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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Neurobiol Aging. Author manuscript; available in PMC 2012 December 1.
Published in final edited form as:
PMCID: PMC2939965

Effects of aging and calorie restriction on white matter in rhesus macaques


Rhesus macaques on a calorie restricted diet (CR) develop less age-related disease, have virtually no indication of diabetes, are protected against sarcopenia, and potentially live longer. Beneficial effects of CR likely include reductions in age-related inflammation and oxidative damage. Oligodendrocytes are particularly susceptible to inflammation and oxidative stress, therefore, we hypothesized that CR would have a beneficial effect on brain white matter and would attenuate age-related decline in this tissue. CR monkeys and controls underwent diffusion tensor imaging (DTI). A beneficial effect of CR indexed by DTI was observed in superior longitudinal fasciculus, fronto-occipital fasciculus, external capsule, and brainstem. Aging effects were observed in several regions, although CR appeared to attenuate age-related alterations in superior longitudinal fasciculus, frontal white matter, external capsule, right parahippocampal white matter and dorsal occipital bundle. The results, however, were regionally specific and also suggested that CR is not salutary across all white matter. Further evaluation of this unique cohort of elderly primates to mortality will shed light on the ultimate benefits of an adult-onset, moderate CR diet for deferring brain aging.

Keywords: Rhesus macaque, diffusion tensor imaging, magnetic resonance imaging, aging, caloric restriction, white matter

1. Introduction

Caloric restriction (CR)—reduced calorie intake without essential nutrient deficiency— has been associated with improved cognition, a slowing of brain aging, and potential protection against neurodegenerative diseases in animal models (Gillette-Guyonnet and Vellas, 2008). Although the exact mechanisms underlying beneficial effects of CR are not known, mechanisms may include reductions in inflammatory and oxidative processes (Anson et al., 2003; Forster et al., 2000; Prolla and Mattson, 2001). Oligodendrocytes, the brain's myelin forming cells, are particularly susceptible to inflammation (Felts et al., 2005; Lehnardt et al., 2002). In addition—as has been pointed out by previous investigators (Bartzokis, 2004)—the high lipid and iron content and high metabolic activity of olidodendroglia make them especially vulnerable to oxidative damage (Juurlink et al., 1998; Richter-Landsberg and Vollgraf, 1998; Smith et al., 1999). Although the effect of CR has been measured in neurons (Eckles-Smith et al., 2000; Okada et al., 2003; Shi et al., 2007; Stranahan et al., 2009) and gray matter (Colman et al., 2009) little is known about the effects of CR on brain white matter.

One method of studying white matter in vivo is to use diffusion tensor imaging (DTI), an MR technique that is sensitive to the random thermally driven motion of water molecules and as a consequence is sensitive to the structural organization of white matter (Basser, 1995; Basser and Pierpaoli, 1996). DTI has been widely used to study disease and early brain development, in addition to normal and pathological aging. Two commonly reported DTI measures are fractional anisotropy (FA), a measure of the directionality of water molecule motion, and mean diffusivity (MD), an indicator of isotropic water molecule motion. In human aging, brain white matter exhibits both a decrease in white matter FA and an increase in MD (Abe et al., 2002; Engelter et al., 2000; Pfefferbaum et al., 2000; Salat et al., 2005), with several studies supporting an anterior to posterior gradient of change with age (Bendlin, 2009; Head et al., 2004; Pfefferbaum et al., 2005; Yoon et al., 2007). In rhesus monkeys, structural white matter alterations with age appear to be similar to humans and occur in prominently frontal regions, including the superior longitudinal fasciculus, the cingulum bundle, and anterior corpus callosum (Makris et al., 2007). With the exception of a few studies (Colman et al., 2009; Matochik et al., 2004), little is known about CR's effect on primate brain in aging.

We sought to investigate the effect of CR on white matter microstructure in primates. Rhesus monkeys (Macaca mulatta) from the Wisconsin National Primate Research Center in an ongoing study of caloric restriction (CR) (Colman et al., 2009; Ramsey et al., 2000) underwent MRI that included DTI. Animals were in one of two groups, a group of control animals who were fed ad libitum, or a group of monkeys that have been on a moderate CR diet (approximately 30% reduced) for approximately 12 or 17 years. Voxel-wise analyses were performed on DTI data acquired on CR and control animals. This method of analysis can be largely automated, has high reliability, allows for the evaluation of brain regions that may not have been considered a priori, and produces results that largely concur with region of interest (ROI) based methods (Snook et al., 2007; Zhang et al., 2009). In the case of CR (of which the effects are unknown) it was desirable to consider all white matter tracts as opposed to limiting the analysis to select regions. We hypothesized that CR would exert a beneficial effect on white matter, indexed by higher FA in the CR animals compared to controls. To date, there are few in vivo studies of the effects of aging on rhesus monkey brain; therefore, we also performed a cross-sectional analysis of age in a combined group of CR and control animals. Finally, since CR is known to inhibit mechanisms associated with aging, we hypothesized that CR would interact with age, resulting in the CR animals showing less age-related white matter decline compared to controls. FA was the primary outcome measure to assess benefits of a CR diet.

2. Methods

2.1 Subjects

Forty-eight rhesus monkeys (21 controls; 27 CR) between 18 and 31 years of age were studied from a longitudinal assessment of CR at the Wisconsin National Primate Research Center located in Madison, WI, USA, a facility accredited by the Association for Assessment and Accreditation of Laboratory Animal Care. Details of the CR manipulation have been published previously (Kemnitz et al., 1993; Ramsey et al., 2000). Animals were maintained at 21 °C; humidity 50-65%; 12h/12h light-dark cycle. Water was available ad libitum. Food was present for 6-8 hours per day. Animals in the CR group were fed approximately 70% of their ad libitum diet (Ramsey et al., 2000). They were monitored at least twice daily by Animal Care and research staff. The study was approved by the institutional Animal Care and Use Committee of the University of Wisconsin-Madison. Of the original 48 animals that underwent scanning, 33 were included in the final DTI analysis.

2.2 Imaging

Images were acquired on a General Electric 3.0 T Signa MR unit (GE Medical Systems, Milwaukee, WI, USA) using a quadrature transmit/receive volume coil with an 18 cm diameter at the Waisman Center for Brain Imaging and Behavior. Prior to the MRI scan, monkeys were administered ketamine (up to 15 mg/kg, intramuscular) or alternative anesthesia in consultation with a Wisconsin National Primate Research Center veterinarian and xylazine (up to 0.6 mg/kg, IM). Occasionally, animals were administered a booster injection of ketamine (7-15 mg/kg, IM or intravascular, IV) with or without xylazine (0.2-0.6 mg/kg, IM or IV) during the scan to maintain the plane of anesthesia. DTI was performed in the axial plane using a single-shot, spin-echo, diffusion-weighted echo-planar imaging sequence with diffusion gradients in twelve optimal directions. A diffusion-weighting of b = 816 s/mm2 was used. Other imaging parameters for this sequence were: TR = 10000, TE = 77.2ms, NEX = 6, FOV = 160 mm, matrix = 120×120, section thickness = 2.5mm, no gap. Image distortion was minimized by a higher-order shimming protocol that was run before the DTI scan. Animals received a 2D T2-weighted scan with parameters: TR = 4500, TE = 88.51 ms, NEX = 2, FOV = 160 mm, matrix = 256×192, flip = 90°, section thickness = 1.7mm, gap = 0.3mm that was used to assist in spatial normalization of the DTI scan as detailed below. Other scans collected included T1-weighted, magnetization transfer, and T2-relaxation scans, but these were not analyzed as part of the present study.

Images were inspected for abnormalities or artifacts that could potentially impact analyses of microstructure. Six animals were excluded for lesions visible on T1- and T2-weighted images. Six animals were excluded due to breathing motion, phase artifact, or scanner drift that produced images with poor quality. Additionally, despite sedation, three animals did not complete the DTI scan due to excessive involuntary movement during MRI. Of the total fifteen excluded animals, nine excluded animals were from the CR group and six were controls.

2.3 Image Processing

DTI processing was conducted by personnel blind to diet status of the animals. Image distortions in the DTI data caused by eddy currents were corrected using tools available in the FMRIB Software Library (FSL) (Smith et al., 2004) Diffusion Toolbox (FDT). Three-dimensional maps of the diffusion tensor and derived measures, MD and FA, were calculated using DTIFIT in FSL. DTI maps were further corrected for spatial distortion and brought into a common space for voxel-wise analysis through a combination of two transforms. First, in order to ameliorate warping artifact, we estimated non-linear transforms of each subject's non-diffusion weighted map (b=0 image) to their own co-planar T2-weighted image using Statistical Parametric Mapping software (University College London, London, UK, SPM5). Second, parameters were estimated for a normalization of the subject's corrected T2-weighted images to a rhesus macaque T2-weighted template (McLaren et al., 2009) via 12-parameter affine transformation and non-linear deformations. The two estimated parameters for each subject were combined and then applied to the subjects' non-diffusion-weighted image (b=0), FA, and MD maps. Combining the transformations avoided resampling the data twice. Normalized maps maintained the intensities of the original images. DTI maps that were normalized to the T2-weighted template were visually inspected using the “check registration” function in SPM5 to ensure accurate normalization. In order to optimize signal-to-noise and facilitate comparison across participants, the normalized images were smoothed using a 4mm full-width-at-half maximum Gaussian kernel.

2.4 Statistics and Analysis

First, we assessed the effect of age by performing a cross-sectional linear regression analysis where age was the predictor variable and FA was the dependent variable. In order to test the hypothesis that CR exerts a beneficial effect on white matter, FA was compared between groups using a two-sample t-test. In order to investigate the hypothesis that CR has an age-inhibiting effect on white matter, we tested the interaction between group (CR vs.control) and age using ANOVA. We hypothesized that CR would interact with age; with CR animals showing less age-related decline in white matter integrity compared to control animals. The same analyses were also performed with MD as the dependent variable. Due to the unknown effects of CR on brain white matter, we adopted an exploratory approach and the t-statistic threshold was set at p < 0.005 (uncorrected). In order to reduce the number of multiple comparisons, we employed a binary white matter mask. The mask was generated by thresholding a white matter prior probability mask (McLaren et al., 2009) at 0.4. Results were thresholded at a minimum cluster size of 5 contiguous voxels. Sex differences were controlled by covarying for female or male sex in all analyses.

Results are displayed on a standardized rhesus monkey atlas, the 112RM-SL atlas described by McLaren et al. (2009), an MRI based atlas comprising 112 rhesus monkeys, which is defined by the brain coordinate space of the Saleem-Logothetis atlas (Saleem et al., 2002). The locations of significant results on statistical parametric maps were identified by overlaying the results on either an average FA image or on the T1-weighted 112RM-SL underlay and referring to the results of autoradiography conducted in rhesus monkeys (Schmahmann, 2006).

3. Results

There was no significant difference in years of age at the start of the CR study between the CR group (M = 9.5; SD = 2.6) and control (M = 9.47; SD = 2.4); no difference in age at the time of scanning between the CR group (M = 23.74, SD = 2.66) and control (M = 23.62, SD = 2.98); and no significant difference in length of time (yrs.) in the study between the CR group (M = 13.33, SD = 2.23) and control (M = 13.00, SD = 2.07). The CR monkeys weighed (lbs) significantly less (M = 9.04, SD = 1.74) than control (M = 11.70, SD = 3.35), t = 2.9, df = 31, p < .05. The CR group had a slightly larger proportion of males (67%) than the control group (53%). Table 1 provides descriptive information for individual animals.

Table 1
Individual FA and MD findings

3.1 Fractional Anisotropy

3.1.1 Effect of age

A robust negative relationship between age and FA was evident when the monkeys in both the CR and control conditions were considered together. Brain regions where older age was associated with lower FA included white matter tracts in the thalamus, the body and tapetum of the corpus callosum, anterior internal capsule, superior longitudinal fasciculus, inferior longitudinal fasciculus, medial longitudinal fasciculus, and a small cluster in the inferior cerebellar peduncle. This association between FA and age is shown on a T1-weighted template brain in Figure 1. Table 1 indicates individual FA values for a subset of larger clusters. Table 2 indicates the specific regions where older age was associated with lower FA by 112RM-SL coordinates.

Figure 1
White matter microstructure and age: calorie restricted and control monkeys combined. There was a negative correlation between fractional anisotropy (FA) and age. There was a positive correlation between mean diffusivity (MD) and age. FA is shown on the ...
Table 2
Negative correlation between FA and age

3.1.2 Effect of group

Compared to control, the CR group showed higher FA in several regions including: bilateral superior longitudinal fasciculus, with the most prominent difference apparent on the right, fronto-occipital fasciculus, a small cluster in external capsule and small portions of the brainstem, and a small cluster in right parahippocampal white matter. Regions where CR animals showed higher FA compared to controls are listed by 112RM-SL coordinates in Table 3. Control monkeys had higher FA compared to CR in external capsule, internal capsule, a small cluster of short association fibers in the parietal lobe, superior longitudinal fasciculus and a small cluster in uncinate fasciculus (see Table 4).

Table 3
Greater FA and lower MD in CR
Table 4
Greater FA and lower MD in control

3.1.3 Group by age interaction

An interactive effect on FA was found in several small clusters including fibers in the mediodorsal thalamus, inferior longitudinal fasciculus, short association fibers in the frontal and ventral prefrontal cortex, frontal superior longitudinal fasciculus, external capsule, internal posterior capsule, cingulum, and dorsal occipital bundle. With the exception of small clusters in thalamus, and left inferior longitudinal fasciculus, the majority of the clusters indicated a larger slope in controls. Shown in red in Figure 4 are regions where controls showed a greater negative slope with age compared to CR, that is, where higher age was associated with lower FA in controls. In blue, are those regions where the opposite was true, CR showed a greater negative slope with age compared to controls. Table 5 lists 112RM-SL coordinates for the locations where age interacted with group.

Figure 4
Interaction with age. Both calorie restricted (CR) and control animals showed evidence for greater declines with age in relation to the other group, dependent on the region examined. CR animals showed attenuated age-related decline in several brain regions, ...
Table 5
Age by group interaction

3.2. Mean Diffusivity (MD)

3.2.1 Effect of age

Higher MD with higher age was observed in superior colliculi fibers, lateral geniculate nucleus, thalamus, a small cluster in posterior internal capsule, inferior cerebellar peduncle, superior longitudinal fasciculus, and medial longitudinal fasciculus. The associations between MD and age are shown on a T1-weighted template brain in Figure 1. Table 1 indicates individual MD values for a subset of larger clusters. Table 6 lists coordinates for the regions where higher age was associated with higher MD.

Table 6
Positive correlation between MD and age

3.2.2 Effect of group

When testing for a beneficial effect of CR, we found that the CR group showed lower MD in the tapetum of the corpus callosum, thalamus, and a small portion of superior longitudinal fasciculus. These regions are listed by 112RM-SL coordinates in Table 3. When testing the extent to which the control monkeys showed lower MD compared to CR monkeys, regions that reached significance included the superior colliculi, superior longitudinal fasciculus, short association fibers in the parietal lobe, medial longitudinal fasciculus, dorsal occipital bundle, internal capsule, and external capsule. The coordinates for these regions can be found in Table 4.

3. Group by age interaction

A significant interaction between group and age was found in short association fibers of the occipital lobe. Plotting MD against age in the two groups revealed a significantly greater positive slope in the controls compared to CR animals, where older age was also associated with higher MD. No regions showed the opposite pattern (a greater positive slope in CR). Table 5 lists the 112RM-SL coordinates for regions where age interacted with group.

4. Discussion

A calorically restricted diet is known to impart beneficial effects on health and extend lifespan in a variety of species. Several mechanisms underlying this effect have been proposed (Anderson et al., 2009; Masoro, 2005; Mattson, 2008; Ramsey et al., 2000). It is acknowledged that the mechanisms that underlie CR's many effects are not likely to be mutually exclusive, but strong evidence suggests that a reduction in inflammatory processes underlies at least part of CR's beneficial effects (Bhattacharya et al., 2006; Jung et al., 2009; Kim et al., 2006) along with decreases in oxidative stress (Barja, 2004; Sohal and Weindruch, 1996). With regard to the rhesus monkeys in this cohort, CR is associated with lower levels of proinflammatory cytokines including IL-6 (Willette et al. under review). Furthermore, Zainal et al. have shown in this same cohort that CR decreases oxidative stress, which in turn confers a beneficial effect on skeletal muscle tissue (Zainal et al., 2000). Accordingly, decreased inflammation and oxidation are considered prime candidates for the beneficial effect of CR on lifespan, and protection against age-related degeneration.

4.1 Age-related microstructural alterations

In human aging, brain white matter undergoes several alterations including a well substantiated loss of volume (Courchesne et al., 2000; Jernigan et al., 2001; Raz et al., 2005), and microstructural alterations (Abe et al., 2002; Bendlin, 2009; Engelter et al., 2000; Pfefferbaum et al., 2000; Salat et al., 2005; Sullivan et al., 2006). Postmortem study of the brain in rhesus monkeys indicates that white matter is significantly compromised with age in the anterior commissure (Sandell and Peters, 2003), frontal lobe white matter, corpus callosum (Peters and Sethares, 2002), and primary visual cortex (Peters et al., 2008) due to the inclusion of dense cytoplasm, ballooning of myelin sheaths, formation of redundant myelin, and the circumferential splitting of thick sheaths (Feldman and Peters, 1998; Peters et al., 2000; Peters and Sethares, 2002). Brain imaging studies of volume indicate that the rhesus monkey undergoes a decline in forebrain white matter with age (Wisco et al., 2008). Age-associated white matter microstructural alterations have also been measured with DTI in superior and inferior longitudinal fascicles, cingulum bundle and corpus callosum (Makris et al., 2009; Makris et al., 2007).

In the present study, age range was somewhat restricted compared to previous studies of brain aging, with animals falling along the spectrum of middle-age adult to old/old (18–31 years of age). The median survival age of laboratory fed rhesus monkeys is ~25 years of age (Bodkin et al., 2003), with a maximum life span of ~35 years (Tigges et al., 1988). Similar to previous studies (Makris et al., 2009; Makris et al., 2007), we nevertheless found a relationship between age and white matter microstructure (as indexed by FA) in the corpus callosum (body and tapetum) and superior longitudinal fasciculus. Additional tracts identified using voxel-wise analysis in the present study included inferior longitudinal fasciculus, medial longitudinal fasciculus, inferior cerebellar peduncles, thalamic fibers, superior colliculi fibers, and tracts in the vicinity of lateral geniculate nucleus.

Although several of the affected regions were frontal, the majority of the age associations were clustered in midbrain. Moderate results in frontal brain regions are plausible given the age of the animals studied. Frontal white matter decline typically commences around the 4th decade of life in humans. In rhesus monkey, myelin alterations such as circumferential splitting of the myelin sheath is present at 12–15 years of age (Sandell and Peters, 2003). It is possible that in later years, alterations in frontal white matter drop off from previous decline, and hence were not detected using correlation analysis in this older cohort.

One of the largest areas where an effect of age was detected was in a significant cluster situated over lateral geniculate nucleus. The lateral geniculate is the main structure through which visual information passes on the way to cortex, and studies indicate that neuronal counts in this region do not differ between young and old monkeys (Ahmad and Spear, 1993; Spear et al., 1994). However older animals do show differences in neuronal responses in this region compared to young animals. A lack of neuronal difference coupled with a behavioral difference may suggest alterations to myelin; post mortem examination of white matter in elderly rhesus may provide further information on age-related changes in this region.

4.2 Age and caloric restriction

Experiments in this cohort of animals have shown an interaction between age and diet that clearly favors CR animals. Monkeys undergoing CR develop less age-related disease, have virtually no indication of diabetes, are protected against sarcopenia, and tend to have an overall lower rate of mortality (Colman et al., 2009). In the present analysis of white matter, we hypothesized that the interaction between age and CR would show a similar pattern, where CR animals would exhibit less age-related declines when compared to controls. The analyses revealed several regions where this pattern was present, in particular, frontal superior longitudinal fasciculus and external capsule, small portions of cingulum, and dorsal occipital bundle. These results do suggest a protective effect of CR; however, the detected clusters were small and widespread compared to the main effect of age on white matter, and did not encompass large areas of the affected tract. Although we speculate that a reduction in inflammation or oxidative stress underlies the attenuation in age-related decline observed in this study, we can not rule out that other health differences between the CR animals and control may have exerted independent effects on the brain diffusion measures.

Contrary to our hypothesis, we also found brain regions where the opposite pattern was true. Clusters where the age slope was steeper in CR animals were found in thalamus, inferior longitudinal fasciculus, and prefrontal cortex. These clusters were also small, but indicate that CR does not exert a global protective effect against FA decline with age. At least one other study has failed to establish a significant interaction between age and CR (Matochik et al., 2004), though striatum was the only area considered in that study.

4.3 Caloric restriction

Although we did not find strong evidence for widespread attenuation of age effects, an examination of CR, while controlling for age, revealed several regions where CR animals had higher FA compared to controls. Higher FA was present in bilateral superior longitudinal fasciculus, a set of long fiber bundles that form arcs through the brain sending branches into frontal, parietal, occipital, and temporal lobes; with CR primarily affecting frontal portions of this tract. Differences were also found in the anterior portions of the fronto-occipital fasciculus, external capsule, portions of the brainstem, dorsal occipital bundle, and a small cluster in right parahippocampal white matter. When MD was taken into account, the beneficial effects of CR were measurable in the thalamus, the tapetum of the corpus callosum (a small bundle of white matter that extends from the corpus callosum into temporal white matter), and superior longitudinal fasciculus.

We did not find a general beneficial effect across the brain; rather the CR animals exhibited localized regions of putative benefit. Bartzokis and others have hypothesized that certain brain regions may be more susceptible to the negative effects of inflammation and oxidation (Bartzokis, 2004; Bartzokis et al., 2007). In particular, brain regions that show late myelination in the course of development are hypothesized to show a greater susceptibility to brain insult compared to early myelinating regions. Our present results reflect this possibility, with beneficial effects of CR clustering to the anterior of the brain.

Interestingly, CR also showed effects opposite to our hypothesized direction of action. That is, we found brain regions where CR was associated with lower FA compared to control. Few studies have examined the effect of dieting on the brain, a handful of studies have examined extreme nutritional deficiency. In contrast to CR (which is a state of undernutrition without malnutrition), low body weight associated with starvation in both intentional (e.g., anorexia nervosa, bulimia, and hunger strike) and unintentional cases (war) is linked with smaller brain volume (Drevelengas et al., 2001; Krieg et al., 1989). Dieting in obesity indicates that loss of body weight is linked to a corresponding loss of brain volume, specifically white matter volume (Haltia et al., 2007). Additionally, obesity is associated with larger white matter volumes in superior, middle, and inferior temporal gyri, fusiform gyrus; parahippocampal gyrus, brain stem, and cerebellum (Haltia et al., 2007), and greater volume in the putamen (Pannacciulli et al., 2006). Similarly, Matochik et al. have found larger putamen volumes in rhesus monkeys that were fed ad libitum compared to CR animals. (Matochik et al., 2004).

Although our study differs in species and comparative age range from the majority of existing studies of CR and body weight, the results of several studies suggest that either CR or body weight is related to brain volume, in particular, white matter volume. As is reported elsewhere (Colman et al., 2009), despite comparable nutrient intake, the CR animals weighed significantly less than control animals. It is not unreasonable to postulate that weight differences between groups could be accompanied by alterations in the diffusion properties of white matter, particularly if a CR diet is associated with a decrease in myelin lipids. Regions where we found higher FA and lower MD in the controls compared to CR monkeys included tracts in the vicinity of regions identified in previous human studies of body weight. These include the uncinate fasciculus, which connects the anterior temporal lobe to the orbital cortex, the internal and external capsules which carry axons from putamen (Ai et al., 2003), and occipital white matter fibers. Furthermore, in contrast to the primarily anterior pattern that was found in favor of CR, brain regions where controls showed higher FA and MD compared to CR animals were located primarily in mid and posterior brain regions.

4.4 Limitations

A limitation of this study is the small sample size. Larger sample sizes would provide more power to examine interactions and would likely obviate the need for the liberal threshold used in this study. It is important to note that the results are presented at an uncorrected (for multiple comparisons) threshold of p<.005. However, the effects of a CR diet on brain white matter are unknown, and consequently, we adopted an exploratory approach to the data. The number of multiple comparisons was somewhat limited by employing a conservative white matter mask, and by the inherently smaller brain size of rhesus compared to humans. Nevertheless, the chance of Type 1 error was increased compared to analyses performed using more stringent criteria and the results should be considered with that caution in mind.

The majority of the animals in this study are still alive. It will be invaluable to have postmortem information gleaned from the necropsied brain; in particular so that the imaging results can be compared with tissue pathology and cell counts. Furthermore, ultimate lifespan itself will serve as a critical variable in the future analysis of these imaging data. Finally, the results reported here are cross-sectional. Longitudinal analyses of these animals over time will more systematically inform us about which aspects of the inevitable decline in brain integrity can be forestalled at the end of the life span.

Figure 2
The calorie restricted (CR) animals showed several regions of greater fractional anisotropy (FA) and lower mean diffusivity (MD) compared to controls. The cooler colors (green) indicate regions where the CR animals showed lower MD compared to controls. ...
Figure 3
In some regions, the control group showed greater fractional anisotropy (FA) and lower mean diffusivity (MD) compared to the calorie restricted (CR) animals. Regions indicated by blue scale color are regions where the control group showed less MD compared ...


The authors gratefully acknowledge the support of researchers and staff at the Waisman Center, UW–Madison, where MR imaging took place, and the technical assistance provided by S. Baum, J. Christensen, J. A. Adriansjach, and C. E. Armstrong. We also acknowledge the assistance provided by the Animal Care, Veterinary and Pathology Staff of the Wisconsin National Primate Research Center. The project was supported by funding from the NIH to the Wisconsin National Primate Research Center grants AG11915 and RR000167 and NIH grant AG000213. This research was conducted in part at a facility constructed with support from Research Facilities Improvement Program grant numbers RR15459-01 and RR020141-01 from NCRR. The project also benefited from resources and facilities at the Wm. S. Middleton Memorial Veterans Hospital.


Disclosure Statement: The authors declare no actual or potential conflicts of interest.

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  • Abe O, Aoki S, Hayashi N, Yamada H, Kunimatsu A, Mori H, Yoshikawa T, Okubo T, Ohtomo K. Normal aging in the central nervous system: quantitative MR diffusion-tensor analysis. Neurobiology of Aging. 2002;23:433–441. [PubMed]
  • Ahmad A, Spear PD. Effects of aging on the size, density, and number of rhesus monkey lateral geniculate neurons. Journal of Comparative Neurology. 1993;334:631–643. [PubMed]
  • Ai Y, Markesbery W, Zhang Z, Grondin R, Elseberry D, Gerhardt GA, Gash DM. Intraputamenal infusion of GDNF in aged rhesus monkeys: distribution and dopaminergic effects. Journal of Comparative Neurology. 2003;461:250–261. [PubMed]
  • Anderson RM, Shanmuganayagam D, Weindruch R. Caloric restriction and aging: studies in mice and monkeys. Toxicologic Pathology. 2009;37:47–51. [PMC free article] [PubMed]
  • Anson RM, Guo Z, De Cabo R, Iyun T, Rios M, Hagepanos A, Ingram DK, Lane MA, Mattson MP. Intermittent fasting dissociates beneficial effects of dietary restriction on glucose metabolism and neuronal resistance to injury from calorie intake. Proc Natl Acad Sci U S A. 2003;100:6216–6220. [PubMed]
  • Barja G. Aging in vertebrates, and the effect of caloric restriction: a mitochondrial free radical production-DNA damage mechanism? Biological Reviews of the Cambridge Philosophical Society. 2004;79:235–251. [PubMed]
  • Bartzokis G. Age-related myelin breakdown: a developmental model of cognitive decline and Alzheimer's disease. Neurobiology of Aging. 2004;25:5–18. author reply 49-62. [PubMed]
  • Bartzokis G, Lu PH, Tishler TA, Fong SM, Oluwadara B, Finn JP, Huang D, Bordelon Y, Mintz J, Perlman S. Myelin breakdown and iron changes in Huntington's disease: pathogenesis and treatment implications. Neurochemical Research. 2007;32:1655–1664. [PubMed]
  • Bendlin B, Fitzgerald ME, Ries ML, Xu G, Kastman EK, Thiel BW, Carlsson CM, Rowley HA, Lazar M, Alexander AL, Johnson SC. White Matter in Aging and Cognition: A Cross-sectional Study of Microstructure in Adults Aged Eighteen to Eighty-Three. Developmental Neuropsychology In Press 2009 [PMC free article] [PubMed]
  • Bhattacharya A, Chandrasekar B, Rahman MM, Banu J, Kang JX, Fernandes G. Inhibition of inflammatory response in transgenic fat-1 mice on a calorie-restricted diet. Biochemical and Biophysical Research Communications. 2006;349:925–930. [PubMed]
  • Bodkin NL, Alexander TM, Ortmeyer HK, Johnson E, Hansen BC. Mortality and morbidity in laboratory-maintained Rhesus monkeys and effects of long-term dietary restriction. Journals of Gerontology. Series A, Biological Sciences and Medical Sciences. 2003;58:212–219. [PubMed]
  • Colman RJ, Anderson RM, Johnson SC, Kastman EK, Kosmatka KJ, Beasley TM, Allison DB, Cruzen C, Simmons HA, Kemnitz JW, Weindruch R. Caloric restriction delays disease onset and mortality in rhesus monkeys. Science. 2009;325:201–204. [PMC free article] [PubMed]
  • Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, Harwood M, Hinds S, Press GA. Normal brain development and aging: quantitative analysis at in vivo MR imaging in healthy volunteers. Radiology. 2000;216:672–682. [PubMed]
  • Drevelengas A, Chourmouzi D, Pitsavas G, Charitandi A, Boulogianni G. Reversible brain atrophy and subcortical high signal on MRI in a patient with anorexia nervosa. Neuroradiology. 2001;43:838–840. [PubMed]
  • Eckles-Smith K, Clayton D, Bickford P, Browning MD. Caloric restriction prevents age-related deficits in LTP and in NMDA receptor expression. Brain Research. Molecular Brain Research. 2000;78:154–162. [PubMed]
  • Engelter ST, Provenzale JM, Petrella JR, DeLong DM, MacFall JR. The effect of aging on the apparent diffusion coefficient of normal-appearing white matter. AJR Am J Roentgenol. 2000;175:425–430. [PubMed]
  • Feldman ML, Peters A. Ballooning of myelin sheaths in normally aged macaques. Journal of Neurocytology. 1998;27:605–614. [PubMed]
  • Felts PA, Woolston AM, Fernando HB, Asquith S, Gregson NA, Mizzi OJ, Smith KJ. Inflammation and primary demyelination induced by the intraspinal injection of lipopolysaccharide. Brain. 2005;128:1649–1666. [PubMed]
  • Forster MJ, Sohal BH, Sohal RS. Reversible effects of long-term caloric restriction on protein oxidative damage. J Gerontol A Biol Sci Med Sci. 2000;55:B522–529. [PubMed]
  • Gillette-Guyonnet S, Vellas B. Caloric restriction and brain function. Curr Opin Clin Nutr Metab Care. 2008;11:686–692. [PubMed]
  • Haltia LT, Viljanen A, Parkkola R, Kemppainen N, Rinne JO, Nuutila P, Kaasinen V. Brain white matter expansion in human obesity and the recovering effect of dieting. Journal of Clinical Endocrinology and Metabolism. 2007;92:3278–3284. [PubMed]
  • Head D, Buckner RL, Shimony JS, Williams LE, Akbudak E, Conturo TE, McAvoy M, Morris JC, Snyder AZ. Differential vulnerability of anterior white matter in nondemented aging with minimal acceleration in dementia of the Alzheimer type: evidence from diffusion tensor imaging. Cereb Cortex. 2004;14:410–423. [PubMed]
  • Jernigan TL, Archibald SL, Fennema-Notestine C, Gamst AC, Stout JC, Bonner J, Hesselink JR. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging. 2001;22:581–594. [PubMed]
  • Jung KJ, Lee EK, Kim JY, Zou Y, Sung B, Heo HS, Kim MK, Lee J, Kim ND, Yu BP, Chung HY. Effect of short term calorie restriction on pro-inflammatory NF-kB and AP-1 in aged rat kidney. Inflammation Research. 2009;58:143–150. [PubMed]
  • Juurlink BH, Thorburne SK, Hertz L. Peroxide-scavenging deficit underlies oligodendrocyte susceptibility to oxidative stress. Glia. 1998;22:371–378. [PubMed]
  • Kim YJ, Kim HJ, No JK, Chung HY, Fernandes G. Anti-inflammatory action of dietary fish oil and calorie restriction. Life Sciences. 2006;78:2523–2532. [PubMed]
  • Krieg JC, Lauer C, Pirke KM. Structural brain abnormalities in patients with bulimia nervosa. Psychiatry Research. 1989;27:39–48. [PubMed]
  • Lehnardt S, Lachance C, Patrizi S, Lefebvre S, Follett PL, Jensen FE, Rosenberg PA, Volpe JJ, Vartanian T. The toll-like receptor TLR4 is necessary for lipopolysaccharide-induced oligodendrocyte injury in the CNS. Journal of Neuroscience. 2002;22:2478–2486. [PubMed]
  • Makris N, Kennedy DN, Boriel DL, Rosene DL. Methods of mri-based structural imaging in the aging monkey. Methods 2009 [PMC free article] [PubMed]
  • Makris N, Papadimitriou GM, van der Kouwe A, Kennedy DN, Hodge SM, Dale AM, Benner T, Wald LL, Wu O, Tuch DS, Caviness VS, Moore TL, Killiany RJ, Moss MB, Rosene DL. Frontal connections and cognitive changes in normal aging rhesus monkeys: a DTI study. Neurobiology of Aging. 2007;28:1556–1567. [PubMed]
  • Masoro EJ. Overview of caloric restriction and ageing. Mechanisms of Ageing and Development. 2005;126:913–922. [PubMed]
  • Matochik JA, Chefer SI, Lane MA, Roth GS, Mattison JA, London ED, Ingram DK. Age-related decline in striatal volume in rhesus monkeys: assessment of long-term calorie restriction. Neurobiology of Aging. 2004;25:193–200. [PubMed]
  • Mattson MP. Dietary factors, hormesis and health. Ageing Res Rev. 2008;7:43–48. [PMC free article] [PubMed]
  • McLaren DG, Kosmatka KJ, Oakes TR, Kroenke CD, Kohama SG, Matochik JA, Ingram DK, Johnson SC. A population-average MRI-based atlas collection of the rhesus macaque. Neuroimage. 2009;45:52–59. [PMC free article] [PubMed]
  • Okada M, Nakanishi H, Amamoto T, Urae R, Ando S, Yazawa K, Fujiwara M. How does prolonged caloric restriction ameliorate age-related impairment of long-term potentiation in the hippocampus? Brain Research. Molecular Brain Research. 2003;111:175–181. [PubMed]
  • Pannacciulli N, Del Parigi A, Chen K, Le DS, Reiman EM, Tataranni PA. Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage. 2006;31:1419–1425. [PubMed]
  • Peters A, Moss MB, Sethares C. Effects of aging on myelinated nerve fibers in monkey primary visual cortex. Journal of Comparative Neurology. 2000;419:364–376. [PubMed]
  • Peters A, Sethares C. Aging and the myelinated fibers in prefrontal cortex and corpus callosum of the monkey. Journal of Comparative Neurology. 2002;442:277–291. [PubMed]
  • Peters A, Verderosa A, Sethares C. The neuroglial population in the primary visual cortex of the aging rhesus monkey. Glia. 2008;56:1151–1161. [PubMed]
  • Pfefferbaum A, Adalsteinsson E, Sullivan EV. Frontal circuitry degradation marks healthy adult aging: Evidence from diffusion tensor imaging. Neuroimage. 2005;26:891–899. [PubMed]
  • Pfefferbaum A, Sullivan EV, Hedehus M, Lim KO, Adalsteinsson E, Moseley M. Age-related decline in brain white matter anisotropy measured with spatially corrected echo-planar diffusion tensor imaging. Magnetic Resonance in Medicine. 2000;44:259–268. [PubMed]
  • Prolla TA, Mattson MP. Molecular mechanisms of brain aging and neurodegenerative disorders: lessons from dietary restriction. Trends Neurosci. 2001;24:S21–31. [PubMed]
  • Ramsey JJ, Colman RJ, Binkley NC, Christensen JD, Gresl TA, Kemnitz JW, Weindruch R. Dietary restriction and aging in rhesus monkeys: the University of Wisconsin study. Experimental Gerontology. 2000;35:1131–1149. [PubMed]
  • Raz N, Lindenberger U, Rodrigue KM, Kennedy KM, Head D, Williamson A, Dahle C, Gerstorf D, Acker JD. Regional brain changes in aging healthy adults: general trends, individual differences and modifiers. Cereb Cortex. 2005;15:1676–1689. [PubMed]
  • Richter-Landsberg C, Vollgraf U. Mode of cell injury and death after hydrogen peroxide exposure in cultured oligodendroglia cells. Experimental Cell Research. 1998;244:218–229. [PubMed]
  • Salat DH, Tuch DS, Greve DN, van der Kouwe AJ, Hevelone ND, Zaleta AK, Rosen BR, Fischl B, Corkin S, Rosas HD, Dale AM. Age-related alterations in white matter microstructure measured by diffusion tensor imaging. Neurobiology of Aging. 2005;26:1215–1227. [PubMed]
  • Saleem KS, Pauls JM, Augath M, Trinath T, Prause BA, Hashikawa T, Logothetis NK. Magnetic resonance imaging of neuronal connections in the macaque monkey. Neuron. 2002;34:685–700. [PubMed]
  • Sandell JH, Peters A. Disrupted myelin and axon loss in the anterior commissure of the aged rhesus monkey. J Comp Neurol. 2003;466:14–30. [PubMed]
  • Schmahmann JD, Pandya DN. Fiber Pathways of the Brain. Oxford University Press; USA, New York: 2006.
  • Shi L, Adams MM, Linville MC, Newton IG, Forbes ME, Long AB, Riddle DR, Brunso-Bechtold JK. Caloric restriction eliminates the aging-related decline in NMDA and AMPA receptor subunits in the rat hippocampus and induces homeostasis. Experimental Neurology. 2007;206:70–79. [PMC free article] [PubMed]
  • Smith KJ, Kapoor R, Felts PA. Demyelination: the role of reactive oxygen and nitrogen species. Brain Pathology. 1999;9:69–92. [PubMed]
  • Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 1:S208–219. [PubMed]
  • Snook L, Plewes C, Beaulieu C. Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment. Neuroimage. 2007;34:243–252. [PubMed]
  • Sohal RS, Weindruch R. Oxidative stress, caloric restriction, and aging. Science. 1996;273:59–63. [PMC free article] [PubMed]
  • Spear PD, Moore RJ, Kim CB, Xue JT, Tumosa N. Effects of aging on the primate visual system: spatial and temporal processing by lateral geniculate neurons in young adult and old rhesus monkeys. Journal of Neurophysiology. 1994;72:402–420. [PubMed]
  • Stranahan AM, Lee K, Martin B, Maudsley S, Golden E, Cutler RG, Mattson MP. Voluntary exercise and caloric restriction enhance hippocampal dendritic spine density and BDNF levels in diabetic mice. Hippocampus 2009 [PMC free article] [PubMed]
  • Sullivan EV, Adalsteinsson E, Pfefferbaum A. Selective age-related degradation of anterior callosal fiber bundles quantified in vivo with fiber tracking. Cerebral Cortex. 2006;16:1030–1039. [PubMed]
  • Tigges J, Gordon TP, McClure HM, Hall EC, Peters A. Survival rate and life span of rhesus monkeys at the Yerkes regional primate research center. American Journal of Primatology. 1988;13:263–273.
  • Wisco JJ, Killiany RJ, Guttmann CR, Warfield SK, Moss MB, Rosene DL. An MRI study of age-related white and gray matter volume changes in the rhesus monkey. Neurobiology of Aging. 2008;29:1563–1575. [PMC free article] [PubMed]
  • Yoon B, Shim YS, Lee KS, Shon YM, Yang DW. Region-specific changes of cerebral white matter during normal aging: A diffusion-tensor analysis. Arch Gerontol Geriatr 2007 [PubMed]
  • Zainal TA, Oberley TD, Allison DB, Szweda LI, Weindruch R. Caloric restriction of rhesus monkeys lowers oxidative damage in skeletal muscle. FASEB Journal. 2000;14:1825–1836. [PubMed]
  • Zhang Y, Schuff N, Du AT, Rosen HJ, Kramer JH, Gorno-Tempini ML, Miller BL, Weiner MW. White matter damage in frontotemporal dementia and Alzheimer's disease measured by diffusion MRI. Brain 2009 [PMC free article] [PubMed]