# IDENTIFICATION OF MULTIPLE TARGETS IN THE FIGHT AGAINST ALZHEIMER'S DISEASE

EDITED BY : Patrizia Giannoni, Silvia Fossati, Sylvie Claeysen and Elena Marcello PUBLISHED IN : Frontiers in Aging Neuroscience

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ISSN 1664-8714 ISBN 978-2-88963-909-0 DOI 10.3389/978-2-88963-909-0

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# IDENTIFICATION OF MULTIPLE TARGETS IN THE FIGHT AGAINST ALZHEIMER'S DISEASE

Topic Editors: Patrizia Giannoni, University of Nîmes, France Silvia Fossati, Temple University, United States Sylvie Claeysen, Institut National de la Santé et de la Recherche Médicale (INSERM), France Elena Marcello, University of Milan, Italy

Citation: Giannoni, P., Fossati, S., Claeysen, S., Marcello, E., eds. (2020). Identification of Multiple Targets in the Fight against Alzheimer's Disease. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88963-909-0

# Table of Contents

*05 Editorial: Identification of Multiple Targets in the Fight Against Alzheimer's Disease*

Patrizia Giannoni, Silvia Fossati, Elena Marcello and Sylvie Claeysen

*09 Cerebrovascular Smooth Muscle Cells as the Drivers of Intramural Periarterial Drainage of the Brain*

Roxana Aldea, Roy O. Weller, Donna M. Wilcock, Roxana O. Carare and Giles Richardson

*26 Impairment of Dendrodendritic Inhibition in the Olfactory Bulb of APP/PS1 Mice*

Weiyun Li, Shanshan Li, Lianghua Shen, Junbo Wang, Xuewei Wu, Jing Li, Chunlong Tu, Xuesong Ye and Shucai Ling

*42 Identifying Changepoints in Biomarkers During the Preclinical Phase of Alzheimer's Disease*

Laurent Younes, Marilyn Albert, Abhay Moghekar, Anja Soldan, Corinne Pettigrew and Michael I. Miller

*53 Hydroxyurea Improves Spatial Memory and Cognitive Plasticity in Mice and Has a Mild Effect on These Parameters in a Down Syndrome Mouse Model*

Rebecca Deering Brose, Alena Savonenko, Benjamin Devenney, Kirby D. Smith and Roger H. Reeves

Mar Cuadrado-Tejedor, Marta Pérez-González, Cristina García-Muñoz, Damián Muruzabal, Carolina García-Barroso, Obdulia Rabal, Víctor Segura, Juan A. Sánchez-Arias, Julen Oyarzabal and Ana Garcia-Osta

*102 Decoding the Role of Platelets and Related MicroRNAs in Aging and Neurodegenerative Disorders*

Yolanda Espinosa-Parrilla, Christian Gonzalez-Billault, Eduardo Fuentes, Ivan Palomo and Marcelo Alarcón

*120 3D Reconstruction of the Neurovascular Unit Reveals Differential Loss of Cholinergic Innervation in the Cortex and Hippocampus of the Adult Mouse Brain*

Shereen Nizari, Roxana O. Carare, Ignacio A. Romero and Cheryl A. Hawkes

Anne-Laure Hemonnot, Jennifer Hua, Lauriane Ulmann and Hélène Hirbec

Geoffrey Canet, Célia Hernandez, Charleine Zussy, Nathalie Chevallier, Catherine Desrumaux and Laurent Givalois

Patrizia Giannoni, Sylvie Claeysen, Francesco Noe and Nicola Marchi

# Editorial: Identification of Multiple Targets in the Fight Against Alzheimer's Disease

Patrizia Giannoni 1 \*, Silvia Fossati 2 , Elena Marcello3 and Sylvie Claeysen4

1 EA7352 CHROME, University of Nîmes, Nîmes, France, 2 Alzheimer's Center at Temple (ACT), Lewis Katz School of Medicine, Temple University, Philadelphia, PA, United States, 3 Department of Pharmacological and Biomolecular Sciences, Universitá Degli Studi Di Milano, Milan, Italy, 4 IGF, Univ Montpellier, CNRS, INSERM, Montpellier, France

Keywords: inflammation, multi-interventions, neurovascular unit, microbiome, hormones, APP, beta-amyloid

**Editorial on the Research Topic**

**Identification of Multiple Targets in the Fight Against Alzheimer's Disease**

#### INTRODUCTION

This Research Topic is a collection of 20 articles that depict a broad representation of the most impactful advances in Alzheimer's disease (AD) comprehension and therapeutic openings. As it clearly emerges from recent literature, AD is a complex pathology with many different phenotypes and heterogeneous clinical settings. Although in a minority of cases genetic mutations have been linked to its development, for the vast majority of AD patients the triggering event remains to be elucidated. Main hallmarks such as amyloid beta (Aβ) plaques and neurofibrillary tangles have been identified, but understanding their exact role on cognitive consequences, timing of appearance, mechanisms of toxicity, and interplay required years of studies, with many questions still remaining unanswered. To note, the events possibly driving the development of such pathological signs are diverse and much more numerous than expected. For this reason, scientists have been directing their efforts to improving the understanding of the pathways involved in the toxicity mechanisms observed. Indeed, only a profound knowledge of the full process bringing to the different pathological phenotypes will help in figuring out effective treatments and/or preventive actions. With this concept in mind we have developed the present topic, which aims at giving a far-reaching picture of our actual knowledge, from the etiology of AD to mechanistic insights and possible new targets of intervention. Authors present their latest discoveries on a variety of breakthrough AD-related subjects such as inflammation, microbiome, hormones, Aβ production and catabolism, and neurovascular unit (NVU) alterations. Notably, the importance of reliable biomarkers as well as the paramount role of global approaches for the treatment/prevention of AD (see multi-interventions), are granted and finely discussed. We present here a summary of the main fields covered by this collection that we believe will constitute critical hints for future research development.

#### APP METABOLISM

According to the amyloid hypothesis, the increase in brain Aβ levels is a central event in AD pathogenesis. The soluble Aβ oligomers trigger synaptic dysfunction and, thereby, early cognitive deficits. Synaptic failure can occur also in the olfactory bulb leading to olfactory deficits that can be detected in many AD patients. Li et al. revealed a critical mechanism underlying olfactory dysfunction in AD showing that Aβ deposition induces morphological and functional changes in

#### Edited by:

Thomas Wisniewski, New York University, United States

#### Reviewed by:

Miguel Pappolla, University of Texas Medical Branch at Galveston, United States

> \*Correspondence: Patrizia Giannoni<EMAIL_ADDRESS>
> Received: 01 April 2020 Accepted: 14 May 2020 Published: 16 June 2020

#### Citation:

Giannoni P, Fossati S, Marcello E and Claeysen S (2020) Editorial: Identification of Multiple Targets in the Fight Against Alzheimer's Disease. Front. Aging Neurosci. 12:169. doi: 10.3389/fnagi.2020.00169 the synapses of the olfactory processing network in APP/PS1 mice during aging.

Aβ results from the proteolysis of the Amyloid Precursor Protein (APP). β-secretase BACE1 and γ-secretase concerted action on APP releases Aβ peptide, while the α-secretase ADAM10 (A Disintegrin and metalloproteinase domaincontaining protein 10) cleaves APP within Aβ sequence, thereby preventing its generation. Promoting the αcleavage of APP not only precludes the formation of Aβ but also increases the release of the neuroprotective sAPPα fragment. This can be achieved by acitretin, an activator of the α-secretase ADAM10, as assessed by dos Santos Guilherme et al. in the 5XFAD mouse model and in humans.

Even though APP metabolism has been studied in detail, it is still a central question in AD. Haytural et al. highlighted the technical concerns related to a potentially non-specific western blotting band at the expected molecular weight of APP-derived fragments, which calls for precaution when analyzing proteins of this size in human brain tissue. García-González et al. emphasized the importance of the comprehension of APP processing since APP can be a common substrate for several proteases. Among them, MT-MMPs (Membrane-type Metalloproteinases) are at the crossroads of pathological events involving not only amyloidogenesis, but also neuroinflammation and synaptic failure, thus opening up new research perspectives in AD.

#### NEUROINFLAMMATION

Neuroinflammation is definitely a hot-topic and one of the most studied fields in AD and related dementias in recent years. As summarized in the review by Hemonnot et al., many aspects of inflammation are still not completely understood. Microglial cells, main players in inflammatory reactions, are complex and heterogeneous cells. Several activation states exist and a fine-tuned characterization of AD stages is needed in order to design effective preventive and/or curative microglial-targeting interventions. One main aspect that slows down our full understanding of neuroinflammation is represented by the numerous mechanisms that might contribute to its development and progression. Hemonnot et al. presented an analysis of recent GWAS studies and described major genes implicated in microglial cells regulation, such as Apolipoprotein E (APOE) and TREM2 (triggering receptor expressed on myeloid cells 2). Finally, the authors proposed the purinergic signaling as one possible target of intervention for microglial state modulation. Another potential pharmacological target was evidenced by Adorni et al., that highlighted the role of proprotein convertase subtilisin/kexin type 9 (PCSK9) not only in cholesterol homeostasis, but also in neuroinflammation. Finally, Cerovic et al. evidenced in their review the strong link between gut microbiota (GM) and neuroinflammation development and exacerbation. The limitations of experimental protocols constitute a significant obstacle to which researchers are trying to find innovative solutions. Indeed, laboratory models only partially reproduce the complex reality of human AD inflammation. This is true for the microbiome status of animals living in animal facilities, which is far from the "real life" status, transgenic models (Hemonnot et al.) that focus in most cases on specific and limited AD features, and many other constraints, such as the fact that marked immunological differences exist between animals and humans as well as between genders. All these aspects need to be addressed and investigated to globally understand the role of neuroinflammation in AD and develop selective strategies.

#### HORMONES

Sex hormonal variations are a strongly debated risk factor for AD development and, as pointed out recently, they might interplay with other hormones in a refined modulation of neurocognitive functions. Different models have been proposed to study the perimenopause influence on AD development. Marongiu suggested the accelerated ovarian failure as an optimal model to mimic the human process. This model could indeed be used to understand the molecular pathways implicated in sex hormonerelated changes increasing AD risk. Rahman et al. underlined in their review the remarkable variety of pathophysiological conditions that are directly regulated by estrogen or that could be related to an alteration of its levels, like cardiovascular diseases (CVD). In this case, the increased risk of CVD is linked to an altered level of cholesterol, which is frequently observed in association with menopause. Indeed, estrogen seems to play a role in a variety of pathologies going from diabetes to depression, including infections and chronic inflammation. An aspect that should be particularly well-evaluated is the timing of a possible intervention in order to re-establish a hormonal balance. Evidences coming from recent studies are discussed and conclusions are pointing to the need of clinical trials and research investigations considering gender differences and leading to precision medicine approaches capable of analyzing the complexity of a patient clinical history. One of the gender differences that has been investigated concerns the consequences of chronic stress, which could be impacted by the activity of gonadal hormones (Rahman et al.). Indeed, AD has been proposed as a stress-related disorder by Canet et al.. In their up-to-date review, the authors evidenced how a dysregulation of the hypothalamic-pituitary-adrenal axis (HPA axis) has been observed in AD patients and could therefore represent a target for intervention. Molecules acting on the HPA axis hormones and/or receptors are already in clinical trials for other pathologies and the research for AD treatments could take advantage of such information.

#### NEUROVASCULAR UNIT

The neurovascular unit (NVU) is a functional brain unit composed my multiple cell types, including endothelial cells, smooth muscle cells, pericytes, and glial cells. The NVU is responsible for maintaining brain homeostasis by regulating permeability of the blood brain barrier (BBB), as well as clearance of unwanted products (such as Aβ) from the brain. Giannoni et al. emphasized in their review how the regulation of BBB permeability is of outmost importance for brain health, and its deregulation is associated with AD and related vascular dementias through multiple mechanisms, including autoantibody responses and altered gut microbiome. The manuscript by Nizari et al. evaluated the effect of the loss of cholinergic innervation of components of the NVU in hippocampus and cortex, suggesting that cortical arteries are more affected by cholinergic denervation than hippocampal arteries, and pointing to a differential regulation of neurovascular responses in different brain areas. In regard to clearance mechanisms, Aldea et al. proposed through sophisticated mathematical modeling that localized contraction of smooth muscle cells generates the force that drives intramural periarterial drainage of Aβ and other soluble metabolites in the brain.

Circulating blood cells and vesicles may also contribute to BBB function and neurovascular regulation. Interestingly, Espinosa-Parrilla et al. reviewed the link between platelet aging and multiple neurodegenerative diseases including AD, discussing the role of platelets as drivers of protein dysfunctions, and the potential clinical significance of platelets and related miRNAs as peripheral biomarkers of neurodegenerative diseases.

#### MULTI-INTERVENTIONS

Norwitz et al. highlight how AD might be the result of different dysregulations that interplay with each other in a complex and heterogenous matrix. In particular, they focus on the Wnt-signaling, α-synuclein and diabetes hypothesis, that might all contribute at different points and in different ways to the toxicity of the major AD hallmarks, Aβ and tau. In this context, AD therapies engaging simultaneously several molecular actors of the disease might achieve better efficacy in clinical trials. This multiplicity of action can be obtained by multi-target-directed ligands (MTDLs). In the present topic, Cuadrado-Tejedor et al. presented the compound CM-695, a dual inhibitor of histone deacetylase 6 (HDAC6) and phosphodiesterase 9 (PDE9). This MTDL demonstrated a therapeutic effect upon chronic administration to Tg2576 mice. In the same line, Hatat et al. developed an innovative molecule with triple activity of potential therapeutic interest against AD: –activation of serotonin type 4 (5-HT4) receptors, –inhibition of 5-HT6 receptors and –inhibition of acetylcholinesterase (AChE). Acute administration of this compound to mice prevented the memory deficits induced by scopolamine. Pleiotropic synergistic effects can also be demonstrated with molecules targeting α-secretase activity, such as acitretin. Indeed, dos Santos Guilherme et al. demonstrated that this molecule increased cerebrospinal fluid levels of interleukin 6 (IL6). Whether this effect is a direct effect of the molecule on an unknown target or is a consequence of retinoid control on inflammation has to be further investigated.

#### BIOMARKERS

Facing the complexity and the heterogeneity of AD pathology, early, reliable, and easy to access biomarkers will be fundamental for effective therapies. Combining the current knowledge and routinely used biomarkers, such as Aβ and tau quantification and imaging, Younes et al. identified changepoints of these biomarkers far before the clinical symptoms. The future goal would be being able to point out individuals likely to progress to AD as they age. New biomarkers could help to develop an accurate prediction. One strategy could be to implement biomarkers tracking cellular metabolism such as nicotinamide adenine dinucleotide (NAD+). In this Research Topic, Grant et al. presented the fate of NAD+ and its metabolites following an intravenous infusion of NAD+ in a human cohort. As gut microbiota composition seems to be altered in AD patients and to have an impact on amyloid pathology (Cerovic et al.), another strategy to identify innovative biomarkers could be to follow GM composition. Would the huge amount of data collected by metagenomic analysis of bacterial taxa abundancy be informative to point out predictive, stratifying or prognostic biomarkers? This remains to be demonstrated. The success might come from the quantification of bacteriaderived metabolites such as bile acids or short chain fatty acids (SCFA).

## CONCLUDING REMARKS

This series of articles is representative of the complexity of AD pathology. It clearly underlines the importance of transdisciplinary studies that are linking multiple aspects of the pathology or, as pointed out in many reviews, of different subtypes of AD etiology and development. The sharing of pathological features with other diseases, although complex to analyze, may also open numerous treatment possibilities. For exemple, Deering Brose et al. suggest the use of hydroxyurea, an FDA-approved ribonucleotide reductase inhibitor currently prescribed to treat cancer, to ameliorate the cognitive deficits in AD. The use of repurposed drugs can notably accelerate clinical trials bringing to novel therapeutics through a relatively rapid protocol. The example of hydroxyurea, which is tested in a down syndrome mouse model but suggested for a broad range of neurodegenerative diseases (Deering Brose et al.), highlights how future drugs may be able to target pathways shared by multiple pathologies.

## AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

#### ACKNOWLEDGMENTS

We thank all authors that contributed with research and review articles to this wide-ranging topic. We are grateful to the reviewers that granted with their expertise the high-quality of each publication. Finally, we would like to thank all funding agencies that supported our research. PG is funded by the University of Nimes. SF is funded by NIH R01NS104127, NIH R01AG062572, and the Edward N. and Della L. Thome Memorial Foundation. EM is funded by the Italian Ministry of Education, University and Research (PRIN 2017B9NCSX and PON Ricerca e Innovazione PerMedNet project ARS01\_01226), Fondazione Cariplo (Grant no. 2018 - 0511), and University of Milan intramural grants (Fondo di sviluppo unimi- linea2 - PSR2017\_DIP\_022\_03 and PSR2019\_EMARC). SC is funded by the Fondation Vaincre Alzheimer (#FR-15072) the INSERM

cross-cutting Program Microbiota and the FEDER/Région Occitanie (MICMALZ project).

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2020 Giannoni, Fossati, Marcello and Claeysen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Cerebrovascular Smooth Muscle Cells as the Drivers of Intramural Periarterial Drainage of the Brain

Roxana Aldea1 , Roy O. Weller 2 , Donna M. Wilcock 3 , Roxana O. Carare2 \* and Giles Richardson1

1 Mathematical Sciences, University of Southampton, Southampton, United Kingdom, 2 Clinical Neurosciences, Faculty of Medicine, University of Southampton, Southampton General Hospital, Southampton, United Kingdom, 3 Department of Physiology, Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY, United States

The human brain is the organ with the highest metabolic activity but it lacks a traditional lymphatic system responsible for clearing waste products. We have demonstrated that the basement membranes of cerebral capillaries and arteries represent the lymphatic pathways of the brain along which intramural periarterial drainage (IPAD) of soluble metabolites occurs. Failure of IPAD could explain the vascular deposition of the amyloid-beta protein as cerebral amyloid angiopathy (CAA), which is a key pathological feature of Alzheimer's disease. The underlying mechanisms of IPAD, including its motive force, have not been clarified, delaying successful therapies for CAA. Although arterial pulsations from the heart were initially considered to be the motive force for IPAD, they are not strong enough for efficient IPAD. This study aims to unravel the driving force for IPAD, by shifting the perspective of a heart-driven clearance of soluble metabolites from the brain to an intrinsic mechanism of cerebral arteries (e.g., vasomotion-driven IPAD). We test the hypothesis that the cerebrovascular smooth muscle cells, whose cycles of contraction and relaxation generate vasomotion, are the drivers of IPAD. A novel multiscale model of arteries, in which we treat the basement membrane as a fluid-filled poroelastic medium deformed by the contractile cerebrovascular smooth muscle cells, is used to test the hypothesis. The vasomotion-induced intramural flow rates suggest that vasomotion-driven IPAD is the only mechanism postulated to date capable of explaining the available experimental observations. The cerebrovascular smooth muscle cells could represent valuable drug targets for prevention and early interventions in CAA.

Keywords: lymphatic, brain, vasomotion, multi-scale model, poroelastic, Alzheimer's disease, cerebral amyloid angiopathy, perivascular drainage

#### 1.  Of high interest is the clearance of soluble amyloid-beta (Aβ) proteins that are released by neurons into the surrounding extracellular spaces (i.e., interstitium) following normal synaptic activity (Tarasoff-Conway et al., 2015). Inefficient removal of Aβ from the brain leads to parenchymal amyloid plaques and cerebral amyloid angiopathy (CAA), commonly seen in the

#### Edited by:

Silvia Fossati, New York University, United States

#### Reviewed by:

Masafumi Ihara, National Cerebral and Cardiovascular Center, Japan Pia Crone Christensen, Lundbeck, Denmark Yann Decker, Universitätsklinikum des Saarlandes, Germany

#### \*Correspondence:

Roxana O. Carare<EMAIL_ADDRESS>
Received: 25 October 2018 Accepted: 07 January 2019 Published: 23 January 2019

#### Citation:

Aldea R, Weller RO, Wilcock DM, Carare RO and Richardson G (2019) Cerebrovascular Smooth Muscle Cells as the Drivers of Intramural Periarterial Drainage of the Brain. Front. Aging Neurosci. 11:1. doi: 10.3389/fnagi.2019.00001 commonest form of dementia, Alzheimer's disease (AD) (Selkoe, 2001; Ross and Poirier, 2005; Cupino and Zabel, 2014). CAA describes the accumulation of Aβ initially within the basement membranes (BMs) of vascular smooth muscle cells (VSMCs) of cortical and leptomeningeal arteries, but also within the BM of cortical capillaries (with or without recruitment of other vessels). Although CAA has been almost invariably reported in AD, the exact causes of the onset of CAA remain unclear (Charidimou et al., 2012). The hemorrhages and ischemic lesions associated with CAA result in cognitive impairment and dementia (Attems et al., 2011; Reijmer et al., 2015). Currently, 47 million people worldwide suffer from dementia and there is no effective curative or preventive intervention. Aging highly increases the risk for CAA and dementia and, considering increased longevity, the occurrence of dementia by 2050 is evaluated to 131 million people (Prince et al., 2016). This increases the urgency to explore potential unconventional clearance mechanisms for Aβ that may contribute toward maintaining the homeostasis of the brain (Tarasoff-Conway et al., 2015; Bakker et al., 2016).

Aβ produced by neurons is degraded by enzymes (Farris et al., 2003; Marr et al., 2003), transported into the blood via lipoprotein receptor related protein (LRP)-1 (Deane et al., 2008) or cleared along the walls of capillaries and arteries (Hawkes et al., 2014). The Aβ transport along the wall of arteries, generally termed perivascular drainage, has been a subject of considerable controversy over the last few years. The pathways for the elimination of interstitial fluid (ISF) and solutes (including Aβ) from the brain parenchyma have recently been reviewed by Abbott et al. (2018) and Hladky and Barrand (2018). Early experiments by Szentistvanyi et al. (1984) showed that the major pathway by which radioactive solutes drain from the brain to cervical lymph nodes is along the walls of cerebral arteries in the direction counter to the blood flow. This pathway was later located in the BMs interposed between the VSMCs from the tunica media of cerebral arteries (Carare et al., 2008), i.e., the Intramural Peri-Arterial Drainage (IPAD) pathways, effectively the lymphatic drainage routes of the brain (Carare et al., 2014; Morris et al., 2014). An alternative viewpoint has also been postulated which states that, following influx of cerebrospinal fluid (CSF) and solutes from the subarachnoid space into the brain parenchyma, the CSF-ISF mixture is cleared along the walls of intracortical veins, back into CSF (Iliff et al., 2012). However, apart from the original paper, there is little support for this para-venous clearance mechanism. Other studies that injected soluble tracers intracerebrally (Carare et al., 2008; Arbel-Ornath et al., 2013) or into the CSF (Albargothy et al., 2018) have found no evidence of tracer presence along the intracerebral paravenous pathways, but rather along the walls of cerebral arteries, specifically within the IPAD pathways.

Here, we are concerned with shedding light on the mechanisms responsible for transport of ISF and Aβ along the IPAD pathways. The IPAD pathways were mapped by Carare et al. (2008) based on the following observations: soluble tracers injected into the brain parenchyma (i) rapidly enter the BM of capillaries and (ii) are progressively observed in BM in the tunica media of intracerebral arterioles and arteries, and later on, in the walls of leptomeningeal arteries (see **Figure 1**). Extensive work has been done for assessing the transport of solutes out of the brain along the IPAD pathways during aging (Hawkes et al., 2011) and under various physiological and pathological conditions, such as the presence of CAA (Arbel-Ornath et al., 2013; Hawkes et al., 2014), possession of APOE4 (Zekonyte et al., 2016), consumption of a high-fat diet (Hawkes et al., 2015) and after ischemic stroke (Arbel-Ornath et al., 2013). All those experiments showed that soluble tracers injected into the brain interstitium reach the BM of intracerebral and leptomeningeal arteries and their distribution within the arterial wall resembles the pathological deposition of Aβ in CAA (Weller et al., 1998; Preston et al., 2003). These findings indicate that soluble Aβ can be removed from the brain tissue along the IPAD pathways, in the opposite direction to arterial pulsations, and that failure of this clearance mechanism results in the vascular deposition of Aβ as CAA.

Improvement of IPAD holds great promise for treatment of CAA. However, significant advancement is unlikely until the underlying mechanisms of IPAD, including the motive force, are elucidated. The observation that no periarterial drainage occurs following cardiac arrest suggested that arterial pulsations, derived from the heartbeat, might drive IPAD in the brain (Carare et al., 2008). Driving net intramural periarterial flow in the opposite direction to the arterial pulse, as shown in **Figure 1**, appears to be physically difficult. Several mechanisms that could allow periarterial clearance to occur in the reverse direction to arterial pulsations have been mathematically modeled, all of them assuming the arterial pulse to be the motive force for IPAD (Schley et al., 2006; Coloma et al., 2016; Sharp et al., 2016; Diem et al., 2017). Previous suggestions included some degree of attachment between solutes and BM (Schley et al., 2006), different flexible structures within the BM (Sharp et al., 2016) and a valve mechanism (Diem et al., 2017), that would be needed to generate greater resistance to forward periarterial flow than to retrograde periarterial flow (against the direction of arterial pulse). The presence of flexible and valve-like structures within the wall of cerebral arteries still awaits experimental confirmation. More importantly, Diem et al. (2017) have shown that arterial pulsations are incapable of driving intramural fluid flow rates out of the brain of physiological significance, even with valve-like structures within the BM. The very long wavelength of the arterial pulse is inadequate to induce pressure gradients large enough to generate periarterial flow rates comparable with experimental observations (Carare et al., 2008; Arbel-Ornath et al., 2013). Forces other than cardiac pulsations must therefore drive IPAD in the brain and the quest for other candidates is still open.

With this in mind, we propose that the forces generated by cerebral VSMCs can drive IPAD in the brain by acting upon the deformable BM. The VSMCs are contractile cells embedded within the arterial wall and, under physiological conditions, generate a basal vascular tone that is maintained by a combination of various stimuli (e.g., arterial pressure, shear stress, neuronal metabolic activity and several types of innervation) (Cipolla, 2009). Deviations from the basal vascular tone result in significant variations in the diameter of arteries. The evoked vasomotor response is able to spread along the

green), pericyte (purple), and subpial collagen (gray).

artery length, as well as across branch points (Welsh et al., 2018). The conducted vasomotor response may be induced by vasoactive drugs or it may occur spontaneously. The spontaneous rhythmic oscillations of vascular tone are known as vasomotion (Nilsson and Aalkjær, 2003; Aalkjær et al., 2011). The initial, local contractions of the VSMCs are propagated over macroscopic distances as a contraction wave that is linked to the calcium waves mediated via intercellular gap-junctions (Seppey et al., 2009; Pradhan and Chakravarthy, 2011). Vasomotion is independent of pulse rate and respiration and has been observed in the vascular beds of numerous tissues, including the cerebral tissue (Fujii et al., 1990, 1991; Gokina et al., 1996; Mayhew et al., 1996; Obrig et al., 2000; Filosa et al., 2004; Vetri et al., 2007). Vasomotion has been previously modeled as a mechanism aiding the transport of blood (Carew and Pedley, 1997; Ursino et al., 1998) and oxygen (Goldman and Popel, 2001; Hapuarachchi et al., 2010) from blood vessels to various tissues in the body. Di Marco et al. (2015) reviewed the ways in which cerebral vasomotion may be hindered in AD and mentioned that vasomotion could act as a secondary input to the motive force of arterial pulsedriven IPAD. A systematic contribution of the cerebral VSMCs to the periarterial drainage of fluid out of the brain has not been previously inspected, neither experimentally nor by any modeling technique.

Proposed mechanism: vasomotion-driven IPAD. We propose the hypothesis of vasomotion-driven IPAD (denoted V-IPAD) and explain below the underlying mechanisms for this potential clearance process. The position of the contractile VSMCs within the artery wall appears to be ideal for allowing an immediate effect of their contractions on the conformation of the adjoining BM, which is a specialized sheet of extracellular matrix filled with interstitial fluid (see **Figure 2**). In other words, the BM is a soft fluid-filled poroelastic medium whose pores could be closed and reopened during VSMC contraction and relaxation, respectively. In this way, the contractions of two layers of VSMCs will squeeze the interposed BM, pushing fluid out of its pores in the direction of the contraction wave. In order to clear any soluble material from the brain interstitium via the IPAD pathways, the vasomotion wave must propagate from the intracerebral arterioles toward the large arteries on the surface of the brain. The V-IPAD mechanism bears some analogy to an old-fashioned mangle or a wringer which squeezes water

FIGURE 2 | Schematic representation (not to scale) of the V-IPAD model. A leptomeningeal artery is modeled as a long thick-walled cylinder with uniform material properties along its length, maintained at a constant intraluminal pressure P and constant longitudinal stretch and exposed to active contractions of VSMCs. The top layer shows the arterial cross section with a layer of BM (green compartment) embedded in the wall (Left) and the longitudinal section of the BM (Right). For simplicity reasons, only one layer of BM is considered at the middle of the wall and the two layers of the VSMCs surrounding the BM are assumed to behave identically. The remaining wall components are not shown, but their effect on the wall elasticity is captured by the radial (σr ) and circumferential stress (σθ ). The deformed inner radius, middle radius and outer radius of the arterial wall are denoted by ri , rm, and ro, respectively. The position of the inner layer bounding the BM is assumed at rm and, owing to stress continuity across the wall, the radial stress σr at that point represents the external compressive stress 6 which acts on the BM, i.e., 6 = σr (r = rm); thereby, the arterial wall model is coupled with the BM model. Both rm and 6 depend on the prescribed vasomotion wave S(z, t) generated by the contractile VSMCs and are determined from the arterial wall model. The position of the outer layer bounding the BM is assumed at rb with rb = rm + 2h, where 2h denotes the whole BM thickness. Since the BM thickness is significantly smaller than the arterial radius, its upper half is assumed to behave identically to its lower half. The deformed thickness h = h(z, t) of the upper-half BM is determined by solving the BM model in the Cartesian system (y, z) and this is justified by the relationship rb = rm(z, t) + 2y where 2y ≪ rm and 2h ≪ rm. The area delimited by the rectangular is illustrated in the bottom schematic: a sheet of BM modeled as a poroelastic compartment whose fluid-filled pores are squeezed during the VSMC contractions, under the direct actions of −6(S(z, t)), driving the fluid out of the BM pores in the direction of the vasomotion wave.

out of soft materials such as towels. Other parts of the human body, such as the digestive system, use wave-like muscular contractions to propel the content of a tube (e.g., food along the gastro-intestinal tract); this process is also known as peristaltic pumping. We note that the nomenclature of "peristalsis" has also been used in modeling studies of perivascular drainage driven by the heart-derived arterial pulse (Bilston et al., 2003; Wang and Olbricht, 2011; Sharp et al., 2016), rather than by the VSMCs. However, the vasomotion wave has significantly different properties than the arterial pulse. For example, vasomotion has a wavelength of several millimeters, which is at least two orders of magnitude lower than that of arterial pulsations. In addition, the arterial pulse has an approximate frequency of 1 Hz, while the vasomotion frequency, although varies with tissue type and species, is commonly taken to be 0.1 Hz (Nilsson and Aalkjær, 2003; Aalkjær et al., 2011). Hence, comparison between the current work and previous studies should be made with caution because both the arterial pulse and the vasomotion wave are commonly modeled as sinusoidal waves.

Here, we test the role of cerebral vasomotion in the clearance of fluid from the brain by developing a novel physiologicallybased multiscale model of a middle cerebral artery (MCA); this model is denoted the V-IPAD model and presented in section 2.1 (see details in Aldea, 2017). The V-IPAD model couples two models: (i) the arterial wall model which captures the biomechanics of an active elastic cerebral artery and (ii) the BM model which yields the fluid flow rates, along the IPAD pathways, generated by the activation of the VSMCs. We emphasize that this is the first model treating the BM as a fluid-filled poroelastic medium, rather than just a fluid-filled channel (Schley et al., 2006; Coloma et al., 2016) or a fluidfilled porous medium (Wang and Olbricht, 2011; Diem et al., 2017).

The poroelastic BM is a biphasic material composed of: (i) a porous and elastic solid matrix (e.g., the extracellular matrix of BM proteins) and (ii) a fluid component (e.g., the ISF) that occupies the connected pores, an example being a water filled bath sponge. Where the solid matrix deforms in response to an external load it transmits force (in the form of a pressure) to the fluid filling the pores; this subsequently leads to changes in the permeability of the solid matrix to fluid flow as, for example, the pores close up. As it will be shown, the elastic deformations of the poroelastic BM, induced by the contractile VSMCs, are critical in assuring high net fluid flow rates along the IPAD pathways, without the need of any intramural valves; such a behavior could not be obtained in a purely porous, underformable material. Models of poroelasticity have been widely applied to biological materials in applications ranging from fluid movement in bone (Cowin, 1999), to tumor growth (Roose et al., 2007) and biomechanics of brain tissue (Goriely et al., 2015).

The mathematical details of the V-IPAD model are given in section 2. The simulation results are presented in section 3, followed by their discussion in section 4. We have written the paper such that the sections 3 and 4 can be read prior to section 2. In section 4, we also discuss suitable animal models (e.g., hyperhomocystaeinaemia (HHcy) mouse models) for testing experimentally the V-IPAD hypothesis. In **Supplementary Material 1**, we present in detail the arterial wall model which follows popular hyperelastic arterial models from the literature (Rachev and Hayashi, 1999; Kalita and Schaefer, 2008), while in **Supplementary Material 2**, we explain the numerical implementation of the BM-model. Finally, our pilot experimental data from the HHcy animal models are shown in **Supplementary Material 3**.

## 2. MATERIALS AND METHODS

In this section, we first describe the governing equations of the V-IPAD model and then explain their physiological significance. The V-IPAD model consists of two coupled models: (i) the BM model for the vasomotion-induced intramural periarterial flows through the deformable poroelastic BM, coupled to (ii) the arterial wall model for the elastic response of a rat MCA. We recall that the latter model is presented in detail in **Supplementary Material 1**, so below we focus on the novel BM model. We also present a concise formulation of the BM model that is solved numerically in **Supplementary Material 2**. The full derivation of the BM model can be found in Aldea (2017). The parameters used in the V-IPAD model are given in **Table 1**.

#### 2.1. Lubrication Model of the Poroelastic BM

The aim of this model is to quantify the amount of fluid eliminated from the brain tissue along the intramural vascular BM as a consequence of muscular contractions of cerebral arteries. The intramural vascular BM is modeled as a slowly varying sheet of width 2h, running through the wall of a cylindrical vessel which is itself centered along the z-axis, undergoing axisymmetric deformation (i.e., no θ-dependence), as illustrated in **Figure 2**. On its top and bottom boundaries (both assumed impermeable), the BM is exposed to compressive stresses dependent on the contractile activity of the VSMCs. Given its anatomical properties (e.g., a thin, extracellular matrix of fibrous proteins), the BM is treated as deformable spongy material filled with interstitial fluid. More specifically, the BM is modeled as a fluid-filled poroelastic medium comprised of a porous solid phase (the matrix of proteins) denoted by the superscript "s" and a fluid phase (interstitial fluid) denoted by the superscript "f." The pores in the solid matrix provide a path for the movement of fluid. Since the BM thickness (≈ 0.4 µm)

TABLE 1 | Dimensional parameters.

is significantly smaller than the arterial radius (≈ 100 µm), we assume that its upper half behaves identically to its lower half. For visual purposes, the following notation is adopted: 2H is the undeformed thickness of the BM and 2h is the deformed thickness of the BM.

The poroelastic BM is a compressible elastic medium subjected to deformations in response to an external compressive stress and to changes in fluid pressure in the pores of the matrix. Specifically, the external compressive stress, denoted 6, is a known input function of time and position, i.e., 6 = 6(z, t); it depends on the contractile oscillations of the VSMCs and its value is previously determined from the elastic analysis of an active cerebral artery, rather than just being a prescribed function from the literature (see **Supplementary Material 1** and Aldea, 2017). 6(z, t) affects the fluid flow through the BM by inducing deformations of its boundaries. Thus, the system depends on time only through the boundary conditions. Accounting for the symmetry of the system, as illustrated in **Figure 2**, it suffices to solve the BM model in the upper half plane in order to determine the deformed BM thickness (Aldea, 2017).

#### 2.1.1. Governing Equations

In general, solving a three-dimensional poroelastic model results in a highly complex system of equations. However, by exploiting the disparity between the length scales of the contracting arterial wall [BM thickness (0.4 µm) ≪ arterial radius (100 µm) ≪ wavelength of muscular contractions (2000 µm)], we derive a simplified version of the BM model by using a lubrication approximation (see details in Aldea, 2017). Thereby, it is conceivable to assume that the variations in the radial direction of the BM are weak and the variables of the system, at leading order, have only one spatial dependence, e.g., the axial z-dependence. This also makes it sensible to assess the BM model in a twodimensional Cartesian system (y, z), where the Cartesian ycoordinate is related to the cylindrical r-coordinated by the

relationship rb = rm(z, t) + 2y, where 2y ≪ rm and 2h ≪ rm; here, the expressions r = rb (z, t) and r = rm(z, t) describe the radial position of the outer and inner layer bounding the BM, respectively, as illustrated in **Figure 2**.

In order to quantitatively determine the fluid flow rate QBM(z, t) through the BM, as an effect of VSMC activity, we first need to solve the system described below in Equations (1–5) for the following five variables: the deformed thickness of the BM h(z, t), the volume fraction of fluid φ f (z, t), the fluid velocity in the z-direction v f z(z, t), the pore pressure in the basement membrane p(z, t) and the principal effective Cauchy stress σ e y (z, t) (i.e., a measure of the force per unit area acting on a surface element in the deformed BM); t is time and z is the position along the z-axis. The full derivation of this lubrication model of the poroelastic BM is given in Aldea (2017). However, in section 2.1.2, we provide a more intuitive derivation of this model based on the physiology of the BM system. The governing equations are:

$$\frac{\partial (\phi^f h)}{\partial t} + \frac{\partial}{\partial z} (\phi^f \nu\_z^f h) = 0,\tag{1}$$

$$-\frac{\partial \phi^f}{\partial t} + (1 - \phi^f) \frac{1}{h} \frac{\partial h}{\partial t} = 0,\tag{2}$$

$$
\phi^f \nu\_z^f = -\frac{k(h)}{\eta} \frac{\partial p}{\partial z},
\tag{3}
$$

$$
\sigma\_\circ^e - \mathfrak{p} = \Sigma(z, t), \tag{4}
$$

$$
\sigma\_\circ^\varepsilon = f\left(\frac{h}{H}\right),
\tag{5}
$$

where η is the fluid viscosity and k is the deformation dependent permeability of the porous medium (details in Equation 6). H denotes the undeformed thickness of the upper half BM and 6 denotes the external constrictive stress; these two terms are the input of the BM model. The function f h H relates the stress in the BM to its deformation and is derived from a given strain energy function. The reader is referred forward to Equations (8– 9) for the particular forms of the stress-strain relationship and the strain energy function used in this work.

#### 2.1.2. Physiological Interpretation of the System (1–5) The physiological significance of the BM model is outlined below (Aldea, 2017).

Equations (1, 2) represent conservation of fluid and solid mass, respectively, in the deformed configuration of the system. Assuming that the BM is comprised only of fluid and solid phases, the volume fractions φ s (solid) and φ f (fluid) satisfy φ s + φ f = 1.

Equation (3) is the lubrication approximation of Darcy's law which relates the interstitial fluid velocity to the pore pressure gradient, the fluid viscosity and the deformationdependent permeability. Various functions for deformationdependent permeability have been discussed in the literature and, here, we choose the model described in Markert (2005) which stands for a large range of material compression, as well as distension,

$$k(h) = k\_\* \left( \frac{\frac{h}{H} - \phi\_\*^s}{1 - \phi\_\*^s} \right)^\kappa,\tag{6}$$

where k∗ is the permeability of the undeformed BM, κ is a positive parameter and φ s ∗ is the volume fraction of the solid in the fluid-filled reference configuration.

Applying an asymptotic analysis (details in Aldea, 2017) shows that, at leading order, the only dimension of the BM that changes significantly is its thickness, meaning that the term h H represents the Jacobian of the system which gives the change in the volume of the BM. Equations (3, 6) account for the fact that any change in the volume of the BM will affect its porosity and, subsequently, its permeability to fluid flow. The state of zero porosity is reached when h H = φ s ∗ , meaning that the BM pores are fully closed.

Equation (4) represents the force-balance equation derived from the conservation of momentum. A close look at the elasticity of an arterial segment shows that 6 is the radial stress at the radial position of the BM (see **Figure 2**, **Supplementary Material 1**, and Aldea, 2017), so Equation (4) follows from the continuity of radial stress across the BM. In **Supplementary Figures 1**, **2**, we plot the dependence of the radial stress at the middle of the wall on the magnitude of VSMC activation, i.e., 6(S), where S acts as a parameter describing the local level of contractile activity of VSMCs. Here, we model the propagation of the muscular contractions of the VSMCs with the following wave form of the vascular tone

$$S(z,t) = A \cdot \sin^2\left(\frac{\pi}{\lambda\_w} \left(z - c\_w t\right)\right) \cdot S\_m,\tag{7}$$

which was previously used for the muscular activation wave of the ureter wall by Carew and Pedley (1997). For the purposes of this study, the vascular tone wave S(z, t) represents the vasomotion wave from the V-IPAD model, having the same pattern as the arterial vasomotion wave observed experimentally (Bouskela and Grampp, 1992; Mayhew et al., 1996; Haddock and Hill, 2005; Vetri et al., 2007; Rayshubskiy et al., 2014). Sm reflects the physically observed maximal contraction of the VSMCs (Rachev and Hayashi, 1999) and A is the amplitude of change in the maximal muscular contraction. The magnitude of Sm has not been experimentally confirmed in the cerebral muscular arteries. Hence, Sm is taken in accordance with the value reported in carotid arteries (Rachev and Hayashi, 1999). λw and cw denote the wavelength and the speed of the vasomotion wave, respectively; t is time and z is the spatial coordinate. The wavelength λw of cerebral vasomotion has not been experimentally measured, but can be calculated by dividing the wave velocity cw by the wave frequency (or equally by multiplying the wave velocity with the time period of the vasomotion wave). The propagation velocities of the vasomotor response available in the literature describe the induced vasomotor response of arteries, rather than the (spontaneous) vasomotion wave (Duling and Berne, 1970; Dietrich et al., 1996; Seppey et al., 2009).

Finally, Equation (5) represents the constitutive equation, i.e., the stress-strain relationship for the hyperelastic BM. This means that the principal effective Cauchy stress σ e y is derived from a strain energy function (here denoted WBM) that describes the elastic behavior of the material under particular deforming conditions. Given the geometrical setup of the BM model, in which compressive forces are only significant in the y-direction, the relationship between the principal stress in the y-direction and the corresponding deformation is given (in terms of a strain energy function) by

$$
\sigma\_{\mathcal{Y}}^{\epsilon}(h) = f\left(\frac{h}{H}\right) = \frac{\partial \mathcal{W}\_{BM}(\frac{h}{H})}{\partial \lambda\_{\mathcal{Y}}(\frac{h}{H})}.\tag{8}
$$

Here, λy is the principal stretch ratio in the y-direction and is defined as λy( h H ) = h H , considering that the only dimension of the BM that changes significantly is its thickness. The choice of WBM requires careful attention. Limited by the lack of experimental data on the elasticity of the cerebrovascular BM, a minimum number of material parameters able to describe the physiological response is desired. With this in mind, we choose the neo-Hookean model with only two material parameters from the study of Wirth et al. (2010), in which various permeability functions (e.g., Equation 6) and hyperelasticity constitutive laws were combined for modeling choked (or limited) flows through biological poroelastic materials. Hence, within this lubrication model, the elastic response of the poroelastic BM to the external constrictive stress is captured by the strain energy function

$$W\_{BM}\left(\frac{h}{H}\right) = \frac{\mu\_s}{2}\left(\left(\frac{h}{H}\right)^2 - 1 - 2(1 - f\_\*)\log\frac{\frac{h}{H} - f\_\*}{1 - f\_\*}\right)$$

$$+ \frac{1}{2}\left(\lambda\_s - \mu\_s \frac{f\_\*}{1 - f\_\*}\right)\left(\frac{h}{H} - 1\right)^2,\tag{9}$$

which is plotted in **Figure 3**. The material parameters µs and λs denote the first and second Lame´ parameters, respectively. H · J∗ represents the minimum possible deformed thickness h of the membrane. For example, if the solid matrix of proteins is also incompressible, J∗ equals φ s ∗ when all the pore spaces are closed. In other words, J∗ represents the lower limit of physical validity of the employed elastic model, meaning that the BM cannot be compressed to zero volume with finite energy (WBM diverges as h H → J∗). For the Lame´ parameters given in **Table 1**, the BM behavior is denoted by the word "lymphatic" and the corresponding WBM from Equation (9) is plotted in **Figure 3**, showing that it takes a fast increasing energy to deform the BM once its pores are nearly shut (as h H approaches J∗). The Cauchy stress σ e y is obtained according to Equation (8) and shown in **Figure 3**. If the BM were a more deformable material (denoted "spongy"), then its pores could be nearly shut during the maximum activation of the VSMCs, e.g., by a compressive stress of 6 kPa (**Figure 3** and details in section 4.2).

#### 2.1.3. Concise Formulation of the BM Model

The system of Equations (1–5) is simplified by replacing Equations (2, 3) in Equation (1), which yields the non-linear equation for the deformed upper-half BM

$$\frac{\partial h}{\partial t} = \frac{\partial}{\partial z} \left( h \frac{k(h)}{\eta} \frac{\partial p(h, \Sigma(z, t))}{\partial z} \right), \tag{10}$$

recalling that k(h) is given by Equation (6) and p = σ e y (h(z, t)) − 6(S(z, t)). Equation (10) requires one initial condition and two boundary conditions.

The BM is considered initially undeformed and uniform along the vessel, i.e.,

$$h|\_{t=0} = H, \quad 0 \le z \le L\_3,\tag{11}$$

where z = 0 represents the proximal end of the BM, while z = Ls represents the distal end of the BM. Subsequently, both the deformation of the BM induced by the external compressive stress and the resulting flow through the BM, are investigated.

The ends of the BM are assumed to be at the same pressure (i.e., the pressure drop along the BM between the two ends of the vessel does not drive a significant flow)

$$p|\_{z=0} = p|\_{z=L\_s} = 0, \quad t > 0. \tag{12}$$

Equation (10) plays a paramount role in the dynamics of the system and is solved for h(z, t) for a given 6(S(z, t)) and WBM. Once h is determined, all the other variables of the system can be calculated given their dependency on h. Finally, the fluid flow rate through the poroelastic BM is calculated as shown below.

#### 2.1.4. Volumetric Flow Rate Through the Poroelastic BM

An infinitesimal element of the cerebral artery is considered, as illustrated in **Figure 2**. The annular region represents the poroelastic BM. Radial symmetric deformation of the artery is assumed and the deformation of BM depends solely on the external compressive stress induced by the vasomotion wave, i.e., on 6(S(z, t)). The total flow rate of fluid through the infinitesimal volume of BM, generated by the vasomotioninduced deformation of the BM boundaries, is calculated by using the differential form of the Darcy's law and recalling that the cross sectional area of the BM is 2πrm(2h)[µm2 ]; hence, it follows that

$$Q\_{BM}(z,t) = -2\frac{2\pi \, r\_m hk(h)}{\eta} \frac{\partial p}{\partial z},\tag{13}$$

where QBM(z, t)[µm3 ·s −1 ] is the volumetric flow rate through the entire thickness of the BM and rm[µm] denotes the radial position of the BM. Here, the extra factor of 2 appears because we have only been considering the upper half of the BM in our model from sections 2.1.1–2.1.3. It is emphasized that rm is a function of the wave of vascular tone, i.e., rm = rm(S(z, t)), and its form is not prescribed, but is rather determined from the arterial wall model in **Supplementary Material 1**.

The net flow rate over one cycle of vasomotion is

$$
\bar{Q}\_{BM} = \frac{1}{T} \int\_0^T Q\_{BM} dt,\tag{14}
$$

where T represents the time period of the vasomotion wave. The parabolic Equation (10) is solved with the method of lines as described in **Supplementary Material 2**. All the common diagnostic checks were performed to ensure that the numerical method conserves the mass of the system and the errors are acceptably small. The convergence of the employed numerical method is demonstrated in **Supplementary Figure 3**.

#### 3. RESULTS

We have considered the cerebral artery to be an idealized vessel (e.g., thick-walled cylinder) with uniform material properties determined experimentally by Bell et al. (2013). For reasons of simplicity, we have accounted for a single layer of BM positioned at the middle of the arterial wall and modeled it as a fluid-filled poroelastic material with a slow-varying thickness, as illustrated in **Figure 2**. We have assumed the BM boundaries to be impermeable, such that the fluid cannot escape from this compartment, guaranteeing in this way conservation of fluid. The artery deforms due to the non-zero intraluminal pressure and longitudinal stretching, as well as due to VSMC contractions (i.e., active mechanical response). The biomechanical behavior of three MCAs was investigated in detail in **Supplementary Material 1** and the results corresponding to an artery exposed to a physiological intraluminal pressure of 13.3 kPa and an axial stretch of 1.07 are presented below. The deformation of the artery wall triggers the flow in the BM, but the BM is small enough such that it does not alter the elastic stresses in the wall. Hence, there is only one-way coupling, allowing for the elastic analysis of the artery to be handled independently of the BM.

Rather than prescribing the deformation of the arterial wall, we have first solved the arterial wall model from **Supplementary Material 1** in order to assess the variations in arterial diameter as a consequence of the local VSMC contractions, by maintaining both the intraluminal pressure and the axial stretch constant. Once the deformed inner radius of the artery is determined, the deformation and the corresponding stresses at any spatial point within the wall can be calculated. Secondly, by assuming continuity of stress across the wall, we can take the radial stress (denoted σr) at the middle of the arterial wall as the external constrictive stress (denoted 6) acting on the BM, i.e., σr(r = rm) = 6, where rm is the deformed radius r at the middle of the arterial wall (see **Figure 2** and **Supplementary Figure 1**).

Furthermore, the spatiotemporal contractile oscillations of the cerebral VSMCs, i.e., the vasomotion wave, are prescribed by the wave form of the vascular tone S(z, t) with units of stress, as shown in **Figure 4** and defined in Equation (7). The values of S were determined elsewhere (Rachev and Hayashi, 1999) based on experimentally recorded pressure-diameter curves of arteries, under VSMC contraction and control conditions. Thereby, S = 0 kPa reflects the purely passive mechanical response of the arterial wall when the VSMCs are fully relaxed, while S = 100 kPa is taken for maximal muscular contraction. In terms of spatiotemporal properties of the system, we consider the frequency of vascular oscillations to be around 0.1 Hz (i.e., they repeat every 10 s) and investigate the particular case in which the wavelength of the vasomotion wave is comparable with the characteristic length of a MCA. Moreover, for reasons of simplicity, we assume a uniform system and one that is sufficiently long in order to encompass several vasomotions waves simultaneously (see **Table 1**). This latter consideration allows for evaluation of the solution to our poroelastic problem far from the ends of the computational spatial grid where rapid variations in the solution may occur.

Both the deformation of the middle wall of the artery, rm = rm(S(z, t)), and the corresponding radial stress, 6 = 6(S(z, t)),

depend on the vascular tone S(z, t) through the arterial wall model (see details in **Supplementary Material 1**). In turn, these quantities serve as input in the BM model (see **Figure 4**). A whole cycle of vasomotion includes both the contraction and the relaxation of VSMCs and the corresponding oscillation between the maximal and minimal radius of the artery has an amplitude of approximately 20%, as pictured in **Figure 4**. In addition, from **Figure 4**, it is obvious that the highest negative value of 6 (i.e., the strongest constrictive stress on the BM) is generated during maximal muscular contraction. On the other hand, the lowest value of 6 corresponds to the case of fully relaxed VSMCs and is non-zero due to stresses generated by the passive load bearing components of the wall of the artery (e.g., collagen and elastin fibers) during inflation and axial stretching of the artery.

#### 3.1. The VSMCs as the Pump for IPAD in the Brain

The spatiotemporal variations in cerebral vascular tone influence the fluid flow through the BM by inducing deformations of its top and bottom boundaries via the radial constrictive stress 6(S(z, t)). The fluid movement through the BM is governed by Darcy's law. The change in the BM volume affects its porosity and, subsequently, its permeability to fluid flow, according to Equations (3, 6). In this particular scenario, it has been allowed for the fluid volume fraction to drop to zero due to finite compressive forces that reduce the BM pores and, as a consequence, obstruct the path for fluid drainage; this behavior is similar to squeezing shut a fluid-filled sponge.

The response of the poroelastic BM to the external constrictive stress is described by the stress-stretch relationship from Equation (8), which is derived using the strain energy function for neo-Hookean poroelastic materials from Wirth et al. (2010). The ability of the chosen strain energy function from Equation (9) to describe a non-linear elastic behavior for a general large range of deformations is shown in **Figure 3** and further discussed in section 4.2. The cerebrovascular BM considered here has similar properties to the interstitium of the systemic lymphatic system (Smillie et al., 2005) and to the interstitium of the brain (Heppell et al., 2013), therefore it appears as a physiologically reasonable assumption. From **Figures 3**, **5**, it is obvious that the compressive stress generated during maximum VSMC activation (e.g., 6 kPa) decreases the permeability of the BM by 50% compared to its undeformed state. As the BM pores are squeezed by the contractile VSMCs, the reduced BM permeability prevents high reverse flows (in the direction opposing the vasomotion wave), making the VSMCs an efficient pump for driving fluid along the IPAD pathways.

#### 3.2. Non-zero Net Intramural Periarterial Flow Rates During One Cycle of Vasomotion

Owing to the assumed symmetry of the system pictured in **Figure 2**, the vasomotion-induced BM deformation has been determined by solving Equation (10) for the upper-half part of the BM. The BM deformations propagate longitudinally and vary in time, with characteristics specific to the vasomotion wave (e.g., a time period of 10 s or, equally, a frequency of 0.1 Hz). The evolution of the BM thickness h(z, t) and permeability k(z, t), as well as the resulting fluid flow rate QBM(z, t) through the BM are shown in **Figure 5**. The vasomotion-induced fluid flow rate through the entire BM compartment, calculated with Equation (13), depends on the radial deformation of the artery wall, the fluid viscosity, the deformation-dependent permeability of the poroelastic BM and the pore pressure gradient. Given that one vasomotion cycle incorporates both the contraction and the relaxation of the VSMCs, positive (i.e., in the direction of the vasomotion wave) as well as negative flows (i.e., in the reverse direction of the vasomotion wave) occur at different times at a given spatial point of the BM. Although high intramural flow rates of nearly 4000 µm3 ·s −1 are obtained, the net flow rate during one cycle of vasomotion is only 360 µm3 ·s −1 . The positive value indicates that the net intramural fluid flow rate during one cycle of vasomotion is always in the direction of the vasomotion wave (a consequence of the much reduced BM permeability as it is squeezed; Aldea, 2017).

#### 4. DISCUSSION AND CONCLUSION

The small dimension of the clearance pathways of the brain (e.g., 10–400 nm) and their anatomical position within the deep cortical layers make the investigation of brain clearance challenging to conduct in living humans and even in animal models. Physiologically-based mathematical and computational models represent an alternative tool for analysing potential clearance mechanisms of the brain, likely generating new mechanistic insights (Goriely et al., 2015; Holter et al., 2017). Since the motive force for IPAD has remained unresolved (Diem et al., 2017), we have proposed and tested with in silico modeling the idea of vasomotion-driven IPAD (denoted V-IPAD). According to the V-IPAD hypothesis, the contractile VSMCs of cerebral arteries act as the drivers of IPAD in the brain by inducing BM deformations and, subsequently, net intramural fluid flows in the direction of the vasomotion wave. To our knowledge, this is the first quantitative study that explores the contribution of the cerebral VSMCs in the drainage of fluid from the brain. Although the exact physiological roles of vasomotion remain elusive, the most common view is that vasomotion serves as an auxiliary mechanism in tissue perfusion, especially under hypoperfusion conditions (Nilsson and Aalkjær, 2003). The theoretical results from this study indicate another potential role for vasomotion, namely the clearance of fluid and soluble metabolites from the brain along the IPAD pathways.

#### 4.1. Experimental Evidence Supporting the V-IPAD Hypothesis

The most solid evidence for the V-IPAD hypothesis probably comes from the experiments of Beach et al. (2000) who found significant accumulation of Aβ within the abluminal BM of VSMCs as a result of cortical cholinergic deafferentation (i.e., interruption of acetylcholine neurotransmitter to the cerebral blood vessels and other cells of the brain). The authors found it difficult to explain why the Aβ deposition was predominantly in the wall of arterioles and only sparsely in the brain tissue as amyloid plaques. Here, we propose that the cerebrovascular accumulation of Aβ , observed in Beach et al. (2000), resulted from impaired activity of the VSCMs. As the vascular tone of cerebral arteries is modulated by a rich cholinergic innervation (Hamel, 2006), the cholinergic deafferentation induced in the study of Beach et al. (2000) may have hindered the VSMC activity and, subsequently, their contribution to the clearance of Aβ along the BMs that act as the IPAD pathways.

Inefficient periarterial drainage was observed during ischemic stroke (i.e., focally disrupted blood perfusion to the brain) in Arbel-Ornath et al. (2013). Since the contractile filaments of the arterial VSMCs are significantly damaged after 15–45 min of ischemia (Kwon et al., 2002), altered contractile activity of the VSMCs of the vessels deprived of blood flow seems likely and this could explain the impaired periarterial drainage along occluded vessels observed by Arbel-Ornath et al. (2013). On a similar note, Carare et al. (2008) observed no perivascular drainage following cardiac arrest. Further evidence that indicates a link between the cerebral vascular tone and IPAD comes from Maki et al. (2014) where the vasoactive drug, Cilostazol, was administrated into the mouse brain, leading to enhanced periarterial drainage.

#### 4.2. The Most Likely Motive Force for IPAD in the Brain

The simulated amplitude of oscillations in arterial diameter (e.g., 20%), induced by the contracting and relaxing VSMCs, is in line with the experimental observations of vasomotion in large cerebral arteries (Fujii et al., 1990, 1991; Rayshubskiy et al., 2014) and is considerably larger than the amplitude of oscillations induced by systolic pulsations (e.g., on average 3%, Bedussi et al., 2018). Comparison of the resulting intramural fluid flow rates with previous studies is given in **Table 2**. The net fluid flow rate of 360 µm3 ·s −1 generated by the V-IPAD mechanism along only one BM layer is five orders of magnitude higher than the net periarterial flow rate induced by arterial pulsations (Diem et al., 2017). The studies of Asgari et al. (2015, 2016) also yielded perivascular flow rates driven by arterial pulsations, but in a distinct perivascular space positioned

The driving force in the V-IPAD model is the vasomotion wave, while in the models of Diem et al. (2017), Asgari et al. (2015), and Asgari et al. (2016) is the arterial pulse. The net flow rates represent the absolute values. BM is the compartment within the arterial wall, while PVS denotes the perivascular space between the arterial wall and the glial layer of the brain.

between the arterial wall and glial layer of the brain. Nonetheless, their findings offer additional proof that arterial pulsations are incapable of generating significant perivascular flow rates out of the brain. The values reported by Asgari et al. (2015, 2016) differ from those reported by Diem et al. (2017) due to the fact that the former authors assumed the thickness of the perivascular space up to two orders of magnitude higher than in the model of Diem et al. (2017). Here, the BM thickness is taken to be 0.4 µm in the undeformed, hydrated state and decreases to 0.27 µm during its compression by the maximally activated VSMCs. It is worth remarking that when comparison with other studies is made, it should be kept in mind that others may have defined the net flows out of the brain as negative flows relative to the direction of arterial pulsations.

Compared with the arterial pulse, the cerebral vasomotion is a more reasonable driving force of IPAD because its wavelength is orders of magnitude smaller than that of the arterial pulse wave. The wavelength of vasomotion is calculated based on its frequency and velocity. While the frequency of vasomotion is commonly reported with values centred at 0.1 Hz, its velocity is less well known. However, existing data allow a reasonable estimate.  Even when the higher end of velocities is considered, the resulting wavelength of cerebral vasomotion (e.g., 8 mm) is still two orders of magnitude smaller than that of the human arterial pulse wave (e.g., 1 m, calculated based on a velocity of 1 m/s and a frequency of 1 Hz, Stefanovska, 2007). In particular, vasomotion wavelength appears comparable with the length of the cerebral arteries; this feature favors development of pressure gradients that are able to drive significant net ISF flows along the IPAD pathways.

Despite the fact that the V-IPAD mechanism promotes significantly higher net intramural periarterial flow rates than the previously suggested mechanisms (see **Table 2**), it is essential to examine the relative contribution of V-IPAD in the global picture of brain clearance. The model we have designed yields the fluid flow rate along only one BM compartment surrounding the arterial VSMCs. The total number of BMs contributing to IPAD in the whole brain is currently unknown, so instead we consider a smaller block of gray matter and its vascular supply which can be relatively easier to assess. Based on the in vivo imaging of the surface arterial vascular network in the rodent brain (Schaffer et al., 2006), minimum 8 surface arteries branching from the MCA can be detected in an area measuring 4 × 3 mm. It is reasonable to assume that the penetrating arterioles arising from the surface arterial network extend through the entire depth of the cortex (1 mm in the rat brain Vetreno et al., 2017). In this way, a brain volume measuring 4 × 3 × 1 mm appears to be supplied (blood through the lumen) and cleared (ISF and soluble metabolites through the artery wall) by at least 8 surface arteries branching from the MCA. Assuming that each of these surface arteries has on average 3 BM compartments (Lee, 1995), a total of 24 BMs clears a block of gray matter of 12 mm3 , leading to a net ISF flow rate of 720 µm3 ·s −1 ·mm−3 (calculated as 24 · 360 µm3 ·s −1 /12 mm3 ) or, equally, 0.04 µl·min−1 ·g −1 brain. Owing to the lack of consistent experimental data on the passive and active mechanical response of small cerebral arteries, we have considered the vascular response of the branches of MCA to be identical to that of the MCA itself when estimating the total net ISF flow rate drained from a block of gray matter along the IPAD pathways. We note that investigation of IPAD in the MCA is itself an important goal, because the MCAs, together with the Circle of Willis, may be seen as a bottleneck in the outflow of ISF and solutes toward the cervical lymph nodes along the IPAD pathways. Therefore, it is of high significance to evaluate the likely strength of IPAD drainage along the MCAs. The proposed mechanism can also explain vasomotiondriven IPAD in smaller cerebral arteries with at least two layers of VSMCs. Adaptation of the V-IPAD model to intracerebral arterioles with only one layer of VSMCs will be addressed in a future study.

How physiologically-significant is the estimated net ISF flow rate along the IPAD pathways? The exact amount of ISF that requires removing from the brain interstitium along the IPAD pathways vs. other routes (e.g., convection through the cerebral extracellular spaces toward the CSF Abbott, 2004 or the recently proposed glymphatic system; Iliff et al., 2012) has been widely debated (Hladky and Barrand, 2014). By comparison with the average value for ISF flow rate from the brain (e.g., 0.2 µl·min−1 ·g −1 brain), which falls within the range of lymph flow rates and was estimated by Szentistvanyi et al. (1984), our estimated fluid flow rate induced by the V-IPAD mechanism (e.g., 0.04 µl·min−1 ·g −1 brain) has a 20% contribution to the clearance of cerebral ISF secreted at the blood-brain barrier. The amount of ISF transported by the V-IPAD mechanism may prove sufficient to maintain a physiological Aβ concentration within the artery wall by diluting the soluble Aβ that is not cleared at the blood-brain barrier via LRP1 transporters (escaping instead along the BM of cerebral arteries).

The elastic properties of the BM and the pattern of the vasomotion wave prove crucial in inducing net intramural fluid flow rates of physiological importance. If the cerebrovascular BM has the purpose of removing most of the cerebral soluble metabolites, its properties will likely have evolved to improve clearance of the brain and, hence, the BM may be a more deformable material than the one simulated here. The behavior of the BM may resemble the elastic response of the "spongy" material illustrated in **Figure 3**, which could undergo more significant compression of its pores, thereby hindering almost completely the reverse intramural flow. Such a behavior should make the pumping action of cerebral VSMCs even more effective, leading to higher net intramural fluid flow rates than the ones simulated here. This encourages future experimental assessment of the biomechanics of the contractile cerebral VSMCs and their BMs in order to explore the full potential of the V-IPAD mechanism. In the light of the values given in **Table 2**, the vasomotion wave appears as the best candidate proposed to date for the motive force for IPAD in the brain because it has a more appropriate amplitude and wavelength to drive fluid out of the brain at physiologically more significant flow rates than the arterial pulse. This conclusion holds for all the arterial segments described in **Supplementary Material 1** (with material parameters in **Supplementary Table 2**), considering that the resulting net intramural flow rates are 380 µm3 · s −1 and 444 µm3 · s −1 when the artery experiences an axial stretch of 1.09 and 1.13, respectively.

## 4.3. The Direction of the V-IPAD Mechanism

We have shown that the oscillations in the contractile state of the cerebral VSMCs are sufficiently powerful to promote net intramural flow rates of notable magnitude for efficient drainage of fluid along the IPAD routes, but the predominant direction of vasomotion remains elusive. Within the context of IPAD of the brain, the vasomotion wave must propagate from penetrating arterioles toward leptomeningeal arteries on the surface of the cortex. Direct investigations of the cerebral cortex in humans when awake showed distinct regions of pial arterioles exhibiting slow sinusoidal vascular oscillations, with a frequency of ≈ 0.1 Hz, which propagated as spatial waves across the cortex (Rayshubskiy et al., 2014). Spontaneous, lowfrequency oscillations of cerebral arteries within the range 0.1–1 Hz, presumably resulting from vasomotion, were also reported in mice when awake, propagating over several hundred micrometers across the cortex (Drew et al., 2011). Remote communication of local vascular responses (e.g., vasodilation) of arterioles to their parent arteries is common during cerebral functional hyperemia, in order to assure an optimal blood supply to the highly active brain region (Iadecola et al., 1997). Considering the complexity of communication between the active neurons and their supplying blood vessels during cerebral functional hyperemia, the cerebral arterial network seems to be empowered with properties that allow vasomotion to propagate in the optimal direction for IPAD (Secomb and Pries, 2002; Girouard and Iadecola, 2006; Sweeney et al., 2018). Additional experimental recordings of the spatial pattern of vasomotion is required and in vivo optical imaging (e.g., two-photon microscopy, optical intrinsic signal imaging) of contractile cerebral vascular networks (Nishimura et al., 2007; Chen et al., 2011; Rayshubskiy et al., 2014) will prove useful in such an endeavor.

 Soluble metabolites can thus only leave the brain tissue by either diffusion toward the CSF compartments or by bulk flow along less resistant pathways such as the perivascular pathways of blood vessels (Abbott, 2004; Bakker et al., 2016). However, diffusion is only able to operate effectively over very short distances (Syková and Nicholson, 2008). It is not immediately obvious how the vasomotion wave will improve the flow of fluid and soluble metabolites along the other proposed para-vascular clearance pathways, such as the pial-glial basement membranes (Albargothy et al., 2018) or the para-venous pathways (Iliff et al., 2012). According to the recent review of Welsh et al. (2018), there is no evidence of conducted vasomotor responses along venules and veins, or from veins to the arterial system through the capillary network. This indicates that vasomotion can only act as a driving force for periarterial drainage. Furthermore, if the artery wall acts as a two-way traffic system for (i) the efflux of ISF and soluble parenchymal metabolites along the IPAD pathways and (ii) the convective influx of CSF into the brain parenchyma along the pial-glial basement membranes, then two distinct motive forces are required. With respect to the latter, it has been demonstrated by Asgari et al. (2016) that the arterial pulsations propagating into the brain are not sufficient to drive significant net para-vascular flows along arteries, but they may enhance local mixing and diffusion of solutes in the CSF along para-vascular pathways. It remains an open question which exact physical forces are responsible for this influx of CSF into the brain parenchyma. In order for separate longitudinal flows (in opposite directions) to occur along both the IPAD and para-vascular pathways, the vascular BM, adjacent to the VSMCs, needs to be separated from the CSF by an impermeable (or highly resistant) membrane. The pial layer could serve this purpose, but its exact permeability needs further experimental characterization, as it may not only vary between species, but also between intracerebral and extracerebral regions (Alcolado et al., 1988; Abbott et al., 2018). Here, we have shown that vasomotion can drive net ISF flow along the vascular BM positioned between two impermeable layers of VSMCs.

## 4.4. Contributors to the Pathogenesis of CAA

While direct evidence is still missing, the aforementioned experiments (Beach et al., 2000; Arbel-Ornath et al., 2013; Maki et al., 2014) strongly support the proposed hypothesis that the activity of the cerebral VSMCs may be a key element for IPAD in the brain and, subsequently, in preventing the onset of CAA. The contractile and relaxing abilities of the VSMCs are weakened in old arteries (Tümer et al., 2014). The cholinergic innervation of cerebral arteries also decreases with age and in the presence of AD, thus impairing vessel dilatation (Tong and Hamel, 1999; Van Beek and Claassen, 2011). Moreover, the transport of Aβ out of the brain interstitium across the blood-brain barrier also becomes inefficient with age (Shibata et al., 2000; Deane et al., 2009), leading to an elevated burden of intracerebral Aβ that requires removal along alternative routes, presumably along the IPAD pathways. The cumulative effect of all these age-driven changes may translate into an altered response of the VSMCs and, consequently, into a diminished motive force for IPAD, increasing in this way the likelihood of CAA.

Various hypotheses have been advanced to explain CAA. It has been proposed that the Aβ deposited in the walls of arteries in CAA was derived from VSMCs (Vinters, 1987). However, production of Aβ by VSMCs does not explain the deposition of Aβ in the walls of capillaries that lack VSMCs. Furthermore, transgenic mice in which neurons overproduce Aβ develop CAA, both in cortical and leptomeningeal arteries; this suggests that parenchymal Aβ is transported from the brain tissue along the walls of arteries where it accumulates due to amyloid overload of the drainage pathways (Calhoun et al., 1999). It has also been proposed that the origin of Aβ in the walls of arteries in CAA is from the CSF due to convective influx along para-arterial pathways (Iliff et al., 2012). However, subsequent studies have shown that tracers from the CSF enter the brain along pial-glial basement membranes (Morris et al., 2016) and drain from the brain along the IPAD pathways (Albargothy et al., 2018). The most feasible mechanism for the pathogenesis of CAA, therefore, appears to be protein elimination failure angiopathy due to age-related failure of IPAD (Carare et al., 2013).

#### 4.5. Potential Therapeutic Implications

The impact of this study opens avenues for targeting vasomotion toward the facilitation of IPAD and prevention of CAA. This is key for the successful outcome of current immunization trials against Aβ in which plaques are solubilized, but Aβ becomes entrapped in the IPAD pathways, increasing the severity of CAA and most likely resulting in amyloid-related imaging abnormalities (Holmes et al., 2008; Sperling et al., 2012; Weller et al., 2015). Moreover, as neurovascular therapeutic approaches in patients with mild cognitive impairment and AD are developed, e.g., the clinical trial with Cilostazol (Saito et al., 2016), the potential role of cerebral VSMCs in the periarterial clearance of Aβ from the brain may be worth considering in greater detail. The role of the VSMCs in the efficiency of IPAD in the brain could be tested experimentally in animal models with endogenous dysfunction of the VSCMs, which could be induced by dietary interventions (Gooch and Wilcock, 2016) or, in a more specific manner, by genetic mutations (Lee et al., 2012).

Using transgenic models with overproduction of mutant amyloid-beta peptides has many advantages in basic understanding of mechanism of disease (Klohs et al., 2014) but has also led to problems regarding translation into therapies (Jäkel et al., 2017). One model that may be more suitable, as there are no transgenic manipulations, is that of HHcy (Gooch and Wilcock, 2016). The HHcy mouse model develops physiologically relevant cerebrovascular pathology including microhemorrhages, vascular fibrosis, cerebral hypoperfusion and, most importantly, cognitive impairment (Hainsworth et al., 2016; Sudduth et al., 2017). The HHcy model is a dietary intervention that drives the folate cycle and methionine metabolic pathways to accumulate homocysteine through the elimination of vitamins B6, 9, and 12, and enrichment with methionine. The levels of homocysteine achieved in this model equate to moderate HHcy for a mouse. Our pilot data from HHcy models presented in **Supplementary Material 3** show a strong trend of reduced expression of α-smooth muscle actin in the HHcy mouse model compared to the control group (see **Supplementary Figure 4**). This suggests that the normal contractile function of the VSMCs may be impaired in the HHcy cases. In the future stages of our experiments, we will investigate the efficiency of IPAD in the HHcy mouse model.

Drugs such as cholinesterase inhibitors, commonly prescribed in the treatment of Alzheimer's disease, have enhanced high cognitive functions in demented patients who responded to treatment (Birks, 2006). Under the cholinergic-vasculature hypothesis, Claassen and Jansen (2006) have proposed that the observed benefits may be an outcome of the direct action of cholinesterase inhibitors on blood vessels. It therefore remains an interesting question how various vasoactive agents acting on the cerebral VSMCs affect not only the cerebral blood flow, but also the perivascular drainage of Aβ out of the brain.

In conclusion, this study has theoretically proven that the vasomotion wave initiated by the contractile VSMCs of cerebral arteries can represent the motive force for IPAD in the brain, shifting the view from a heart-driven clearance of fluid and solutes from the brain to an intrinsic mechanism of cerebral arteries. The V-IPAD model is the first one that quantitatively examines the contribution of the contractile cerebral VSMCs in the clearance of fluid from the brain, proposing a physically plausible driving force for IPAD. The model offers mechanistic insights into the behavior of the cerebrovascular BM and its interaction with the adjacent VSMCs. Once additional experimental data about the active response of cerebral arteries are provided, the V-IPAD model could be extended in order to improve its accuracy and simulate the effect of cerebral vascular aging on the IPAD of the brain. The hypothesis proposed and tested here stimulates further experimental investigation of the contribution of cerebral VSMCs in the clearance of fluid and solutes from the brain, particularly as these cells represent an accessible therapeutic target for CAA.

## ETHICS STATEMENT

The study was approved by the University of Kentucky Institutional Animal Care and Use Committee and conformed to the National Institutes of Health's Guide for the Care and Use of Laboratory Animals.

#### AUTHOR CONTRIBUTIONS

RA, GR, and RC formulated the hypothesis. RA and GR formulated and solved the model. RC and DW designed the experimental pilot study, obtained, and analyzed the experimental data. All authors contributed to the draft of the manuscript, read, and approved the final version.

#### FUNDING

This work was funded by an EPSRC Doctoral Training Centre grant (EP/G0369X/1) and EPSRC Doctoral Training Partnership grant (EP/N509747/1).

#### REFERENCES

#### ACKNOWLEDGMENTS

The authors acknowledge that parts of this work have been included in the Doctoral Thesis published by University of Southampton as Aldea (2017) at https://eprints.soton.ac.uk/ 420891/.

#### SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fnagi. 2019.00001/full#supplementary-material

mechanism for perivascular drainage in the brain. J. Math. Biol. 73, 469–490. doi: 10.1007/s00285-015-0960-6

Zekonyte, J., Sakai, K., Nicoll, J. A., Weller, R. O., and Carare, R. O. (2016). Quantification of molecular interactions between apoe, amyloid-beta (aβ) and laminin: relevance to accumulation of aβ in alzheimer's disease. Biochim. Biophys. Acta 1862, 1047–1053. doi: 10.1016/j.bbadis.2015.08.025

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 Aldea, Weller, Wilcock, Carare and Richardson. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Impairment of Dendrodendritic Inhibition in the Olfactory Bulb of APP/PS1 Mice

Weiyun Li 1,2† , Shanshan Li 1† , Lianghua Shen1 , Junbo Wang2 , Xuewei Wu1 , Jing Li 1 , Chunlong Tu3 , Xuesong Ye3 \* and Shucai Ling1 \*

1 Institute of Neuroscience and Anatomy, School of Medicine, Zhejiang University, Hangzhou, China, 2Department of Clinical Medicine, Zhejiang University City College, Hangzhou, China, 3Biosensor National Special Laboratory, Key Laboratory of BME of the Ministry of Education, Zhejiang University, Hangzhou, China

Olfactory dysfunction is an early event in Alzheimer's disease (AD). However, the mechanism underlying the AD-related changes in the olfactory bulb (OB) remains unknown. Granule cells (GCs) in the OB regulate the activity of mitral cells (MCs) through reciprocal dendrodendritic synapses, which is crucial for olfactory signal processing and odor discrimination. Nevertheless, the relationships between the morphological and functional changes of dendrodendritic synapses, particularly the local field potentials (LFPs) as a consequence of olfactory disorders in patients with AD have not been investigated. Here, we studied the morphological and functional changes induced by dendrodendritic inhibition in GCs onto MCs in the OB of amyloid precursor protein (APP)/PS1 mice and age-matched control mice during aging, particular, we focused on the effects of olfactory disorder in the dendrodendritic synaptic structures and the LFPs. We found that olfactory disorder was associated with increased amyloid-β (Aβ) deposits in the OB of APP/PS1 mice, and those mice also exhibited abnormal changes in the morphology of GCs and MCs, a decreased density of GC dendritic spines and impairments in the synaptic interface of dendrodendritic synapses between GCs and MCs. In addition, the aberrant enhancements in the γ oscillations and firing rates of MCs in the OB of APP/PS1 mice were recorded by multi-electrode arrays (MEAs). The local application of a GABAAR agonist nearly abolished the aberrant increase in γ oscillations in the external plexiform layer (EPL) at advanced stages of AD, whereas a GABAAR antagonist aggravated the γ oscillations. Based on our findings, we concluded that the altered morphologies of the synaptic structures of GCs, the dysfunction of reciprocal dendrodendritic synapses between MCs and GCs, and the abnormal γ oscillations in the EPL might contribute to olfactory dysfunction in AD.

Keywords: Alzheimer's disease, olfactory dysfunction, olfactory bulb, dendrodendritic inhibition, γ oscillations

#### INTRODUCTION

Alzheimer's disease (AD), the most common neurodegenerative disorder, results in severe memory and learning impairments (Wei et al., 2010). Olfactory deficits have been proposed as a marker of AD because reductions in odor detection thresholds, recognition, and identification occur earlier than dementia in many patients (Wesson et al., 2010; Wu et al., 2013). The driving force of AD pathology is hypothesized to be the formation of toxic amyloid-β (Aβ) peptides that are

#### Edited by:

Patrizia Giannoni, University of Nîmes, France

#### Reviewed by:

Gustavo Provensi, Università degli Studi di Firenze, Italy Federica Bertaso, INSERM U1191 Institut de Génomique Fonctionnelle (IGF), France

\*Correspondence:

Xuesong Ye<EMAIL_ADDRESS>Shucai Ling<EMAIL_ADDRESS>
†These authors have contributed equally to this work

Received: 27 October 2018 Accepted: 08 January 2019 Published: 24 January 2019

#### Citation:

Li W, Li S, Shen L, Wang J, Wu X, Li J, Tu C, Ye X and Ling S (2019) Impairment of Dendrodendritic Inhibition in the Olfactory Bulb of APP/PS1 Mice. Front. Aging Neurosci. 11:2. doi: 10.3389/fnagi.2019.00002

**26**

cleaved from amyloid precursor protein (APP), the subsequent development of neurofibrillary tangles (NFTs), and the resulting, cascade of secondary pathologies (Lachén-Montes et al., 2016). Therefore, studies aiming to reveal the relationship between neuropathological Aβ deposition and olfactory dysfunction have the potential to provide information about early AD pathology, and, ultimately, early diagnosis.

Olfaction involves various processes including sensory neuron inputs to the olfactory bulb (OB), decoding in the primary olfactory cortex, and ultimately transmission to downstream neurons in the amygdala, hippocampus, hypothalamus and nucleus accumbens (Wachowiak and Shipley, 2006; Wesson et al., 2010). The OB constitutes the first relay of the olfactory system (Lepousez and Lledo, 2013). In the OB, the excitatory sensory inputs from excitatory sensory neurons to mitral cells (MCs) trigger the release of glutamate from their lateral dendrites onto the dendrites of granule cells (GCs), and this section mediates the transmission of the GABAergic inhibition back to MCs (Isaacson and Strowbridge, 1998; Lepousez and Lledo, 2013). The recurrent and lateral inhibition supported by dendrodendritic reciprocal synapses between the dendrites of MCs' and GCs' mediates key roles in sensory processing, such as the gain control and odor selectivity of MC responses, which are crucial for proper odor discrimination (Abraham et al., 2010; Tan et al., 2010). As the largest population of interneurons in the OB, GCs are involved in the synchronization and establishment of the slow temporal firing patterns of MC activity (Schild, 1988; Friedrich and Laurent, 2001; Nusser et al., 2001; Lledo and Lagier, 2006). The function of GCs in inhibiting MCs has been studied using various approaches. However, researchers have not yet determined whether the morphology and function of dendrodendritic reciprocal synapses between GCs and MCs in the OB are impaired in patients with AD.

MCs extend lateral dendrites in the external plexiform layer (EPL) that are contacted by pedunculated, headed spines arising from the distal apical dendrites of GCs, which results in the establishment of the reciprocal dendrodendritic synapses between GCs and MCs (Phillips et al., 1963; Rall et al., 1966; Price and Powell, 1970). Reciprocal dendrodendritic synapses between GCs and MCs are involved in the generation of γ oscillations (40–100 Hz) in the OB (Lagier et al., 2007). As one band of the local field potential (LFP) oscillations in the OB, γ oscillations are induced by odorants and reflect the synchronized spike discharges from principal neurons (Beshel et al., 2007; Lagier et al., 2007; Lepousez and Lledo, 2013; Osinski and Kay, 2016). Notably, γ oscillations are observed in awake and anesthetized animals, as well as in brain slices in vitro. However, the role of γ oscillations in sensory coding, particularly in patients with AD, remains unclear.

In the present study, we identified the relationship between Aβ deposits within the olfactory processing network and odor perception in APP/PS1 mice during aging. In addition, we investigated the morphological and functional changes in the reciprocal dendrodendritic synapses by employing a multielectrode array (MEA) to illustrate the network-level dynamics in response to pharmacological stimulation under excessive Aβ depositions in APP/PS1 mice at sequential age stages. The results showed that Aβ deposition induced morphological and functional changes in the dendrodendritic synapses in the EPL, and thus, contribute to a more in-depth illustration of the mechanism of olfactory disorders in AD.

## MATERIALS AND METHODS

#### Animals

B6/JNju-Tg (APPswe, PSEN1dE9)/Nju (APP/PS1) mice, which coexpress human PS1 encoding the E9 deletion and mouse APP containing humanized APP and Swedish mutations (K594N, M595L), were employed in this study. The mice were bred and maintained within the animal facility at the Laboratory Animal Center of Zhejiang University, Chinese Academy of Sciences. Age-matched nontransgenic littermate, C57BL/6Nju (C57) mice, were used as controls. The animals were housed with three-five same-sex littermates per cage under standard conditions (20–22◦C; 40%–60% humidity; 12-h light/dark cycle; and ad libitum access to water and food). All animal experiments were carried out in accordance with the National Institutes of Health guidelines for the care and use of laboratory animals (NIH Publication No. 85- 23, revised 1996), and the protocols were approved by the Institutional Animal Care and Use Committee of Zhejiang University. We studied 3–4-month-old (mo), 6–7-mo and 9–10-mo APP/PS1 mice and C57 mice to examine the possible contributions of accumulating Aβ deposits on olfaction over time. Both female and male mice were used in all the experiments. The ratio of female and male mice was approximately 1:1. No differences were observed between female and male mice.

#### Buried Food Test

A buried food test, which measures how rapidly an overnightfasted animal locates a small piece of familiar palatable food, was performed as previously published described with minor modifications (Hu et al., 2016). Briefly, at approximately 24 h prior to testing, the 3–4-mo, 6–7-mo and 9–10-mo APP/PS1 and age-matched C57 mice were weighed and subjected to a food-restricted diet. On the testing day, all the mice were habituated to the testing room for 1 h prior to testing, and the mice were then allowed to acclimate to the cage for 5 min before being transferred to an empty clean cage. A small piece (10 mm cube) of the same food that the mouse was fed daily was then randomly placed in a random corner of a clean mice cage with ∼3 cm of woodchip bedding. Before the mouse was transferred, a small piece (10-mm cube) of the same food that the mouse was fed daily was placed ∼1 cm beneath the bedding in the clean mice cage. The experimental mouse was then placed in the testing cage at a constant distance from the hidden food. The time it takes the mice to find the food was recorded, and whether the food was consumed was also noted. If the mouse failed to find the buried food within 5 min, the test was stopped, and the latency score was recorded as 300 s. Twelve mice from each group were used in the buried food test.

#### Fine Olfactory Discrimination Test

The fine olfactory discrimination test was used to measure the olfactory discrimination ability of the mice by associating olfaction with taste aversion. The test was conducted using previously published protocols (Enwere et al., 2004; Zhu et al., 2014). After the buried food test, the same mice were separated into individual cages and deprived of water for 24 h. Each individual mouse was subjected to two stages of testing, a training stage and a testing stage, to obtain each data point. The training experiment was designed to encourage the mice to associate mango smells with palatable drinks and almond smells with bitterness. For the first training stage, a mixture of 10 µl of double-distilled water and 1 µl of mango extract (Mgo) was placed in a sterile 35 × 10-mm dish to allow the mice to habituate to the Mgo smell. The combination of distilled water and Mgo, which served as a reward for response, was designated [+]. The mice were allowed 2 min to find [+]. Thirty seconds after the mouse finished drinking the solution, a fresh [+] solution was provided. In the trials, the amount of Mgo was sequentially increased to 2.5, 4, 5.5, 7 and 8.5 µl. We repeated the last trial five times, and for the sixth trial, we presented the mice with 8.5 µl of almond extract (ALM) with 10 µl of a 1% denatonium benzoate (DB) solution (Sigma-Aldrich, St. Louis, MO, USA). The combination of ALM and DB was designated [−]. The experimental mice found the [−] solution extremely aversive, learned to associate the bitter taste with the smell of ALM because DB is extremely bitter, and subsequently avoided drinking the [−] solutions. An additional four trials were conducted with the [−] solution to ensure that the mice had learned the association between bitter taste and the smell of ALM. In the testing stage, the mouse was presented with two dishes, one of the dishes mainly contained [+], and the other contained [−]. For example, a 60:40 ratio of the odor components in task indicates that the composition ratio of Mgo in distilled water to almond in 1% DB in one dish was 60:40, and that in the other dish was 40:60. Successful discrimination occurred if the mouse drank [+] and did not perform any the following behaviors: (a) chose [−] rather than [+] or (b) chose both [+] and [−] within 30 s. A task was designated a total failure if the mouse tasted the [−] first. The mice that did not choose within 2 min were excluded from the analysis. Ten trials were conducted in each testing stage. In the additional testing sessions, which incorporated decreased contents of Mgo and ALM in [+] and [−], respectively, an appropriate training stage using the new [+] and [−] solutions was included prior to the testing stage.

#### Morris Water Maze Test

The Morris water maze (MWM) test, which was conducted according to previously described protocols (Guo et al., 2017), was performed to test the spatial learning and memory of the mice. After experiencing the buried food and fine olfactory discrimination tests, the same mice were subjected to the MWM test. To test their spatial acquisition abilities, all the mice were trained to find the platform and underwent four trials per day for five consecutive days. During the acquisition phase trials, if the mouse was able to escape onto the hidden platform within 90 s it was allowed to stay on the platform for 5 s. If the mouse failed to find the hidden platform within 90 s, it was guided to the platform by the experimenter and allowed to remain on the platform for 15 s to help it remember the platform's location. The probe trial was performed 24 h after the last acquisition trial to access the retention of the spatial memory. In this phase, the hidden platform was removed, and the mice were allowed to swim for 90 s in the pool. The numbers of platform crossings were recorded using a computerized tracking system.

#### Tissue Preparation

After the MWM tests, six randomly selected mice from each group were sacrificed under deep anesthesia with sodium pentobarbital (50 mg/kg) and perfused with 25 ml of 0.01 M phosphate-buffered saline (PBS, pH 7.4) and then with 100 ml of 4% paraformaldehyde in 0.1 M PB solution (pH 7.4). The OBs were removed, postfixed in the same fixative for 2 h and then cryoprotected for 36 h at 4◦C in 0.1 M PB containing 30% sucrose. Olfactory sections (25 µm) were cut coronally using a freezing microtome (Leica CM2100, Germany) at −20◦C and collected in a cryoprotectant fluid containing 30% ethanediol and 30% glycerinum in 0.01 M PBS. The olfactory sections were then stored at −20◦C for immunofluorescence staining and Nissl staining. Six mice from each group that underwent the behavioral tests were anesthetized, perfused transcardially with ice-cold PBS (pH 7.4), and fixed with 0.5% paraformaldehyde. The brain tissues were then submerged in a Golgi-Cox solution containing 5% potassium dichromate, 5% mercuric chloride and 5% potassium chromate in distilled water. A new set of mice from each group were used for electron microscopy, Western blotting and MEA assays.

#### Immunofluorescence Staining

The olfactory sections of each group stored at −20◦C were washed three times with PBS, blocked with 10% normal goat serum in PBS, and incubated with primary antibodies against Aβ (6E10, Invitrogen, Carlsbad, CA, USA, 1:500) overnight at 4 ◦C. The sections were then rinsed with PBS and incubated with secondary antibodies conjugated to Alexa Fluor 549 (Invitrogen Technologies, 1:500) for 2 h at room temperature. The sections were subsequently rinsed twice with PBS, mounted on gelatincoated glass slides with fluorescent sealant and cover-slipped. The sections were subsequently observed and imaged with a laser-scanning confocal microscope (Olympus FV1000, Japan). The photomicrographs were saved as TIFs and quantitatively analyzed using ImageJ software.

#### Nissl Staining

For Nissl staining, the olfactory sections from each group were rinsed three times with 0.01 M PBS, mounted on gelatin-coated glass slides and naturally dried at room temperature overnight. The sections were defatted by incubation with 75% ethanol at 37◦C for 2 h, stained within 0.1% cresyl violet solution for 10 min at room temperature, and rinsed with water.  Permount was added to the slides, and the slides were then covered with coverslips. Images were captured using an Olympus microscope.

#### Golgi-Cox Staining

The brain tissues of each group were submerged in Golgi-Cox solution in the dark for 8 days, and the solution was replaced every 2–3 days. The brains were then dehydrated with a 30% sucrose solution, and the OBs were mounted on the vibratome platform and sectioned at 150 µm. The sections were rinsed twice (5 min each) with distilled water to remove any traces of the impregnating solution. The sections were dehydrated by incubation in 50% alcohol for 5 min, incubated with an ammonia solution (3:1, ammonia: distilled water) for 10 min, rinsed twice with distilled water, and incubated with 5% sodium thiosulfate solution for 10 min in the dark. The sections were then rinsed twice with distilled water, sequentially dehydrated in 50% ethanol I (5 min), 50% ethanol II (5 min), 70% ethanol (5 min), 80% ethanol (5 min), 95% ethanol (5 min), 100% ethanol I (5 min), add 100% ethanol II (10 min), cleared with xylene I (10 min) and xylene II (30 min) and mounted on gelatinized slides using Permount. A confocal microscope was then used to obtain the spine density in apical dendrites of GCs. A Z-stack of the optical section was captured using an Olympus FV1000 instrument with an 60× oil-immersion objective lens and a 10× optical zoom. When capturing the fluorescent signals, an excitation wavelength of 559 nm was used, and the light path was adjusted to mirror and receive the reflected light. For the analyses of the dendritic spine density and the neuron maturity, we adopted the criteria detailed in the published literature (Matsuda and Hisatsune, 2017). The dendritic spines of GCs were divided into mature spines (mushroom and thin spines) and immature spines (filopodia and stubby spines) according to their morphology. In this study, all the dendritic spines were categorized and quantified manually as immature or mature.

#### Western Blotting

Western blotting was conducted as described previously (Yang et al., 2016). Six OBs were dissected from APP/PS1 mice and age-matched C57 mice at different ages and then lysed using RIPA buffer supplemented with an EDTA-free protease inhibitor cocktail and phosphatase inhibitors. Twenty micrograms of total protein from each group were separated on 10% SDS-PAGE gels and transferred to PVDF membranes. Protein-bound PVDF membranes were incubated with rabbit anti-postsynaptic density 95 (anti-PSD95), β-actin (1:1,000, Cell Signaling Technology, Danvers, MA, USA) synaptophysin and synapsin I antibodies (1:1,000, Sigma, USA) overnight at 4◦C. the membranes were then washed with TBST for 15 min, incubated with a horseradish peroxidase-conjugated goat anti-rabbit antibody (1:2,000; Cell Signaling Technology, Danvers, MA, USA) for 2 h, and then washed with TBST. The membranes were subsequently processed for detection using the ECL system. The protein levels were quantified by assessing their optical densities using Quantity One software and are expressed as ratios relative to β-actin.

#### Electron Microscopy

Six mice in each group were anesthetized, and their brains were removed. The OB was sliced coronally into 0.2-mm slices using a vibrating slicer and then postfixed with 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4) for 12 h. After three washes with 0.1 M PBS (10 min each), the OB slices were exposed to 1% osmium tetroxide for 2 h, washed several times with water, and dehydrated with a gradient series of alcohol solutions (2 × 10 min with 50%, 2 × 10 min with 70%, 2 × 10 min with 90%, and 2 × 10 min with 100%). The sections were subsequently embedded in epon resin, and randomly selected ultrathin sections were stained with uranyl acetate and lead citrate. Three slides per animal and three fields within each granule layer and EPL per slide were randomly selected to quantify the number of synapses and measure the thickness of the PSD. Each field was imaged at 26,500× magnification using a transmission electron microscope (Tecnai G2 F20 S-TWIN, FEI). The number of synapses and the thickness of the PSD in the granule layer and EPL were analyzed in 15 images from each mouse by an experimenter who was blinded to the treatment and genotype using Image Pro Plus 6.0 software.

### MEA Assay

Six mice from each group were deeply anesthetized, and their brains were rapidly excised from their skulls and submerged in ice-cold cutting solution containing 2.34 mM sucrose, 5 mM KCl, 1.25 mM NaH2PO4, 5 mM MgSO4, 26 mM NaHCO3, 25 mM glucose, and 1 mM CaCl2 for 5 min. Slices were cut from the brain samples using a vibratome (Leica Microsystems, Germany) while the samples were submerged in a chamber filled with ice cold cutting solution. The OB was cut into 250 µm coronal sections, and the tissue slices were allowed to recover in an oxygenated artificial cerebrospinal fluid (ACSF) solution (119 mM NaCl, 2.5 mM KCl, 1.0 mM NaH2PO4, 26.2 mM NaHCO3, 11 mM glucose, 1.3 mM MgSO4, and 2.5 mM CaCl2) at 34◦C for 0.5 h and then incubated at 28◦C for at least for 1 h. OB slices were transferred to an MEA chip (60 Square MEA200/50iR-Ti-gr), continuously perfused with oxygenated ACSF, and saturated with 95% O2/5% CO2 (pH 7.4 and 325 mOsm/kg) at 28◦C at a rate of 1 ml/min. All the experiments were conducted through a 6-min recording in ACSF. In addition, control slices that were continuously treated with ACSF after the initial 5 min showed no change in frequencies, sites of initiation, and level of propagation. We treated the OB slices with a GABAA receptor antagonist (bicuculline, 10 µM) and an agonist (muscimol, 100 µM) to assess the synaptic events between GCs and MCs in response to pharmacological stimulation. The drugs were perfused over the slices through gravity at a rate of 3 ml/min. The chemicals were acquired from Tocris Bioscience (Ellisville, MO, USA) and Sigma-Aldrich Canada (Oakville, ON, Canada). The neuronal network activity was recorded using an MEA (MEA2100- System, Reutlingen, Germany) with 60 platinum electrodes, a 50-µm electrode diameter and a 200-µm interelectrode spacing. An OB slice was positioned over the array such that the slice was in close contact with the microelectrode array electrodes throughout the recording. The electrical activity recorded from each channel was digitized at a sampling rate of 25 kHz and acquired using a MEA2100 amplifier (Multi Channel Systems, Reutlingen, Germany). The temperature in the recording chamber was constantly monitored and maintained at 28◦C by a heated perfusion cannula (TC02, Reutlingen, Germany).

#### Experimental Design and Statistical Analysis

The data were analyzed and performed using GraphPad Prism version 6.0 (GraphPad software). The escape latency data obtained from the MWM test were analyzed by two-way repeated-measures analysis of variance (ANOVA) with Bonferroni post-tests. For the rest of the data, the statistical significance of the differences in the means between two groups was assessed by Mann-Whitney or two-tailed unpaired t-tests, as appropriate. A value of p < 0.05 was considered to indicate statistical significance. All the data are expressed as the means ± standard errors of the means (SEMs). In all the figures, ∗p < 0.05, ∗∗p < 0.01, and ∗∗∗p < 0.001.

#### RESULTS

#### Olfactory Dysfunction Occurs Earlier Than Cognitive Impairment in APP/PS1 Mice

We performed two behavioral tests to assess whether the olfaction function of APP/PS1 mice was altered: the buried food test and the olfactory discrimination test. In the buried food test, the time each individual mouse required to find the hidden food was recorded (**Figure 1A**). As shown in

FIGURE 1 | Atypical olfactory and cognitive behaviors of amyloid precursor protein (APP)/PS1 mice. (A) Design of the buried food test. (B) Quantitative statistical analysis of latency of 3–4-, 6–7 and 9–10-month-old (mo) APP/PS1 and C57 mice to find the hidden food particle. (C) Design of the fine odor discrimination test. Mice were deprived of water for 24 h and trained to identify distilled water. In one dish, the distilled water was coupled with an odor mixture of mango and almond, in which mango was the main component. The other dish contained bitter water with an odor mixture in which almond was the main component. Different odor proportions of mango and almond were used in the performance test. (D–F) Performance of APP/PS1 and C57 mice at 3–4 (D), 6–7 (E), and 9–10 months of age (F). (G) Design of the Morris water maze (MWM) test. (H) Quantitative statistics of the crossing platform times of APP/PS1 and C57 mice at 3–4, 6–7 and 9–10 months of age. (I–K) Escape latencies of APP/PS1 and C57 mice at 3–4 (I), 6–7 (J) and 9–10 months of age (K). The data are presented as the means ± standard errors of the means (SEMs). ∗p < 0.05; ∗∗p < 0.01.

**Figure 1B**, the 3–4-mo (p < 0.05), 6–7-mo (p < 0.05) and 9–10-mo (p < 0.01) APP/PS1 mice needed more time to find the hidden food than the age-matched C57 mice, indicating an impairment in their olfactory performance. The same groups of mice that were subjected to the hidden food test were subsequently subjected to the olfactory discrimination test. In the olfactory discrimination test, the mice were acquainted with mango flavor mixed with distilled water and with an almond flavor mixed with bitter water. The olfactory capabilities of the mice were determined based on their ability to distinguish between mixtures containing different fractions of mango and almond smells, which have distinct aromas (**Figure 1C**). The accuracy of the discrimination between mixtures containing more than 60% of either mango flavor or almond odor based on their smells was comparable. The variable trends in the results from the olfaction discrimination test were consistent with those from the buried food test. Compared with the C57 mice, the 3–4 mo (p < 0.05, **Figure 1D**), 6–7-mo (p < 0.05, **Figure 1E**) and 9–10-mo (p < 0.05, **Figure 1F**) APP/PS1 mice showed decreases in the numbers of correct olfactory discrimination responses per trial, indicating that the level of olfactory discrimination became increasingly impaired as the animals aged. Thus, APP/PS1 mice exhibited olfactory dysfunction at 3–4 months of age.

We then evaluated the hippocampal-dependent spatial learning and memory abilities of APP/PS1 and age-matched C57 mice at sequential age stages using the MWM test (**Figure 1G**). In this test, the escape latencies of the mice were measured during the 5-day acquisition phase, and the time of platform site crossings was recorded in the probe trial. There were no significant differences in the crossing plate times between the APP/PS1 and age-matched C57 mice at 3–4 months of age (p < 0.05, **Figure 1H**). In addition, no significant differences in escape latency were found in the acquisition phase trials between the APP/PS1 and C57 mice at 3–4 months of age (F = 3.883; p = 0.0628 > 0.05, **Figure 1I**), indicating that the 3–4-mo APP/PS1 and control mice have equal abilities to encode and remember the spatial coordinates of the platform. At 6–7 months of age, significantly decreased crossing plate times in probe trial (p < 0.05, **Figure 1H**) and a tendency to require a longer time to find the platform (F = 0.2689; p = 0.1049 > 0.05, **Figure 1J**) were observed in the APP/PS1 mice compared with the control mice, indicating that the spatial memory of APP/PS1 mice was impaired. The 9–10-mo APP/PS1 mice needed a longer time to find the platform (F = 9.12; p = 0.0056 < 0.01, **Figure 1K**) and exhibited fewer crossings over the former platform site than the control mice (p < 0.05, **Figure 1H**). The above-mentioned results showed that the spatial discrimination learning and memory impairments in APP/PS1 mice started to occur at 6–7 months of age. Based on the results from the behavioral tests, the 3–4-mo APP/PS1 mice suffered olfactory deficits but not cognitive impairments. We thus hypothesized that the 3–4-mo APP/PS1 mice can be

considered to be at a relative early stage of AD. In addition, the 9–10-mo APP/PS1 mice suffered olfactory deficits and cognitive impairments and were considered representative of late-stage AD.

#### Deposition of Soluble Aβ Aggregates in the Olfactory Bulb

According to recent evidence, soluble Aβ is strongly correlated with olfactory dysfunction in patients with AD (Wu et al., 2013; Wang et al., 2016). We characterized Aβ deposition in APP/PS1 mice through immunostaining for Aβ using the anti-Aβ antibody 6E10 (1:2,000 diluted) to investigate the spatiotemporal pattern of Aβ aggregates in the OB (**Figure 2A**). The results showed gradual increases in the amount, degree of aggregation, and spatial distribution of Aβ deposits from the outer to the inner layers in APP/PS1 mice with increasing age and negative staining for Aβ in 9–10-mo C57 mice (**Figure 2B**). At 3–4 months of age, the MCL developed obvious Aβ deposits (**Figure 2C**). In addition, Aβ began to accumulate in the GC layer (GCL) and was secreted into the extracellular space (**Figure 2C**). At 6–7 months of age, the Aβ burden spread throughout the GCL (**Figure 2D**), and an increased Aβ burden was observed in all layers in 9–10-mo APP/PS1 mice (**Figure 2E**). In both 6–7-mo and 9–10-mo APP/PS1 mice, Aβ was almost exclusively located within the GCL and MCL in APP/PS1 mice (**Figure 2F**), and only rare Aβ deposits were observed in the glomerular layer (GL), EPL, or inner plexiform layer (IPL).

#### Effect of Aβ Exposure on the Laminar Organization of the OB

Nissl staining for Nissl bodies was performed to visualize the structure of the OB and thus explore the effects of Aβ depositions on the overall morphology of the OB. A distinct laminar organization, with intact glomeruli and clear layers, was observed

bars = 500 µm in (F; applies to A–F) and 100 µm in (F1; applies to A1–F1).

in the OB. During aging, the morphology of the OB remained unchanged in APP/PS1 mice compared with the age-matched C57 mice (**Figures 3A–F,A1–F1**).

#### The Dendritic Spine Density of GCs Is Reduced in the OB of APP/PS1 Mice

We analyzed the dendritic spine density of GCs in the OB through Golgi staining to investigate the mechanisms through which Aβ mediates olfactory deficits in APP/PS1 mice. The morphology of the dendritic spines in GCs in the adult OB is heterogeneous based on the dendrite distributions and dendrodendritic synaptic partners (Nagayama et al., 2014; McDole et al., 2015). An examination of the dendritic spine density of GCs indicated a qualitatively significant decrease in the APP/PS1 mice compared with the age-matched C57 mice (**Figure 4A**). The dendritic spine density of GCs in APP/PS1 mice tended to decrease at 3–4 months of age (p > 0.05, **Figure 4B**), but significant decreases were observed in both 6–7-mo (p < 0.05, **Figure 4B**) and 9–10-mo (p < 0.001, **Figure 4B**) APP/PS1 mice compared with their age-matched control mice. The spines of GCs were classified as mature and immature, and confocal observations revealed that the mature spine density was approximately the same in 3–4-mo APP/PS1 and C57 mice (p > 0.05, **Figure 4C**), but significantly impaired in 6–7 and 9–10-mo APP/PS1 mice compared with their age-matched C57 mice (p < 0.05, **Figure 4C**). Compared with that in the age-matched control mice, the immature spine density of GCs in APP/PS1 mice tended to decline at 3–4 and 6–7 months of age (p > 0.05, **Figure 4D**) but was significantly reduced at 9–10 months of age (p < 0.01, **Figure 4D**). We further explored the molecular mechanisms underlying Aβ overexpression-induced olfactory deficits. Specifically, a Western blotting analysis was performed to examine the levels of several synaptic proteins, including a postsynaptic protein (PSD95) and two presynaptic proteins (synaptophysin and synapsin I), and the results revealed that their expression was gradually decreased in APP/PS1 mice compared with C57 mice (**Figures 4E–H**).

#### Amyloid-Beta Overexpression Impairs Synaptic Ultrastructure Parameters

An electron microscopy analysis was performed to investigate possible changes in the morphology of the reciprocal dendrodendritic synapses in the EPL. Specifically, we examined various synaptic components, including the minor axis diameter of the synaptic vesicle, the synaptic cleft size, and the thickness of PSD, to demonstrate the distinctive features of reciprocal dendrodendritic synapses. At 3–4 months of age, the synaptic vesicles surrounding the synaptic membrane were transparent and clear in both APP/PS1 and C57 mice (p > 0.05, **Figure 5A**). However, the presynaptic and postsynaptic membrane structures of asymmetric synapses, which are considered excitatory synapses, were damaged. At 6–7 and 9–10 months of age, an unclear and broken membrane structure and blurred boundaries for the synaptic vesicles were observed in APP/PS1 mice. Significant differences in the synaptic cleft (p > 0.05, **Figure 5B**) and the diameters of synaptic vesicles (p > 0.05, **Figure 5C**)

immature spine density (D) from randomly selected dendritic segments of GCs in APP/PS1 and age-matched C57 mice. (E–H) Western blotting assays of postsynaptic density95 (PSD95; F), synapsin I (G) and synaptophysin (H) in the OB; the levels of these proteins were reduced in APP/PS1 mice compared with age-matched C57 mice. The data are expressed as the means ± SEMs. ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001.

were not observed between APP/PS1 and C57 mice. However, compared with the C57 mice, the thickness of the PSD (p < 0.05, **Figure 5D**) in asymmetric synapses was thinner in 3–4-, 6–7-, and 9–10-mo APP/PS1 mice. We subsequently examined the structures of the symmetric synapses in the EPL (**Figure 5E**), which are also known as inhibitory synapses, and the thickness of the presynaptic membrane was similar to that of the postsynaptic membrane. The integrated structures of synaptic membranes