Region-specific age at onset of β-amyloid in dogs☆

Neurobiology of Aging 21 (2000) 89 –96

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Region-specific age at onset of ␤-amyloid in dogs夞 E. Heada,*, R. McClearya, F. F. Hahnb, N. W. Milgramc, C. W. Cotmana a

Institute for Brain Aging and Dementia, University of California, Research Faculty, 1226 Gillespie Neuroscience, Irvine, CA, 92697-4540, USA b Lovelace Respiratory Research Institute, Albuquerque, NM, 87185, USA c Division of Life Sciences, Scarborough Campus, University of Toronto, Toronto, Ontario, Canada M1C 1A4 Received 23 July 1999; received in revised form 2 December 1999; accepted 30 December 1999

Abstract Cortical patterns of ␤-amyloid (A␤) deposition were evaluated in 40 beagle dogs ranging in age from 2 to 18 years. A␤ deposition in the prefrontal, occipital, parietal and entorhinal cortices was visualized by using an antibody against A␤1– 42. A logistic regression was used to estimate differences in age-at-onset and rate of deposition of A␤ as a function of brain region. The earliest and most consistent site of A␤ deposition with age was in the prefrontal cortex. Entorhinal A␤ deposition was not consistently observed until the age of 14 years, but was present in a subset of dogs under the age of 14 years. These regional vulnerabilities to A␤ accumulation are similar to those seen in the aging human. By using parameters derived from regression analyses, it may be possible to predict the presence of A␤ within specific brain regions in individual dogs. We propose that these models will be a useful tool to evaluate interventions that delay the age of onset or slow the rate of accumulation of A␤ in the dog. © 2000 Elsevier Science Inc. All rights reserved. Keywords: ␤-amyloid; Canine model; Logistic regression; Cortical pattern

1. Introduction Cognitive and neurobiological aging in dogs parallels human aging [8]. For example, ␤-amyloid (A␤) deposition, in the form of diffuse senile plaques, is a widely reported feature of the brains of aged dogs [5,12,21,29,33,34]. These A␤ deposits are not thioflavin S-positive nor are they associated with neurofibrillary degeneration. Despite the lack of neuritic involvement, the extent of A␤ deposition is correlated with learning and memory impairment in aging dogs [7,8,14]. In both dogs and humans, A␤ deposition varies across brain regions [3,4]. Previous reports demonstrate significant A␤ deposition in the entorhinal cortex of dogs as young as 10 years [7,14,28]. A more recent study of 10 dogs ranging in age from 12 to 17 years found A␤ in the prefrontal cortex and temporal lobes but not in the brain stem or cerebellum; the entorhinal cortex was rarely affected [17]. The purpose of the present study is to determine the cortical distribution of A␤ in the dog brain as a function of 夞

This research was funded by NIA Grant AG12694. * Corresponding author. Tel.: ⫹1-949-824-6071; fax: ⫹1-949-8242071. E-mail address: [email protected] (E. Head).

age. To accomplish this goal, we have used an approach that allows us to model the age of onset and rate of accumulation of A␤ and then compare these measures between different brain regions. The parameters we derive from these models, in turn, provide us with information that can be used to predict the location of A␤ pathology in dogs of various ages. Further, we can develop hypotheses regarding the types of cognitive function we may expect to be the earliest indicator of A␤ deposition. For example, does prefrontal cortex A␤ deposition occur early in the aging process, and can we detect this pathology by using frontal-dependent cognitive tasks? In addition, another goal for understanding the patterns of A␤ deposition in the aged dog brain is to be able to evaluate the effects of interventions that may slow or halt A␤ deposition. Attempts to estimate the variability in A␤ deposition across brain regions in either human or animal brain are confounded by limitations on the brain regions analyzed and problems associated the lack of power in small-scale studies. The present study addresses this confound by using a large sample (N ⫽ 40) of beagles from the same colony, and by examining four regions (prefrontal, entorhinal, occipital, and parietal cortices) suggested by the Braak and Braak staging model [3,4]. The statistical model applied in our design was developed for explicit analyses of the extent of

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A␤ deposition, in a large number of samples [18]. A mixedeffects logistic regression allows for precise estimates of the age-at-onset of A␤ deposition and the rate of accumulation for each region sampled. The results of this analysis provide a systematic picture of the progression of A␤ deposition in the aging dog brain.

2. Materials and methods 2.1. Brain tissue All of the dogs were bred and reared at the Lovelace Respiratory Research Institute in Albuquerque, New Mexico and all received the same extent of veterinary care and diets. There were 18 unneutered male and 22 unspayed females. Dogs were euthanized with sodium pentobarbital (Nembutal) anesthesia and a complete necropsy was performed. The brains were removed and fixed in either 4% paraformaldehyde at 4°C for 72 to 80 h (20 brains) or fixed in 10% buffered formalin. The tissue was transferred into phosphate-buffered saline (PBS), pH 7.4, with 0.02% sodium azide and stored at 4°C. Two 50-␮m vibratome sections, separated by at least 500 ␮m, were taken from four brain regions, the dorsolateral prefrontal cortex, the entorhinal cortex, the occipital cortex, and the parietal cortex, according to the canine brain atlas by Kreiner [20]. For the dorsolateral prefrontal cortex, the anterior tip of the brain was sectioned and the dorsal aspect, called the proreus, was used for analysis. The entorhinal cortex section was taken midway between the rostral and caudal poles of the hippocampus. The parietal cortex section was sampled from the same coronal section as the entorhinal cortex using the most dorsal aspect of the brain corresponding to area marginalis posterior. Finally, the occipital cortex section was derived from the most posterior coronal section of the brain and the most dorsal aspect was removed for analysis. This section is also called the area marginalis posterior but was much further posterior than the area taken from the parietal cortex. Due to the large number of tissue sections, it was necessary to conduct several smaller experiments to process two samples from four brain regions for 40 dogs (total of 160 ⫻ 2 ⫽ 320 samples). Thus, dogs were split into two groups of 20 animals per group with Group 1 dogs ranging in age from 4.5 to 15.3 years. Group 2 dogs were selected to include and extend the age range of the first group and ranged from 6.7 to 17.8 years. The ages of individual dogs used in this experiment are illustrated in Fig. 1 on the x-axis. In the first group of dogs, each brain region was immunostained in four separate experiments by using an identical procedure for all experiments. To control for the possibility of region-specific differences in A␤ immunoreactivity being due to variability in immunocytochemical staining runs, an additional 20 dogs were used in a replication. In the final

analysis, all dogs were combined because neither group differed significantly from the other when simple difference-of-means tests were conducted for all the variables used in the analysis. 2.2. Immunocytochemistry A␤ was detected with a rabbit polyclonal antibody, designated A␤, raised against a synthetic amyloid peptide containing amino acids 1 through 42 and detects senile plaques in the brains of Alzheimer’s Disease cases [6,36]. Previous studies have shown that this antibody is specific for the longer 1 to 42 species, typically found in the aged dog brain rather than the shorter 1 to 40 species [9]. Sections were pretreated for 4 min with 90% formic acid before overnight incubation at room temperature in A␤ (1 : 500) in Trissaline with 2.0% bovine serum albumin (BSA) and 0.1% Triton X-100. Bound antibody was detected by using a biotinylated anti-rabbit ABC peroxidase kit from Vector Labs (Burlingame, CA, USA). A␤ was visualized using a DAB substrate kit from Vector Labs (Burlingame, CA, USA). All sections were observed by light microscopy. In control experiments, the primary or secondary antibody was omitted with negative results. 2.3. A␤ quantification Two sections from each of the four brain regions were examined. Five semi-random fields from each of the two sections were digitized (for a total of 40 fields per animal ⫻ 40 dogs ⫽ 1600 samples) and the cross-sectional area occupied by A␤ was quantified using gray scale thresholding [6,7]. Each group of 20 dogs was analyzed in two sessions. To ensure consistent sampling procedures and A␤ load values across sessions, a single A␤-positive section of tissue from the first session was used to standardize the A␤ loads in the second session. It was not feasible to differentiate A␤ present in the form of diffuse senile plaques from A␤ present in blood vessel walls using this technique and thus the load values represent total A␤ deposition. To estimate age at onset, imaged samples from each section, brain region and individual dogs were binary-coded for presence (A␤ load ⱖ 1%) or absence (A␤ load ⬍ 1%) of A␤ deposition. The rationale for selecting 1% as a cutoff was based upon preliminary work where a sample of young dogs without A␤ deposition obtained load values that fell between 0.001% and 0.7%, which represented background staining. Thus, a conservative cutoff of 1% was used. The binary-coded tissue samples were then regressed on age-atdeath, sex, and other explanatory variables with a logistic link function. To control for sampling error within and between regions, random effects were assumed.

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Fig. 1. The average A␤ load is plotted as a function of age for individual dogs to illustrate differences in the age of onset and total amount of A␤ deposition in different brain regions.

2.4. Data analysis To estimate the relationship between region-specific A␤ deposition and age, extent of A␤ deposition was regressed on brain region (prefrontal, parietal, occipital, or entorhinal cortex) and age-at-death in a mixed-effects or multi-level model [13]. The basic model consists of two linked equations. In the first, binary-coded A␤ deposition is regressed on indicator variables binary-coded for each of the four areas. That is, A␤ij ⫽ b1j Prefrontalij ⫹ b2j Entorhinalij ⫹ b3j Occipitalij ⫹ b4j Parietalij ⫹ uij

(1)

Subscripts in Eqn. (1) index the brain (j ⫽ 1, . . . , 40 brains) and within each brain, a tissue sample (i ⫽ 1, . . . , nj, where nj ⱕ 40). On the left-hand side of Eqn. (1), A␤ij is binarycoded to indicate the presence (A␤ⱖ1%, A␤ij ⫽ 1) or absence (A␤⬍1%, A␤ij ⫽ 0) of A␤ deposition in the ith

sample of the jth brain. On the right-hand side, Prefrontalij, Entorhinalij, Occipitalij, and Parietalij are binary-coded for the region from which A␤ij was sampled. A log-linear link function allows parameters b1j, b2j, b3j, and b4j to be interpreted as region-specific mean logits (mean log-odds ratios) for each of the 40 brains. A second set of equations regresses parameters b1j, b2j, b3j, b4j on age-at death. That is, for k ⫽ 1, . . . , 4 regions, bkj ⫽ ck ⫹ dkj Agej ⫹ Vkj

(2.1–2.4)

Substituting Eqs. (2.1) through (2.4) into Eq. (1), the region-specific logits are specified as fixed effects of ageat-death; hypothetically, as age increases, the log-odds (and hence, the risk) of A␤ deposition increases. Because the A␤ij are randomly sampled, a realistic model of the A␤-age relationship will also include random effects. Normal error

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E. Head et al. / Neurobiology of Aging 21 (2000) 89 –96 Table 2 Parameter estimates for Eqs. (2.1)–(2.4)

Table 1 Descriptive statistics for dogs and brain tissue samples N Dog’s age at death (years) Dog’s sex: (female ⫽ 1) Dog’s body weight (kg) A␤ load (%): N ⫽ 1555 samples Prefrontal cortex Entorhinal cortex* Occipital cortex** A␤ Load Parietal cortex: Aß Load Total A␤ load (%): N ⫽ 741 samples with A␤ ⱖ1% Prefrontal cortex Entorhinal cortex* Occipital cortex** Parietal cortex Total

Mean

SD

40 40 40

12.04 0.58 9.32

4.15 0.50 2.28

400 365 390 400 1555

8.57 6.15 5.88 3.86 6.19

12.81 11.00 11.11 6.29 10.70

222 160 168 191 741

15.36 13.98 13.55 7.99 12.75

13.97 12.93 13.54 7.08 12.49

* Statistics based on 37 dogs who had at least one tissue sample. ** Statistics based on 39 dogs who had at least one tissue sample.

terms are specified in each of Eqs. (2.1) through (2.4). The vkj terms allow for sampling error within each of the j ⫽ 1, . . . , 40 brains.

3. Results Descriptive statistics for the 40 dogs (age, weight, and sex) and the 1555 images taken from the dogs’ brains are reported in Table 1. The design called for 1600 images in total (10 images from each of 4 regions from each of 40 brains). However, 35 entorhinal cortex images and ten occipital cortex images were not available because these brain regions could not be obtained from individual dogs. The multi-level models that were used in this study were developed for this purpose and are robust with respect to missing values. Of the 1555 images, 814 had A␤ loads of less than 1% and 741 had A␤ loads of 1% or greater. These were scored as A␤ij ⫽ 0 and A␤ij ⫽ 1, respectively. As shown in Table 1, the distribution of A␤ load varied significantly across brain regions and dogs. The average A␤ load for individual dogs varied as a function of age of onset and brain region (Fig. 1). A␤ deposition was consistently seen in the parietal, occipital and entorhinal cortex around the age of 14 years. On the other hand, prefrontal A␤ deposition was consistently deposited at an earlier age of approximately 9 years. A subset of aged dogs exhibited A␤ deposition in the entorhinal cortex before 14 years. The total amount of A␤ deposition was lowest in the parietal cortex and maximal levels were consistent in the other three brain regions. Fig. 2 plots the observed mean A␤ij load by region and age-at-death rounded to the nearest year. The proportion of samples with A␤ present (A␤ij ⫽ 1) is an S-shaped function of age that varies across brain regions.

Age ⫺8.03 0.64* H1 Baseline Model H2 Prefrontal ⫺4.96 0.40* Entorhinal ⫺4.78 0.42* Occipital ⫺5.16 0.40* Parietal ⫺4.65 0.38* H3 Entorhinal effect ⫽ parietal effect H4 Four regions ⫹ body weight H4 Four regions ⫹ sex

SE

Risk

0.06 0.02 0.02 0.02 0.02

1.89 1.49 1.52 1.49 1.46

␹2 345.12*

5.45* 3.05 2.70

* Statistically significant; P ⬍0.05.

To estimate the expected A␤-age relationship, parameters of Eqs. (2.1) through (2.4) were estimated with a penalized quasi-likelihood algorithm as implemented in public domain software (MIXOR: [15]). Parameter estimates and their standard errors are reported in Table 2. The rows of Table 2 are organized around a set of nested hypotheses, labeled H1 through H4. Ignoring statistical significance, the parameter estimates in Table 2 give the expected relationships between the presence or absence of A␤ deposits and age for each of the four regions. These expected relationships are plotted as probabilities in Fig. 3 and odds-ratios, or “risks,” in Fig. 4. As shown in Fig. 3, the probability of A␤ ⱖ 1% increases with age in every brain region. However, at the median age the probability of A␤ ⱖ 1% is highest in the prefrontal cortex and lowest in the occipital cortex. Between these two extremes, the probability of A␤ ⱖ 1% in the entorhinal and parietal cortices are visually indistinguishable. This raises the possibility that the age-A␤ relationship is not significantly different in these two regions. This and other relevant hypotheses are tested with the standard errors and ␹2 statistics reported in Table 2. H1: The first null hypothesis holds that there is no relationship between age and A␤ deposition. Under H1, the estimated logit for age will be statistically insignificant. Because the estimate reported in Table 2 (0.638) is approximately 11 times larger than its SE (0.058), H1 is rejected at a high confidence level (t ⫽ 10.96; P ⬍ 0.00093). The odds ratio for the estimated logit (OR ⫽ 1.892) is interpreted to mean that the risk of onset at any age is approximately 1.9⫻ higher than the risk in the preceding year. H2: The second null hypothesis holds that the relationship between age and A␤ deposition, confirmed in the test of H1, does not vary across the four regions. This implies that the region-specific age logits (b1j, b2j, b3j and b4j) are identically zero. That is, H2: b1j ⫽ b2j ⫽ b3j ⫽ b4j ⫽ 0 As shown in Table 2, the estimated logits for the entorhinal, prefrontal, occipital, and parietal regions (0.401, 0.417,

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Fig. 2. Observed proportions (P) of A␤ deposition (A␤ ⱖ 1%) for 40 dogs are plotted by age in each of four regions. Prefrontal A␤ deposition is the earliest and most consistent feature of the aged dog brain. Entorhinal A␤ accumulation only occurs in a subset of dogs under 14 years but after this also becomes more consistent. Parietal and occipital lobe A␤ deposition does not consistently occur until dogs are quite old, well past the average lifespan of 13.6 years in this colony of beagles [29].

Fig. 3. Expected proportions (P) of A␤ deposition (A␤ ⱖ 1%) for 40 dogs are plotted as a function of age in each of four regions. Consistent with the observed proportions (Fig. 1), prefrontal A␤ deposition is the earliest and each structure shows different age of onset and rate of accumulation. Age functions are derived from a logistic transformation of the parameter estimates reported in Table 2.

Fig. 4. Expected odds ratio or “risk” of A␤ deposition (A␤ ⱖ 1%) for 40 dogs is plotted as a function of age in each of four regions. Age functions are based on an exponentiation of the parameter estimates reported in Table 2. The risk of A␤ onset at any age is approximately 1.9⫻ higher than the risk in the preceding year and is highest in the prefrontal cortex.

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0.401, 0.375) are each at least fifteen times larger than their standard errors (0.023, 0.024, 0.023, and 0.024). A more conservative test of H2 is based on the likelihood ratio ␹2 statistic reported in the last column of Table 2. Compared to a ␹2 distribution with three degrees of freedom, the probability of a value of ␹2 ⫽ 345.12 is infinitesimally small (P ⬍ 10⫺15). H3: The third null hypothesis holds that A␤ deposition in the entorhinal and parietal cortices is not different. That is, H3: b2j ⫽ b4j Under H3, the likelihood ratio statistic is distributed as a ␹2 with one degree of freedom. But because the value of ␹2 ⫽ 5.45 is statistically significant (P ⬍ 0.0196), H3 is rejected. Though the lines representing the entorhinal and parietal A␤ age relationships seem to be visually indistinct in Fig. 3, A␤ deposition in these two brain regions are significantly different. H4 –5: As a final step in the analysis, two additional potential explanatory variables, sex and body weight, were entered into Eqs. (2.1) through (2.4). As shown in Table 2; however, neither variable added significantly to the model. For body weight and sex respectively (H4 and H5), likelihood ratios of ␹2 ⫽ 3.05 (P ⬍ 0.0801) and ␹2 ⫽ 2.70 (P ⬍ 0.1003) are statistically insignificant. Neither variable improves the region-specific A␤-age relationships confirmed in H2.

4. Discussion The present results demonstrate that age of onset and rate of A␤ deposition varies as a function of brain region in beagles. A␤ deposition appeared earliest in the prefrontal cortex at about the age of 9 years. The entorhinal cortex also develops A␤ deposits in dogs as young as 9 years but only in a subset of dogs and was not consistently affected until the age of 14 years. Of 12 dogs between the ages of 9 and 14 years, 41.7% of dogs exhibited entorhinal cortex A␤ but 75% had accumulated A␤ in the prefrontal cortex. These findings parallel some [5,12,17,29 –31,34], but not all studies of the relationship between A␤ deposition and age in dogs [28,32,37]. One factor that might account for this difference between this and other studies was the method used to measure A␤ deposition. Some of the studies used silver-based stains, which are not as sensitive to canine A␤ deposits as is the use of immunocytochemical techniques [1,32,34]. The current study used an antibody that detects A␤1– 42; however, most commercially available antibodies do not discriminate between A␤1– 42 and A␤1– 40 (for example, 6E10 recognizes A␤1–17; 4G8 recognizes A␤17–24). This is an important

fact when considering that A␤ deposits in dog brains consist largely of the A␤1– 42 species but it is entirely possible that using other antibodies will result in higher or lower A␤ loads accordingly [9,22,28,35]. In addition, the extent of A␤ immunoreactivity in the dog brain is heavily dependent upon pretreatment protocols such as the use of formic acid to reveal epitopes in amyloid protein [19]. There is also the possibility that we are not detecting all of the A␤ that is present in the dog brain as recent evidence suggests that more sensitive techniques, such as enzyme assays, may detect A␤ in cases where senile plaques were not detected with immunocytochemistry [11]. Dog selection procedures are another factor that can contribute to individual variability in the deposition of A␤ leading to a lack of age-associated deposition. Including a variety of breeds in a study provides a potential confound because breed and source are significant contributors to the extent of A␤ deposition in aged dog brain [2]. Some breeds of dogs seem to develop A␤ deposits at an earlier age than other breeds, with beagles showing the highest percentage of A␤ deposition [2]. Neither the weight of dogs before euthanasia nor the sex of the dog were significant factors to predict the accumulation of A␤. The lack of effect of body weight indicates that other health issues do not contribute significantly to A␤ deposition. In addition, this is consistent with the fact that a single breed of dog was used for this study. If several different breeds of dogs had been included, this may have been a significant factor, as larger breeds of dogs have a shorter lifespan; this may translate into more A␤ pathology than a similarly aged smaller breed of dog. The lack of sex effect on A␤ deposition is also not surprising considering that all of the dogs were reproductively-intact but it may be interesting to consider the effects of gonadectomy on A␤ deposition in future canine studies. This is particularly relevant to the study of the mechanisms underlying the role of low estrogen levels in the development of brain pathology in humans. Postmenopausal elderly women are at higher risk for age-related neurodegenerative disorders such as Alzheimer’s disease [16,23–25]. Possibly the most important aspect of the present findings is the similarity in pattern of A␤ deposition to that seen in aged human brain. The sequence of events seems to be that A␤ deposition occurs first in the prefrontal cortex, moves into the entorhinal and parietal cortices and then is last detected within the occipital cortex (Fig. 5). Based on this pattern we have developed a staging schematic to indicate the extent of A␤ pathology in the aged dog brain based upon the number of cortical regions affected using a similar approach as that used by Braak and Braak (1991). The pattern of canine A␤ deposition is consistent with that reported in aged demented and nondemented human brain where the isocortex is the predilection site for the deposition of A␤ [3]. The early stages of A␤ deposition in the human brain are subject to large individual variability as it is in the dog, particularly in the entorhinal cortex [32]; current

E. Head et al. / Neurobiology of Aging 21 (2000) 89 –96

Fig. 5. A schematic representation of the pattern of A␤ deposition in four different brain regions in aging dogs. Stage 1 is characterized by A␤ deposition only in the prefrontal cortex. Stage 2 shows that at least two cortical regions are affected by A␤ deposition. Stage 3 is defined as extensive A␤ deposition in many cortical regions and with maximal prefrontal deposition. A␤ seems to be deposited first in the prefrontal cortex, then in the parietal cortex, followed by the entorhinal cortex and finally in the occipital lobe. The key at the top right corner indicates the range of A␤ loads represented by each color.

study). In addition, the basal portion of the frontal cortex is one of the earliest sites of A␤ deposition in human brain, which is consistent with the dog. Further, dogs under the age of 11 years have a pattern of A␤ deposition that is similar to Stage A in the Braak and Braak staging scheme. Older dogs exhibit A␤ deposits in several cortical regions, which more closely resembles Stage B. Although not systematically studied in the current study, we and others have found that old dogs also develop A␤ deposits in subcortical structures such as the basal ganglia and thalamus, which is consistent with Stage C [34,37]. Individuals diagnosed clinically with dementia were consistently characterized as falling into stages B or C in terms of A␤ pathology [3]. Based on A␤ deposition in the entorhinal cortex, dogs under the age of 14 years can be classified as belonging to one of two groups, one showing early and marked A␤ deposition and a second showing little deposition. The current findings are consistent with a report of clusters of aged dogs showing very little or extensive cortical A␤ [32]. Individual differences in environmental and genetic factors may be two factors underlying this dichotomy. However, the current sample of dogs shared the same environment, minimizing the role of environmental explanations. On the other hand, studies showing a significantly high congruence in A␤ deposition within sibling pairs [27] suggest genetic influences. The functional consequences of this proposed pattern of deposition of A␤ suggest that the appearance of cognitive deficits may reflect progression of neuropathology. We have evidence that deficits on specific cognitive tasks are strongly related to the extent of A␤ neuropathology in specific brain regions thought to underlie intact performance [14]. The current findings suggest, first, that deficits in frontal-dependent tasks may be the earliest indicator of A␤ accumulation

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in the brain of aging dogs. Second, it is possible that only a subset of dogs under the age of 14 years will exhibit behavioral deficits on tasks sensitive to hippocampal or entorhinal cortex function. If the extent of A␤ deposition is associated with functional deficits then one interpretation of these studies is that A␤ is playing a significant role in neuronal dysfunction. A␤ is toxic to neurons in vitro and as such, is a good candidate for manipulation experiments designed to evaluate effects on behavior [10,26]. Our data indicate that any type of manipulation or intervention that is thought to prevent or slow the rate of A␤ deposition would be best implemented before the age of 8 years in dogs. Second, in neuropathology studies evaluating the effects of A␤ manipulation, the prefrontal cortex is likely to be most sensitive because A␤ deposition is the most consistent in this brain region in contrast to sampling from the entorhinal cortex. The statistical techniques described here that were used to develop these models and determine the risk of A␤ deposition as a function of age and brain region is a useful method based upon a simple dichotomous yes/no pathology decision. Provided that the sample size is large enough, we suggest that logistic regression analysis techniques will be exquisitely sensitive to interventions that may slow or halt the development of A␤ deposits in aging dogs. The results of this study and others suggest that the canine model of human aging and dementia will be useful for evaluating the effects of interventions that are intended to modify A␤ deposition.

Acknowledgments We thank Theresa Dinise for her technical assistance. We are also grateful to Cheryl Cotman for providing the illustration in Fig. 5.

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