Reproducibility of Three Whole-Brain N-Acetylaspartate Decline Cohorts in Relapsing-Remitting Multiple Sclerosis

O. Gonen; T.A. Oberndorfer; M. Inglese; J.S. Babb; J. Herbert; R.I. Grossman

Abstract

BACKGROUND AND PURPOSE: The cross-sectional rate of whole-brain N-acetylaspartate (NAA, a neuronal cell marker) loss in clinically similar relapsing-remitting multiple sclerosis (RRMS) patients has recently been shown to fall into 3 distinct decline rate strata. Our goal was to test the reproducibility of this observation in a new cohort of RRMS patients.

MATERIALS AND METHODS: Sixteen serial patients (12 women, 4 men, median age 38 [27–55] years) with clinically definite RRMS for an average of 5 (0.3–18) years’ disease duration and a mean Expanded Disability Status Score of 2.0 (0–6) were studied, once each. Their whole-brain NAA (WBNAA) amounts, obtained with proton MR spectroscopy, were divided by brain volumes (segmented from MR imaging) to yield concentrations suitable for cross-sectional comparisons.

RESULTS: Three distinct strata of cross-sectional NAA decline rates were found: −0.031, −0.32, and −1.71 mmol/L/y when disease duration was estimated from confirmed diagnosis, or −0.057, −0.20, and −1.38 mmol/L/y when measured from the first clinical symptom. These rates and their corresponding fractions of the study population were indistinguishable from those reported previously in a different group of 49 clinically similar (mean Expanded Disability Status Score also 2.0) RRMS patients.

CONCLUSION: Reproducing the previous cohort’s cross-sectional WBNAA decline characteristics in this new group of clinically similar RRMS patients indicates that 3 WBNAA loss strata may be a general attribute of MS. Consequently, WBNAA could serve as a surrogate marker for the global load of neuronal and axonal dysfunction and damage in this disease.

Multiple sclerosis (MS), the most prevalent demyelinating neurologic disorder, is the primary cause of nontraumatic disability in young and middle-aged adults, affecting more than 350,000 Americans.¹ Although MS is incurable at present, several disease-modifying drugs are now available for its treatment.² Given the cost and side effects of these drugs, as well as the widely variable course of this disease, an ability to predict the outcome of MS from factors accessible early in its course could influence therapeutic decisions, the planning and interpretation of clinical trials.

Studies on the natural history of MS have focused on identifying early clinical indicators of relative risk for rapid progression.^3–7 Based on some parameters (eg, frequency of relapses in the first 2 years; interval between the first and the second relapses; and time to reach moderate, level 3 on the Kurtzke Disability Status Scale), it has been possible, to some extent, to distinguish patients with “benign” disease from those with “moderate” or “rapidly progressive” disease. For example, Confavreux et al found that the mean duration between the first and second relapse among 349 patients was about 6 years in benign and intermediate disease versus 0.9 years in “hyper acute” MS and 2.4 years in “acute” MS.⁸ However, extensive interindividual variations in disability accumulation rates and the fact that exacerbations most often do not result in permanent disability preclude accurate prediction of the clinical course of MS.⁶

Because the irreversible neurologic deficits of MS are thought to be caused by neuronal cell loss,^9–11 surrogate markers specific for the degeneration and dysfunction of these cells correlate better with clinical disability¹² and might better predict outcome.¹² Key among these markers is N-acetylaspartate (NAA), the second most abundant amino acid in the mammalian brain.^13–16 Being almost exclusive to neurons and their processes, NAA is widely considered a specific marker for their health and concentration,^17–19 and its noninvasive acquisition renders it the most frequently examined brain metabolite.²⁰ With the sole known exception of Canavan disease,²¹ NAA concentration has been reported to decline in all neurodegenerative disorders.^14,22

In a previous study we have shown 3 differential strata of whole-brain NAA (WBNAA) decline rates,²³ which probably reflect axonal dysfunction, in 49 clinically similar relapsing-remitting (RR) MS patients. Based on their cross-sectional rates of NAA decline—which comprised one WBNAA measurement per patient combined with the assumption that when they were diagnosed, their WBNAA levels were at the “normal” level—these strata were “stable” (exhibiting level WBNAA), “moderate” (sustaining 2.7% annual NAA loss), and “rapid” (suffering a 10-fold faster decline).²³ The goal of the present study was to examine whether these findings are reproducible (ie, constitute a general characteristic of the disease) in another cohort of clinically similar RR MS patients. This was done by analyzing their WBNAA concentrations in patients with the same protocol as in the original study.²³

Methods

Patients

Sixteen RRMS patients (12 women, 4 men, median age 38 [27–55] years) were recruited serially based on 4 criteria: 1) clinically definite (CD) disease,²⁴ 2) MS of less than 12 years’ duration, 3) no relapse experienced in the last 3 months, and 4) no contraindications to MR imaging. Their age, sex, disease duration, Expanded Disability Status Scale (EDSS) scores, and medications are shown in Table 1. All patients were briefed on the procedure they were to undertake and gave their Institutional Review Board–approved written consent.

View this table:

Table 1:

Cohort details

Because months and even years may elapse between the 2 clinical events that confirm MS diagnosis,²⁴ we defined 2 disease durations: ΔY₁, the time from the second (confirming) event: $\text{[math]}$ 1)

and ΔY₂, the time from the first symptom (FS), which better approximates true disease duration: $\text{[math]}$ 2)

MR Imaging and Brain Volume

Patients’ brain volumes (V_B) were segmented from their T1-weighted sagittal magnetization-prepared rapid acquisition of gradient-echo images (TE/TR/TI of 7.0/14.7/300 ms, 128 sections 1.5 mm thick, 256² matrix, 210 × 210 mm² FOV) by using the MIDAS package.²⁵ Specifically, a “seed” region was placed in periventricular white matter. Following selection of all pixels at or above the gray matter intensity, a brain mask was constructed in 3 steps: 1) morphologic erosion, 2) recursive region growth-retaining pixels connected to the seed region, and 3) morphologic inflation to reverse the erosion. The masks were truncated at the foramen magnum to incorporate the brain stem and cerebellum but not the cord, and V_B was obtained by multiplying the number of pixels in the mask by their volume.

MR Spectroscopy and WBNAA Quantification

The amount of WBNAA, Q_NAA, was obtained in the same session. Following auto-shimming that yielded consistent 15 ± 3-Hz full-width-at-half-maximum head water linewidth, a nonlocalizing, nonecho (TR/TE of 10.0/0.0 seconds) ¹H-MR spectroscopy sequence yielded the whole-head NAA signal intensity.^26–28 Absolute quantification was done against a reference 3-L sphere of 1.5 × 10⁻² moles NAA in water. Patient and reference NAA peak areas, S_P and S_R, were integrated and Q_NAA was estimated: $\text{[math]}$ 3) where V_R^180° and V_S^180° are the voltages into 50 Ω of radiofrequency power needed for nonselective 1-millisecond, 180° inversion on the reference and patient, reflecting their relative receive sensitivities.

Finally, to account for natural variations in brain size, Q_NAA from Equation (3) was divided by the patient’s V_B to obtain the WBNAA concentration: $\text{[math]}$ 4) which is independent of brain size and is therefore suitable for cross-sectional comparison. The intra- and interpatient variability of such concentrations in healthy adults have been shown to be approximately ±0.6 mmol/L.^26,29

WBNAA Decline Rate Estimates and Group Partitioning

The original study estimated individual WBNAA decline rates with only one measurement by using 3 assumptions: 1) The second time point can be obtained by assuming each patient’s WBNAA to be the average of healthy controls (13.2 mmol/L)^23,26 before disease onset. 2) NAA loss begun at either CD or FS (ie, its duration, ΔY₁ or ΔY₂) is known. 3) WBNAA changes during ΔY₁ or ΔY₂ were linear. Consequently, the decline rate for the ith patient from either CD (R_1i) or FS (R_2i) was²³ $\text{[math]}$ 5a) or $\text{[math]}$ 5b)

Patients were classified as stable if their R was less than 0 mmol/L/y, moderate if between 0 and 1.7 mmol/L/y, and rapid if greater than 1.7 mmol/L/y, as shown in Figs 1 and 2.²³

Fig 1.

Dot-plot distribution of individual patients as a function of average rate of WBNAA decline per year of CD disease duration (R₁), as defined by Equations (1) and (5a). The vertical dotted lines at R₁ = 0 and 1.7 mmol/L/y partition the cohort into stable (○), moderate (□), and rapid (▿) according to criteria defined in the text.²³

Fig 2.

Same as Fig 1, except that the average individual rates of WBNAA decline per year, R₂, were estimated for disease duration from FS, ΔY₂, as defined by Equations (2) and (5b). The vertical dotted lines at R₁ = 0 and 1.7 mmol/L/y partition the cohort into stable (○), moderate (□), and rapid (▿) decline rates according to criteria defined in the text.²³

Statistical Analyses

Constrained least-squares regression was used to construct a model to predict WBNAA as a linear function of elapsed time from either CD or FS, subject to the first assumption above; that is, that at either time point of CD or FS, the WBNAA level was within the normal range of 13.2 ± 3 SD, 3 × 0.6 mmol/L.^26,29,30 This cutoff was chosen to ascertain 99% likelihood that a given patient was outside the normal WBNAA range. Comparison of 2 independent binomial proportions was used to calculate the similarity of group distributions between the current and previous datasets.

Results

Individual WBNAA decline rates were derived according to Equation (5). Based on the previous group assignment rules outlined above,²³ for disease duration from CD, ΔY₁, 5 of the 16 patients were classified as stable, 7 as moderate, and 4 as rapid, as shown in Fig 1. For ΔY₂, 5 patients were classified as stable, 6 as moderate, and 5 as rapid, as shown in Fig 2.

Cross-sectional rates of WBNAA decline derived from constrained linear regression in each of these subgroups (stable, moderate, and rapid, respectively) were −0.031, −0.32, and −1.71 mmol/L/y when based on ΔY₁ and −0.057, −0.20, and −1.38 mmol/L/y when based on ΔY₂. The individual WBNAA values versus ΔY₁ and ΔY₂ are plotted in Figs 2 and 4, respectively, which are overlaid with the current regression (solid) lines and the 95% confidence intervals for the regressions of these subgroups in the original study.²³ The slopes of the current regression lines were not different from those in the original study (P > .05). Summary statistics of the previous and current cohorts are compared in Table 2.

View this table:

Table 2:

Comparison between present and previous cohorts

Discussion

This study reproduced the 3 key findings of the original report.²³ Specifically, 1) both clinically similar (ie, mean EDSS score of 2.0) patient cohorts were readily subdivided into 3 distinct groups of cross-sectional WBNAA decline rates, 2) these decline rates were statistically similar to their respective counterparts, and 3) each subgroup’s percentage of the entire cohort population was statistically indistinguishable from that of the original study.

Number of Decline Subgroups.

Because any unconstrained number of data points can be divided into separate groups, no statistical methodology can objectively determine an optimal number of subgroups. Visual inspection of individual WBNAA levels plotted against disease duration for either ΔY₁ or ΔY₂ in Figs 3 and 4, however, readily reveals 3 such subgroups. This determination is bolstered by the resulting regression lines that so closely match the respective ones from the original study on a different patient cohort.

Fig 3.

Individual WBNAA levels as a function of ΔY₁ (from clinical diagnosis) of Equation (1) (labeled according to Fig 1). Solid lines are the regressions calculated for each subgroup. The dashed lines are the ±95% confidence intervals calculated for the subgroups of the previous cohort.²³ Note how well the current regressions fit into the previous cohort’s confidence intervals.²³

Fig 4.

Individual WBNAA levels as a function of ΔY₂ (from fist symptom) of Equation (2) (labeled according to Fig 2). Solid lines are regressions for each subgroup. The dashed lines are the ±95% confidence intervals calculated for the subgroups of the previous cohort.²³ Note how well the regression lines for the current patients fit into the previous cohort’s confidence intervals.²³

Subgroup Cross-Sectional Decline Rates.

For each subgroup, the linear prediction models derived from the current data fall within the 95% confidence intervals of the previous patients’ data (Figs 3 and 4). Accordingly, a test of 2 binomial proportions determined that the group WBNAA decline rates were not different from those in the original study (P > .05).

Subgroup Populations as Fraction of Total Cohort.

A test of 2 binomial proportions determined that the group size percentages of the current and previous cohorts, shown in Table 2, are indistinguishable, despite the smaller sample size in this study. In the CD groups, most patients are defined as moderate, with fewer being found in the stable and rapid groups. Although the partitioning from FS is more uniform, its similarity to the group distribution percentage of the original data is nonetheless statistically significant.

These observations are bolstered by the known epidemiology of MS (eg, as described by Weinshenker), which supports the notion of 3 levels of severity: “benign,” “intermediate,” and “acute”.³¹ Ascertaining these levels for individual patients, however, is currently possible only retrospectively, years after diagnosis. This delay poses serious limitations for several reasons. First, substantial WBNAA losses (exceeding 22%) were observed in monosymptomatic patients only suspected of having MS, indicating, together with irreversible tissue loss, that early widespread damage occurs even before a confirmed diagnosis of MS.^30,32,33 Second, early drug treatment after the first clinical symptom has been shown to delay conversion to CD MS,^32,34 underscoring the need to triage patients for treatment based on some surrogate marker, especially considering the absence of a clinical indicator. Third, since the administration and pace of treatment (type, dose, and frequency of administration) must be weighed versus cost and side effects, the a priori knowledge of future disease course could identify patients who would benefit most from therapy and help determine its level. Finally, recruiting patients who exhibit the most common form of the disease (as opposed to its mild or virulent forms) is important in setting up clinical drug trials. Identification of such patients could help avert errors of the kind where an ineffective drug is assumed efficacious due to the influence of “stable” patients (a type I statistical error) or when an effective drug is judged to be a failure because of the undue influence of rapidly declining patients (a much more expensive type II error).

Note that although 3 assumptions were used for the analyses above, we argue that these assumptions are reasonable. First, that the WBNAA decline started from approximately the average level of healthy controls (13.2 mmol/L)^26,29,30 is easily justified by the low variability in the latter (±6%–7%).^26,29,30 The second assumption, that disease duration is known (either ΔY₁ or ΔY₂), is more equivocal; that is, because MS etiology is unknown, so is its true time of onset. Nevertheless, since ΔY₂ approximates disease duration better than does ΔY₁, the minimal changes in subgroup traits based on either one (only one patient [no. 7] in Table 1 switched assignment) indicate that either is sufficient. The fact that the moderate and rapid subgroup regression lines do not intersect the normal range at t = 0 (ie, we take this intercept to be the “true” time of disease onset) is just an overall indicator of the “softness” of the approximation that the disease started at CD or FS. Furthermore, the distinction between ΔY₁ and ΔY₂ continues to diminish as radiologic criteria to confirm MS after a single clinical episode become ubiquitous.^35–37 Finally, the assumption that the WBNAA decline was linear over ΔY₁ or ΔY₂, though the least robust of the 3 assumptions, is still reasonable. Although De Stefano et al reported that NAA declines fastest early in the course of the disease and slows down with the transition from RR into the secondary progressive form,¹² that process may, in most patients, take many years, even decades. A linear approximation initially, therefore, is reasonable when patients are younger, when the future course of their disease is least well known, and when they are more likely to benefit from treatment.

Importantly, although this study estimates rates of decline based on the above-mentioned assumptions, these rates can only be validated in a longitudinal study. In such a study, each patient would be assigned an estimated rate category based on his or her WBNAA at entry, and his or her true individual WBNAA decline rate would then be measured over time. Although clearly important, such a study would require much more time and a larger cohort to establish statistical power of prediction. It would be even more interesting if the estimated WBNAA decline rate predicted initially would not only be validated longitudinally, but would also be correlated with the clinical disability accumulated by these patients over time.

Conclusion

This study successfully reproduced the partitioning of a population of clinically similar RRMS patients into 3 separate cohorts of cross-sectional WBNAA decline. This supports the premise that the existence of different subgroups of NAA decline rates is probably a general characteristic of RRMS. Because decreased NAA has been demonstrated in MS lesions, and its decline is associated with neuronal dysfunction and subsequent clinical disability, these 3 distinct rates of WBNAA dynamics are likely to correlate with disease progression.^11,38–40 If validated in a longitudinal study, WBNAA could be used as a marker to predict each patient’s future disease course, a forecast for which the current laboratory and clinical markers are still inadequate.

Footnotes

This work was supported by NIH grants EB01015, NS050520, and NS29029 and by the Martin S. Davis Research Fellowship Endowment from the National Multiple Sclerosis Society.

References

↵
Hauser SL. Multiple sclerosis and other demyelinating diseases. In: Isselbacher KJ, Wilson JD, Martin JB, et al. Harrison’s Principles of Internal Medicine. New York: McGraw-Hill,1994 :2287–95
↵
Bryant J, Clegg A, Milne R. Systematic review of immunomodulatory drugs for the treatment of people with multiple sclerosis: is there good quality evidence on effectiveness and cost? J Neurol Neurosurg Psychiatry 2001;70:574–79
Abstract/FREE Full Text
↵
Weinshenker BG, Bass B, Rice GP, et al. The natural history of multiple sclerosis: a geographically based study. 2. Predictive value of the early clinical course. Brain 1989;112:1419–28
Abstract/FREE Full Text
Weinshenker BG, Rice GP, Noseworthy JH, et al. The natural history of multiple sclerosis: a geographically based study. 4. Applications to planning and interpretation of clinical therapeutic trials. Brain 1991;114:1057–67
Abstract/FREE Full Text
Kremenchutzky M, Cottrell D, Rice G, et al. The natural history of multiple sclerosis: a geographically based study. 7. Progressive-relapsing and relapsing-progressive multiple sclerosis: a re-evaluation. Brain 1999;122:1941–50
Abstract/FREE Full Text
↵
Weinshenker BG. The natural history of multiple sclerosis: update 1998. Semin Neurol 1998;18:301–07
PubMed
↵
Confavreux C, Vukusic S. Natural history of multiple sclerosis: implications for counselling and therapy. Curr Opin Neurol 2002;15:257–66
CrossRef PubMed
↵
Confavreux C, Aimard G, Devic M. Course and prognosis of multiple sclerosis assessed by the computerized data processing of 349 patients. Brain 1980;103:281–300
FREE Full Text
↵
Bjartmar C, Wujek JR, Trapp BD. Axonal loss in the pathology of MS: consequences for understanding the progressive phase of the disease. J Neurol Sci 2003;206:165–71
CrossRef PubMed
Bjartmar C, Battistuta J, Terada N, et al. N-acetylaspartate is an axon-specific marker of mature white matter in vivo: a biochemical and immunohistochemical study on the rat optic nerve. Ann Neurol 2002;51:51–58
CrossRef PubMed
↵
Bjartmar C, Kidd G, Mork S, et al. Neurological disability correlates with spinal cord axonal loss and reduced N-acetyl aspartate in chronic multiple sclerosis patients. Ann Neurol 2000;48:893–901
CrossRef PubMed
↵
De Stefano N, Narayanan S, Francis GS, et al. Evidence of axonal damage in the early stages of multiple sclerosis and its relevance to disability. Arch Neurol 2001;58:65–70
CrossRef PubMed
↵
Birken DL, Oldendorf WH. N-acetyl-L-aspartic acid: a literature review of a compound prominent in 1H-NMR spectroscopic studies of brain. Neurosci Biobehav Rev 1989;13:23–31
CrossRef PubMed
↵
Danielsen EA, Ross B. In: Magnetic Resonance Spectroscopy Diagnosis of Neurological Diseases. New York: Marcel Dekker,1999
Arnold DL, de Stefano N, Matthews PM, et al. N-acetylaspartate: usefulness as an indicator of viable neuronal tissue. Ann Neurol 2001;50:823 ; discussion 824–25
PubMed
↵
Baslow MH. Functions of N-acetyl-L-aspartate and N-acetyl-L-aspartylglutamate in the vertebrate brain: role in glial cell-specific signaling. J Neurochem 2000;75:453–59
CrossRef PubMed
↵
Simmons ML, Frondoza CG, Coyle JT. Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies. Neuroscience 1991;45:37–45
CrossRef PubMed
Jenkins BG, Klivenyi P, Kustermann E, et al. Nonlinear decrease over time in N-acetyl aspartate levels in the absence of neuronal loss and increases in glutamine and glucose in transgenic Huntington’s disease mice. J Neurochem 2000;74:2108–19
CrossRef PubMed
↵
Moffett JR, Namboodiri MA, Cangro CB, et al. Immunohistochemical localization of N-acetylaspartate in rat brain. Neuroreport 1991;2:131–34
PubMed
↵
Baslow MH. N-acetylaspartate in the vertebrate brain: metabolism and function. Neurochem Res 2003;28:941–53
CrossRef PubMed
↵
Baslow MH, Resnik TR. Canavan disease. Analysis of the nature of the metabolic lesions responsible for development of the observed clinical symptoms. J Mol Neurosci 1997;9:109–25
CrossRef PubMed
↵
Ross BD, Bluml S. Magnetic resonance spectroscopy of the human brain. Anat Rec 2001;265:54–84
CrossRef PubMed
↵
Gonen O, Moriarty DM, Li BS, et al. Relapsing-remitting multiple sclerosis and whole-brain N-acetylaspartate measurement: evidence for different clinical cohorts initial observations. Radiology 2002;225:261–68
PubMed
↵
Poser CM, Paty DW, Scheinberg L, et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 1983;13:227–31
CrossRef PubMed
↵
De Santi S, de Leon MJ, Rusinek H, et al. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging 2001;22:529–39
CrossRef PubMed
↵
Gonen O, Viswanathan AK, Catalaa I, et al. Total brain N-acetylaspartate concentration in normal, age-grouped females: quantitation with non-echo proton NMR spectroscopy. Magn Reson Med 1998;40:684–89
PubMed
Gonen O, Catalaa I, Babb JS, et al. Total brain N-acetylaspartate: a new measure of disease load in MS. Neurology 2000;54:15–19
Abstract/FREE Full Text
↵
Gonen O, Grossman RI. The accuracy of whole brain N-acetylaspartate quantification. Magn Reson Imaging 2000;18:1255–58
CrossRef PubMed
↵
Inglese M, Ge Y, Filippi M, et al. Indirect evidence for early widespread gray matter involvement in relapsing-remitting multiple sclerosis. Neuroimage 2004;21:1825–29
CrossRef PubMed
↵
Filippi M, Bozzali M, Rovaris M, et al. Evidence for widespread axonal damage at the earliest clinical stage of multiple sclerosis. Brain 2003;126:433–37
Abstract/FREE Full Text
↵
Weinshenker BG. Natural history of multiple sclerosis. Ann Neurol 1994;36:S6–S11
↵
Predictors of short-term disease activity following a first clinical demyelinating event: analysis of the CHAMPS placebo group. Mult Scler 2002;8:405–09
Abstract/FREE Full Text
↵
Rovaris M, Gambini A, Gallo A, et al. Axonal injury in early multiple sclerosis is irreversible and independent of the short-term disease evolution. Neurology 2005;65:1626–30
Abstract/FREE Full Text
↵
Comi G, Filippi M, Barkhof F, et al. Effect of early interferon treatment on conversion to definite multiple sclerosis: a randomised study. Lancet 2001;357:1576–82
CrossRef PubMed
↵
McDonald WI, Compston A, Edan G, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–27
CrossRef PubMed
Barkhof F, Filippi, M, Miller DH, et al. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain 1997;120:2059–69
Abstract/FREE Full Text
↵
Offenbacher H, Fazekas F, Schmidt R, et al. Assessment of MRI criteria for a diagnosis of MS. Neurology 1993;4:905–09
↵
Narayanan S, Fu L, Pioro E, et al. Imaging of axonal damage in multiple sclerosis: spatial distribution of magnetic resonance imaging lesions. Ann Neurol 1997;41:385–91
CrossRef PubMed
De Stefano N, Matthews PM, Fu LQ, et al. Axonal damage correlates with disability in patients with relapsing-remitting multiple sclerosis—results of a longitudinal magnetic resonance spectroscopy study. Brain 1998;121:1469–77
Abstract/FREE Full Text
↵
Trapp BD, Peterson J, Ransohoff RM, et al. Axonal transection in the lesions of multiple sclerosis. N Engl J Med 1998;338:278–85
CrossRef PubMed

Received January 2, 2006.
Accepted after revision March 22, 2006.

View Abstract

In this issue

Download PDF

Email Article

Citation Tools

Cited By...

Global N-Acetylaspartate Declines Even in Benign Multiple Sclerosis

More in this TOC Section

Show more BRAIN

[1] ↵
Hauser SL. Multiple sclerosis and other demyelinating diseases. In: Isselbacher KJ, Wilson JD, Martin JB, et al. Harrison’s Principles of Internal Medicine. New York: McGraw-Hill,1994 :2287–95

[2] ↵
Bryant J, Clegg A, Milne R. Systematic review of immunomodulatory drugs for the treatment of people with multiple sclerosis: is there good quality evidence on effectiveness and cost? J Neurol Neurosurg Psychiatry 2001;70:574–79
Abstract/FREE Full Text

[3] ↵
Weinshenker BG, Bass B, Rice GP, et al. The natural history of multiple sclerosis: a geographically based study. 2. Predictive value of the early clinical course. Brain 1989;112:1419–28
Abstract/FREE Full Text

[4] Weinshenker BG, Rice GP, Noseworthy JH, et al. The natural history of multiple sclerosis: a geographically based study. 4. Applications to planning and interpretation of clinical therapeutic trials. Brain 1991;114:1057–67
Abstract/FREE Full Text

[5] Kremenchutzky M, Cottrell D, Rice G, et al. The natural history of multiple sclerosis: a geographically based study. 7. Progressive-relapsing and relapsing-progressive multiple sclerosis: a re-evaluation. Brain 1999;122:1941–50
Abstract/FREE Full Text

[6] ↵
Weinshenker BG. The natural history of multiple sclerosis: update 1998. Semin Neurol 1998;18:301–07
PubMed

[7] ↵
Confavreux C, Vukusic S. Natural history of multiple sclerosis: implications for counselling and therapy. Curr Opin Neurol 2002;15:257–66
CrossRef PubMed

[8] ↵
Confavreux C, Aimard G, Devic M. Course and prognosis of multiple sclerosis assessed by the computerized data processing of 349 patients. Brain 1980;103:281–300
FREE Full Text

[9] ↵
Bjartmar C, Wujek JR, Trapp BD. Axonal loss in the pathology of MS: consequences for understanding the progressive phase of the disease. J Neurol Sci 2003;206:165–71
CrossRef PubMed

[10] Bjartmar C, Battistuta J, Terada N, et al. N-acetylaspartate is an axon-specific marker of mature white matter in vivo: a biochemical and immunohistochemical study on the rat optic nerve. Ann Neurol 2002;51:51–58
CrossRef PubMed

[11] ↵
Bjartmar C, Kidd G, Mork S, et al. Neurological disability correlates with spinal cord axonal loss and reduced N-acetyl aspartate in chronic multiple sclerosis patients. Ann Neurol 2000;48:893–901
CrossRef PubMed

[12] ↵
De Stefano N, Narayanan S, Francis GS, et al. Evidence of axonal damage in the early stages of multiple sclerosis and its relevance to disability. Arch Neurol 2001;58:65–70
CrossRef PubMed

[13] ↵
Birken DL, Oldendorf WH. N-acetyl-L-aspartic acid: a literature review of a compound prominent in 1H-NMR spectroscopic studies of brain. Neurosci Biobehav Rev 1989;13:23–31
CrossRef PubMed

[14] ↵
Danielsen EA, Ross B. In: Magnetic Resonance Spectroscopy Diagnosis of Neurological Diseases. New York: Marcel Dekker,1999

[15] Arnold DL, de Stefano N, Matthews PM, et al. N-acetylaspartate: usefulness as an indicator of viable neuronal tissue. Ann Neurol 2001;50:823 ; discussion 824–25
PubMed

[16] ↵
Baslow MH. Functions of N-acetyl-L-aspartate and N-acetyl-L-aspartylglutamate in the vertebrate brain: role in glial cell-specific signaling. J Neurochem 2000;75:453–59
CrossRef PubMed

[17] ↵
Simmons ML, Frondoza CG, Coyle JT. Immunocytochemical localization of N-acetyl-aspartate with monoclonal antibodies. Neuroscience 1991;45:37–45
CrossRef PubMed

[18] Jenkins BG, Klivenyi P, Kustermann E, et al. Nonlinear decrease over time in N-acetyl aspartate levels in the absence of neuronal loss and increases in glutamine and glucose in transgenic Huntington’s disease mice. J Neurochem 2000;74:2108–19
CrossRef PubMed

[19] ↵
Moffett JR, Namboodiri MA, Cangro CB, et al. Immunohistochemical localization of N-acetylaspartate in rat brain. Neuroreport 1991;2:131–34
PubMed

[20] ↵
Baslow MH. N-acetylaspartate in the vertebrate brain: metabolism and function. Neurochem Res 2003;28:941–53
CrossRef PubMed

[21] ↵
Baslow MH, Resnik TR. Canavan disease. Analysis of the nature of the metabolic lesions responsible for development of the observed clinical symptoms. J Mol Neurosci 1997;9:109–25
CrossRef PubMed

[22] ↵
Ross BD, Bluml S. Magnetic resonance spectroscopy of the human brain. Anat Rec 2001;265:54–84
CrossRef PubMed

[23] ↵
Gonen O, Moriarty DM, Li BS, et al. Relapsing-remitting multiple sclerosis and whole-brain N-acetylaspartate measurement: evidence for different clinical cohorts initial observations. Radiology 2002;225:261–68
PubMed

[24] ↵
Poser CM, Paty DW, Scheinberg L, et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 1983;13:227–31
CrossRef PubMed

[25] ↵
De Santi S, de Leon MJ, Rusinek H, et al. Hippocampal formation glucose metabolism and volume losses in MCI and AD. Neurobiol Aging 2001;22:529–39
CrossRef PubMed

[26] ↵
Gonen O, Viswanathan AK, Catalaa I, et al. Total brain N-acetylaspartate concentration in normal, age-grouped females: quantitation with non-echo proton NMR spectroscopy. Magn Reson Med 1998;40:684–89
PubMed

[27] Gonen O, Catalaa I, Babb JS, et al. Total brain N-acetylaspartate: a new measure of disease load in MS. Neurology 2000;54:15–19
Abstract/FREE Full Text

[28] ↵
Gonen O, Grossman RI. The accuracy of whole brain N-acetylaspartate quantification. Magn Reson Imaging 2000;18:1255–58
CrossRef PubMed

[29] ↵
Inglese M, Ge Y, Filippi M, et al. Indirect evidence for early widespread gray matter involvement in relapsing-remitting multiple sclerosis. Neuroimage 2004;21:1825–29
CrossRef PubMed

[30] ↵
Filippi M, Bozzali M, Rovaris M, et al. Evidence for widespread axonal damage at the earliest clinical stage of multiple sclerosis. Brain 2003;126:433–37
Abstract/FREE Full Text

[31] ↵
Weinshenker BG. Natural history of multiple sclerosis. Ann Neurol 1994;36:S6–S11

[32] ↵
Predictors of short-term disease activity following a first clinical demyelinating event: analysis of the CHAMPS placebo group. Mult Scler 2002;8:405–09
Abstract/FREE Full Text

[33] ↵
Rovaris M, Gambini A, Gallo A, et al. Axonal injury in early multiple sclerosis is irreversible and independent of the short-term disease evolution. Neurology 2005;65:1626–30
Abstract/FREE Full Text

[34] ↵
Comi G, Filippi M, Barkhof F, et al. Effect of early interferon treatment on conversion to definite multiple sclerosis: a randomised study. Lancet 2001;357:1576–82
CrossRef PubMed

[35] ↵
McDonald WI, Compston A, Edan G, et al. Recommended diagnostic criteria for multiple sclerosis: guidelines from the international panel on the diagnosis of multiple sclerosis. Ann Neurol 2001;50:121–27
CrossRef PubMed

[36] Barkhof F, Filippi, M, Miller DH, et al. Comparison of MRI criteria at first presentation to predict conversion to clinically definite multiple sclerosis. Brain 1997;120:2059–69
Abstract/FREE Full Text

[37] ↵
Offenbacher H, Fazekas F, Schmidt R, et al. Assessment of MRI criteria for a diagnosis of MS. Neurology 1993;4:905–09

[38] ↵
Narayanan S, Fu L, Pioro E, et al. Imaging of axonal damage in multiple sclerosis: spatial distribution of magnetic resonance imaging lesions. Ann Neurol 1997;41:385–91
CrossRef PubMed

[39] De Stefano N, Matthews PM, Fu LQ, et al. Axonal damage correlates with disability in patients with relapsing-remitting multiple sclerosis—results of a longitudinal magnetic resonance spectroscopy study. Brain 1998;121:1469–77
Abstract/FREE Full Text

[40] ↵
Trapp BD, Peterson J, Ransohoff RM, et al. Axonal transection in the lesions of multiple sclerosis. N Engl J Med 1998;338:278–85
CrossRef PubMed

Main menu

User menu

Search

American Journal of Neuroradiology

Reproducibility of Three Whole-Brain N-Acetylaspartate Decline Cohorts in Relapsing-Remitting Multiple Sclerosis

Abstract