Brain, Vol. 125, No. 10, 2342-2352,
October 2002
© 2002 Oxford University Press
Brain metabolite changes in cortical grey and normal-appearing white matter in clinically early relapsing–remitting multiple sclerosis
1 NMR Research Unit, Institute of Neurology, University College London, London, 2 MRI Unit, National Society for Epilepsy, Chalfont St Peter, UK and 3 Department of Neurology, Karl-Franzens University, Graz, Austria
Correspondence to: Professor D. H. Miller, NMR Research Unit, Institute of Neurology, Queen Square, London WC1N 3BG, UK E-mail: d.miller{at}ion.ucl.ac.uk
Received April 11, 2002. Revised May 12, 2002. Accepted May 13, 2002.
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Summary |
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While much work has concentrated on focal white matter (WM) lesions in multiple sclerosis, there is growing evidence to suggest that normal-appearing WM (NAWM) and grey matter (GM) are also involved in the disease process. This study investigated multiple sclerosis disease effects on NAWM and cortical GM (CGM) metabolite concentrations, and the relationships between these metabolite concentrations and clinical impairment. Proton magnetic resonance spectroscopic imaging (1H-MRSI) data acquired using point resolved spectroscopic (PRESS) localization (echo time 30 ms, repetition time 3000 ms, nominal voxel volume 2.3 ml) from 27 relapsing–remitting multiple sclerosis and 29 normal control (NC) subjects were processed using LCModel to estimate metabolite concentrations in millimoles per litre. 1H-MRSI voxel tissue contents were estimated using SPM99 tissue and semi-automatic lesion segmentations of three-dimensional fast spoiled gradient recall scans acquired during the same scanning session. NAWM and CGM metabolite concentrations estimated were: choline-containing compounds (Cho); creatine and phosphocreatine (Cr); myo-inositol (Ins); N-acetyl-aspartate plus N-acetyl-aspartyl-glutamate (tNAA); and glutamate plus glutamine (Glx). CGM data came from 24 of the multiple sclerosis (mean age 35.2 years, mean disease duration 1.7 years) and 25 of the NC (mean age 34.9 years) subjects. NAWM data came from 25 of the multiple sclerosis (mean age 35.0 years, mean disease duration 1.7 years) and 28 of the NC (mean age 36.7 years) subjects. Metabolite concentrations were compared between multiple sclerosis and NC subjects using multiple (linear) regression models allowing for age, gender, 1H-MRSI voxel tissue and CSF contents, and brain parenchymal volume. At a significance level of P < 0.05, CGM Cho, CGM and NAWM tNAA, and CGM Glx were all significantly reduced, and NAWM Ins was significantly elevated. Spearman correlations of multiple sclerosis functional composite scores with tissue metabolite concentrations were significant for the following: CGM Cr (rs = 0.524, P = 0.009), CGM Glx (rs = 0.580, P = 0.003) and NAWM Ins (rs = –0.559, P = 0.004). These results indicate that metabolite changes in NAWM and CGM can be detected early in the clinical course of multiple sclerosis, and that some of these changes relate to clinical status. The correlation of clinical impairment with CGM Cr and Glx but not tNAA suggests that it is more closely associated with neuronal metabolic dysfunction rather than loss in clinically early relapsing–remitting multiple sclerosis. The correlation of clinical impairment with a raised NAWM Ins may indicate that glial proliferation also relates to function at this stage of the disease.
Keywords: grey matter; metabolite concentrations; multiple sclerosis; normal-appearing white matter; proton magnetic resonance spectroscopic imaging
Abbreviations: 1H-MRSI = proton magnetic resonance spectroscopic imaging; 9HPT = nine hole peg test; BP = brain parenchymal; BPF = brain parenchymal fraction; CGM = cortical grey matter; Cho = choline containing compounds; Cr = creatine and phosphocreatine; EDSS = expanded disability status scale; FSE = fast spin echo; Glx = glutamate plus glutamine; GM = grey matter; GMF = grey matter fraction; Ins = myo-inositol; MSFC = multiple sclerosis functional composite; NAWM = normal-appearing white matter; NC = normal control; PASAT = paced auditory serial addition test; PRESS = point resolved spectroscopic; TI = total intracranial; tNAA = N-acetyl-aspartate plus N-acetyl-aspartyl-glutamate; TWT = timed walk test; WM = white matter; WMF = white matter fraction
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Introduction |
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There is growing evidence to suggest that the effects of multiple sclerosis on brain tissues are not limited to focal white matter (WM) lesions or even WM per se. In the normal-appearing WM (NAWM), there is evidence from MRI and histopathology studies indicating extensive involvement despite the apparent absence of overt lesions on conventional T1- or T2-weighted MRI images. Grey matter (GM) is also involved, although it has not been the focus of much investigation until recently. Despite the considerable volume of GM, its complex anatomy has meant that it has been difficult to study using MRI. The introduction of additional MRI techniques and refinements to conventional imaging methodologies has made the study of this potentially important but under-investigated tissue possible.
This present study investigates clinically early multiple sclerosis disease effects in both NAWM and cortical GM (CGM), using proton magnetic resonance spectroscopic imaging (1H-MRSI) to determine tissue-specific metabolite concentrations and by assessing their relationships with clinical outcome. It builds upon previous preliminary work (Kapeller et al., 2001
), expanding the multiple sclerosis and normal control (NC) cohorts, updating the 1H-MRSI processing technique and tissue segmentation methodology (with inclusion of formal lesion quantification), and employing statistical analyses to account for age, gender, tissue type and partial volume effects associated with differences in brain parenchymal volumes.
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Methods |
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Subjects
Data came from a cohort of 27 multiple sclerosis and 29 NC subjects. The multiple sclerosis subjects all had clinically definite (Poser et al., 1983
) relapsing–remitting multiple sclerosis (Lublin and Reingold, 1996); none had received ß-interferon at any stage prior to scanning, nor had they been treated with corticosteroids within the previous month. The project had approval from the ethics committee of the National Hospital for Neurology and Neurosurgery, Queen Square, London, UK. All subjects gave informed consent.
Scan acquisition
Scanning was performed using a General Electric Signa 1.5 Tesla system (General Electric Medical Systems, Milwaukee, WI, USA). A single inversion-prepared three-dimensional, fast spoiled gradient recall (3D FSPGR) sequence [repetition time (TR) = 16 ms, echo time (TE) = 4.2 ms, inversion time = 450 ms, number of excitations (NEX) = 1, matrix = 160 x 256 interpolated to 192 x 256, field of view (FOV) = 225 x 300 mm, final in-plane resolution = 1.2 x 1.2 mm, with 124 1.5-mm slices covering the whole brain] was acquired in each subject for tissue segmentation. The plane of the excitation volume for 1H-MRSI scan acquisition was defined as orthogonal axial, immediately superior to the roof of the lateral ventricles (Fig. 1). The 1H-MRSI scans were acquired using a point resolved spectroscopic (PRESS) localization scheme (TE = 30 ms, TR = 3000 ms, NEX = 1, 24 x 24 phase encodes over a 300 x 300 mm FOV, slice thickness = 15 mm, spectral width = 2500 Hz, 2048 points), with outer-volume suppression bands contiguous with the PRESS-selected volume in all three dimensions. The size of PRESS-selected volume varied between subjects: the mean anterior to posterior extent was 97 mm (median 98 mm, range 69–118 mm) and the mean left to right extent was 67 mm (median 67 mm, range 54–80 mm). The anterior to posterior and left to right dimensions of the outer-volume suppression bands matched those of the PRESS-selected volume in a given subject. Automated pre-scanning optimized the shim, water suppression, and set both transmitter and receiver gains (Webb et al., 1994).
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During a second scanning session, dual echo fast spin echo (FSE) sequences (TR = 2000 ms, TE = 19/95 ms, NEX = 2, in-plane resolution = 0.9 x 0.9 mm, with 28 5-mm slices covering the whole brain) and T1-weighted spin echo sequences (TR = 540 ms, TE = 20 ms, NEX = 1, in-plane resolution = 0.9 x 0.9 mm, with 28 5-mm slices covering the whole brain) were acquired in all subjects. In multiple sclerosis subjects only, the latter was also acquired 20 min post-intravenous gadolinium administration [0.3 mmol/kg body weight of Magnevist (Schering AG, Berlin, Germany)]. The mean delay between scanning sessions in the multiple sclerosis subjects was 9 days (median 7 days, range 2–60 days), during which time they did not report additional clinical events. All data were acquired using a standard quadrature head-coil.
Image processing
1H-MRSI metabolite quantification
Spectroscopy pre-processing was performed using SAGE 7.0 (General Electric Medical Systems, Milwaukee, WI, USA). Grids of voxels with individual voxel dimensions 12.5 x 12.5 x 15.0 mm (2.3 ml) were automatically extracted and passed to LCModel (version 5.2) (Provencher, 1993) for metabolite quantification in mmol/l. Metabolite concentrations estimated were: choline containing compounds (Cho), creatine and phosphocreatine (Cr), myo-inositol (Ins), N-acetyl-aspartate (NAA) plus N-acetyl-aspartyl-glutamate (together designated tNAA), and glutamate plus glutamine (Glx). GM, NAWM, cerebrospinal fluid (CSF) and lesion masks were derived from the 3D FSPGR images using a previously described methodology (Chard et al., 2002), based upon SPM99 (Wellcome Department of Cognitive Neurology, Institute of Neurology, Queen Square, London, UK) (Ashburner and Friston, 2000) tissue segmentations and semi-automatic lesion segmentations (as described below). These masks were used to quantify the tissue contents for each 1H-MRSI voxel.
1H-MRSI voxels were excluded from further analysis if they were not entirely within the PRESS excitation volume. Voxels were retained if LCModel estimates of the quantifi cation error [as a percentage standard deviation (SD)] for all metabolites was less than the estimated upper 95% limit (mean quantification error + 2 SD of the quantification error) for those voxels kept after the previous stage. Voxels were retained if they contained >80% GM plus NAWM and <1% lesion. Voxels were classified as CGM or NAWM if they contained >60% of a given tissue type. From 3188 voxels acquired, 166 CGM and 694 NAWM voxels were extracted. In a given subject, average tissue contents and metabolite concentrations for a particular voxel type were estimated from the extracted voxels.
Lesion load determination
T1 hypo-intense, gadolinium enhancing and T2 lesion loads were determined using a previously described methodology based upon tools within Dispimage (Plummer, Department of Medical Physics and Bioengineering, University College London Hospitals NHS Trust, London, UK) (Plummer, 1992; Sailer et al., 1999).
Whole brain and tissue-specific fractional volume determination
Using the tissue masks derived from the 3D FSPGR for 1H-MRSI voxel tissue contents estimation, brain parenchymal (BP) and tissue specific volumes were estimated (Chard et al., 2002). Results were assessed as fractions of total intracranial (TI) volume as determined by adding the GM, NAWM, lesion and CSF volumes. Brain parenchymal (BP) volume was calculated as GM + NAWM + lesion volumes, and the brain parenchymal fraction (BPF) was calculated as BP volume/(TI volume). White matter fraction (WMF) was calculated as (NAWM + lesion volume)/(TI volume). The grey matter fraction (GMF) was calculated as the (GM volume)/(TI volume).
Clinical assessments
Multiple sclerosis subjects underwent examination to estimate their expanded disability status scale (EDSS) (Kurtzke, 1983), 25 foot timed walk test (TWT) (Cutter et al., 1999), nine-hole peg test (9HPT) (Goodkin et al., 1988) and paced auditory serial addition test (PASAT; 3 s stimulus interval) (Gronwell, 1977) scores. The average of two trials for the TWT and average of four trials of the 9HPT (averaged as reciprocals of the mean times from two trials for each hand) (Fischer et al., 1999) were calculated and, along with the PASAT and multiple sclerosis functional composite (MSFC) scores (calculated using the preferred method) (Fischer et al., 1999), were used in further analyses.
Statistical analyses
Statistical analyses were performed using SPSS 10.0 (SPSS Inc., Chicago, IL, USA). Multiple sclerosis disease effects on metabolite concentrations were estimated using multiple (linear) regression models, which included gender and disease status (multiple sclerosis or NC) as categorical variables, and age, WM contents of CGM voxels (and vice versa for NAWM voxels), voxel CSF contents and BP volume as continuous covariates. This was designed to allow for differences in GM and WM metabolite concentrations, the potential effects of age and gender, and both overt voxel CSF contamination and partial volume effects associated with differences in whole brain volumes.
The relationships between metabolite concentrations and lesion load measures, disease duration, fractional brain tissue measures (BPF, GMF and WMF) and clinical parameters (EDSS, MSFC, 9HPT, TWT and PASAT) were assessed using Spearman correlations.
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Subject demographics, clinical parameters and brain tissue volume measures
CGM data came from 24 of the multiple sclerosis subjects [mean age 35.2 years, median 34.3 years, range 24.1–48.4 years; 19 females and five males; mean disease duration (estimated from first symptom onset) 1.7 years, median 1.6 years, range 0.5–2.7 years] and 25 of the NC subjects (mean age 34.9 years, median 32.6 years, range 23.2–55.2 years; 13 females and 12 males). NAWM data came from 25 of the multiple sclerosis subjects (mean age 35.0 years, median 33.7 years, range 24.1–48.4 years; 19 females and six males; mean disease duration 1.7 years, median 1.6 years, range 0.5–2.7 years) and 28 of the NC subjects (mean age 36.7 years, median 33.4 years, range 23.2–55.2 years; 15 females and 13 males). Table 1 summarizes the values of the clinical parameters and lesion loads in the multiple sclerosis subjects who yielded usable NAWM voxels, while Table 2 summarizes the values of brain tissue volume measures in NC and multiple sclerosis subjects who produced usable NAWM voxels.
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Disease effects upon tissue metabolite concentrations
After allowing for age, gender, voxel tissue contents and brain tissue volumes, significant disease effects (at a P < 0.05 level) were found in the concentrations of CGM Cho, CGM tNAA, CGM Glx, NAWM tNAA and NAWM Ins (Tables 3 and 4).
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Relationships between tissue metabolite concentrations and both lesion load and clinical parameters
Spearman correlations between metabolite concentrations and other MRI parameters were significant for T2 lesion load and NAWM Ins (rs = 0.435, P = 0.030), but not for T1 hypo-intense or gadolinium-enhancing lesion loads, or fractional brain tissue volumes. Disease duration did not correlate significantly with any metabolite concentrations.
Correlations between metabolite concentrations and measures of disability were significant for CGM Glx with EDSS (rs = –0.431, P = 0.035), MSFC (rs = 0.580, P = 0.003), 9HPT (rs = –0.424, P = 0.039) and PASAT (rs = 0.541, P = 0.006); NAWM Ins with MSFC (rs = –0.559, P = 0.004), 9HPT (rs = 0.612, P = 0.001), TWT (rs = 0.454, P = 0.023) and PASAT (rs = –0.413, P = 0.040); and CGM Cr (noting that a significant overall disease effect was not observed) with MSFC (rs = 0.524, P = 0.009) and 9HPT (rs = –0.536, P = 0.007). Figure 2A and B show the relationship between MSFC and both CGM Glx and NAWM Ins, respectively. In those subjects who contributed NAWM voxel data, lesion load measures and disease duration did not correlate significantly with clinical impairment. In these subjects, 9HPT scores correlated modestly with WMF (rs = –0.403, P = 0.046), but otherwise fractional tissue volumes did not correlate significantly with clinical impairment.
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Discussion |
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These results show that in multiple sclerosis compared with NC subjects, tNAA is reduced in both CGM and NAWM, that Ins is elevated in NAWM, and that Cho and Glx are both reduced in CGM. They were obtained allowing for age and gender effects, voxel tissue contents and potential partial volume effects related to brain tissue volumes. Before considering these results further we will discuss some factors that need to be recalled when reviewing them, and some potential pathological interpretations of changes in metabolite concentrations.
Methodological and analytical considerations
Given the volume of 1H-MRSI voxels utilized in this study (2.3 ml), it was not possible to select pure CGM or NAWM voxels. Instead we elected to use selection criteria that would yield voxels with minimal lesion contamination (< 1%); limited CSF and non-brain tissue contents (GM plus NAWM >80%); and approximately equal proportions of a given tissue type (excluding CSF) to a given voxel type (
79% for both CGM and NAWM voxels). Lesion contamination was predominantly an issue with NAWM voxels, whereas brain tissue content was a limiting factor when setting the criteria to yield CGM voxels due to the proximity of CGM to CSF spaces.
In order to assess intrinsic disease effects upon a given tissue type, we utilized multiple regression models. These allowed for voxel tissue contents and CSF contamination, variability in whole brain volume, age and gender. Previous work has shown tissue-specific (Doyle et al., 1995; Noworolski et al., 1999; McLean et al., 2000; Schuff et al., 2001) and regional (Pouwels and Frahm, 1998; Grachev and Apkarian, 2000; Schuff et al., 2001) differences in metabolite concentration, along with age and gender effects (Grachev and Apkarian, 2000; Leary et al., 2000; Harada et al., 2001; Schuff et al., 2001). The inclusion of these factors in the model was designed to ensure the robustness of our overall disease effect conclusions against confounding, rather than to offer further insight into the role these factors may have in determining metabolite concentrations. These should be explored again in future work.
There are no clearly established methods for constructing appropriate or optimal data models, and there are disadvantages to both under- and over-parameterization (Freund and Wilson, 1998). We decided to err on the side of caution and include all potential confounding factors for which measures were available (age, gender, 1H-MRSI tissue and CSF contents, and BP volume). The relationships between outcome variables and covariates were assumed to be linear and, while this appeared adequate for the present data, this may not be optimal and should be reconsidered in future work. Rather than try to include other disease related parameters such as lesion loads, disease duration and separate brain tissue volume measures in an all-encompassing but potentially markedly over-parameterized model, we decided to explore these separately with Spearman correlations. When considering the results, it should also be recalled that multiple comparisons have been made, and as such some of the results considered significant at a level of P < 0.05 may be due to chance alone.
We have previously detected a small amount of brain atrophy in this clinically early disease cohort (Chard et al., 2002) and the question arises of whether the tissue metabolite changes seen, particularly in CGM, reflect partial volume effects or not. We consider this unlikely for two reasons. First, the previously reported magnitude of the reduction in the GMF (2.8%) is less than those observed for the significant metabolite decreases in CGM (approximately 6.6–15.1%). Secondly, our statistical models allowed for 1H-MRSI voxel tissue contents and whole brain volume, yet still identified significant changes in tissue metabolite concentrations.
Metabolite localization and function
Of the metabolites assessed in this study, tNAA and Ins appear to be relatively well localized to specific cell types. The former is predominantly found in neurones (Simmons et al., 1991) although it may also be present in mature oligodendrocytes (Bhakoo and Pearce, 2000): the latter is relatively concentrated in glial cells when compared with neurones (Glanville et al., 1989; Brand et al., 1993). Metabolic dysfunction may transiently affect the concentrations of these metabolites, and this has been highlighted with regard to NAA (Narayana et al., 1998; Mader et al., 2000; Demougeot et al., 2001). Having noted this, NAA has been confirmed as a marker of axonal density in multiple sclerosis lesions (Bitsch et al., 1999), mature WM tracts (Bjartmar et al., 2002) and the spinal cord (Bjartmar et al., 2000). Cr, Cho and Glx offer less cell-specific information. Observable Cho may relate to membrane turnover (Bluml et al., 1999; Boulanger et al., 2000), while both Cr and Glx appear to be linked to energy flux (Rothman et al., 1999; Wyss and Kaddurah-Daouk, 2000), although how this is reflected in overall tissue concentrations is less clear. Elevations in both Cho and Ins concentrations have been linked to glial proliferation in multiple sclerosis lesions (Bitsch et al., 1999).
Cortical grey matter observations
In CGM, Cho, tNAA and Glx were all reduced in multiple sclerosis compared with NC subjects, and this was unrelated to lesion load measures. The marked reduction in Cho may indicate both reduced cellular density and metabolic activity. The absence of a significant decrease in Ins would suggest that glial loss is not a major contributing factor, assuming that intracellular Ins concentrations remain relatively unchanged in multiple sclerosis. Reductions in tNAA point to more specific neuronal involvement, which may be related to cell loss or metabolic dysfunction or both. Reductions in Glx could mark metabolic dysfunction and loss of neurones and glial cells; once again, the lack of a concurrent significant decrease in Ins favours interpretation of the present results as representing neuronal metabolic dysfunction or loss or both.
Other proton magnetic resonance GM studies in multiple sclerosis have, to date, been limited (Kapeller et al., 2001; Sharma et al., 2001), and have not consistently shown reductions in NAA. Histopathology studies have found cortical lesions (Brownell and Hughes, 1962; Kidd et al., 1999; Bo et al., 2000), with neuronal involvement including axonal and dendritic transection, and neuronal apoptosis (Peterson et al., 2001). Our present observations appear consistent with these findings.
Normal-appearing white matter observations
Metabolite changes in NAWM were more limited than for CGM. NAWM Ins was significantly elevated and was modestly related to T2 lesion loads. This may indicate glial proliferation in NAWM and, given that T2 lesions are to a significant degree associated with previous focal inflammation (Miller et al., 1988; Katz et al., 1993; Lai et al., 1996; Bruck et al., 1997; Ciccarelli et al., 1999), this would also suggest that focal inflammatory activity is related to a more widespread WM process. The reduction in tNAA in NAWM is compatible with axonal metabolic dysfunction or loss or both.
Pathology studies have shown both gliosis (Allen and McKeown, 1979) and axonal loss (Evangelou et al., 2000; Bjartmar et al., 2001) in NAWM. Proton spectroscopy studies of NAWM have found reductions in absolute NAA concentrations (Davie et al., 1997; van Walderveen et al., 1999; Suhy et al., 2000; Kapeller et al., 2001) and relative (to Cr) (Fu et al., 1998; Leary et al., 1999; Tourbah et al., 1999), although in the case of ratio analyses this may in part be due to increased Cr concentrations (Rooney et al., 1997; Suhy et al., 2000). Ins concentrations have also been found to be absolutely (Kapeller et al., 2001) and relatively (to Cr) (Tourbah et al., 1999) elevated. Our present findings appear consistent with previous studies and mirror known histopathology.
Clinical outcome and MRI parameters
CGM Glx and NAWM Ins concentrations correlated most consistently with clinical outcome in this clinically early cohort, while tNAA did not correlate with any outcome measure. An overall disease effect on CGM Cr was not found to be significant and caution is needed when interpreting its relationship with clinical parameters. Taken together, the present findings could be interpreted as showing a relationship between neuronal metabolic dysfunction and clinical impairment, hinting that the former may be an important factor in determining clinical status at an early stage in the clinical evolution of relapsing–remitting multiple sclerosis. This may not be the case in the long-term: absolute axonal and neuronal loss may be more important as the disease progresses. This may, in part, explain the discrepancy between the present results and those from some previous studies that have investigated cohorts with longer disease durations and shown a correlation between disability and reductions in NAA:Cr ratios (Fu et al., 1996, 1998; De Stefano et al., 1998, 2001).
The relationship between clinical impairment and NAWM Ins implies that glial proliferation is associated with a negative effect on clinical function. While NAWM Ins concentrations appear to be modestly related to T2 lesions (rs = 0.435, P = 0.030), only 19% (rs2 = 0.189) of variability can be accounted for by this link, suggesting that glial proliferation is to a large extent independent of focal lesion genesis. Furthermore, clinical status was not related to T2 lesion load, and this would support the concept that glial proliferation intrinsically, or another pathological process leading to it, has a significant role to play in determining impairment.
Study limitations
In addition to those considerations mentioned at the start of this discussion, there are a number of other limitations that should be noted when comparing results from this present work with others. While the metabolite quantification technique employed yields values in millimoles per litre, it is not possible to calibrate these against actual in vivo values. Thus, different metabolite quantification techniques may produce different values and so a direct comparison cannot be made. Furthermore, imperfect 1H-MRSI slice excitation profiles will also influence estimates of metabolite concentrations, although we would anticipate that this would affect multiple sclerosis and NC subjects similarly. Brain tissue volume quantifications also cannot be considered entirely accurate, with scan acquisition and processing methodologies all affecting the apparent tissue volumes, with no definitive gold standard in vivo results to calibrate these against. Further, the estimated magnitude of disease effects will, to a degree, be dependent upon the statistical model employed and this must be remembered when looking at the values given in Table 4.
Returning to tissue segmentation, another consideration is lesion identification in GM and WM. Apparent WM lesion volumes are dependent upon scan acquisition and segmentation parameters (Molyneux et al., 1998; Rovaris et al., 1999). The 3D FSPGR scan used for tissue segmentation yields lesion load values that are very similar to those obtained from two-dimensional T2 images (Chard et al., 2002) (the current gold standard for identifying focal WM lesions), but this may still not completely mark all focal tissue damage.
As noted above, cortical lesions are found in multiple sclerosis. Such lesions are visible on MRI scans (Boggild et al., 1996; Rovaris et al., 2000; Bakshi et al., 2001), although this is hampered by the structural complexity of GM and the relatively similar MRI signal intensity characteristics of GM and lesions on some sequences. Given this, tissue classified as CGM in the present study is likely to contain some focal lesions and it is not possible to determine whether the observed CGM metabolite changes reflect the effects of lesions or more diffuse abnormality. The lack of increases in CGM Ins contrasts with observations made on focal WM lesions (Koopmans et al., 1993; Davie et al., 1994; De Stefano et al., 1995; Brex et al., 2000; Kapeller et al., 2001) and may reflect relatively less glial proliferation in GM lesions when compared with those found in WM.
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Conclusions |
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This study provides evidence for neuronal and axonal metabolic dysfunction or loss or both in CGM and NAWM, and glial proliferation in NAWM, early in the clinical course of multiple sclerosis. The results suggest that metabolic dysfunction, particularly evident in CGM, may be more closely related to clinical impairment in the early stages of multiple sclerosis than absolute neuronal and axonal loss. Further work is required to clarify the metabolic and structural contributions towards disability throughout the clinical course of multiple sclerosis.
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Acknowledgements |
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The authors wish to thank the subjects who kindly agreed to take part in this study and Daniel Altmann for statistical advice. The Multiple Sclerosis Society of Great Britain and Northern Ireland supported the NMR Research Unit with a programme grant, Schering AG sponsored D.C., and the Medical Research Council sponsored M.M.
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