Cerebral amyloid angiopathy (CAA) is a neurovascular disease characterized by accumulation of the amyloid-β peptide in the brain vasculature, which ultimately leads to stroke and cognitive decline (Banerjee et al., 2017). In both hereditary and sporadic variants of CAA, measurements in patients have shown that impairments in cerebrovascular function can be found early in the disease process (Dumas et al., 2012; van Opstal et al., 2017). These measurements were performed with blood oxygenation level dependent-functional magnetic resonance imaging (BOLD-fMRI) and a visual stimulation paradigm, allowing to characterize the cerebrovascular response to neuronal activation in the occipital cortex, where visual processing occurs. The occipital cortex is also where CAA burden is highest (Yamada et al., 1987), likely contributing to the sensitivity of the fMRI measurement.

Mouse models of cerebral amyloidosis are invaluable tools for testing safety and effectiveness of greatly needed novel therapies for amyloid-β-related diseases, including CAA. However, for the results to be translatable to the clinic, it is essential that the amyloidosis model shows similar structural and functional phenotypes as the patient. The transgenic Swedish Dutch Iowa (Tg-SwDI) mouse model is an amyloidosis model expressing low levels of the human amyloid-β precursor protein (APP) gene with three familial mutations, of which the Dutch and Iowa mutations are located in the amyloid-β coding region of APP (Davis et al., 2004). The neuronal expression of the mutated APP in this model leads to early amyloid-β accumulation in the brain, starting around 6 months. Amyloid-β accumulates predominantly around capillaries in the Tg-SwDI model, which is similar to a subtype of CAA pathology observed in pathological examinations of patient tissue and sometimes referred to as capCAA (Thal et al., 2002). It is unknown to what extent amyloid-β accumulation around capillaries contributes to the observed impairments in cerebrovascular function in patients. Unlike patients, vascular pathology in the Tg-SwDI model is most severe in the thalamus (Miao et al., 2005). Previous studies have measured cortical vascular reactivity using laser Doppler flowmetry (LDF) after removal of the skull in Tg-SwDI and wild-type (WT) mice. They reported, similar to CAA patients, early impairments in cerebrovascular reactivity (CVR) in Tg-SwDI mice (Chow et al., 2007; Park et al., 2014). It is unknown, however, whether skull removal has affected the outcome, and whether the thalamus is more strongly affected, as the thalamus is not readily accessible with LDF.

Here, we therefore used the noninvasive arterial spin labeling (ASL)-magnetic resonance imaging (MRI) technique to study the cerebrovascular function in the Tg-SwDI brain. With ASL, arterial blood is magnetically labeled and used as endogenous tracer flowing into the tissue of interest, which is most often the brain. The distribution of the label over the different brain regions reflects local tissue perfusion and can be converted into absolute cerebral blood flow (CBF) values, expressed as mL/100 g/min. When combined with a hypercapnic challenge, both CBF and CVR can be determined for different brain regions. A known unfavorable characteristic of ASL is a possible underestimation of CBF in case of slow flow. In that case, a delayed arterial transit time (ATT) – the time that it takes for the label to travel from the labeling plane to the brain tissue – could be misinterpreted as decreased CBF. ATT evaluation is therefore valuable to prevent a potential underestimation of the ASL-based CBF estimates, as well as indicative in itself of vascular pathology (Alsop et al., 2015).

The noninvasive nature of ASL was fully exploited in our study with the use of a longitudinal study design in which CBF and CVR were repeatedly measured during increasing amyloid-β accumulation in the brain vasculature of Tg-SwDI mice. As it is conceivable that high microvascular amyloid burden could lead to delayed ATT in Tg-SwDI mice, an ATT measurement was added to the protocol by means of a modified ASL sequence optimized to capture the inflow of the tracer into the brain tissue (Hirschler et al., 2018a). To allow for repeated measurements, a minimally invasive isoflurane anesthesia protocol was used. Moreover, to be able to compare the results to literature, additional end-point measurements were performed under a terminal anesthesia protocol with urethane and α-chloralose (U and A). Furthermore, a subgroup of mice was used to directly compare ASL-MRI to LDF. The study design is summarized in Figure 1.

Figure 1

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Hypercapnia consistently induced a CBF increase in both WT and Tg-SwDI mice under isoflurane anesthesia (Figure 2 and Figure 3), which was mainly located in cortical regions (Figure 2). Surprisingly, the CBF maps and CBF time profiles acquired in the mid-brain of the Tg-SwDI mice resembled those of the WT mice at every time point. Hence no significant differences were observed in baseline CBF nor CVR between the two genotypes at any time point (Figure 3b; see Figure 3—figure supplement 1 for individual animal trends). A significant effect of age on CBF was, however, observed in both WT and Tg-SwDI mice, χ²(3)=13.00, p=0.005 and χ²(3)=8.49, p=0.037, respectively. Post hoc analysis indicated that this was attributable to a decrease in baseline CBF between the ages of 3 and 6 months. Between these time points, median (iqr) CBF significantly decreased from 155 (143–159) to 121 (112–124) mL/100 g/min in WT mice, p=0.008. In Tg-SwDI mice, a similar trend was observed, from 147 (133–151) to 126 (103–135) mL/100 g/min, p=0.036, but this was not significant (cut-off p-value of 0.017 after Bonferroni correction). From 6 months of age, the baseline CBF remained stable inside each group. Age also had a significant effect on CVR in WT mice, χ²(3)=8.33, p=0.040. No differences were observed with post hoc analysis, however, besides a trend toward increased CVR between the ages of 3 and 6 months, from 13 (7–17) to 31 (23–37) %, p=0.038. Age had no significant effect on CVR in Tg-SwDI mice, χ²(3)=6.94, p=0.074. Additional analysis in cortical and thalamic areas did not reveal any difference between WT and Tg-SwDI mice either (Figure 3—figure supplement 2). Similarly, no differences were observed in brain volume, body weight, change in tc-pCO₂ upon the hypercapnia challenges, respiration rate, and inversion efficiency between WT and Tg-SwDI mice (Figure 3—figure supplement 1, Figure 3—figure supplement 3 and Figure 3—figure supplement 4). Of note, the CBF at 12 months showed higher variability in the WT group, and the body weights showed higher variability in the Tg-SwDI group.

Figure 2

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Figure 3 with 4 supplements see all

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Median (iqr) baseline ATT values in the mid-brain were also similar for both WT and Tg-SwDI mice, i.e. 206 (186-240) milliseconds (ms) and 223 (202–246) ms respectively at 12 months of age (Figure 4). The hypercapnia challenge shortened the ATT to 192 (189–205) for WT and 197 (187–207) for Tg-SwDI mice, which was significant for Tg-SwDI mice (Z = −2.20 and p=0.028), but did not reach significance for WT mice (Z = −1.84 and p=0.066).

Figure 4

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To evaluate if a functional deficit could have been masked by the vasodilatory effect of isoflurane during the MRI sessions, additional CBF and CVR measurements were performed in the same cohort of mice under urethane and alpha-chloralose (U and A) anesthesia. This was done at 12.3 months, 10 days after the last MRI measurement under isoflurane. The change of anesthesia protocol resulted in profound hemodynamic changes: baseline CBF was markedly reduced and the hypercapnic response was higher in amplitude, but also slower (Figure 5a), and more widespread in the brain tissue (Figure 5b). The CBF and CVR estimates were indeed significantly impacted by the change in anesthesia protocol (Figure 5c), with the median CBF (iqr) decreasing from 126 (84–141) to 28 (26–30) mL/100 g/min in WT mice, Z = −2.67 and p=0.008, and median (iqr) CVR increasing from 26 (6–47) to 233 (193–245) %, Z = −2.67 and p=0.008. These changes were again comparable to those in Tg-SwDI mice, with the median CBF (iqr) and CVR (iqr) respectively changing from 114 (103–130) to 25 (21–40) mL/100 g/min, Z = −2.37 and p=0.018, and from 30 (17–34) to 265 (178–312) %, Z = −2.37 and p=0.018. The CBF response at the induction phase of U and A anesthesia was also similar in Tg-SwDI and WT mice (Figure 5—figure supplement 1). The higher CVR during U and A was unlikely due to a higher CO₂ absorption, as the tc-pCO₂ responses to the hypercapnia challenges only increased from an average of 15 mmHG during isoflurane to an average of 19 mmHG during U and A (Figure 5—figure supplement 2).

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An additional smaller second cohort of mice was used to cross-validate our MRI findings with the previously used LDF readout as imaging modality to assess cerebrovascular function in this mouse model (Chow et al., 2007; Park et al., 2014). After a unilateral craniotomy (right side), LDF measurements were performed with two probes at the same time: one through the skull in the left hemisphere and the other directly above the brain tissue in the right hemisphere. MRI measurements were performed directly after the LDF measurement in the same mice and the brain region analyzed with the MRI data was restricted to the somatosensory cortex, where the LDF measurements were also collected. No differences in CVR could be observed between WT and Tg-SwDI mice, neither with MRI nor with LDF (Figure 6). Of note, removal of the skull severely reduced the CVR for both imaging modalities (Figure 6—figure supplement 1).

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Lastly, the brain tissue was stained for amyloid-β to assess the degree of pathological burden. All Tg-SwDI mice developed extensive amyloid-β plaque pathology by the end of the experiment, with mainly diffuse parenchymal plaques in the cortex and microvascular plaques in the hippocampus and thalamus, but none of the WT mice displayed any amyloid-β deposition (Figure 7).

Figure 7

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This longitudinal study characterized cerebrovascular function in the Tg-SwDI mouse model of microvascular amyloidosis over the full course of amyloid-β pathogenesis. No significant impairment in cerebrovascular function could be found in the Tg-SwDI model, no matter the age or functional parameter explored. This contradicts previous findings, which showed early impairments in CVR in the cerebral cortex in Tg-SwDI mice using LDF (Chow et al., 2007; Park et al., 2014). In our study, cerebrovascular function was first assessed using ASL-MRI and a CO₂ challenge, allowing to measure absolute CBF as well as CVR in the entire brain. The ASL-MRI perfusion data were acquired longitudinally using a low-level isoflurane anesthesia protocol to capture the dynamics of decreasing cerebrovascular function over increasing microvascular amyloid-β loads. This study setup was substantially different from literature studies, where relative CBF measurements performed using LDF under a terminal anesthesia protocol with urethane and α-chloralose (U and A), after removal of the skull, showed impaired hemodynamics in the Tg-SwDI mouse model (Chow et al., 2007; Park et al., 2014). Therefore, additional experiments were performed to determine whether differences in experimental design could explain this discrepancy. A likely candidate was the difference in anesthesia protocol, as this has been shown to significantly influence CVR experiments (Petrinovic et al., 2016; Munting et al., 2019). Another likely candidate was the difference in imaging modality, as ASL-MRI and LDF are sensitive to different blood components, namely flux of blood plasma (ASL-MRI) or velocity of red blood cells (LDF). However, after differences in anesthesia protocol and imaging modality were accounted for, cerebrovascular function was in our hands still found to be preserved in the Tg-SwDI model. Practically, it is very difficult to replicate an experiment up to the smallest detail in a different laboratory, as small differences might remain. However, by showing similar findings for two modalities and two anesthesia protocols, including the ones used before, we think that our study convincingly shows that microvascular amyloidosis in the Tg-SwDI mouse model does not induce cerebrovascular dysfunction. Moreover, the robustness to sense local hemodynamic changes could be confirmed based on the observed reduced CBF and CVR as a function of age and anesthesia. The CBF time profiles of Tg-SwDI mice were in fact remarkably similar to their WT controls, even when CBF was monitored for up to 1.5 hr (Figure 5—figure supplement 1). Everything considered, our functional results indicate that the causal link between microvascular amyloidosis and cerebrovascular function, which was established in past studies in the Tg-SwDI model, is to be mitigated and remains to be fully uncovered.

Some remaining differences between our approach and those of others are, however, useful to mention and could possibly provide explanations for the different outcomes found between studies. For instance Chow et al., 2007 and also Park et al., 2014 used heterozygotic Tg-SwDI mice, whereas in this study, homozygotic Tg-SwDI mice were used. Homozygotic Tg-SwDI mice have been reported to develop more extensive amyloid-β pathology, but with a similar distribution on the micro- and macro-scale in the brain, and similar amyloid-β−40/amyloid-β−42 ratios as hemizygous mice (Xu et al., 2007). It seems, however, unlikely that the more severe pathology would result in a reversal of the functional phenotype. Furthermore, the amyloid-β pathology found here (Figure 7) is comparable to what is described in the literature for both hemizygous and homozygous mice, namely diffuse parenchymal plaques in the cortex and microvascular accumulation in the thalamus and hippocampus (Miao et al., 2005). Second, we used a higher percentage of CO₂ for our vascular challenge, that is, 7.5% for 7 min here, versus 5% for 5 min in literature (Park et al., 2014). However, it is unlikely that higher pCO₂ intake would reverse the phenotype of the Tg-SwDI mice. Furthermore, the pCO₂ increase measured here through the skin during U and A is comparable to that reported with blood gas sampling in Park et al., 2014 (approximately 20 mmHg). Possibly, a lower sensitivity of the transcutaneous pCO₂ measurement versus blood gas sampling could explain why our pCO₂ increase was not higher. It is also important to mention that in most patient studies (Dumas et al., 2012; van Opstal et al., 2017) as well as in Chow et al., 2007 vascular responses to neuronal activity were measured, not baseline perfusion and hypercapnia, which might be differently affected by vascular amyloid-β. However, this is not likely to explain the differences in outcomes, as Park et al., 2014 showed impaired responses in Tg-SwDI mice to both hypercapnia and neuronal activity. Furthermore, patient studies have also shown baseline perfusion deficits (van Opstal et al., 2017). Lastly, in Chow et al., 2007 and Park et al., 2014 a craniotomy was performed right before the LDF measurement, whereas here, most of the measurements were performed noninvasively. An attempt was made to account for this difference in experimental set-up by preparing an acute craniotomy in the second cohort of mice. However, even though the brain surface in these animals was visually normal (Figure 6—figure supplement 1) after the craniotomy, edema was observed with MRI, and with both imaging modalities marked reductions in CBF and CVR were measured in the underlying brain regions in both WT and Tg-SwDI mice. A likely explanation for the functional impairments after craniotomy is the occurrence of a cortical spreading depression (CSD), which occurs even at minor manipulations on the dura (Ayata et al., 2004). In mice specifically, CSDs have been reported to cause a CBF reduction of 40–50%, and enhanced resistance to relaxation by acetylcholine (Ayata et al., 2004). It could be hypothesized that the presence of amyloid-β in the Tg-SwDI model enhances the sensitivity of the brain tissue to a CSD. Thus, when measured just after craniotomy, the unnoticed presence of a CSD might give the false idea of direct amyloid-β induced cerebrovascular dysfunction. Interestingly, a recent study also reported that skull removal was necessary to detect cerebrovascular dysfunction in a different mouse model of amyloid-β accumulation, providing some support for this hypothesis (Sharp et al., 2020). Nevertheless, because our WT mice showed similar functional deficits as the Tg-SwDI mice, we currently cannot substantiate this hypothesis. It is important to mention that the researcher that performed the craniotomy in this study is highly skilled in this procedure, underlining that this is not merely a matter of experience. Hence, it is of interest to elucidate in future research if our surgical procedure indeed triggers a CSD, and whether Tg-SwDI mice are more susceptible to CSDs.

Alternatively, our results could indicate that the mouse cerebral circulation is less affected by amyloid-β accumulation than the human cerebral circulation. However, other amyloidosis models did show early impairments in cerebrovascular function, even in a noninvasive set-up, such as the APP23 model (Mueggler et al., 2002; Maier et al., 2014). Because the APP23 model mainly shows arteriolar CAA pathology (Sturchler-Pierrat et al., 1997), altogether this might indicate that predominantly CAA pathology on the arteriolar side of the vasculature is responsible for the observed CBF and CVR impairments in patients. It is important to note, however, that even without CBF limiting pathology, capillary dysfunction could lead to inefficient oxygen extraction from the capillary network (Østergaard et al., 2016). Indeed, hypoxia-induced factor angiopoietin-4 was found to be highly expressed in capCAA patients (Chakraborty et al., 2018), indicating that possibly capCAA could induce hypoxic conditions. It would thus be interesting to validate whether similar conditions are found in the Tg-SwDI mouse model.

To the best of our knowledge, this is the first study with ASL-MRI in mice where the CBF responses to hypercapnia under isoflurane and U and A anesthesia were directly compared. Large differences in hemodynamic parameters were observed, with the very low CBF during U and A anesthesia as the most striking observation. The low CBF during U and A is non-physiological and thus forms a limitation of this study. Interestingly, U and A is considered one of the more suitable anesthesia protocols for cerebral hemodynamic studies in mice, as this protocol has been reported to have relatively mild hemodynamic effects (Janssen et al., 2004; Wang et al., 2010) and to maintain cerebral autoregulation (Dalkara et al., 1995). However, these studies did not provide absolute CBF estimates, and our results indicate that the stable hemodynamics come together with very low baseline CBF, which may not always be favorable and certainly do not represent normal baseline physiology. It would be of interest to identify the underlying cause for this low CBF.

This is also one of the few studies where a direct comparison was established between hypercapnic CBF responses measured with ASL-MRI versus LDF. The results must, however, be interpreted with care due to the small group size and the ±0.5 hr delay between the two measurements. Both readouts consistently reported preservation of cerebrovascular function in Tg-SwDI mice and a severe reduction of CVR after craniotomy, but CVR measurements with ASL-MRI were nearly twice as high compared to those obtained with LDF. It is unclear where this difference comes from. As the two imaging modalities are sensitive to different blood components (plasma or red blood cells), it could be argued that local hematocrit changes during hypercapnia could explain this difference. Indeed, hematocrit has been reported to decrease during hypercapnia in the rat brain (Keyeux et al., 1995), but with only a 10% decrease during a 10% CO₂ challenge, which is too low to explain the observed difference. A more convincing explanation could be found in an interesting study where ASL-MRI and LDF were performed simultaneously (He et al., 2007) and point in the direction of an underestimation by LDF. The authors showed that by varying the fiber separation distance in the LDF probe, which changes the measured cortical depth, a different CBF response was measured. Consequently, the magnitude of the CBF response was either comparable to that measured with ASL-MRI, or up to two times lower, which is comparable to our results. The authors measured CBF responses to electrical whisker stimulation through a thinned skull in rats under urethane anesthesia, and concluded that when larger cortical depths were measured, LDF underestimated the CBF response. It is unclear how these results would exactly translate to our different set-up (smaller cortical thickness in the mouse, intact skull, and different vascular challenge), but indicates that investigating the cortical depth measured with a set-up like ours is a compelling area for future research. Interestingly, the absolute CBF responses measured with ASL-MRI by He et al., 2007 were comparable to those measured here.

Lastly, another finding in our study that is of interest is the observation of edema and the local reduction in CBF and CVR with MRI after craniotomy. This was observed in all animals, while the brain tissue was normal by visual inspection. Normal appearance of brain tissue is therefore not enough to conclude that the brain tissue is healthy when working with acutely prepared cranial windows.

In conclusion, this study shows that cerebrovascular function in the Tg-SwDI mouse model of microvascular amyloidosis is preserved up to 12 months of age, despite the high amyloid-β burden observed at that age. This observation was confirmed using two different anesthesia protocols and two different imaging modalities. These observations call into question to what extent microvascular amyloidosis as seen in the Tg-SwDI mouse model is a correct model for cerebrovascular dysfunction as observed in CAA patients, and calls for further research to clear up the discrepancy in results.

Imaging data have been deposited in the EASY online archiving system, and is accessible under: https://doi.org/10.17026/dans-znx-cvf3.

1. 1. Munting LP

   (2020) ASL-MRI

   Tg-SwDI ASL-MRI study.

   https://doi.org/10.17026/dans-znx-cvf3

1. 1. Alsop DC
   2. Detre JA
   3. Golay X
   4. Günther M
   5. Hendrikse J
   6. Hernandez-Garcia L
   7. Lu H
   8. MacIntosh BJ
   9. Parkes LM
   10. Smits M
   11. van Osch MJ
   12. Wang DJ
   13. Wong EC
   14. Zaharchuk G

   (2015) Recommended implementation of arterial spin-labeled perfusion MRI for clinical applications: a consensus of the ISMRM perfusion study group and the European consortium for ASL in dementia

   Magnetic Resonance in Medicine 73:102–116.

   https://doi.org/10.1002/mrm.25197

   • PubMed
   • Google Scholar
2. 1. Ayata C
   2. Shin HK
   3. Salomone S
   4. Ozdemir-Gursoy Y
   5. Boas DA
   6. Dunn AK
   7. Moskowitz MA

   (2004) Pronounced hypoperfusion during spreading depression in mouse cortex

   Journal of Cerebral Blood Flow & Metabolism 24:1172–1182.

   https://doi.org/10.1097/01.WCB.0000137057.92786.F3

   • PubMed
   • Google Scholar
3. 1. Banerjee G
   2. Carare R
   3. Cordonnier C
   4. Greenberg SM
   5. Schneider JA
   6. Smith EE
   7. Buchem Mvan
   8. Grond J
   9. Verbeek MM
   10. Werring DJ

   (2017) The increasing impact of cerebral amyloid angiopathy: essential new insights for clinical practice

   Journal of Neurology, Neurosurgery & Psychiatry 88:982–994.

   https://doi.org/10.1136/jnnp-2016-314697

   • Google Scholar
4. 1. Brossard C
   2. Montigon O
   3. Boux F
   4. Delphin A
   5. Christen T
   6. Barbier EL
   7. Lemasson B

   (2020) MP3: medical software for processing Multi-Parametric images pipelines

   Frontiers in Neuroinformatics 14:16.

   https://doi.org/10.3389/fninf.2020.594799

   • PubMed
   • Google Scholar
5. 1. Buxton RB
   2. Frank LR
   3. Wong EC
   4. Siewert B
   5. Warach S
   6. Edelman RR

   (1998) A general kinetic model for quantitative perfusion imaging with arterial spin labeling

   Magnetic Resonance in Medicine 40:383–396.

   https://doi.org/10.1002/mrm.1910400308

   • PubMed
   • Google Scholar
6. 1. Chakraborty A
   2. Kamermans A
   3. van Het Hof B
   4. Castricum K
   5. Aanhane E
   6. van Horssen J
   7. Thijssen VL
   8. Scheltens P
   9. Teunissen CE
   10. Fontijn RD
   11. van der Flier WM
   12. de Vries HE

   (2018) Angiopoietin like-4 as a novel vascular mediator in capillary cerebral amyloid angiopathy

   Brain 141:3377–3388.

   https://doi.org/10.1093/brain/awy274

   • PubMed
   • Google Scholar
7. 1. Chow N
   2. Bell RD
   3. Deane R
   4. Streb JW
   5. Chen J
   6. Brooks A
   7. Van Nostrand W
   8. Miano JM
   9. Zlokovic BV

   (2007) Serum response factor and myocardin mediate arterial hypercontractility and cerebral blood flow dysregulation in Alzheimer's phenotype

   PNAS 104:823–828.

   https://doi.org/10.1073/pnas.0608251104

   • PubMed
   • Google Scholar
8. 1. Dalkara T
   2. Irikura K
   3. Huang Z
   4. Panahian N
   5. Moskowitz MA

   (1995) Cerebrovascular responses under controlled and monitored physiological conditions in the anesthetized mouse

   Journal of Cerebral Blood Flow & Metabolism 15:631–638.

   https://doi.org/10.1038/jcbfm.1995.78

   • PubMed
   • Google Scholar
9. 1. Davis J
   2. Xu F
   3. Deane R
   4. Romanov G
   5. Previti ML
   6. Zeigler K
   7. Zlokovic BV
   8. Van Nostrand WE

   (2004) Early-onset and robust cerebral microvascular accumulation of amyloid β-Protein in transgenic mice expressing low levels of a vasculotropic Dutch/Iowa mutant form of amyloid β-Protein precursor

   Journal of Biological Chemistry 279:20296–20306.

   https://doi.org/10.1074/jbc.M312946200

   • Google Scholar
10. 1. Denis de Senneville B
    2. Zachiu C
    3. Ries M
    4. Moonen C

    (2016) EVolution: an edge-based variational method for non-rigid multi-modal image registration

    Physics in Medicine and Biology 61:7377–7396.

    https://doi.org/10.1088/0031-9155/61/20/7377

    • PubMed
    • Google Scholar
11. 1. Dobre MC
    2. Uğurbil K
    3. Marjanska M

    (2007) Determination of blood longitudinal relaxation time (T1) at high magnetic field strengths

    Magnetic Resonance Imaging 25:733–735.

    https://doi.org/10.1016/j.mri.2006.10.020

    • PubMed
    • Google Scholar
12. 1. Dumas A
    2. Dierksen GA
    3. Gurol ME
    4. Halpin A
    5. Martinez-Ramirez S
    6. Schwab K
    7. Rosand J
    8. Viswanathan A
    9. Salat DH
    10. Polimeni JR
    11. Greenberg SM

    (2012) Functional magnetic resonance imaging detection of vascular reactivity in cerebral amyloid angiopathy

    Annals of Neurology 72:76–81.

    https://doi.org/10.1002/ana.23566

    • PubMed
    • Google Scholar
13. 1. Faul F
    2. Erdfelder E
    3. Lang AG
    4. Buchner A

    (2007) G*power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences

    Behavior Research Methods 39:175–191.

    https://doi.org/10.3758/BF03193146

    • PubMed
    • Google Scholar
14. 1. He J
    2. Devonshire IM
    3. Mayhew JE
    4. Papadakis NG

    (2007) Simultaneous laser Doppler flowmetry and arterial spin labeling MRI for measurement of functional perfusion changes in the cortex

    NeuroImage 34:1391–1404.

    https://doi.org/10.1016/j.neuroimage.2006.11.017

    • PubMed
    • Google Scholar
15. 1. Herscovitch P
    2. Raichle ME

    (1985) What is the correct value for the brain--blood partition coefficient for water?

    Journal of Cerebral Blood Flow & Metabolism 5:65–69.

    https://doi.org/10.1038/jcbfm.1985.9

    • PubMed
    • Google Scholar
16. 1. Hirschler L
    2. Munting LP
    3. Khmelinskii A
    4. Teeuwisse WM
    5. Suidgeest E
    6. Warnking JM
    7. van der Weerd L
    8. Barbier EL
    9. van Osch MJP

    (2018a) Transit time mapping in the mouse brain using time-encoded pCASL

    NMR in Biomedicine 31:e3855.

    https://doi.org/10.1002/nbm.3855

    • Google Scholar
17. 1. Hirschler L
    2. Debacker CS
    3. Voiron J
    4. Köhler S
    5. Warnking JM
    6. Barbier EL

    (2018b) Interpulse phase corrections for unbalanced pseudo-continuous arterial spin labeling at high magnetic field

    Magnetic Resonance in Medicine 79:1314–1324.

    https://doi.org/10.1002/mrm.26767

    • PubMed
    • Google Scholar
18. 1. Janssen BJ
    2. De Celle T
    3. Debets JJ
    4. Brouns AE
    5. Callahan MF
    6. Smith TL

    (2004) Effects of anesthetics on systemic hemodynamics in mice

    American Journal of Physiology-Heart and Circulatory Physiology 287:H1618–H1624.

    https://doi.org/10.1152/ajpheart.01192.2003

    • PubMed
    • Google Scholar
19. 1. Keyeux A
    2. Ochrymowicz-Bemelmans D
    3. Charlier AA

    (1995) Induced response to hypercapnia in the two-compartment total cerebral blood volume: influence on brain vascular reserve and flow efficiency

    Journal of Cerebral Blood Flow & Metabolism 15:1121–1131.

    https://doi.org/10.1038/jcbfm.1995.139

    • PubMed
    • Google Scholar
20. 1. Kilkenny C
    2. Browne WJ
    3. Cuthill IC
    4. Emerson M
    5. Altman DG

    (2010) Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research

    PLOS Biology 8:e1000412.

    https://doi.org/10.1371/journal.pbio.1000412

    • PubMed
    • Google Scholar
21. 1. Maier FC
    2. Wehrl HF
    3. Schmid AM
    4. Mannheim JG
    5. Wiehr S
    6. Lerdkrai C
    7. Calaminus C
    8. Stahlschmidt A
    9. Ye L
    10. Burnet M
    11. Stiller D
    12. Sabri O
    13. Reischl G
    14. Staufenbiel M
    15. Garaschuk O
    16. Jucker M
    17. Pichler BJ

    (2014) Longitudinal PET-MRI reveals β-amyloid deposition and rCBF dynamics and connects vascular amyloidosis to quantitative loss of perfusion

    Nature Medicine 20:1485–1492.

    https://doi.org/10.1038/nm.3734

    • PubMed
    • Google Scholar
22. 1. Miao J
    2. Xu F
    3. Davis J
    4. Otte-Höller I
    5. Verbeek MM
    6. Van Nostrand WE

    (2005) Cerebral microvascular amyloid beta protein deposition induces vascular degeneration and neuroinflammation in transgenic mice expressing human vasculotropic mutant amyloid beta precursor protein

    The American Journal of Pathology 167:505–515.

    https://doi.org/10.1016/S0002-9440(10)62993-8

    • PubMed
    • Google Scholar
23. 1. Mueggler T
    2. Sturchler-Pierrat C
    3. Baumann D
    4. Rausch M
    5. Staufenbiel M
    6. Rudin M

    (2002) Compromised hemodynamic response in amyloid precursor protein transgenic mice

    The Journal of Neuroscience 22:7218–7224.

    https://doi.org/10.1523/JNEUROSCI.22-16-07218.2002

    • PubMed
    • Google Scholar
24. 1. Munting LP
    2. Derieppe MPP
    3. Suidgeest E
    4. Denis de Senneville B
    5. Wells JA
    6. van der Weerd L

    (2019) Influence of different isoflurane anesthesia protocols on murine cerebral hemodynamics measured with pseudo-continuous arterial spin labeling

    NMR in Biomedicine 32:e4105.

    https://doi.org/10.1002/nbm.4105

    • PubMed
    • Google Scholar
25. 1. Østergaard L
    2. Engedal TS
    3. Moreton F
    4. Hansen MB
    5. Wardlaw JM
    6. Dalkara T
    7. Markus HS
    8. Muir KW

    (2016) Cerebral small vessel disease: capillary pathways to stroke and cognitive decline

    Journal of Cerebral Blood Flow & Metabolism 36:302–325.

    https://doi.org/10.1177/0271678X15606723

    • PubMed
    • Google Scholar
26. 1. Park L
    2. Koizumi K
    3. El Jamal S
    4. Zhou P
    5. Previti ML
    6. Van Nostrand WE
    7. Carlson G
    8. Iadecola C

    (2014) Age-dependent neurovascular dysfunction and damage in a mouse model of cerebral amyloid angiopathy

    Stroke 45:1815–1821.

    https://doi.org/10.1161/STROKEAHA.114.005179

    • PubMed
    • Google Scholar
27. 1. Petrinovic MM
    2. Hankov G
    3. Schroeter A
    4. Bruns A
    5. Rudin M
    6. von Kienlin M
    7. Künnecke B
    8. Mueggler T

    (2016) A novel anesthesia regime enables neurofunctional studies and imaging genetics across mouse strains

    Scientific Reports 6:24523.

    https://doi.org/10.1038/srep24523

    • PubMed
    • Google Scholar
28. 1. Ramos-Cabrer P
    2. Weber R
    3. Wiedermann D
    4. Hoehn M

    (2005) Continuous noninvasive monitoring of transcutaneous blood gases for a stable and persistent BOLD contrast in fMRI studies in the rat

    NMR in Biomedicine 18:440–446.

    https://doi.org/10.1002/nbm.978

    • PubMed
    • Google Scholar
29. 1. Sharp PS
    2. Ameen-Ali KE
    3. Boorman L
    4. Harris S
    5. Wharton S
    6. Howarth C
    7. Shabir O
    8. Redgrave P
    9. Berwick J

    (2020) Neurovascular coupling preserved in a chronic mouse model of Alzheimer's disease: Methodology is critical

    Journal of Cerebral Blood Flow & Metabolism 40:2289–2303.

    https://doi.org/10.1177/0271678X19890830

    • PubMed
    • Google Scholar
30. 1. Sturchler-Pierrat C
    2. Abramowski D
    3. Duke M
    4. Wiederhold KH
    5. Mistl C
    6. Rothacher S
    7. Ledermann B
    8. Bürki K
    9. Frey P
    10. Paganetti PA
    11. Waridel C
    12. Calhoun ME
    13. Jucker M
    14. Probst A
    15. Staufenbiel M
    16. Sommer B

    (1997) Two amyloid precursor protein transgenic mouse models with Alzheimer disease-like pathology

    PNAS 94:13287–13292.

    https://doi.org/10.1073/pnas.94.24.13287

    • PubMed
    • Google Scholar
31. 1. Thal DR
    2. Ghebremedhin E
    3. Rüb U
    4. Yamaguchi H
    5. Del Tredici K
    6. Braak H

    (2002) Two types of sporadic cerebral amyloid angiopathy

    Journal of Neuropathology & Experimental Neurology 61:282–293.

    https://doi.org/10.1093/jnen/61.3.282

    • PubMed
    • Google Scholar
32. 1. van Opstal AM
    2. van Rooden S
    3. van Harten T
    4. Ghariq E
    5. Labadie G
    6. Fotiadis P
    7. Gurol ME
    8. Terwindt GM
    9. Wermer MJH
    10. van Buchem MA
    11. Greenberg SM
    12. van der Grond J

    (2017) Cerebrovascular function in Presymptomatic and symptomatic individuals with hereditary cerebral amyloid angiopathy: a case-control study

    The Lancet Neurology 16:115–122.

    https://doi.org/10.1016/S1474-4422(16)30346-5

    • PubMed
    • Google Scholar
33. 1. Wang Z
    2. Schuler B
    3. Vogel O
    4. Arras M
    5. Vogel J

    (2010) What is the optimal anesthetic protocol for measurements of cerebral autoregulation in spontaneously breathing mice?

    Experimental Brain Research 207:249–258.

    https://doi.org/10.1007/s00221-010-2447-4

    • PubMed
    • Google Scholar
34. 1. Xu F
    2. Grande AM
    3. Robinson JK
    4. Previti ML
    5. Vasek M
    6. Davis J
    7. Van Nostrand WE

    (2007) Early-onset subicular microvascular amyloid and neuroinflammation correlate with behavioral deficits in vasculotropic mutant amyloid beta-protein precursor transgenic mice

    Neuroscience 146:98–107.

    https://doi.org/10.1016/j.neuroscience.2007.01.043

    • PubMed
    • Google Scholar
35. 1. Yamada M
    2. Tsukagoshi H
    3. Otomo E
    4. Hayakawa M

    (1987) Cerebral amyloid angiopathy in the aged

    Journal of Neurology 234:371–376.

    https://doi.org/10.1007/BF00314080

    • PubMed
    • Google Scholar

Author details

1. Leon P Munting

   1. Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
   2. Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands

   Contribution

   Conceptualization, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-2352-4451

2. Marc Derieppe

   1. Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
   2. Princess Máxima Center for Pediatric Oncology, Utrecht, Netherlands

   Contribution

   Software, Formal analysis, Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9099-1746

3. Ernst Suidgeest

   Department of Radiology, Leiden University Medical Center, Leiden, Netherlands

   Contribution

   Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8015-9238

4. Lydiane Hirschler

   Department of Radiology, Leiden University Medical Center, Leiden, Netherlands

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2379-0861

5. Matthias JP van Osch

   Department of Radiology, Leiden University Medical Center, Leiden, Netherlands

   Contribution

   Conceptualization, Supervision, Writing - review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7034-8959

6. Baudouin Denis de Senneville

   1. Department of Radiotherapy, University Medical Center Utrecht, Utrecht, Netherlands
   2. Institut de Mathématiques de Bordeaux, Université Bordeaux/CNRS UMR 5251/INRIA, Bordeaux-Sud-Ouest, France

   Contribution

   Software, Formal analysis

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5284-8474

7. Louise van der Weerd

   1. Department of Radiology, Leiden University Medical Center, Leiden, Netherlands
   2. Department of Human Genetics, Leiden University Medical Center, Leiden, Netherlands

   Contribution

   Conceptualization, Resources, Supervision, Funding acquisition, Project administration, Writing - review and editing

   For correspondence

   L.van_der_Weerd@lumc.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5997-2125

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