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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Neurosci. Author manuscript; available in PMC 2009 September 16.
Published in final edited form as:
PMCID: PMC2744887
NIHMSID: NIHMS135717

In Vivo Magnetic Resonance Microimaging of Individual Amyloid Plaques in Alzheimer’s Transgenic Mice

Abstract

The ability to detect individual Alzheimer’s amyloid plaques in vivo by magnetic resonance microimaging (MRI) should improve diagnosis and also accelerate discovery of effective therapeutic agents for Alzheimer’s disease (AD). Here we perform in vivo and ex vivo MRI on double transgenic AD mice as well as wild type mice at varying ages and correlate these with Thioflavin-S and iron staining histology. Quantitative counts of individual plaques on MRI increase with age and correlate with histologically determined plaque burden. Plaques 20 μm in diameter can be detected in AD mice as young as 3 months of age with ex vivo MRI. Plaques 35 μm in diameter can be detected by 9 months of age with in vivo MRI. In vivo MRI of individual Alzheimer’s amyloid plaques provides a non-invasive estimate of plaque burden in transgenic AD mice which might be useful in assessing the efficacy of amyloid reduction therapies.

Keywords: aging, amyloid, Alzheimer’s disease, magnetic resonance microimaging, plaque, transgenic mice

One of the cardinal pathologic features of Alzheimer’s disease (AD) is formation of senile, or amyloid, plaques (Hardy and Selkoe, 2002). Murine models of AD have been created by inserting human mutations leading to familial AD into the mouse genome, allowing controlled study of amyloid plaque biology (Hsiao et al., 1996; Holcomb et al., 1998). Doubly transgenic {amyloid precursor protein (APP) – presenilin 1 (PS1)} mice develop extensive “human-like” plaque formation at an early age (10 weeks), whereas plaques are not found in wild type mice. Plaque number and size increase with age in a stereotypical manner in these transgenic AD mice with relatively low inter-animal variation in this pattern (Wengenack et al., 2000b).

Direct imaging of amyloid plaques in the brain is feasible. Scintigraphic probes label plaques in human AD post mortem tissue sections as well as living AD subjects (Skovronsky et al., 2000; Wengenack et al., 2000a; Shoghi-Jadid et al., 2002; Bacskai et al., 2003; Klunk et al., 2004). Several groups have also pursued imaging of amyloid plaques with magnetic resonance microimaging (MRI) (Beneviste et al., 1999; Dhenain et al., 2002; Poduslo et al., 2002; Wadghiri et al., 2003; Helpern et al., 2004; Jack et al., 2004; Zhang et al., 2004; Higuchi et al., 2005). A motivation for this effort is that, unlike other modalities, MRI can resolve individual plaques noninvasively. The work in MRI has taken several directions. One is imaging of plaques in ex vivo human or transgenic mouse specimens, typically with long imaging times that may exceed 10 hours (Beneviste et al., 1999; Dhenain et al., 2002; Poduslo et al., 2002; Helpern et al., 2004; Lee et al., 2004; Zhang et al., 2004). Another is imaging of plaques in AD mice following administration of exogenous plaque labeling contrast media (Poduslo et al., 2002; Wadghiri et al., 2003; Higuchi et al., 2005). The final direction is MRI of plaques without exogenous contrast media in the living AD mouse (Jack et al., 2004; Vanhoutte et al., 2005). The basis for intrinsic MRI contrast (i.e. without administration of an exogenous labeling agent) between individual plaques and normalbackground tissue is presumed to be related to iron content of plaques which accelerates T2 and T2 * relaxation rates of tissue water protons in and adjacent to plaques (Beneviste et al., 1999).

These early efforts at establishing the ability of MRI to visualize plaques are essential steps in verifying the feasibility of this technique. However, in order to employ this approach to address biologically interesting questions, appropriate correlations with the known biology of plaque development must be demonstrated. To this end, the broad objective of this study was to validate in vivo MRI of individual plaques as a non-invasive estimate of plaque burden in transgenic AD mice.

Materials and Methods

We addressed the following questions in this study.

  1. Do in vivo and ex vivo MRI quantitatively capture the known age-dependent increase in plaque burden?
  2. Does plaque burden quantified by in vivo and ex vivo MRI correlate with a histological gold standard measure, Thioflavin – S (Thio-S)?
  3. Does the appearance of individual plaques on in vivo and ex vivo MRI correlate qualitatively with Thio-S and iron staining histology?
  4. What is the youngest age at which plaques can be detected by in vivo and ex vivo MRI in this AD mouse model?
  5. What are the smallest size AD plaques that can be resolved by in vivo and ex vivo MRI?

Imaging Methods

Experiments were performed using both APP/PS1 transgenic AD and B6SJL wild type mice. Mice were anesthetized using 1.0% – 1.5% isoflurane and O2/N2O and positioned in a custom built cradle for imaging. The mouse was positioned in the holder with its abdomen/thorax on top of a respiration pressure transducer of an MRI compatible ECG and respiratory monitoring device (SA Instr., Stony Brook, NY). The same unit also controlled the cardiorespiratory gating that triggered the scanner. ECG was monitored via disposable adhesive electrodes affixed to the left forelimb and right hindlimb. Body temperature was monitored by a rectal probe and maintained with a hot water circulating system. Experimental protocols were approved by the Institutional Animal Care and Use Committees of both the University of Minnesota and Mayo Clinic in accordance with the NIH Guide for the Care and Use of Laboratory Animals.

AD and wild type mice at ages 3, 6, 9, 12, and 24 months were imaged in vivo with a previously described T2–weighted spin echo sequence specifically designed for this application (Jack et al., 2004). Imaging parameters were: TR = 2 s; TE = 52 ms; x,y,z matrix 256 × 96 × 32 at corresponding fields of view of 15.36 mm × 5.76 mm × 3.84 mm resulting in voxel dimensions of 60 μm × 60 μm × 120 μm respectively and a scan time of 1 hour 40 minutes. The images were reconstructed with in-plane 2D zero-padding to produce final voxel dimensions of 30 μm × 30 μm × 120 μm. MR images were filtered to smooth noise while preserving edges. Experiments were performed with a Unity Inova spectometer (Varian, Palo Alto, CA) which was interfaced to a 9.4 T/31 cm horizontal bore magnet equipped with actively-shielded gradients capable of 300 mT/m in a rise-time of 500 μs (Magnex Scientific, Abingdon, UK). RF transmission and reception was performed with a in-house-built quadrature surface coil consisting of two 1 cm diameter loops.

Animal and Brain Tissue Handling

Following in vivo MRI, the animals were sacrificed to perform correlations with ex vivo MRI and in vitro histology. These animals were perfused with phosphate buffered saline and fixed with neutral buffered, 10% formalin following an overdose with sodium pentobarbital (200 mg/kg, IP). The brain was removed and fixed further in formalin over night and then equilibrated in 0.1 M sodium phosphate, pH 7.4 for 24 hours. The brain was embedded vertically in 2% agar in a 15 mm outer diameter glass tube for ex vivo MRI. The fixed brains were imaged ex vivo with the same imaging sequence described above.

After cryoprotecting in 10% followed by 30% sucrose in 0.1 M phosphate, pH 7.4, frozen coronal sections (30 μm) were cut with a sliding microtome throughout the whole extent of the cerebral cortex. The brains were sectioned in the same coronal orientation histologically as in the MRI scan. Adjacent 30 μm sections were stained for Thio-S and iron. The sections were mounted on slides, dried, and then stained with fresh, filtered, aqueous 1% Thio-S. The Thio-S positive beta-amyloid plaques were visualized with a fluorescence microscope using filters for fluorescein isothiocyanate.

In order to visualize tissue iron, a diaminobenzidine (DAB)-enhanced method of the Prussian Blue reaction was performed (LeVine, 1991) that is sensitive enough to detect trace amounts of iron in Alzheimer’s plaques (LeVine, 1997). Briefly, mounted 30 μm sections were hydrated in PBS and then incubated in 10 mg/ml sodium borohydride (Sigma) in PBS for 30 min. The sections were washed between each step in PBS twice for five min. each. The sections were incubated for 20 min. in 30 μg/ml proteinase K (Sigma), 0.1% Triton X in PBS. The sections were then incubated in 1% potassium ferrocyanide (Sigma), 1% HCl, and 1% Triton X in distilled water for 30 min. Lastly, the sections were incubated in 0.5 mg/ml 3,3′-diaminobenzidine tetrahydrochloride (Sigma) and 2 μl/ml 30% hydrogen peroxide (Sigma) in 0.05 M Tris HCl, pH7.6, for 15 min. in the dark. The sections were rinsed for five minutes in three changes of distilled water, coverslipped, and then imaged without counterstaining. In negative control sections in which the potassium ferrocyanide step is omitted but the DAB/H2O2 step is included to test for the presence of endogenous peroxidases, no plaques are stained, confirming the specificity of the staining for iron and its presence in plaques.

Qualitative MRI - Histological Correlation

Correlating anatomic features between in vivo and ex vivo MRI of the same mouse was accomplished using image analysis software with a linked cursor system (Robb, 1990). The ex vivo MRI volume was spatially matched to the in vivo volume using readily identifiable anatomic landmarks common to both image volumes. Following affine transformation, the ex vivo MRI volume was re-sampled in the space of the in vivo volume with windowed sinc interpolation.

In order to correlate histological sections with MRI, medium resolution images of the individual histological sections were combined to create a digitized 3D volume of the specimen (32 images per stain per mouse). Appropriate multiples of the 30 μm histological sections were used to match the 120 μm through-plane resolution of MRI. Adjacent 30 μm sections were stained with Thio-S and DAB-enhanced Prussian Blue, therefore matching between the MRI, Thio-S, and iron stained sections contained inherent partial volume averaging approximations. The digitized histological volume was then spatially matched to the in vivo MRI volume using anatomic landmarks common to both volumes as described above. A linked cursor system in the image analysis software program was used to identify plaques common to the four spatially registered volume datasets; in vivo MRI, ex vivo MRI, in vitro Thio-S, and in vitro iron stain.

Quantification Methods

As outlined in the discussion, it is impossible to spatially register an entire in vivo MRI volume to an ex vivo MRI or histological volume with accuracy at the tens of μm level. The different image types were therefore globally affine registered for quantitative plaque counting. Following affine registration of ex vivo MRI and Thio-S sections to the appropriate in vivo MRI sections, plaques were quantified by manual counting. Circular regions-of-interests (ROIs) 0.75 mm in diameter were placed electronically over the cortex – two ROIs per hemisphere near the midline as presented in Figure 1. This was repeated on five coronal sections evenly spaced throughout the cortex for a total 20 ROIs per image volume. The number of plaques in each ROI was counted manually. The mean number of plaques per ROI was summed for each type of image (in vivo MRI, ex vivo MRI, and Thio-S). Plaque counts were repeated four times on successive days to assess precision of the method. The standard deviation over four repeated measures at each ROI was averaged across all plaque containing ROIs in each animal.

Figure 1
Regions-of-interest used for plaque counting

The minimum plaque size resolvable on in vivo or ex vivo MRI was established by measuring plaque diameters on the corresponding Thio-S stained sections using high resolution photomicrographs. Areas of cortex were selected in which individual plaques could be identified both on spatially matched in vivo (or ex vivo) MRI and Thio-S images in each of the APP/PS1 mice. The diameters of these MR visible plaques were measured on the Thio-S photomicrographs using Zeiss Axiovision image analysis software.

Results

Plaques seen in the cortex and hippocampus on both in vivo and ex vivo MRI appear as discrete dark foci against the brighter intensity background of adjacent normal tissue. This is illustrated in Figure 2 which is a composite figure of 4 different image types in a 24 month AD mouse. In this image, in vivo MRI, ex vivo MRI, Thio-S, and iron stained images have been precisely spatially registered over a circumscribed area of the cortex, indicated by the box. This permits accurate point to point correlation of individual plaques in the 4 different types of images. Figure 2 demonstrates that individual plaques documented by positive Thio-S staining can be resolved with both in vivo and ex vivo MRI. The figure also demonstrates the correlation between iron staining of individual plaques and MRI conspicuity. The iron stain appears to label only plaques which are Thioflavin S positive. Figures 36 are similarly formed composite figures of AD mice aged 12, 9, 6 and 3 months respectively. Comparison of Figures 26 demonstrates that both plaque density and size increase with the age of AD mice on histological sections, and that these age-dependent features of plaque appearance are mirrored on MRI. No plaques were seen in wild type mice at any age. A composite figure (Figure 7) of a 9 month wild type mouse demonstrates no evidence of plaques on Thio-S or iron stained sections and no evidence of plaques on in vivo or ex vivo MRI.

Figure 2
24 month AD mouse
Figure 3
12 month AD mouse
Figure 6
3 month AD mouse
Figure 7
9 month wild type mouse

Quantification of plaque burden demonstrates that the mean number of plaques per ROI increases with the age of AD mice (Table 1). This is true in each of the image types evaluated quantitatively - in vivo MRI, ex vivo MRI, and Thio-S. Thus, plaque density on MRI scaled appropriately with the histological gold standard. Ex vivo MRI detected fewer plaques than Thio-S staining at each age, and in vivo MRI detected even fewer plaques at each age. Fewer plaques were resolved with both in vivo and ex vivo MRI compared to the Thio-S sections (Table 1). Taking the Thio-S plaque counts as the gold standard, the degree to which in vivo MRI underestimated true plaque burden varied with age. In order to calibrate MRI estimates of plaque burden with respect to the Thio-S stain, we regressed mean plaque count per ROI at each age in MRI on the corresponding mean value in the Thio-S sections. Linear regression of in vivo MRI on Thio-S revealed a slope = 0.35 and intercept = −2.2 (Figure 8, closed box). This equation can be used to estimate true plaque burden (plaques per ROI in the Thio-S preparations) from in vivo MRI plaque counts. Linear regression of ex vivo MRI on Thio-S revealed a slope = 0.38 and intercept = 0.46 (Figure 8, open box). The reproducibility of plaque counting was best with Thio-S and worst with in vivo MRI. Reproducibility increased with both age and plaque density for all three image types, but this effect was most pronounced with in vivo MRI.

Figure 8
Mean number of plaques per ROI
Table 1
Number of plaques per region-of-interest by age and image type

One benchmark we wished to establish was the youngest age at which plaques could be reliably detected with MRI. Figures 26 demonstrate that the conspicuity of individual plaques is consistently superior with ex vivo compared to in vivo MR images and as a result, the youngest age at which plaques can be resolved in vivo differs from ex vivo MRI. Plaques can be resolved at 3 months and above by ex vivo MRI. Candid assessment indicates that plaques can not be resolved from background noise on stand-alone in vivo MRI in the 3 and 6 month animals without the visual cues provided by other spatially registered image types. We therefore conclude that plaques can be resolved reliably by in vivo MRI at 9 months and above.

Another benchmark we wished to establish was the smallest plaque size detectable by in vivo and ex vivo MRI. The median diameters of plaques that were visible on in vivo MRI by age are presented in Figure 9A. As expected, the median diameter of MR visible plaques increases with age. The minimum plaque diameter on Thio-S that was detected by ex vivo MRI was as small as 10 μm. This undoubtedly represents a partial volume effect as MR resolution was 30 μm × 30 μm × 120 μm after 2D zero-padding reconstruction and the histological sections were 30 μm thick. Because it is not possible to completely control for partial volume mismatches between the MR and Thio-S sections, a better indicator of minimum resolvable plaque size might be the median values (Figure 9A). As indicated in the previous paragraph, plaques can be resolved reliably by in vivo MRI only at 9 months and above. We therefore determined the median plaque diameter at 9 months, 35 μm, to be the minimum plaque size resolvable by in vivo MRI (Figure 9A). Plaques could be reliably detected on ex vivo MRI at 3 months. We therefore determined the median diameter at 3 months, 20 μm, to be the minimum plaque size resolvable by ex vivo MRI (Figure 9B).

Figure 9
Diameter of plaques visible on in vivo (A) and ex vivo (B) MRI by age

Discussion

The major findings of this study are as follows. Quantitative counts of individual amyloid plaques on MRI increase with age in transgenic AD mice in a manner consistent with known pathobiology. This increase is monotonic with age on ex vivo MRI, and also with in vivo MRI after a detection threshold somewhere between 6–9 months is reached._MRI quantification of plaque burden correlates with histologically established plaque burden in the same animals. Plaques can be resolved in APP/PS1 mice as young as 3 months with ex vivo MRI and by 9 months with in vivo MRI. Plaques histologically measured to be 35 μm in diameter are detectable by in vivo MRI and plaques 20 μm diameter are detectable by ex vivo MRI.

We were able to resolve plaques as small as 20 μm ex vivo with a 2D zero-padded reconstructed MRI voxel size of 30 μm × 30 μm × 120 μm. A relatively small plaque inside a larger voxel therefore must produce enough local spin dephasing, and thereby signal loss, to cause that voxel to be resolved from surrounding normal tissue. Plaques that stained with Thio-S also typically stained for iron indicating that, as in humans, amyloid plaques in this animal model do contain iron. The source of the contrast between plaques and normal appearing adjacent brain tissue is presumably related to iron within plaques. The accelerated T2 relaxation observed in plaques is most likely due to the magnetic susceptibility effect induced by the iron itself. The faster T2 relaxation in plaques compared to normal brain could also be partially due to exchange between tissue water protons and protons in plaque associated proteins. A caveat about our data on plaque diameter resolvable by MRI is that estimates of plaque size might be different had a different plaque staining technique been used. (Styren et al., 2000)

The number of plaques per ROI seen on Thio-S exceeds the number of plaques per ROI seen on ex vivo and in vivo MRI. This is due to the fact that only plaques exceeding the 20 μm and 35 μm diameter threshold are resolvable by ex vivo and in vivo MRI respectively. The implication is that MRI can be considered an estimate rather than an absolute measure of plaque burden. Moreover, since the proportion of Thio-S staining plaques exceeding 20 μm or 35 μm increases with age, the degree to which MRI underestimates Thio-S plaque burden decreases with age. Wengenack et al. (Wengenack et al., 2000b) have previously quantified plaque morphology by age in this mouse model. The proportion of the total plaque load in APP/PS1 mice that exceeds the 35 μm threshold for detection by in vivo MRI is 6% at 9 months, 16% at 12 months, and 20% at 24 months. As indicated above it is not plaque size per se, but rather plaque iron content that renders a plaque visible on T2 (or T2*) weighted MRI. The contrast mechanism is not so much replacement of normal tissue in a voxel by plaque, as it is achieving sufficiently different T2 relaxation in a voxel with a plaque relative to surrounding voxels containing normal tissue. While not absolute, larger more mature plaques tend to have higher iron content.

We estimated plaque density by MRI as the number of plaques per ROI, which is analogous to the number of plaques per high power field in histology. We considered a stereological approach (Hyman et al., 1998) for plaque quantification with MRI, but decided against it for the following reason. In order to improve SNR, our MR imaging was performed with a surface coil. This results in intrinsic non-uniformity in signal sensitivity over the field of view — i.e. image SNR declines with increasing distance from the surface coil. With non-uniform SNR, conventional stereological plaque sampling methods would suffer a spatial bias.

Although many plaques in each section could be unequivocally matched between the histological and MR images (in vivo and ex vivo), some plaques visible in the histological sections were not visible on MRI and vice versa. In reality, perfect one to one correspondence between every plaque in the MRI and histological images is not possible. The histological section thickness was 30 μm while the MRI sections were 120 μm thick. Differences in partial volume averaging between MRI and histology were inevitable.

Furthermore, because adjacent 30 μm histological sections were stained for Thio-S and iron respectively, some though-plane spatial mismatching was unavoidable. In addition, the histological specimens were sliced manually. Even for a highly skilled individual it is impossible to create sections with perfect 30 μm uniformity throughout each tissue section. With MRI however, tissue was sectioned in a rectilinear grid and therefore some mismatch in section thickness between MRI and histological preparation was unavoidable. Perfect plaque matching without employing an algorithm that introduces spatial warping was likewise not possible between in vivo and ex vivo MRI. The ex vivo MRI was performed after the brain had been removed and formalin fixed, creating nonlinear anatomic distortions.

The voxel size we selected for the MRI experiments was dictated by intrinsic tradeoffs between signal-to-noise (SNR), spatial resolution, and imaging time in MRI. It is possible to increase spatial resolution, SNR, or both by simply increasing the imaging time used for ex vivo MRI. However, it is not possible to increase imaging time in vivo indefinitely in order to meet arbitrarily established specifications for image spatial resolution and SNR. Comparison of in vivo and ex vivo MRI in Figure 26 demonstrate that in vivo MRI is clearly SNR limited. Imaging conditions were identical for in vivo and ex vivo MRI and plaque conspicuity was always superior on ex vivo images. This may be in part due to shorter T1 relaxation times in fixed vs. in vivo tissue which would result in greater steady state magnetization at a fixed repetition time of 2 seconds at 9.4T for the former. However, the predominant reason for the discrepancy is the presence of additional sources of noise in vivo that are not present ex vivo. The major noise source is physiologic, specifically, fluctuations related to the cardiorespiratory cycle. Although our in vivo MRI was cardiorespiratory triggered, complete correction of view to view cardiorespiratory variation is difficult to achieve at the present time. However, with engineering improvements in cardiorespiratory monitoring and triggering, this could change. Other strategies to improve image quality include increasing SNR – for example with even higher field strength and an RF coil design with multiple elements or with better coupling.

Several useful applications of this approach can be envisioned in transgenic AD mice. The one hour plus imaging time we used for in vivo MRI can be repeated many times over the life span of an animal. Because individual plaques can be resolved non-invasively, in theory MRI could be used to study plaque biology longitudinally. Natural history studies can be envisioned in which the temporal and regional characteristics of plaque development are characterized. Perhaps of greater interest is employing this approach to assess the effect of experimental amyloid reduction therapeutic interventions at the level of the individual plaque. One major caveat however relates to the fact that visualization of individual plaques on MRI (without administration of a plaque labeling contrast agent) is due to relaxation effects of plaque associated iron. It is possible that amyloid reduction may not be associated removal or dispersal of the iron associated with plaque. The equations we provide relating plaque load on MRI to that on Thio-S staining likewise might differ if a therapeutic approach altered the ratio of plaque iron to plaque amyloid concentration.

The demonstrated ability to visualize plaques in vivo in mice naturally raises the question of extension of the technique to humans. Although this possibility should not be summarily excluded, a number of significant technical barriers must be solved for this technique to be viable in the living human subject. First, scan times would have to be reduced by roughly an order of magnitude and the reduction in scan time would have to be achieved with no significant loss of image SNR or spatial resolution compared with the acquisition in mice. Second, improved methods of reducing artifacts from physiologic motion and correction of bulk head motion would be needed in human subjects. Finally, our data indicates that only 20% of the total plaque burden can be resolved by in vivo MRI in 24 month APP/PS1 mice. And, the plaque load in APP/PS1 mice at this age significantly exceeds that found in the typical human AD patient (Takeuchi et al., 2000).

In summary, we have documented a correlation between in vivo and ex vivo MRI quantification of plaques and histological plaque counting in AD transgenic mice of various ages. In vivo and ex vivo MRI can estimate plaque burden at the resolution level of individual plaques. This study serves as a foundation for using in vivo MRI of transgenic mice (or other animal models) as a surrogate measure of plaque burden in natural history studies of plaque biology as well as drug discovery studies of Alzheimer’s amyloid plaque prevention and reduction.

Figure 4
9 month AD mouse
Figure 5
6 month AD mouse

Acknowledgments

This work was supported by AG22034, RR08079, WM Keck Foundation, Mind Institute, and the Minnesota Partnership for Biotechnology and Medical Genomics. The authors wish to thank Dawn M. Gregor, B.S., for mouse breeding and genotyping, Stephen Weigand MS and Peter C. O’Brien PhD for statistical assistance, and Dr. Karen Duff for the PS1 transgenic mouse line.

References

  • Bacskai BJ, Hickey GA, Skoch J, Kajdasz ST, Wang Y, Huang GF, Mathis CA, Klunk WE, Hyman BT. Four-dimensional multiphoton imaging of brain entry, amyloid binding, and clearance of an amyloid-beta ligand in transgenic mice. Proc Natl Acad Sci U S A. 2003;100:12462–12467. [PubMed]
  • Beneviste H, Einstein G, Kim KR, Hulette C, Johnson GA. Detection of neuritic plaques in Alzheimer’s disease by magnetic resonance microscopy. Proc Natl Acad Sci U S A. 1999;96:14079–14084. [PubMed]
  • Dhenain M, Privat N, Duyckaerts C, Jacobs RE. Senile plaques do not induce susceptibility effects in T2*-weighted MR microscopic images. NMR in Biomedicine. 2002;15:197–203. [PubMed]
  • Hardy J, Selkoe DJ. The amyloid hypothesis of Alzheimer’s disease: progress and problems on the road to therapeutics. Science. 2002;297:353–356. [PubMed]
  • Helpern JA, Lee S-P, Falangola MF, Dyakin V, Bogart A, Ardekani B, Duff K, Branch C, Wisniewski T, de Leon MJ, Wolf O, O’Shea J, Nixon RA. MRI assessment of neuropathology in a transgenic mouse model of Alzheimer’s disease. Magnetic Resonance in Medicine. 2004;51:794–798. [PubMed]
  • Higuchi M, Iwata N, Matsuba Y, Sato K, Sasamoto K, Saido T. 19F and 1H MRI detection of amyloid Beta plaques in vivo. Nature Neuroscience. 2005;8:527–533. [PubMed]
  • Holcomb L, Gordon M, McGowan E, Yu X, Benkovic S, Jantzen P, Wright K, Saad I, Mueller R, Morgan D, Sanders S, Zehr C, O’Campo K, Hardy J, Prada CM, Eckman C, Younkin SG, Hsiao K, Duff K. Accelerated Alzheimer-type phenotype in transgenic mice carrying both mutant amyloid precursor protein and presenilin 1 transgenes. Nature Medicine. 1998;4:97–100. [PubMed]
  • Hsiao K, Chapman P, Nilsen S, Eckman C, Harigaya Y, Younkin D, Yang F, Cole GM. Correlative memory deficits, A-beta elevation, and amyloid plaques in transgenic mice. Science. 1996;274:99–102. [PubMed]
  • Hyman BT, Gomez-Isla T, Irizarry MC. Stereology: a practical primer for neuropathy. Journal of Neuropathology and Experimental Neurology. 1998;57:305–310. [PubMed]
  • Jack CR, Jr, Garwood M, Wengenack TM, Borowski B, Curan GL, Lin J, Adriany G, Grohn OHJ, Grimm RC, Poduslo JF. In vivo visualization of Alzheimer’s amyloid plaques by MRI in transgenic mice without a contrast agent. Magnetic Resonance in Medicine. 2004;52:1263–1271. [PMC free article] [PubMed]
  • Klunk WE, Engler H, Nordberg A, Wang Y, GB, PHD, Bergstrom MIS, Huang GF, Estrada S, Ausen B, Debnath ML, Barletta J, Price JC, Sandell J, Lopresti BJ, Wall A, Koivisto P, Antoni G, Mathis CA, Langstrom B. Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Annals of Neurology. 2004;55:306–319. [PubMed]
  • Lee S-P, Falangola MF, Nixon RA, Duff K, Helpern JA. Visualization of amyloid plaques ina transgenic mouse model of Alzheimer’s disease using MR microscopy without contrast reagents. Magnetic Resonance in Medicine. 2004;52:538–544. [PubMed]
  • LeVine SM. Oligodendrocytes and myelin sheaths in normal, quaking, and shiverer brains are enriched in rion. J Neuroscience. 1991;29:413–419. [PubMed]
  • LeVine SM. Iron deposits in multiple sclerosis and Alzheimer’s disease brains. Brain Research. 1997;760:298–303. [PubMed]
  • Poduslo JF, Wengenack TM, Curran GL, Wisniewski T, Sigurdsson EM, SI M, Borowski BJ, Jack CR., Jr Molecular targeting of Alzheimer’s amyloid plaques for contrast-enhanced magnetic resonance imaging. Neurobiology of Disease. 2002;11:315–329. [PubMed]
  • Robb RA. A software sytem for interactive and quantitative analysis of biomedical images. In: Hohne KH, Fuchs H, Pizer SM, editors. 3D Imaging in Medicine. Springer Verlag; Berlin-Heidelberg: 1990. pp. 333–361.
  • Shoghi-Jadid K, Small GW, Agdeppa ED, Kepe V, Ercoli LM, PS, Read S, Satyamurthy N, AP, Huang SC, Barrio JR. Localization of neurofibrillary tangles and beta-amyloid plaques in the brains of living patients with Alzheimer disease. American Journal of Psychiatry. 2002;10:24–35. [PubMed]
  • Skovronsky DM, Zhang B, Kung M-P, Kung HF, Trojanowski JQ, Lee VM. In vivo detection of amyloid plaques in a mouse model of Alzheimer’s disease. Proc Natl Acad Sci USA. 2000;97:7609–7614. [PubMed]
  • Styren SD, Hamilton RL, C SG, Klunk WE. X-34, A fluorescent derivative of congo red: a novel histochemical stain for Alzheimer’s disease pathology. The Journal of Histochemistry and Cytochemistry. 2000;48:1223–1232. [PubMed]
  • Takeuchi A, Irizarry MC, Duff K, Saido TC, Ashe KH, Hasegawa M, Mann DVA, Hyman BT, Iwatsubo T. Age-related amyloid beta-deposition in transgenic mice overexpressing both Alzheimer mutant presenilin 1 and amyloid beta-precursor protein Swedish mutant is not associated with global neuronal loss. American Journal of Pathology. 2000;157:331–339. [PubMed]
  • Vanhoutte G, Dewachter I, Borghgraef P, Van Leuven F, Van der Linden A. Noninvasive in vivo MRI detection of neuritic plaques associated with iron in APP[V717I] transgenic mice, a model for Alzheimer’s disease. Magnetic Resonance in Medicine. 2005;53:607–613. [PubMed]
  • Wadghiri YZ, Sigurdsson EM, Sadowski M, Elliott JI, Li Y, Scholtzova H, Tang CY, Aguinaldo G, Pappolla M, Duff K, Wisniewski T, Turnbul lDH. Detection of Alzheimer’s amyloid in transgenic mice using magnetic resonance microimaging. Mag Res in Med. 2003;50:293–302. [PubMed]
  • Wengenack TM, Curran GL, Poduslo JF. Targeting Alzheimer amyloid plaques in vivo. Nature Biotechnology. 2000a;18:868–872. [PubMed]
  • Wengenack TM, Whelan S, Curran GL, Duff KE, Poduslo JF. Quantitative histological analysis of amyloid deposition in Alzheimer’s double transgenic mouse brain. Neuroscience. 2000b;101:939–944. [PubMed]
  • Zhang J, Yarowsky P, Gordon MN, DiCarlo G, Munireddy S, van Zijl PCM, Mori S. Detection of amyloid plaques in mouse models of Alzheimer’s disease by magnetic resonance imaging. Mag Res in Med. 2004;51:452–457. [PubMed]