Elsevier

Neurobiology of Aging

Volume 29, Issue 10, October 2008, Pages 1563-1575
Neurobiology of Aging

An MRI study of age-related white and gray matter volume changes in the rhesus monkey

https://doi.org/10.1016/j.neurobiolaging.2007.03.022Get rights and content

Abstract

We applied the automated MRI segmentation technique Template Driven Segmentation (TDS) to dual-echo spin echo (DE SE) images of eight young (5–12 years), six middle-aged (16–19 years) and eight old (24–30 years) rhesus monkeys. We analyzed standardized mean volumes for 18 anatomically defined regions of interest (ROI's) and found an overall decrease from young to old age in the total forebrain (5.01%), forebrain parenchyma (5.24%), forebrain white matter (11.53%), forebrain gray matter (2.08%), caudate nucleus (11.79%) and globus pallidus (18.26%). Corresponding behavioral data for five of the young, five of the middle-aged and seven of the old subjects on the Delayed Non-matching to Sample (DNMS) task, the Delayed-recognition Span Task (DRST) and the Cognitive Impairment Index (CII) were also analyzed. We found that none of the cognitive measures were related to ROI volume changes in our sample size of monkeys.

Introduction

In selecting an appropriate animal model in which to study the effects of normal aging, the most important criteria that must be met is how closely the animal model reflects human neurobiology. The rhesus monkey (Macaca mulatta) qualifies as an excellent non-human primate model for the study of normal aging for several reasons. First, the anatomy of the brain more closely resembles that of the human brain than other available laboratory animals. Second, unlike the human brain, the rhesus monkey brain does not exhibit the characteristic neuropathological loss of neurons that characterizes Alzheimer's disease (Peters et al., 1998) and hence normal aging can be studied without risk of intrusion of AD cases into the sample. Third, the life span of the human compared to the rhesus monkey approximately follows a ratio of 3:1 (Bowden and Williams, 1984, Tigges et al., 1988); Based on this, monkeys age 5–12 years are considered “young adults”, those between 13 and 19 are considered middle aged and those beyond 20 years are considered “aged adults” (Peters et al., 1996). Fourth, monkeys can be evaluated on batteries of behavioral tasks that tap cognitive domains that clearly correspond to human cognitive functions (Herndon et al., 1997). Fifth, monkeys and humans show similar patterns of age-related cognitive changes (Moss and Albert, 1988).

Studies of human aging post-mortem have been faced with the difficult problem of obtaining optimally prepared brain tissue from behaviorally well-characterized subjects. Recent advances in MRI technology, however, have allowed in vivo anatomical assessments to be conducted with closely monitored human subjects. These studies of the human brain have reported a variety of age-related changes in the volumes of different tissue components and have reported loss of gray matter, loss of white matter and loss of both (Bartzokis et al., 2001, Condon et al., 1986, Ge et al., 2002, Gunning-Dixon et al., 1998, Guttmann et al., 1998, Jernigan et al., 1991, Ketonen, 1998, Krishnan et al., 1990, McDonald et al., 1991, Meier-Ruge et al., 1992, Raz et al., 1995, Raz et al., 1997, Raz et al., 2003, Raz et al., 2004, Resnick et al., 2003, Salat et al., 1999). For example, using manual tracing methods, Raz et al., 1997, Raz et al., 2004 reported gray matter and white matter volume decrease in the prefrontal cortex and medial temporal lobe from high resolution spoiled gradient recalled (SPGR) images and Bartzokis et al. (2001) reported gray matter volume loss in the frontal and temporal lobes accompanied by a quadratic (increase then decrease) change of white matter volume with increasing age in the same lobes from dual-echo spin echo images. However, using automated segmentation methods, Guttmann et al. (1998) reported white matter volume decrease, with a minor change in gray matter volume on dual-echo spin echo images, and Ge et al. (2002) reported total gray and white matter volume loss with age in both men and women on dual-echo fast spin echo images.

Studies involving the rhesus monkey as an animal model for normal aging have focused on behavioral (Bachevalier and Mishkin, 1989, Bachevalier et al., 1991, Herndon et al., 1997, Lai et al., 1995, Li et al., 2004, Mahut et al., 1982, Mishkin, 1978, Moore et al., 2003, Moore et al., 2005, Moss et al., 1988, Moss et al., 1997, Presty et al., 1987, Rapp and Amaral, 1989, Rapp and Amaral, 1991) and anatomical (Andersen et al., 1999, Matochik et al., 2000) endpoints, but few have examined both (Peters et al., 1996, Peters et al., 1998, Peters, 1999). The rhesus monkey model provides the opportunity to study age-related brain structural changes using MRI with the assurance that Alzheimer's disease pathology will not confound the interpretation of results. Unfortunately unlike human aging studies, only a handful of rhesus monkey studies have utilized structural MRI analysis for the study of aging.

For example, Andersen et al. (1999) used a signal intensity classification algorithm to segment female monkey brains into gray matter, white matter and cerebrospinal fluid (CSF) classes. They found a decrease in parenchyma volume (normalized to the intracranial cavity volume). Andersen and colleagues attributed the decline in parenchyma volume to a decrease in gray matter volume and a compensatory increase in CSF volume, with some white matter volume loss up to 15 years of age. However, for later years of the monkeys’ lives, they attributed parenchyma volume loss mostly to white matter volume loss.

In another example, Matochik et al. (2000) used a manual tracing segmentation method to measure the volume of the rhesus monkey striatum. In 19 male monkeys between the ages of 3 and 30 years, they found age-related declines in normalized caudate nucleus and putamen volumes when comparing young, middle-aged and aged groups. Matochik and colleagues postulated that the volume loss could be due to both neuronal- and non-neuronal-related atrophy (Matochik et al., 2000, Morris et al., 1999).

The rhesus monkey model of normal aging also provides the opportunity to study cognitive decline (Bachevalier and Mishkin, 1989, Bachevalier et al., 1991, Herndon et al., 1997, Lai et al., 1995, Mahut et al., 1982, Mishkin, 1978, Moore et al., 2003, Moore et al., 2005, Moss et al., 1988, Moss et al., 1997, Peters et al., 1996, Peters et al., 1998, Peters, 1999, Presty et al., 1987, Rapp and Amaral, 1989, Rapp and Amaral, 1991) using behavioral tasks that are similar to clinical tests (Barbeau et al., 2005, Dickerson et al., 2004, Li et al., 2004, Pfefferbaum et al., 2001). Similar to anatomical studies, the advantage of using the rhesus monkey as an animal model for cognitive decline in normal aging is the absence of Alzheimer's disease-associated cognitive impairment.

The goal of this investigation was to utilize MRI methods to determine if there are age-related changes in the major components of the forebrain in behaviorally characterized monkeys participating in ongoing investigations of normal aging (Herndon et al., 1997, Lai et al., 1995, Moore et al., 2003, Moore et al., 2005, Moss et al., 1988, Moss et al., 1997, Peters et al., 1996, Peters et al., 1998, Peters, 1999). To accomplish this efficiently we applied the automated MRI segmentation technique Template Driven Segmentation (TDS) to legacy rhesus monkey MRI data. The technique has been validated in human studies of normal aging (Guttmann et al., 1999, Iosifescu et al., 1997, Warfield et al., 1995, Wei et al., 2002). We created a rhesus monkey anatomical atlas template for the segmentation pipeline to ensure that all voxels were assigned to anatomically validated components. Age-related volume changes calculated by TDS were then compared to cognitive performance as measured by the Delayed Non-matching to Sample (DNMS), the Delayed Recognition Span Task (DRST) and the Cognitive Impairment Index (CII), which is a composite score of DNMS acquisition, DNMS 2-min delay and DRST spatial condition tasks. The underlying hypothesis for this investigation was that age-related brain structure volume decreases would be associated with impaired cognitive performance.

Section snippets

Selection of animal subjects and housing accommodations

A total of 22 male rhesus monkeys were selected for this study, 8 of whom were young (5–12 years), 6 of whom were middle-aged (16–19 years) and 8 of whom were old (24–30 years) at the time of MRI scan acquisition. All subjects were selected from the rhesus monkey population at the Yerkes National Primate Research Center (YNPRC) according to explicit criteria which excluded subjects with histories that included any of the following: splenectomy or thymectomy, exposure to radiation, organ

ROI volume as a function of age group

One-way ANOVA analysis revealed a significant overall main effect of age group for the following standardized ROI volumes (Fig. 2): total forebrain [F(2,19) = 20.76, P < 0.0001], forebrain parenchyma [F(2,19) = 20.40, P < 0.0001], forebrain white matter [F(2,19) = 12.89, P = 0.0003], forebrain gray matter [F(2,19) = 3.96, P = 0.0365], caudate nucleus [F(2,19) = 11.48, P = 0.0005], globus pallidus [F(2,19) = 5.39, P = 0.0140] and third ventricle [F(2,19) = 4.53, P = 0.0247, data not shown]. Cerebral cortex [F(2,19) = 0.76, P =

Summary of results

We have reported two principal results. First, there was a significant age-related decrease in the ROI volumes of total forebrain, forebrain parenchyma, forebrain white matter, forebrain gray matter, caudate nucleus and globus pallidus. We also found a significant increase in third ventricle volume, but the volume changes were less than 0.2 cm3. Volume changes this small could be accounted for by partial volume effects, and so further analysis was not done for this ROI. Second, none of the ROI

Acknowledgements

This research was funded in part by the following grants: NIH grants F31-AG05897, P01-AG00001, P41-RR13218-01, P51-RR-00165, R01-NS35142, R37-AG17609 and R21 MH067054, Boston University School of Medicine, Graduate Student Research Fellowship and National Multiple Sclerosis Society grant RG 3478A2/2. No authors had any conflict of interest with the work presented in this paper.

The authors would like to thank Svetlana Egorova, Ying Wu, Zsusanna Liptak and Jeremy Warner for their assistance with

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