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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Obesity (Silver Spring). Author manuscript; available in PMC 2010 April 1.
Published in final edited form as:
PMCID: PMC2847061
NIHMSID: NIHMS166585

BMI and Neuronal Integrity in Healthy, Cognitively Normal Elderly: A Proton Magnetic Resonance Spectroscopy Study

Abstract

Recent studies associated excess body weight with brain structural alterations, poorer cognitive function, and lower prefrontal glucose metabolism. We found that higher BMI was related to lower concentrations of N-acetyl-aspartate (NAA, a marker of neuronal integrity) in a healthy middle-aged cohort, especially in frontal lobe. Here, we evaluated whether NAA was also associated with BMI in a healthy elderly cohort. We used 4 Tesla proton magnetic resonance spectroscopy (1H MRS) data from 23 healthy, cognitively normal elderly participants (69.4 ± 6.9 years; 12 females) and measured concentrations of NAA, glutamate (Glu, involved in cellular metabolism), choline-containing compounds (Cho, involved in membrane metabolism), and creatine (Cr, involved in high-energy metabolism) in anterior (ACC) and posterior cingulate cortices (PCC). After adjustment for age, greater BMI was related to lower NAA/Cr and NAA/Cho ratios (β < −0.56, P < 0.008) and lower Glu/Cr and Glu/Cho ratios (β < −0.46, P < 0.02) in ACC. These associations were not significant in PCC (β > −0.36, P > 0.09). The existence of an association between NAA and BMI in ACC but not in PCC is consistent with our previous study in healthy middle-aged individuals and with reports of lower frontal glucose metabolism in young healthy individuals with elevated BMI. Taken together, these results provide evidence that elevated BMI is associated with neuronal abnormalities mostly in frontal brain regions that subserve higher cognitive functions and impulse control. Future studies need to evaluate whether these metabolite abnormalities are involved in the development and maintenance of weight problems.

Introduction

A growing body of evidence suggests that excess body weight is associated with brain structural and functional alterations. In older adults, higher BMI was associated with smaller global brain volumes (1). A higher waist-to-hip ratio (a measure sensitive to abdominal obesity) was related to smaller hippocampal volumes and more white matter (WM) lesions in the elderly (2). In younger and healthy cohorts some reductions in gray matter (GM) volume and WM enlargement were recorded, mostly in frontal lobe, with WM enlargements (at least) partially reversible with weight loss (e.g., ref. 3,4). Additionally, a positron emission tomography study found higher BMI associated with lower glucose metabolism in prefrontal GM and cingulate gyrus and that this hypometabolism was related to poorer memory and poorer executive functions (5). Consistent with these findings of glucose hypometabolism, our 1.5 Tesla proton magnetic resonance spectroscopy (1H MRS) study of healthy middle-aged individuals with BMI covering a range from normal to obese (6) showed that higher BMI was related to lower absolute concentrations of N-acetyl-aspartate (NAA) in frontal GM and in frontal, parietal, and temporal WM. NAA, a marker of neuronal integrity, reflects neuronal loss, lower neuronal density, atrophied dendrites and axons, and/or deranged neuronal metabolism (7). Additionally, in frontal WM, we found that lower concentrations of choline-containing metabolites (Cho, involved in membrane turnover) were related to higher BMI. As about 80% of obese individuals (BMI >30 kg/m2) are insulin resistant (8), we speculated that these associations may be mediated to some extent by impaired insulin functions. Interestingly, among patients with type 2 diabetes mellitus and impaired glucose tolerance, lower regional NAA and Cho levels were associated with higher fasting insulin levels and insulin resistance (9), which suggests the NAA and Cho abnormalities observed in our earlier study may have been, at least partially, modulated by insulin resistance.

These metabolic characteristics may be functionally significant, as excess weight among healthy individuals was related to poorer cognition (10,11), especially of executive function (5,12,13). In fact, another study linked poorer executive function to lower NAA concentrations in frontal WM (14). Additionally, elevated BMI at midlife and across lifetime was associated with increased risk for developing Alzheimer's disease (AD) and/or vascular dementia decades later (reviewed in ref. 15). In one of these studies, women who developed dementia in their eighties, had consistently higher average BMI in their seventies than those who did not develop dementia (16). However, it has been reported that patients start loosing weight several years before the onset of dementia (e.g., ref. 17).

In this study, we used magnetic resonance imaging and 1H MRS data obtained at 4 Tesla from a small cohort of healthy, nondemented elderly individuals, who served as controls in a study on healthy aging and dementia (18). This retrospective analysis was performed as aging is associated with increasing amounts of abdominal fat (even at constant BMI: e.g., ref. 19) and as approximately half of adults over 60 years of age experience insulin resistance (8). One spectroscopic voxel was positioned in posterior cingulate cortex (PCC, a region that demonstrates volumetric and spectroscopic changes in preclinical (20) and symptomatic AD (e.g., ref. 21). The other spectroscopic voxel was placed on the border between the affective and cognitive divisions of the anterior cingulate cortex (ACC) (see Figure 1) (22). The ACC is a critical component of the brain reward system and is implicated in emotional and cognitive regulation, decision making, self-monitoring, and goal directed behaviors (22,23).

Figure 1
Position of ACC (left) and PCC (right) voxels for 1H MRS. ACC, anterior cingulate cortex; 1H MRS, proton magnetic resonance spectroscopy; PCC, posterior cingulate cortex.

Compared to studies at lower fields, 1H MRS at 4 Tesla allows measuring concentrations of glutamate (Glu), an important molecule in cellular metabolism that is mostly found within neurons; it is also the main excitatory neurotransmitter and a potential marker of neuronal integrity (24). Striatal Glu levels were related to neurocognition in the elderly (25).

The main goal of this study was to replicate in a healthy cohort of elderly individuals the regional pattern of associations between BMI and NAA observed in our previous study of middle-aged adults. As higher BMI has been recognized as a risk factor for AD, we also were interested to investigate whether such associations were present in brain regions affected early in AD. We hypothesized that higher BMI is associated with lower NAA and Glu (relative to Cr and relative to Cho) in the ACC and with a smaller volume of the ACC. Additionally, we predicted that higher BMI will be associated also with lower NAA and Glu (scaled to Cr and to Cho) in the PCC and with smaller PCC volume.

Methods and Procedures

Participants

We used spectroscopic and volumetric data from 23 healthy elderly, nondemented participants (mean age 69.4 ± 6.9, 12 females, age range: 59–82 years, mean mini mental state examination: 29.4 ± 1.0), who were recruited from the community for a pilot study of normal aging and dementia. The exclusion criteria were described previously (e.g., ref. 26). In short, participants were screened for neurological and psychiatric conditions that are known to affect the brain, such as history of stroke, seizures, brain surgery, or history of psychiatric diseases. Additionally, diabetes, untreated thyroid disease, use of medication/substances that could affect brain function, a history of brain trauma, or brain surgery were exclusionary. Finally, any evidence for ischemic events (stroke but not WM hyperintensities or small lacunes) and skull defects on the magnetic resonance imaging were exclusionary. Cognitive function was assessed with a neuropsychological test battery that included the mini mental state examination, the California verbal learning test (short form), Rey–Osterrieth complex figures, verbal fluency, and the Wechsler adult intelligence score (digit symbol, digit span) (see Table 1). The outcomes were converted to z-scores based on normative data for each measure, and participants who scored −1.5 or less on more than two of these tests were not included in the study. Depressive symptomatology and ability to execute the activities of daily living were assessed with the geriatric depression scale and the functional activities questionnaire, respectively.

Table 1
Demographics and cognition

All participants gave written informed consent before any procedures. The studies were approved by the institutional review boards of the University of California San Francisco and the San Francisco Veterans Administration Medical Center.

Data acquisition and processing

All data was acquired on a Bruker MedSpec 4T system controlled by a Siemens Trio console (Siemens, Erlangen, Germany). Volumetric T1-weighted gradient echo magnetic resonance imaging (MPRAGE: TR/TE/TI = 2,300/3/950 ms, 7° flip angle, 1.0 × 1.0 × 1.0 mm3 resolution) and a 3D T2-weighted turbo spin-echo sequence (TR/TE = 8,390/70 ms, 15° flip angle, 0.9 × 0.9 × 3 mm3 nominal resolution, 54 slices) were used to place MRS voxels and measure volumes of ACC and PCC. The MR spectra were acquired with a stimulated echo acquisition mode sequence (TE/TM/TR = 15/10/2,000 ms, voxel size = 2 × 2 × 2 cm3, spectral bandwidth = 2,000 Hz, 128 scans). The sequence used Shinnar-le-roux radiofrequency localization pulses (bandwidth = 4.5 kHz) designed with the Matlab-based software Matpulse (27). Water suppression was achieved using a variable pulse power and optimized relaxation delays scheme (28). After eddy-current and phase corrections with an in-house software, the spectroscopic data were processed with a freely available software (SI-TOOLS) (29). We fitted NAA, Cho, Cr, myo-inositol, scyllo-inositol, glutamine, Glu, as well as six macromolecular resonances (e.g., ref. 30; see Figure 2). The absolute metabolite concentrations were not calculated as cerebral spinal fluid contribution to the MRS voxels was not available. Instead, we calculated metabolite concentrations relative to Cr and Cho. Finally, we used automatic FreeSurfer software (31) to calculate volumes of ACC (defined as a sum of rostral and caudal ACC volumes) and PCC. All FreeSurfer results were visually inspected and manually corrected when necessary.

Figure 2
Exemplary ACC spectrum: (a) one experimental spectrum with fitted baseline and (b) fitted spectrum overlaid on experimental spectrum after subtraction of the fitted baseline. ACC, anterior cingulate cortex.

Statistical analysis

For statistical analyses, we adopted a linear modeling approach used in our previous publication (6). In short, the metabolite ratios and volumes were modeled as a linear function of BMI and age. To assess robustness of the results, we repeated the analyses with the following predictors used one at a time: (i) sex, (ii) interaction between age and BMI, (iii) volumes of corresponding regions (proxy measures for GM contributions to the spectroscopic volumes of interest). The relationship of BMI as well as NAA and Glu ratios to cognitive performance were evaluated with Spearman's correlation coeffcients (ρ). To account for multiple comparisons, we multiplied the P values by the number of evaluated regions (ACC and PCC). We did not correct for the number of metabolites as their associations with BMI pertain to different scientific questions. NAA and Glu were standardized to both Cr and Cho in the metabolite ratios to assure the observed relationships with BMI are driven by the metabolite of interest (NAA or Glu), not by the metabolite used for normalization. All analyses were performed with SPSS-16 for Windows (SPSS, Chicago, IL). A significance level of P = 0.05 or lower was considered significant.

Results

Thirty percent of participants were classified as overweight (25 < BMI < 30, n = 7, including 4 females) and 70% were in the normal weight range (18.5 < BMI < 25, n = 16, including 8 females). No participant was classified as obese or underweight. Nineteen participants (83%) were white. BMI was not related to age in this cohort (ρ = −0.16, P = 0.46). According to readings of a clinical neuroradiologist, no participant had significant cerebrovascular disease or atrophy patterns consistent with AD. No participant reported hypertension.

Males and females had similar age, education, and BMI (P > 0.11). There were no significant differences in age, education, and height between weight groups (all P > 0.28, see Table 1). No participant had clinically significant depressive symptoms. No participant was homozygous for the apolipoprotein E-ε4 allele and there were no differences in the frequency of single-copy apolipoprotein E-ε4 carriers between healthy weight and overweight groups (P = 0.62; see Table 1). Kolmogorov–Smirnov tests demonstrated that the distributions of the metabolite ratios and cortical thicknesses did not significantly depart from normality (P > 0.36).

In ACC, higher BMI was associated with lower NAA/Cr (β = −0.57, P = 0.006, Figure 3a), lower NAA/Cho (β = −0.56, P = 0.008), lower Glu/Cr (β = −0.46, P = 0.02, Figure 3c), and lower Glu/Cho (β = −0.53, P = 0.009). These associations were not significant in PCC (β > −0.32, P > 0.15, Figure 3b), except for a trend between higher BMI and lower Glu/Cr (β = −0.38, P = 0.09, Figure 3d). The Cho/Cr, myo-inositol/Cr, and myo-inositol/Cho ratios were not associated with BMI in any region (β > −0.33, P > 0.14, uncorrected). Age was independently associated with Glu/Cr (β = −0.46, P = 0.023) and Glu/Cho (β = −0.38, P = 0.05) in ACC, but not in PCC (β > −0.15, P > 0.50). NAA/Cr and NAA/Cho were not related to age in any region (β > 0.05, P > 0.23).

Figure 3
NAA/Cr (not corrected for age) as a function of BMI in (a) ACC and (b) PCC. Glu/Cr as a function of BMI in (c) ACC and (d) PCC. Note that the statistical outlier in quadrant b (see arrow) did not significantly affect the outcome. ACC, anterior cingulate ...

To assess robustness of the results, we repeated the analyses with the following predictors used one at a time: (i) sex, (ii) interaction between age and BMI, (iii) volumes of corresponding regions (proxy measures for GM contributions to the spectroscopic volumes of interest). None of these covariates significantly affected the results (P > 0.42). Finally, when BMI was removed from the models, only the Glu/Cr ratio in ACC demonstrated a trend for decrease with age (ρ = −0.37, P = 0.08). This confirms that the reported associations with BMI were not statistically mediated by BMI increasing with age.

NAA and Glu were strongly related. NAA/Cr correlated with Glu/Cr in ACC (ρ = 0.47, P = 0.01) and PCC (ρ = 0.56, P =0.003) and NAA/Cho was associated with Glu/Cho in ACC and PCC (ρ = 0.66 and ρ = 0.63, both P = 0.001). BMI was not associated with volumes of ACC or PCC (P > 0.37). Performance on any of our cognitive tests described in Methods and Procedures section was not significantly related to any of the metabolite ratios in ACC or PCC (ρ < 0.42, P > 0.10).

Discussion

The major finding of this neuroimaging study was that higher BMI in healthy elderly individuals was significantly associated with lower NAA/Cr, NAA/Cho, Glu/Cr, and Glu/Cho in ACC and that these associations were not observed in PCC. NAA ratios were not related to age, but they were positively associated with Glu ratios, consistent with a previous 4 Tesla report on normal aging (24). Glu ratios were lower at older age. No significant associations of BMI with volumes of ACC and PCC, cognition, or other metabolite ratios were observed.

Lower NAA is consistent with derangement of neurometabolism, lower dendritic/axonal density, and/or neuronal/axonal loss. The results suggest that higher BMI is related to lower neuronal integrity in a frontal region involved in emotional and cognitive regulation, decision making, self-monitoring, and goal directed behaviors. The spatial pattern of associations between NAA and BMI is consistent with our previous findings in a healthy middle-aged cohort, where BMI was inversely related to lower NAA in frontal GM, but not parietal GM (6). Furthermore, lack of associations between BMI and volumes of ACC and PCC may suggest that neuronal dysfunction rather than neuronal loss underlies our findings.

Lower NAA levels may be related to lower brain glucose metabolism, via derangement of neurometabolism (e.g., ref. 32) and/or they may reflect lower neuronal/dendritic density in those with higher BMI (33). Lower glucose metabolism in ACC and prefrontal cortex, as well as deregulation of their functions were recently found in obese individuals (5) and these findings were related to lower striatal dopamine D2 receptor availability (34). Lower availability of dopamine D2 receptors was also reported among healthy individuals with BMI in the overweight range (35).

Our findings may also relate to insulin resistance that is often found among the obese and is more prevalent in the elderly than in the young individuals (8,19). Insulin resistance may be related to impaired insulin signaling in the brain, which has been shown to be associated with impaired glucose utilization (8) and possibly lower NAA (see ref. 32). Furthermore, insulin resistance is often accompanied by increased levels of certain cytokines, such as interleukin-6 or tumor necrosis factor-α, that are able to cross the blood–brain barrier and directly influence the brain or other processes that in turn may affect the brain (e.g., ref. 36,37) and potentially lead to change in concentrations of NAA and Glu. Last but not least, unhealthy weight is associated with increased risk for cerebrovascular disease (16). Although significant cerebrovascular disease was not observed in our participants, we believe that future studies should evaluate whether presence of cerebrovascular disease influences the relationship between BMI and metabolite concentrations.

The ACC plays a crucial role in the reward circuitry, especially in reward-based learning, decision making, and emotional and cognitive regulation (22). Thus, lower concentrations of NAA and Glu in ACC might be related to poorer health decisions by individuals with weight problems.

In our cohort, the associations between BMI and NAA/Cr or NAA/Cho were not significant in PCC, a region demonstrating volumetric and spectroscopic changes in preclinical (20) and symptomatic AD (21). However, we cannot exclude the possibility that some of our participants had lost weight related to preclinical dementia (e.g., ref. 17) and/or our study did not have enough power to detect significant associations between BMI and metabolites in PCC. A larger and better controlled study may reveal significant associations between NAA and BMI in PCC, then suggesting a potential link between midlife obesity and increased risk of AD.

Limitations

The major limitation of this study was use of BMI as a marker of body fat. In elderly populations, the increase in amount of body fat is accompanied by fat redistribution into the muscle and toward the abdomen. This increase in the amount of adipose tissue may be accompanied by loss of lean body mass and bone mass as well as changes in body height (19). Thus, future studies need to measure the actual amount of body fat, especially abdominal fat, evaluate the body fat distribution, and measure levels of hormones and cytokines that are associated with elevated abdominal fat storage (see ref. 38). Additionally, patients start loosing weight several years before onset of dementia (e.g., ref. 17), thus similar studies of healthy elderly in future should follow-up on their participants for at least 5 years to exclude those who will develop cognitive decline and/or dementia. The relatively narrow age range of our cohort did not allow a meaningful evaluation for interactions between age and BMI. In addition, absolute metabolite concentrations were not available for analyses, thus our results could have been potentially confounded by potential age-related increases in Cr or Cho concentrations (e.g., ref. 24, but not ref. 21) and GM volume decreases with advancing age (e.g., ref. 39). However, these factors would lead to decreasing NAA/Cr and NAA/Cho ratios with age. As such decreasing relationships were not observed, it suggests that our results reflect associations between NAA or Glu levels and BMI. We screened participants during telephone interviews; thus the possibility exists that physical examination would reveal some undiagnosed conditions that could affect the data. Furthermore, we did not assess potentially important factors such as overall physical health, cardiorespiratory fitness, diet, total cholesterol, systolic and diastolic blood pressure, and glucose and insulin levels and we need to stress that our data do not allow us to discern whether lower NAA and Glu concentrations in ACC are pre-existing (genetic) or whether they are a consequence of a lifestyle leading to excess weight. This question could be addressed in prospective longitudinal studies of individuals in weight loss procedures, such as in ref. (3). Finally, our ACC voxels were positioned on the border between the affective and cognitive divisions of the ACC (22), thus potentially reducing our ability to detect any associations between metabolite ratios and cognitive performance (e.g., executive functions).

In summary, this retrospective analysis in cognitively normal elderly individuals demonstrate a pattern of association between elevated BMI and poorer neuronal integrity in the ACC but not PCC, which was not accompanied by significant structural changes in these regions. This pattern is consistent with our previous findings in a healthy middle-aged cohort. Taken together, these results provide evidence that excess weight is associated with poorer neuronal integrity mostly in frontal brain regions that subserve higher cognitive functions (e.g., executive functions) and impulse control. Thus, poorer neuronal integrity in ACC may be involved in the development and maintenance of weight problems.

Acknowledgments

The study was supported by AA10788 (D.J.M.), AG010897 (M.W.W.), and AG12435 (M.W.W.). This material is the result of work supported with resources and the use of facilities at the Radiology Research Service of the Veterans Administration Medical Center in San Francisco. We thank Jeffrey Kasten, Kristen Peek, and Sky Raptentsetsang of the Center for Imaging of Neurodegenerative Diseases for data acquisition and processing. We extend our gratitude to Dr Lustig of the University of California San Francisco for critical review of this manuscript.

Footnotes

Disclosure: The authors declared no conflict of interest.

References

1. Ward MA, Carlsson CM, Trivedi MA, Sager MA, Johnson SC. The effect of body mass index on global brain volume in middle-aged adults: a cross sectional study. BMC Neurol. 2005;5:23. [PMC free article] [PubMed]
2. Jagust W, Harvey D, Mungas D, Haan M. Central obesity and the aging brain. Arch Neurol. 2005;62:1545–1548. [PubMed]
3. Haltia LT, Viljanen A, Parkkola R, et al. Brain white matter expansion in human obesity and the recovering effect of dieting. J Clin Endocrinol Metab. 2007;92:3278–3284. [PubMed]
4. Pannacciulli N, Del Parigi A, Chen K, et al. Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage. 2006;31:1419–1425. [PubMed]
5. Volkow ND, Wang GJ, Telang F, et al. Inverse association between BMI and prefrontal metabolic activity in healthy adults. Obesity (Silver Spring) 2009;17:60–65. [PMC free article] [PubMed]
6. Gazdzinski S, Kornak J, Weiner MW, Meyerhoff DJ. Body mass index and magnetic resonance markers of brain integrity in adults. Ann Neurol. 2008;63:652–657. [PMC free article] [PubMed]
7. Ross B, Bluml S. Magnetic resonance spectroscopy of the human brain. Anat Rec. 2001;265:54–84. [PubMed]
8. Craft S. Insulin resistance and Alzheimer's disease pathogenesis: potential mechanisms and implications for treatment. Curr Alzheimer Res. 2007;4:147–152. [PubMed]
9. Sahin I, Alkan A, Keskin L, et al. Evaluation of in vivo cerebral metabolism on proton magnetic resonance spectroscopy in patients with impaired glucose tolerance and type 2 diabetes mellitus. J Diabetes Complicat. 2008;22:254–260. [PubMed]
10. Elias MF, Elias PK, Sullivan LM, Wolf PA, D'Agostino RB. Obesity, diabetes and cognitive deficit: The Framingham Heart Study. Neurobiol Aging. 2005;26(Suppl 1):11–16. [PubMed]
11. Waldstein SR, Katzel LI. Interactive relations of central versus total obesity and blood pressure to cognitive function. Int J Obes (Lond) 2006;30:201–207. [PubMed]
12. Gunstad J, Paul RH, Cohen RA, et al. Elevated body mass index is associated with executive dysfunction in otherwise healthy adults. Compr Psychiatry. 2007;48:57–61. [PubMed]
13. Pignatti R, Bertella L, Albani G, et al. Decision-making in obesity: a study using the Gambling Task. Eat Weight Disord. 2006;11:126–132. [PubMed]
14. Charlton RA, McIntyre DJ, Howe FA, Morris RG, Markus HS. The relationship between white matter brain metabolites and cognition in normal aging: the GENIE study. Brain Res. 2007;1164:108–116. [PubMed]
15. Beydoun MA, Beydoun HA, Wang Y. Obesity and central obesity as risk factors for incident dementia and its subtypes: a systematic review and meta-analysis. Obes Rev. 2008;9:204–218. [PubMed]
16. Gustafson D, Rothenberg E, Blennow K, Steen B, Skoog I. An 18-year follow-up of overweight and risk of Alzheimer disease. Arch Intern Med. 2003;163:1524–1528. [PubMed]
17. Stewart R, Masaki K, Xue QL, et al. A 32-year prospective study of change in body weight and incident dementia: the Honolulu-Asia Aging Study. Arch Neurol. 2005;62:55–60. [PubMed]
18. Mueller SG, Stables L, Du AT, et al. Measurements of hippocampal subfields and age-related changes with high resolution MRI at 4T. Neurobiol Aging. 2007;28:719–726. [PMC free article] [PubMed]
19. Zamboni M, Mazzali G, Zoico E, et al. Health consequences of obesity in the elderly: a review of four unresolved questions. Int J Obes (Lond) 2005;29:1011–1029. [PubMed]
20. Jagust W, Gitcho A, Sun F, et al. Brain imaging evidence of preclinical Alzheimer's disease in normal aging. Ann Neurol. 2006;59:673–681. [PubMed]
21. Schuff N, Meyerhoff DJ, Mueller S, et al. N-acetylaspartate as a marker of neuronal injury in neurodegenerative disease. Adv Exp Med Biol. 2006;576:241–62. discussion 361. [PMC free article] [PubMed]
22. Bush G, Luu P, Posner MI. Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci (Regul Ed) 2000;4:215–222. [PubMed]
23. Kalivas PW, Volkow ND. The neural basis of addiction: a pathology of motivation and choice. Am J Psychiatry. 2005;162:1403–1413. [PubMed]
24. Kaiser LG, Schuff N, Cashdollar N, Weiner MW. Age-related glutamate and glutamine concentration changes in normal human brain: 1H MR spectroscopy study at 4 T. Neurobiol Aging. 2005;26:665–672. [PMC free article] [PubMed]
25. Zahr NM, Mayer D, Pfefferbaum A, Sullivan EV. Low striatal glutamate levels underlie cognitive decline in the elderly: evidence from in vivo molecular spectroscopy. Cereb Cortex. 2008;18:2241–2250. [PMC free article] [PubMed]
26. Mueller SG, Stables L, Du AT, et al. Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T. Neurobiol Aging. 2007;28:719–726. [PMC free article] [PubMed]
27. Matson GB. An integrated program for amplitude-modulated RF pulse generation and re-mapping with shaped gradients. Magn Reson Imaging. 1994;12:1205–1225. [PubMed]
28. Tkác I, Starcuk Z, Choi IY, Gruetter R. In vivo 1H NMR spectroscopy of rat brain at 1 ms echo time. Magn Reson Med. 1999;41:649–656. [PubMed]
29. Soher BJ, Vermathen P, Schuff N, et al. Short TE in vivo (1)H MR spectroscopic imaging at 1.5 T: acquisition and automated spectral analysis. Magn Reson Imaging. 2000;18:1159–1165. [PubMed]
30. Mader I, Seeger U, Karitzky J, et al. Proton magnetic resonance spectroscopy with metabolite nulling reveals regional differences of macromolecules in normal human brain. J Magn Reson Imaging. 2002;16:538–546. [PubMed]
31. Desikan RS, Ségonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968–980. [PubMed]
32. Baslow MH. N-acetylaspartate in the vertebrate brain: metabolism and function. Neurochem Res. 2003;28:941–953. [PubMed]
33. O'Neill J, Eberling JL, Schuff N, et al. Method to correlate 1H MRSI and 18FDG-PET. Magn Reson Med. 2000;43:244–250. [PMC free article] [PubMed]
34. Volkow ND, Wang GJ, Telang F, et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage. 2008;42:1537–1543. [PMC free article] [PubMed]
35. Haltia LT, Rinne JO, Merisaari H, et al. Effects of intravenous glucose on dopaminergic function in the human brain in vivo. Synapse. 2007;61:748–756. [PubMed]
36. Whitmer RA. The epidemiology of adiposity and dementia. Curr Alzheimer Res. 2007;4:117–122. [PubMed]
37. Whitmer RA, Gustafson DR, Barrett-Connor E, et al. Central obesity and increased risk of dementia more than three decades later. Neurology. 2008;71:1057–1064. [PubMed]
38. Whitmer RA, Gunderson EP, Quesenberry CP, Jr, Zhou J, Yaffe K. Body mass index in midlife and risk of Alzheimer disease and vascular dementia. Curr Alzheimer Res. 2007;4:103–109. [PubMed]
39. Jernigan TL, Archibald SL, Fennema-Notestine C, et al. Effects of age on tissues and regions of the cerebrum and cerebellum. Neurobiol Aging. 2001;22:581–594. [PubMed]