredient underlying the observed vessel shapes must be the establishment of a compressive force, with faster transition among successive modes favored by increased blood pressure, but dampened by increased PVS pressure.

Moreover, the stability analysis (Supplementary Results SI2) shows that, for a given vessel, planar solutions (T2-T4) will always be observed for smaller compressive forces than 3D ones (T5). When additionally considering unbinding from the matrix (delamination), neither the smallest critical force nor the shape of the corresponding planar mode are changed since the instability takes places at null amplitude. However, as its amplitude increases, delamination is first observed at places of largest displacements where tension forces are maximal, consistent with the intermittent occurrence of EPVS at the sinusoid bends in T3. When a helical mode develops, material points further away from the centreline will first delaminate. As its amplitudes increase, all points reaching that critical distance will delaminate and one expects a cavity to form within the helix, similar to T5. Further evidence in favor of a mechanical instability is the negative association between myelin density in the WM and severity of EPVS in post-mortem human tissue [19]. Thus, we conclude that all qualitative aspects of PVS enlargement are consistent with a generic buckling instability.

Previous research suggests that CAA may locally impair CSF flow in the PVS [52, 53, 54], potentially leading to fluid stagnation and PVS enlargement in the WM portion of the vessel [19]. While EPVS surround vessels directly connected to vascular Aβ deposits in CAA1, we also found that vascular Aβ deposition in the upstream cortical portion is not required to trigger PVS enlargement, consistent with independence of MRI visible PVS volume fractions and amyloid load in cognitively normal and mild-cognitive impairment individuals [55]. Thus, our results suggest additional mechanisms may be at play, including buckling-
induced delamination, consistent with the presence of EPVS in aging and multiple pathologies [19, 20, 21, 22, 23, 24].

Finally, each observed morphotype must result from a dynamic process by which its shapes evolved continuously from T1. Despite the cross-sectional nature of our data, the ordered coexistence of the observed morphotypes in each sample, with type *N* + 1 only present when type *N* is also present (Fig. 3e-g and Supplementary Tab. S1) is consistent with the above stability analysis. One can therefore speculate that compressive forces have established over time in our samples, so that each observed morphotype may indeed have appeared after the other, as suggested in Fig. 3a. While the origin of such compressive forces still needs to be determined, the simplest explanation would be an overall displacement of WM boundaries induced by WM atrophy, which has been reported at early stages in patients with CAA [56], in animal models of CAA that recapitulate many of the pathological aspects of human CAA, including microhemorrhages, [57], and has been found to be correlated with the number of MRI visible tortuous medullary arteries in cognitively-normal humans [45]. Designing longitudinal experiments in which the local mechanical properties of vessel walls (with or without $A\beta$) and nearby tissue are probed (e.g., by atomic force microscopy [58]) in animal models will be crucial to refine our theoretical understanding of how $A\beta$ accumulation in the cortex could interact with WM loss to favor morphogenetic changes in the WM. In this same conceptual framework, EPVS with no detectable vessel inside could result from gradual loss of lectin labeling or even complete vessel regression, the mechanisms of which are still to be elucidated. Such mechanisms could lead to progressive vessel disconnection from the surrounding microvasculature at earlier stages. More refined analysis considering vessel remodeling (e.g., [59]) and its impact on vessel stiffness, as well as studies focusing on the associations between occurrence and severity of abnormal morphotypes and regional variations of WM atrophy in CAA [56] and other diseases [55, 60], will be needed to identify these mechanisms. Notwithstanding, our findings provide evidence of topological alterations of the brain microvascular network in CAA. Compared to normal-appearing vessels, the connectivity to the surrounding microvasculature of vessels within EPVS was significantly reduced. Because of their high position in the vascular hierarchy, this is likely to yield an overall increase of the resistance of the associated trees [28], beyond that induced by the increased tortuosity only. This should drive a global decrease of the regional blood flow, consistent with the report of hypoperfusion in CAA [61]. Moreover, a local increase of blood flow in vessels surrounding these trees (inverse to vascular steal [62]) is expected, which could explain why microhemorrhages are often found in blood vessels without $A\beta$ in CAA [16].

This study had several limitations. First, the sample size was small, which is related to the inherent complexity of the tissue processing and analysis pipeline. These findings should therefore be considered exploratory and need to be reproduced in larger studies. Second, there is a potential risk for selection bias as the cases included in this study may not be fully representative of typical individuals with CAA or healthy individuals. As such, the findings from this study require external validation. Finally, for the purpose of this study, we operationally defined EPVS as dark spots or rings around vessels visible on LSM, by analogy with the STRIVE criteria [37]. The cross-sectional nature of the study did not allow us to establish whether these are truly the result of pathological dilation of the PVS. However, the above mechanisms are highly generic. They provide a simple and plausible bio- mechanical rationale linking PVS enlargement to the formation of tortuous vessels, in which white matter atrophy plays a key role. Thus, observation of tortuous vessels within EPVS, not only in CAA but also in aging and other forms of small vessel and neurodegenerative disease, suggest they could be the signature of cerebral atrophy. Moreover, disconnection of such vessels from the surrounding microvasculature in CAA uncovers a biomechanical link between $A\beta$ pathology and cerebrovascular manifestations of CAA.
## Acknowledgments

We gratefully acknowledge F. Cassot, F. Lauwers and J.-P. Marc-Vergnes for sharing human anatomical data, V. Perosa for providing expert advice on the identification of EPVS, P. Swider and P. Assemat for inspiring discussions about mechanical buckling, J. Le Dreff who prepared a preliminary version of Fig. 3a, A. Shih who connected the Boston and Toulouse groups, N. Renier and his group for making Tubemap available and the Lorentz Center in Leiden. This work has been funded by NIH-NINDS RF1NS110054, NIH-NIA R00AG059893 and the Leducq Fondation (23CVD03). MG was awarded an international mobility grant from ED MEGEP.

## Author Contributions

Designed research: MG, LPM, KC, BJB, SMG, MPF, SJvV, SL; Performed research: MG, LPM, AG, SJvV, SL; Prepared anatomical material: DHY, SJvV; Acquired data: DHY; Analyzed data: MG, LPM, AG, SJvV, SL; Contributed to theory: AG, SL; Prepared figures: MG, AG, SJvV, SL; Wrote the paper: LPM, MG, DHY, AG, SJvV, SL; Reviewed the manuscript: MG, DHY, LPM, KC, BJB, SMG, MPF, AG, SJvV, SL; Supervised research: SJvV, SL
Acquired Funding: BJB, SJvV, SL.

## Competing Interests

Except SJvV, the authors have no competing interests to declare. SJvV has consulted for Eisai and serves on the board of the international CAA association. She also receives funding from the BrightFocus Foundation and the American Heart Association, unrelated to this work. Her lab receives industry support from Therini Bio and Sanofi.

## Data Availability Statement

Raw data containing the graph representations of microvascular networks in all samples, as displayed in Supplementary Fig. S6, as well as Supplementary Movies S1 and S2 and a high resolution version of all Figures and Supplementary material, have been deposited in the Harvard Dataverse (https://dataverse.harvard.edu/) and are publicly available from https://doi.org/10.7910/DVN/BRYPB2.

## Supplementary Information Content

- Supplementary Figures S1 to S23

- Supplementary Tables S1 and S2

- Captions for Supplementary Movies S1 and S2

- Supplementary Methods

- Supplementary Results: Theoretical analysis of the buckling of a blood vessel embedded in a matrix
Figures

<figure><img src="image_1.png" /><figcaption>**Figure 1:** Enlarged perivascular spaces (EPVS). I-III. Examples of visible enlarged EPVS in the Aβ channel of optical sections imaged by light-sheet microscopy (LSM) from a CAA case versus a control case. (a) Photograph of formalin-fixed sample. (b) Vessel-channel for a representative optical section of the whole sample. (c) Corresponding Aβ channel; the red box highlights the selected ROI. (d) Enlarged view of the selected ROI with blue arrows pointing to large visible EPVS. I: CAA1o; II: CAA1f; III: CTRLf. IV. Examples of the diversity of EPVS shapes in the CAA1o sample. (a) Representative optical section from the Aβ channel. (b) Corresponding optical section from the vessel channel. (c) Zoom on both channels: ribbon-like EPVS (blue boxes), tube-like EPVS (red boxes), EPVS with no detectable vessel inside (black boxes) and typical illumination artifact, which mainly shows up in the Aβ channel (gray boxes). (d) The surface of the projection of an EPVS in the image plane, S<sub>EPVS</sub>, corresponds to the hatched area. The non-hatched white area corresponds to the vessel projection.</figcaption></figure>
<figure><img src="image_2.png" /><figcaption>**Figure 2:** Volumetric visualizations of tree-like vessels and EPVS reconstructions. (a) Vessel elements in the tree-like structures in 3 CAA samples (color-coded by diameter). For clarity of the visualization, vessel elements with mean diameter below 12 µm have not been included. (b) Examples of typical EPVS tree-like structures in CAA1o (left) and non-EPVS-trees in CTRLp (right, same trees as in Supplementary Fig. S3a). (c) Same tree-like structures as in panel b, left, with their associated EPVS highlighted in gray; (d) with automatically identified elongated Aβ deposits (CAA) highlighted in orange. (e) Visualization of all EPVS in a larger volume of CAA1o, with the central EPVS element displayed in red, and CAA in orange. (f) Enlarged view of typical examples of a sinusoid element associated with a ribbon-like EPVS (Type 4, top) and a helical element associated with a tube-like EPVS (Type 5, bottom), both extracted from the volume in (e). (g) Thin-cut paraffin sections processed for immunohistochemistry from the occipital WM of a different CAA case in the MGH CAA autopsy case series, revealing a ribbon-like EPVS with aligned dots (top) and a tube-like EPVS with scattered dots corresponding to the cut of a sinusoid and helical vessels, respectively, by a plane intersecting the vessel main axis.</figcaption></figure>
<figure><img src="image_3.png" /><figcaption>**Figure 3: Morphological alterations of vessels within EPVS are associated with PVS enlargement and correlate with neuropathological findings.** (a) Schematics of vessel/EPVS morphotypes (top) and their projections in two imaging planes A and B (bottom). (b-d) Comparison between metrics characterizing vessels visually classified as T4 (sinusoidal) or T5 (helical), see more details in Section *Morphological alterations of vessels within EPVS are associated with PVS enlargement*, (b) and their associated EPVS (c-d). EPVS with no detectable vessel inside are also considered in (c-d). (b) Mean deviation from plane (MDP). (c) Mean eccentricity. (d) EPVS surface. (e-f) Proportion of vessel elements of all types depending on case, in the whole ROI (e), WM (f) and GM (g). Filled areas: T4 to T6 (continuous EPVS); empty areas: T1 (normal vessels). For more details on legend, see top of panel f.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 4:** PVS enlargement is associated to increased tortuosity of vessels within EPVS and decreased connectivity with surrounding vascular network. Statistics (median and interdecile range) of MAR (a), ICM (b), MDP (c) and lineic connectivity (d) in sample CAA1o for vessels in morphotypes 1 to 5 (T1 to T5) (left) and comparison of EPVS elements, all in the WM (T4-T5 (WM)), with non-EPVS ones in the WM (T1-T3 (WM)) and GM (T1-T3 (GM)), and with reference GM human data (Cassot) [26] (right). \* *p* < 0.05, \*\* *p* < 0.01, \*\*\* *p* < 0.001.</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>**Figure 5:** Typical examples of morphotypes 4 (a) and 5 (b) from CAA1o and of theoretical shapes predicted by **stability analysis** (c-e). (c): Planar solution with parameters $K_1=1$, $K_2=1.1$, $k = 16$ and amplitude $A_1 = 0.15$. (d): Helical-solution with same parameters and amplitudes $A_1 = A_2 = 0.225$. (e): Helix-like solution with $K_2$ increased to 1.5 and $A_1 = 4/3A_2 = 1$; for more details see Eq. S24 in Supplementary Results SI2.</figcaption></figure>
Tables

<table><caption>**Table 1:** Case characteristics: #=number of samples; M=Male; F=Female; f=frontal; o=occipital; t=temporal; p=parietal. ¹CAA was assessed on histopathological sections from the contralateral hemisphere (see Supplementary Methods S11.1 and Supplementary Fig. S13) that underwent ex vivo MRI and subsequent neuropathological examination in the lab. Histopathological sections underwent immunohistochemistry against Aβ and were assessed for leptomeningeal and cortical CAA severity according to the Love et al. criteria [63]. ²Extracted from final neuropathological reports, based on routine neuropathological examination. ABC score reflects the National Institute on Aging-Alzheimer Association score for Alzheimer disease neuropathological changes (ADNC) [64].</caption><thead><tr><th>Case<br>(#: areas)</th><th>Age<br>at death</th><th>Sex</th><th>Cumulative CAA score<sup>1</sup><br>(leptomeningeal / cortical)</th><th>Main neuro-<br>pathological findings<sup>2</sup></th></tr></thead><tbody><tr><td>CAA1<br>(2: f,o)</td><td>76</td><td>M</td><td>12/11</td><td>Severe CAA (Vonsattel grade 4)<br>Severe arteriolosclerosis<br>ADNC A3B3C3</td></tr><tr><td>CAA2<br>(1: f)</td><td>57</td><td>F</td><td>12/7</td><td>Severe CAA (Vonsattel grade 3)</td></tr><tr><td>CTRL<br>(3: f,t,p)</td><td>82</td><td>F</td><td>2/1</td><td>Moderate arteriolosclerosis<br>ADNC A0B1C0</td></tr></tbody></table>

<table><caption>**Table 2:** Global statistics of the tree-like part of the vasculature in the studied samples. The capillary part of the vasculature has been removed from the microvessel network, thus revealing the tree-like structures (see, e.g., Supplementary Fig. S3a), whose vessel elements have been ranked according to Strahler's classification (Supplementary Fig. S3b). Elements inside the segmented volume of each EPVS have been automatically labeled as elements in EPVS (see, e.g., Fig 3c), while EPVS without any detectable vessel inside, such as those highlighted in blue in Fig. 2e, have also been included.</caption><thead><tr><th>Sample</th><th>CAA1o</th><th>CAA1f</th><th>CAA2f</th><th>CTRLf</th><th>CTRLp</th><th>CTRLt</th></tr></thead><tbody><tr><td>Nb of trees</td><td>92</td><td>68</td><td>158</td><td>44</td><td>231</td><td>105</td></tr><tr><td>Total nb of elements</td><td>3286</td><td>2571</td><td>6104</td><td>2765</td><td>2764</td><td>3376</td></tr><tr><td>Strahler 4</td><td>0</td><td>3</td><td>2</td><td>13</td><td>29</td><td>11</td></tr><tr><td>Strahler 3</td><td>93</td><td>88</td><td>155</td><td>129</td><td>72</td><td>119</td></tr><tr><td>Strahler 2</td><td>564</td><td>349</td><td>713</td><td>346</td><td>266</td><td>507</td></tr><tr><td>Strahler 1</td><td>795</td><td>778</td><td>1369</td><td>608</td><td>888</td><td>968</td></tr><tr><td>Strahler 0</td><td>1834</td><td>1353</td><td>3865</td><td>1669</td><td>1509</td><td>1771</td></tr><tr><td>Elements in EPVS</td><td>31</td><td>2</td><td>11</td><td>6</td><td>0</td><td>0</td></tr><tr><td>EPVS with no detectable vessel inside</td><td>13</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td></tr></tbody></table>
References

[1] P. C. Nahirney and M.-E. Tremblay. "Brain Ultrastructure: Putting the Pieces Together". *Frontiers in Cell and Developmental Biology* 9 (2021), 629503.

[2] A. A. Linninger et al. "Cerebrospinal fluid mechanics and its coupling to cerebrovascular dynamics". *Annual Review of Fluid Mechanics* 48 (2016), 219-257.

[3] J. Tithof et al. "Hydraulic resistance of periarterial spaces in the brain". *Fluids and Barriers of the CNS* 16 (2019), 19.

[4] L. Bojarskaite et al. "Sleep cycle-dependent vascular dynamics in male mice and the predicted effects on perivascular cerebrospinal fluid flow and solute transport". *Nature Communications* 14 (2023), 953.

[5] M. Kozberg et al. "A practical approach to the management of cerebral amyloid angiopathy". *International Journal of Stroke* 16 (2021), 356-369.

[6] A. Charidimou et al. "The Boston criteria version 2.0 for cerebral amyloid angiopathy: a multicentre, retrospective, MRI-neuropathology diagnostic accuracy study". *The Lancet Neurology* 21 (2022), 714-725.

[7] C. G. Janson. "AD and CAA: Independent risk factors for dementia". *Science Translational Medicine*. 7 (2015), 318ec214

[8] H. H. Adams et al. "A priori collaboration in population imaging: The Uniform Neuro-Imaging of Virchow-Robin Spaces Enlargement consortium". *Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring* 1 (2015), 513-520.

[9] J. M. Wardlaw et al. "Perivascular spaces in the brain: anatomy, physiology and pathology". *Nature Reviews Neurology* 16 (2020), 137-153.

[10] S. Greenberg et al. "Cerebral amyloid angiopathy and Alzheimer disease — one peptide, two pathways". *Nature Reviews Neurology* 16 (2019), 1-13.

[11] V. W. Fischer, A. Siddiqi, and Y. Yusufaly. "Altered angioarchitecture in selected areas of brains with Alzheimer's disease". *Acta Neuropathologica* 79 (1990), 672-679.

[12] J. Davis et al. "A Novel Transgenic Rat Model of Robust Cerebral Microvascular Amyloid with Prominent Vasculopathy". *The American Journal of Pathology* 188 (2018), 2877-2889.

[13] L. Jaickel et al. "Prevalence of cerebral amyloid angiopathy: A systematic review and meta-analysis". *Alzheimers and Dementia* 18 (2022), 10-28.

[14] R. A. Corriveau et al. "The Science of Vascular Contributions to Cognitive Impairment and Dementia (VCID): A Framework for Advancing Research Priorities in the Cerebrovascular Biology of Cognitive Decline". *Cellular and Molecular Neurobiology* 36 (2016), 281-288.

[15] M. Pasi and C. Cordonnier. "Clinical relevance of cerebral small vessel diseases". *Stroke* 51 (2020), 47-53.

[16] S. J. van Veluw et al. "Reduced vascular amyloid burden at microhemorrhage sites in cerebral amyloid angiopathy". *Acta Neuropathologica* 133 (2017), 409-415.
[17] S. J. van Veluw et al. "Different microvascular alterations underlie microbleeds and microinfarcts". *Annals of Neurology* 86 (2019), 279-292.

[18] M. G. Kozberg et al. "Blood-brain barrier leakage and perivascular inflammation in cerebral amyloid angiopathy". *Brain Communications* 4 (2022), fcac245.

[19] V. Perosa et al. "Perivascular space dilation is associated with vascular amyloid-β accumulation in the overlying cortex". *Acta Neuropathologica* 143 (2022), 331-348.

[20] W. R. Brown et al. "Venous collagenosis and arteriolar tortuosity in leukoaraiosis". *Journal of the Neurological Sciences* 203-204 (2002), 159-163.

[21] H. Nonaka et al. "The Microvasculature of the Cerebral White Matter: Arteries of the Subcortical White Matter". *Journal of Neuropathology & Experimental Neurology* 62 (2003), 154-161.

[22] W. R. Brown and C. R. Thore. "Review: Cerebral microvascular pathology in ageing and neurodegeneration". *Neuropathology and Applied Neurobiology* 37 (2011), 56-74.

[23] B. V. Ineichen et al. "Dilated Virchow-Robin spaces are a marker for arterial disease in multiple sclerosis". *eBioMedicine* 92 (2023), 104631.

[24] E. S. Poni et al. "Tortuosity of terminal arterioles in the basal ganglia is increased in status lacunaris." *Invest Clin.* 44 (2003), 137-145.

[25] H. Duvernoy, S. Delon, and J. Vannson. "Cortical blood vessels of the human brain". *Brain Research Bulletin* 7 (1981), 519-579.

[26] F. Cassot et al. "A Novel Three-Dimensional Computer-Assisted Method for a Quantitative Study of Microvascular Networks of the Human Cerebral Cortex". *Microcirculation* 13 (2006), 1-18.

[27] F. Lauwers et al. "Morphometry of the human cerebral cortex microcirculation: General characteristics and space-related profiles". *NeuroImage* 39 (2008), 936-948.

[28] F. Cassot et al. "Scaling Laws for Branching Vessels of Human Cerebral Cortex". *Microcirculation*, 16 (2009), 331-344

[29] S. Lorthois and F. Cassot. "Fractal analysis of vascular networks: Insights from morphogenesis". *Journal of Theoretical Biology* 262 (2010), 614.

[30] A. Smith et al. "Brain capillary networks across species: a few simple organizational requirements are sufficient to reproduce both structure and function". *Frontiers in Physiology* 10 (2019), 1-22.

[31] G. S. Kassab, D. H. Lin, and Y. C. Fung. "Morphometry of pig coronary venous system". *American Journal of Physiology-Heart and Circulatory Physiology* 267 (1994). PMID: 7810711, H2100-H2113.

[32] S. J. van Veluw et al. "Histopathology of diffusion imaging abnormalities in cerebral amyloid angiopathy". *Neurology* 92 (2019), e933-e943.

[33] S. Kim et al. "Stochastic electrotransport selectively enhances the transport of highly electromobile molecules". *PNAS* 112 (2015), E6274-E6283.

[34] Y.-G. Park et al. "Protection of tissue physicochemical properties using polyfunctional crosslinkers". *Nature Biotechnology* 37 (2019), 73-83.
[35] D. H. Yun et al. "Ultrafast immunostaining of organ-scale tissues for scalable proteomic phenotyping". *bioRxiv* (2019), 660373.

[36] J. Swaney et al. "Scalable image processing techniques for quantitative analysis of volumetric biological images from light-sheet microscopy". *bioRxiv* (2019), 576595.

[37] J. M. Wardlaw et al. "Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration". *Lancet Neurology* 12 (2013), 822–838.

[38] C. Kirst et al. "Mapping the Fine-Scale Organization and Plasticity of the Brain Vasculature". *Cell*. 180 (2020), 780–795.e25.

[39] C. Fouard. "Extraction de paramètres morphométriques pour l'étude du réseau micro-vasculaire cérébral". Thèse de doctorat de l'université de Nice 2005. PhD thesis. 2005, 184 p.

[40] E. Bullitt et al. "Measuring tortuosity of the intracerebral vasculature from MRA images". *IEEE Transactions on Medical Imaging* 22 (2003), 1163–1171.

[41] A. B. Brummer, D. Hunt, and V. Savage. "Improving Blood Vessel Tortuosity Measurements via Highly Sampled Numerical Integration of the Frenet-Serret Equations". *IEEE Transactions on Medical Imaging* 40 (2021), 297–309.

[42] D. H. Salat et al. "White Matter Alterations in Cerebral Amyloid Angiopathy Measured by Diffusion Tensor Imaging". *Stroke* 37 (2006), 1759–1764.

[43] M. Todorov et al. "Machine learning analysis of whole mouse brain vasculature". *Nature Methods* 17 (2020), 1–8.

[44] A. Di Giovanna et al. "Whole-brain vasculature Reconstruction at the Single Capillary Level". *Scientific Reports* 8 (2018), 12573.

[45] Z. Sun et al. "In vivo detection of age-related tortuous cerebral small vessels using ferumoxytol-enhanced 7T MRI". *Aging and disease* 15, 1110 (2024), 1–14.

[46] O. Hassler. "Vascular changes in senile brains". *Acta Neuropathologica* 5 (1965), 40–53.

[47] T. Su et al. "Buckling of an elastic rod embedded on an elastomeric matrix: planar vs. non-planar configurations". *Soft Matter* 10 (2014), 6294–6302.

[48] H.-C. Han. "Blood vessel buckling within soft surrounding tissue generates tortuosity". *Journal of Biomechanics* 42 (2009), 2797–2801.

[49] H.-C. Han. "The Theoretical Foundation for Artery Buckling Under Internal Pressure". *Journal of Biomechanical Engineering* 131 (2009). 124501.

[50] S. Budday et al. "Mechanical properties of gray and white matter brain tissue by indentation". *Journal of the Mechanical Behavior of Biomedical Materials* 46 (2015), 318–330.

[51] C. R. Thore et al. "Morphometric Analysis of Arteriolar Tortuosity in Human Cerebral White Matter of Preterm, Young, and Aged Subjects". *Journal of Neuropathology and Experimental Neurology* 66 (2007), 337–345.

[52] M. Li et al. "Impaired Glymphatic Function and Pulsation Alterations in a Mouse Model of Vascular Cognitive Impairment". *Frontiers in Aging Neuroscience* 13 (2022), 788519.

[53] S. J. van Veluw et al. "Vasomotion as a Driving Force for Paravascular Clearance in the Awake Mouse Brain". *Neuron* 105 (2020), 549–561.e5.

[54] M. Arbel-Ornath et al. "Interstitial fluid drainage is impaired in ischemic stroke and Alzheimer's
disease mouse models”. *Acta neuropathologica* (2013).

[55] Sepehrband et al. "Volumetric distribution of perivascular space in relation to mild cognitive impairment". *Neurobiology of Aging* 99 (2021), 28e43.

[56] P. Fotiadis et al. "White matter atrophy in cerebral amyloid angiopathy". *Neurology* 95 (2020), e554-e562.

[57] H. Lee et al. "Diffuse white matter loss in a transgenic rat model of cerebral amyloid angiopathy". *Journal of Cerebral Blood Flow & Metabolism* 41 (2021), 1103-1118.

[58] E. Moeendarbary et al. "The soft mechanical signature of glial scars in the central nervous system". *Nature Communications* 8 (2017), 14787.

[59] D. Weiss et al. "Mechanics-driven mechanobiological mechanisms of arterial tortuosity". *Science Advances* 6 (2020), eabd3574.

[60] G. Barisano et al. "Robust, fully-automated assessment of cerebral perivascular spaces and white matter lesions: a multicentre MRI longitudinal study of their evolution and association with risk of dementia and accelerated brain atrophy". *eBioMedicine* 111 (2025), 105523.

[61] Y.-A. Chung et al. "Hypoperfusion and Ischemia in Cerebral Amyloid Angiopathy Documented by 99mTc-ECD Brain Perfusion SPECT". *Journal of Nuclear Medicine* 50 (2009), 1969-1974.

[62] S. Lorthois, F. Cassot, and F. Lauwers. "Simulation study of brain blood flow regulation by intra-cortical arterioles in an anatomically accurate large human vascular network. Part II: Flow variations induced by global or localized modifications of arteriolar diameters". *NeuroImage* 54 (2011), 2840-2853.

[63] S. Love et al. "Development, appraisal, validation and implementation of a consensus protocol for the assessment of cerebral amyloid angiopathy in post-mortem brain tissue". *American journal of neurodegenerative disease* 3 (2014), 19-32.

[64] B. Hyman et al. "National Institute on Aging-Alzheimer's Association guidelines for the neuropathologic assessment of Alzheimer's disease". English. *Alzheimer's Dement* 8 (2012), 1-13.