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Magnetization transfer MRI metrics predict the accumulation of disability 8 years later in patients with multiple sclerosis

Federica Agosta, Marco Rovaris, Elisabetta Pagani, Maria Pia Sormani, Giancarlo Comi, Massimo Filippi
DOI: http://dx.doi.org/10.1093/brain/awl208 2620-2627 First published online: 2 September 2006


In multiple sclerosis, the relationship between conventional MRI findings and the clinical evolution of the disease is weak. Magnetization transfer (MT) MRI can provide markers reflecting the more disabling features of multiple sclerosis pathology. The aim of the present study was to assess the value of MT MRI quantities and their short-term changes in predicting the long-term accumulation of disability in multiple sclerosis patients. Conventional and MT MRI scans of the brain were obtained at baseline and after 12 months in 73 patients, who were followed prospectively with clinical visits for a median period of 8 years. At baseline and at 12 months, T2-hyperintense and T1-hypointense lesion volume, normalized brain volume [with grey (GM) and white matter (WM) fractions] and average lesion MT ratio (MTR) were measured. At the two time points, metrics derived from the MTR histograms of the whole-brain parenchyma, GM and normal-appearing WM were also computed. A multivariate analysis, adjusted for follow-up duration, was performed to establish which variables were significant predictors of long-term neurological deterioration. At the end of follow-up, 44 patients (60%) showed a significant disability worsening. A multivariable model included baseline GM MTR histogram peak height [P = 0.029, odds ratio (OR) = 0.97], and average lesion MTR percentage change after 12 months (P = 0.016, OR = 0.88) as independent predictors of disability worsening at 8 years (r2 = 0.28). The discriminating ability of such a model in predicting the individual patients' outcome was 66%. MT MRI provides useful prognostic markers for the prediction of the long-term evolution of multiple sclerosis. This study also suggests that GM damage is one of the key factors associated with disability accumulation in this ‘white matter’ condition.

  • disability
  • grey matter damage
  • multiple sclerosis
  • magnetic resonance imaging
  • magnetization transfer MRI


The sensitivity of conventional MRI (cMRI) for the detection of focal white matter (WM) lesions has improved our ability to diagnose (Polman et al., 2005) and monitor (Miller, 1996; Rovaris and Filippi, 1999) multiple sclerosis. The extent of cMRI abnormalities in patients at presentation with clinically isolated syndrome (CIS) suggestive of the illness is a robust predictor of the occurrence of subsequent relapses and, albeit at a lesser magnitude, also accounts for the long-term accumulation of disability (Miller et al., 2005a, b). Unfortunately, this is not the case in a more advanced phase of the disease, where the strength of the relationship between cMRI findings and the subsequent clinical manifestations remains modest at best (Filippi et al., 1995; Kappos et al., 1999; Molyneux et al., 2001; Rovaris et al., 2003). This is, at least partially, the result of the limited specificity of cMRI to the various features (i.e. oedema, demyelination, remyelination, gliosis and axonal loss) of multiple sclerosis pathology, which can in turn be associated with very different clinical outcomes, and of its inability to detect and quantify the ‘occult’ damage known to occur in the normal-appearing brain tissues (Filippi and Grossman, 2002).

In an attempt to overcome these limitations and to define new magnetic resonance (MR) markers more closely linked with the most disabling pathological features of multiple sclerosis (i.e. irreversible demyelination and neuroaxonal injury), magnetization transfer (MT) MRI has recently received considerable attention (Filippi and Rocca, 2004a). MT MRI is based on the exchange of magnetization between the protons bound to macromolecules and those in free water (Wolff and Balaban, 1994) and allows the calculation of the MT ratio (MTR), which reflects the efficiency of such an exchange. Post-mortem studies have shown unambiguously that MTR is strongly associated with the percentage of residual axons and the degree of demyelination of T2-visible lesions and normal-appearing white matter (NAWM) of multiple sclerosis (van Waesberghe et al., 1999; Schmierer et al., 2004). MTR histogram-derived measures were found to correlate moderately to strongly with physical disability (Filippi et al., 1999a; Kalkers et al., 2001) and cognitive impairment (Rovaris et al., 1998; van Buchem et al., 1998) in cross-sectional studies, and to worsen over time in all major multiple sclerosis phenotypes (Filippi et al., 2000; Inglese et al., 2003). In addition, two studies have found that MT MRI markers can contribute to the prediction of the disease evolution in the subsequent 4 to 5 years (Santos et al., 2002; Rovaris et al., 2003).

Against this background, we present an extended clinical follow-up of the cohort of patients participating in a longitudinal MT MRI study (Rovaris et al., 2003) and re-analysed the imaging data set with the aim to ascertain whether the severity of damage of various brain tissue categories [T2-visible lesions, whole-brain parenchyma, NAWM, grey matter (GM)] in patients with multiple sclerosis can contribute to the prediction of the long-term clinical evolution.

Material and methods


Patients included had either suffered from definite multiple sclerosis (Poser et al., 1983) for at least 2 years, with a relapsing–remitting (RR) or secondary progressive (SP) disease course (Lublin and Reingold, 1996), or from CIS suggestive of the disease, with the first clinical attack in the preceding 3 months and paraclinical evidence of spatial disease dissemination (Polman et al., 2005). All patients are participating in a prospective, long-term follow-up study. Sex- and age-matched controls with no previous history of neurological diseases and with a normal neurological examination were also studied. A comprehensive description of inclusion/exclusion criteria and study design are provided elsewhere (Rovaris et al., 2003).

At the time MRI scans were performed, patients were assessed neurologically by a single physician, unaware of the MRI results, and disability was measured using the Expanded Disability Status Scale (EDSS) (Kurtzke, 1983). Neurological assessment and EDSS rating were repeated after a median follow-up period of 8 years (mean = 7.7 years). For each patient, neurological examination and EDSS rating were performed by the same observer at baseline and follow-up visits. At follow-up evaluation, patients were considered clinically worsened if they had an EDSS score increase ≥1.0, when baseline EDSS was <6.0, or an EDSS score increase ≥0.5, when baseline EDSS was ≥6.0. EDSS changes had always to be confirmed by a second visit after a 3-month, relapse-free period. Local Ethical Committee approval and written informed consent from all the subjects were obtained before study initiation.

Image acquisition

Brain MRI scans were obtained at study entry and after 12 months (±10 days), using the same 1.5 T scanner. During each session, the following scans were performed without moving the patient from the gantry: (i) dual-echo conventional spin echo (CSE); (ii) T1-weighted CSE; and (iii) 2D gradient echo with and without a saturation pulse for MT MRI. Additional details about image acquisition are provided elsewhere (Rovaris et al., 2003).

Image review and analysis

Multiple sclerosis lesions were first identified by agreement by two experienced observers on the hard copies of proton density (PD) weighted and T1-weighted scans. T2-weighted scans were always used to increase confidence in lesion identification. Total lesion volume (LV) measurements were then performed using a semi-automated, computer-assisted technique (Rovaris et al., 1997). Additional details on lesion identification and LV quantification are reported elsewhere (Rovaris et al., 2003). Using SPM2 and maximum image in-homogeneity correction (Ashburner et al., 1997) to segment PD- and T2-weighted images, we obtained three maps representing fractions of GM, WM and CSF in each voxel. Since T2-visible lesions can be misclassified, these were all assigned to the WM map with probability one and removed from the other two maps, before the computation of the corresponding tissue volumes. Then, brain parenchymal fraction (BPF), GM fraction (GMF) and WM fraction (WMF) were calculated by dividing each tissue volume by overall intracranial volume (i.e. WM + GM + CSF).

MTR maps were derived pixel-by-pixel as described elsewhere (Cercignani et al., 2001). Extracerebral tissue was removed from MTR maps using a local thresholding segmentation technique, and the resulting images were co-registered with the PD-weighted images. On the MTR maps, MTR values were calculated in regions of interest corresponding to hyperintense lesions on PD-weighted images. Average lesion MTR was calculated as described elsewhere (Filippi et al., 1999b). In order to generate mutually exclusive masks for GM, WM and CSF and minimize partial volume effects, the maps obtained from the SPM2 segmentation were thresholded to a value >0.75 (Fernando et al., 2005). The resulting masks were superimposed onto the MTR maps (on which hyperintense lesions were masked out previously), and the corresponding normalized MTR histograms of the whole-brain parenchyma, NAWM and GM were produced. For each histogram, the average MTR, the peak height and the peak location were measured. Given the strong correlation existing between average MTR and MTR histogram peak location (Filippi et al., 1999b), the latter quantity was a priori not considered for this study, in order to minimize the number of comparisons and, therefore, reduce the risk of type I errors.

Statistical analysis

The baseline and 12-month follow-up values of conventional and MT MRI-derived metrics were compared using a Student's t-test for paired data. To compare the 12-month changes of conventional and MT MRI-derived measures between groups, a one-way analysis of variance (ANOVA) model was used and two comparisons were decided a priori (a priori contrasts): CIS versus RR and RR versus SP. The number of a priori contrasts was determined by the available degrees of freedom and their nature was decided on the basis of clinical relevance. A univariate logistic regression model adjusted for follow-up duration was used to investigate the role of clinical and MRI-derived variables as independent predictors of the probability to have an EDSS deterioration at final follow-up. To account for a potential effect of the disease stage on the predictive value of clinical and MRI-derived variables, the univariate logistic regression model was also run after adjusting for patients’ clinical phenotype (CIS + RR versus SP). Those variables with a P-value < 0.20 entered a multivariate analysis where the presence or absence of EDSS deterioration was the dependent variable. The discriminating ability of the final multivariable model was tested with the leave-one-out cross-validation method. With this method and for each observation, the final model is refit after leaving that observation out of the data set, and the predicted value for that observation is then computed. This procedure is run n times (n = sample size) and the discriminating ability of the final model is estimated as the proportion of patients whose clinical evolution is correctly predicted. A univariate analysis, adjusted for the actual follow-up duration, was also carried out with the evolution to a more severe stage of the disease (i.e. from CIS/RR to SP) as the dependent variable.


Seventy-three patients and 16 healthy controls were studied (Rovaris et al., 2003). Six patients (1 CIS, 3 RR and 2 SP) of the original patients' cohort (Rovaris et al., 2003) did not undergo the present long-term follow-up visit, because of unwillingness (three patients) or inability (three patients) to attend (these patients, however, were not considered as lost at follow-up since they left open the possibility to come back at the subsequent, pre-planned 10-year visit). For all these patients, EDSS scores rated at the medium-term visit (Rovaris et al., 2003) entered the present analysis, which was adjusted for the actual follow-up duration. In two other SP multiple sclerosis patients, EDSS score at long-term follow-up was assessed by telephone (Lechner-Scott et al., 2003). Whereas tissue-type segmentation worked well on all images obtained from patients, this was not the case for three controls. As a consequence, 13 healthy volunteers constituted the control group for the present analysis. At study entry, the clinical disease category was CIS in 20, RR multiple sclerosis in 34 and SP multiple sclerosis in 19 patients. The demographic and clinical characteristics for the three patient subgroups at baseline are reported elsewhere (Rovaris et al., 2003). In the whole cohort, median EDSS scores were 2.5 (range = 0.0–6.5) at baseline and 3.5 (range = 0.0–9.5) at 8-year follow-up. During the follow-up period, 12 CIS patients developed clinically definite multiple sclerosis (11 with an RR course and 1 with an RR course subsequently entering the SP phase) and 8 RR patients entered an SP course. At final assessment, 44 patients (11 CIS, 19 RR and 14 SP) were considered clinically worsened.

No macroscopic abnormalities were ever detected on dual-echo brain MRI scans from healthy subjects. In controls, the mean values [standard deviation (SD)] of BPF, GMF and WMF at baseline were 80.5% (2.1%), 46.8% (2.0%) and 34.0% (1.6%). None of these values significantly changed at 12-month follow-up (data not shown). cMRI findings of patients at baseline and at 12-month follow-up are reported elsewhere (Rovaris et al., 2003). In Table 1, volumetry data of patients at study entry and 12-month follow-up are shown. In the whole cohort, only GMF decreased significantly, but such 12-month GMF decrease did not differ when CIS were contrasted with RR patients or when RR were contrasted with SP patients (P-values: 0.18 and 0.58).

View this table:
Table 1

Brain atrophy measurements in healthy controls and patients at study entry and after a 12-month follow-up

VariablesHealthy controlsAll patientsCISRRMSSPMS
BPF—baseline [%]
    Mean (SD)80.7 (1.8)80.4 (3.3)82.7 (1.5)78.8 (3.4)80.7 (3.1)
BPF—FU [%]
    Mean (SD)80.6 (1.8)80.0 (3.4)82.5 (1.5)78.2 (3.9)80.1 (1.8)
GMF—baseline [%]
    Mean (SD)46.3 (2.4)46.2 (3.1)47.4 (2.4)46.6 (3.0)44.4 (3.3)
GMF—FU [%]
    Mean (SD)46.6 (2.6)45.5 (3.4)47.3 (1.8)45.5 (3.9)43.6 (2.9)
WMF—baseline [%]
    Mean (SD)34.6 (1.4)34.2 (2.8)35.3 (1.9)32.1 (2.3)36.3 (2.2)
WMF—FU [%]
    Mean (SD)34.0 (1.3)34.4 (2.8)35.2 (1.5)32.7 (2.6)36.5 (2.5)
  • BPF = brain parenchymal fraction; CIS = clinically isolated syndrome; FU = 12-month follow-up; GMF = grey matter fraction; RRMS = relapsing–remitting multiple sclerosis; SD = standard deviation; SPMS = secondary progressive multiple sclerosis; WMF = white matter fraction.

  • *Pairwise comparisons versus baseline values (see the text for details and statistical analysis).

In healthy controls, the mean values (SD) of average MTR and histogram peak height at baseline were 48.1% (1.6%) and 11.2% (1.1%) for GM, 50.6% (1.7%) and 15.3% (2.3%) for NAWM. No significant differences between baseline and 12-month follow-up values were found for any of these metrics (data not shown). Table 2 reports the mean values and ranges of NAWM and GM MTR histogram-derived quantities of patients at study entry and 12-month follow-up [whole-brain MTR histogram and average lesion MTR data are reported elsewhere (Rovaris et al., 2003)]. The values of all MTR histogram-derived quantities were lower in multiple sclerosis patients than in healthy controls (P-values ranged from 0.01 to <0.001), both at baseline and 12-month follow-up. There were greater 12-month decreases of average GM and NAWM MTR in CIS than in RR patients (P-values: 0.009 and 0.01), as well as greater 12-month decreases of average GM/NAWM MTR and GM histogram peak height in SP than in RR patients (P < 0.001). At baseline, average lesion, GM and NAWM MTR values were correlated with T2 LV (r-values ranged from −0.25 to −0.43; P-values ranged from 0.035 to <0.001) and brain tissue volumes (r-values ranged from 0.24 to 0.47; P-values ranged from 0.05 to <0.001).

View this table:
Table 2

NAWM and GM MT MRI findings in 73 patients at study entry and after a 12-month follow-up

VariablesHealthy controlsAll patientsCISRRMSSPMS
Average GM MTR—baseline [%]
    Mean (SD)48.4 (0.8)43.9 (3.2)45.8 (1.6)42.8 (3.2)43.9 (3.7)
Average GM MTR—FU [%]
    Mean (SD)49.0 (6.2)42.1 (3.0)43.7 (1.7)42.5 (3.2)39.7 (2.4)
GM histogram peak height [%]—baseline
    Mean (SD)11.4 (1.4)8.2 (2.1)9.8 (1.4)7.6 (1.9)7.5 (2.3)
GM histogram peak height [%]—FU
    Mean (SD)11.2 (1.1)7.9 (2.0)9.6 (1.2)7.8 (1.9)6.4 (1.6)
Average NAWM MTR—baseline [%]
    Mean (SD)50.8 (0.8)49.2 (1.9)49.9 (1.6)48.2 (2.0)49.9 (1.8)
Average NAWM MTR—FU [%]
    Mean (SD)50.4 (1.1)47.2 (1.5)47.7 (1.3)49.1 (19.5)48.9 (9.8)
NAWM histogram peak height [%]—baseline
    Mean (SD)14.9 (2.9)13.8 (2.4)14.7 (1.9)13.1 (2.8)14.1 (1.9)
NAWM histogram peak height [%]—FU
    Mean (SD)15.3 (2.3)13.6 (2.6)14.6 (2.0)12.6 (2.9)14.1 (2.0)
  • CIS = clinically isolated syndrome; FU = 12-month follow-up; GM = grey matter; MTR = magnetization transfer ratio; NAWM = normal-appearing white matter; RRMS = relapsing–remitting multiple sclerosis; SD = standard deviation; SPMS = secondary progressive multiple sclerosis.

  • *Pairwise comparisons versus baseline values (see the text for details and statistical analysis).

Table 3 reports the results of the univariate logistic regression analysis, where EDSS deterioration at final follow-up was the dependent variable. Disease duration, baseline EDSS, baseline T2 LV, baseline GMF, baseline average GM MTR and histogram peak height, as well as the 12-month percentage change of average whole brain and lesion MTR, were all associated with the long-term worsening of disability. After adjusting for multiple sclerosis clinical phenotype, all these variables remained significantly associated with the long-term EDSS worsening (data not shown). The final multivariable model retained baseline GM histogram peak height [P = 0.029, odds ratio (OR) = 0.97, 95% confidence interval (CI) = 0.94–0.99], and average lesion MTR percentage change after 12 months (P = 0.016, OR = 0.88, 95% CI = 0.80–0.98) as independent predictors of EDSS worsening (Nagelkerke r2 = 0.28). The discriminating ability of this model, adjusted for follow-up duration, was 66% (16 out of 29 patients were correctly classified as clinically stable and 30 out of 41 as clinically worsened at final follow-up). Higher EDSS scores at baseline and greater 1-year percentage decreases of average lesion MTR were associated with an increased probability of evolution from the RR to the SP stage of multiple sclerosis (OR: 7.32 and 0.73, P-values: 0.006 and 0.009).

View this table:
Table 3

Univariate logistic regression analysis of the predictive value of clinical and MRI-derived quantities for patients' EDSS worsening at long-term follow-up (dependent variable)

Independent variablesOdd Ratios (95% CI)P-values
Patients' age1.03 (0.98–1.09)0.25
Disease duration*1.08 (0.98–1.19)0.11
MS clinical phenotype (CIS + RRMS versus SPMS)1.93 (0.58–6.48)0.29
Baseline EDSS*1.14 (1.01–1.30)0.048
Baseline T2 LV*1.06 (1.01–1.12)0.03
Baseline T1 LV1.08 (0.93–1.26)0.31
T2 LV percentage change1.21 (0.41–3.51)0.73
T1 LV percentage change0.98 (0.87–1.09)0.70
Baseline BPF0.93 (0.73–1.10)0.40
Baseline GMF*0.84 (0.71–1.003)0.054
Baseline WMF1.11 (0.93–1.33)0.23
Baseline average whole-brain MTR1.00 (0.98–1.03)0.90
Baseline average GM MTR*0.986 (0.969–1.003)0.10
Baseline average NAWM MTR1.003 (0.978–1.029)0.81
Baseline average lesion MTR1.00 (0.99–1.02)0.49
Baseline whole-brain MTR histogram peak height0.98 (0.96–1.01)0.23
Baseline GM histogram peak height*0.97 (0.95–0.99)0.029
Baseline NAWM histogram peak height0.99 (0.98–1.02)0.72
BPF percentage change0.99 (0.80–1.22)0.92
GMF percentage change0.99 (0.91–1.08)0.38
WMF percentage change1.01 (0.93–1.10)0.82
Average whole-brain MTR percentage change*0.91 (0.80–1.03)0.12
Average GM MTR percentage change0.99 (0.92–1.08)0.78
Average NAWM MTR percentage change0.96 (0.83–1.11)0.59
Average lesion MTR percentage change*0.88 (0.81–0.97)0.02
  • *Variables entering the multivariate analysis. Percentage changes were for 12-month follow-up versus baseline scans. CI = confidence intervals; EDSS = Expanded Disability Status Scale; LV = lesion volume. See Tables 1 and 2 for other abbreviations and the text for further details.

  • P-values in bold are those less than 0.20, thus those related to the variables entering the multivariate analysis.


Despite their sensitivity for multiple sclerosis lesion detection, cMRI-derived metrics do not allow the prediction of the subsequent evolution of the disease reliably, especially when it comes to the long-term accumulation of disability (Filippi and Grossman, 2002). This is probably due to both the inherent weaknesses of the clinical scales used to rate disability (Willoughby and Paty, 1988) and the limitations of cMRI, which include the lack of pathological specificity to the various substrates of the pathology and, perhaps most importantly, its inability to detect ‘occult’ changes occurring in the normal-appearing brain tissues (Filippi and Grossman, 2002). These limitations can be overcome, at least partially, by non-cMRI techniques, such as MT MRI (Filippi and Rocca, 2004a). Against this background, we planned a long-term perspective study of a large cohort of patients (Rovaris et al., 2003) to ascertain whether a combination of clinical, conventional and MT MRI features may contribute to the identification of factors associated with an increased risk of a more severe clinical evolution. The present study reports interim findings after a median follow-up of 8 years and adds to the previous report from the same cohort (Rovaris et al., 2003) the analysis of MT MRI changes in NAWM and GM assessed separately. It is worth noting that this cohort of patients, which is representative of the whole spectrum of relapse-onset phenotypes, was studied with MT MRI soon after its availability to clinical research. This accounts for some technical limitations, including the partial brain coverage (i.e. 20 contiguous, 5-mm thick axial slices). At the same time, however, this study provides us the unique opportunity of investigating the potential impact of a multiparametric MR assessment on the prediction of the long-term evolution of multiple sclerosis.

Our findings confirmed that ‘cMRI-occult’ pathology is present in all multiple sclerosis phenotypes and in both NAWM and GM (Filippi et al., 2000; Tortorella et al., 2000; Bozzali et al., 2002; Rovaris et al., 2002; Santos et al., 2002; De Stefano et al., 2003; Inglese et al., 2003; Rovaris et al., 2003; Sailer et al., 2003; Chen et al., 2004; Oreja-Guevara et al., 2005; Rovaris et al., 2005; Tedeschi et al., 2005; Valsasina et al., 2005). This fits with post-mortem data showing astrocytic hyperplasia, patchy oedema, perivascular infiltration, myelin thinning and axonal loss in the NAWM (Allen and McKeown, 1979; Trapp et al., 1998), as well as diffuse demyelination (Kutzelnigg et al., 2005), axonal transection and apoptotic loss of neurons (Peterson et al., 2001) in the GM of patients. Among these pathological features, irreversible demyelination and neuroaxonal damage are likely to be the major contributors to the observed MTR decreases (van Waesberghe et al., 1999; Schmierer et al., 2004). The absence of relevant GMF and WMF decreases in patients when compared with controls, together with the normalization process preliminary to MTR histogram production, indicates that the observed MTR changes are most likely independent of partial volume effect from the CSF. Moreover, the absence of significant tissue volume decreases after 12 months in all patient subgroups, in contrast to the significant worsening of several MT MRI metrics, confirms that the measurement of end-stage phenomena like brain atrophy can, on the short-term, underestimate the actual progression of multiple sclerosis-related damage (Filippi and Grossman, 2002). Surprisingly, we found a higher value of WMF in SP multiple sclerosis than in RR patients. Given the high inter-individual variability of atrophy measures, our relatively small cohort of RR patients might be biased for behaving differently from other, larger ones (Tedeschi et al., 2005). Moreover, owing to the lack of 3D sequences tailored for volumetric measurements, our estimation of brain tissue fractions might be less reliable than those obtained using more sophisticated approaches (Miller et al., 2002). We also found that the severity of MTR changes within lesions, GM and NAWM were significantly, albeit weakly, correlated with brain tissue volumes and the burden of T2-visible lesions. This suggests that, at least in part, NAWM and GM changes can be secondary to Wallerian and retrograde degeneration of axons passing through macroscopic WM lesions and that not only the extent of such lesions but also subtle, cMRI-undetectable tissue damage can contribute to the development of brain atrophy (Tortorella et al., 2000). Interestingly, the rates of ‘occult’ NAWM and GM changes were found to be higher in both CIS and SP multiple sclerosis than in RR multiple sclerosis, thus suggesting the possibility of a ‘bimodal’ distribution of tissue damage accrual rate. Albeit this is only speculative and future confirmatory studies are needed, the observed variable rates of NAWM and GM MTR deterioration with an intermediate ‘resting’ period might reflect the fact that different damaging mechanisms are operative or not yet operative at different phases of multiple sclerosis (inflammatory demyelination in the earlier phase and neurodegeneration later on), thus reinforcing the notion of a ‘two-stage’ disease (Confavreux and Vukusic, 2006). Admittedly, this finding disagrees with those by Davies et al. (2005), who reported a significant decrease of both NAWM and GM MTR values in patients with ‘early’ RR multiple sclerosis after a 2-year follow-up, but it suggests that the rate of progression of tissue damage may not be a function of the disease phenotype, but rather of the duration of the disease process. The discrepancy between our findings and previous ones might also be due to the waxing and vaning nature of the inflammatory process in RR multiple sclerosis, which might have reduced our ability to identify a significant time trend for MT MRI changes, since we just had two observations taken 1 year apart.

The most interesting part of this study is that a 12-month multiparametric MRI follow-up can provide prognostic markers of the long-term clinical outcome of patients. Similarly to what was found for the medium-term follow-up analysis (Rovaris et al., 2003), the presence and duration of disease-modifying treatments did not affect the long-term disease evolution (data not shown). As extensively discussed elsewhere (Rovaris et al., 2003), this finding is likely to be the result of the absent or modest efficacy of immunomodulating treatments on the accumulation of multiple sclerosis-related disability (Frohman et al., 2005). Disease duration, baseline EDSS, T2 LV, GMF, average GM MTR and histogram peak height, as well as the 12-month percentage change of average lesion MTR, were all associated with the long-term worsening of disability at the univariate logistic regression analysis. However, the final multivariable model retained only GM MTR histogram peak height and average lesion MTR percentage change after 12 months as independent predictors of such an evolution. This model was able to explain ∼30% of the observed EDSS changes at follow-up, and ∼70% of patients were correctly classified as clinically stable or worsened when the discriminating ability of the model was tested in individual cases.

The potential prognostic value of MT MRI findings has been highlighted by two previous studies (Santos et al., 2002; Rovaris et al., 2003). The first one (Santos et al., 2002), conducted in a small sample of 18 multiple sclerosis patients with either RR or SP disease, found that more severe MTR abnormalities in the NAWM predicted a poorer clinical outcome after a 5-year follow-up. In the same patient cohort of the present study, we found that baseline T2-hyperintense LV and average overall brain MTR percentage change after 12 months were independent predictors of disability accumulation after 4.5 years (Rovaris et al., 2003). It is intriguing that, after a longer follow-up, the severity of GM damage and the short-term progression of multiple sclerosis pathology within chronic lesions came out as the strongest predictors of unfavourable disease evolution. Albeit at 4.5-year follow-up we did not assess GM and NAWM MTR changes separately, this again suggests that different factors (broadly speaking, ‘inflammatory demyelination’ versus ‘neurodegeneration’) have a different impact on disease evolution with increased disease duration. We also found that higher EDSS scores at baseline and greater 1-year percentage decreases of average lesion MTR were associated with the probability of evolving from the relapsing to the progressive stage. The latter findings, albeit derived from relatively small subcohorts of patients, may indicate that the degenerative processes taking place within chronic T2-visible lesions are among the main substrates of disability worsening in the later phases of the disease. If confirmed, these observations should lead to the design of specific MRI protocols to monitor multiple sclerosis evolution (either natural or in the course of clinical trials) rather than to a generic application of cMRI metrics in all possible contests.

In several cross-sectional studies, the severity of GM damage (De Stefano et al., 2003; Sailer et al., 2003; Chen et al., 2004; Tedeschi et al., 2005) has been identified as one of the main factors associated with the severity of physical disability or cognitive impairment. On the other hand, a previous 3-year, longitudinal MT MRI study highlighted that MTR changes in newly formed lesions were more pronounced in SP than in RR patients (Rocca et al., 1999). The differences between the former medium-term (Rovaris et al., 2003) and the present long-term prognostic model may therefore be consistent with the epidemiological notion that the longer disease duration is, the higher the probability of EDSS worsening due to secondary progression rather than to irreversible disability following multiple sclerosis relapses. In any case, the prognostic value of MT MRI calls for a more extensive use of this technique as an adjunctive paraclinical tool to monitor multiple sclerosis evolution, at least in the more advanced phase of the disease. The preliminary results obtained in two treatment trials (Inglese et al., 2003; Filippi et al., 2004) of SP multiple sclerosis support this conclusion.

Admittedly, the present predictive model based on MT MRI quantities is still not completely satisfactory, since ∼30% of the patients were wrongly classified as worsened/stable. A potential explanation for the suboptimal prognostic value of this model (but which applies to any model based on structural MRI findings) is the role that might be played by cortical reorganization in limiting the impact of multiple sclerosis injury on the severity of the clinical outcome in individual patients. This aspect of the disease, which has been shown by several functional MRI studies (Filippi and Rocca, 2004b), is likely to be important in terms of disability accrual, since the extent of cortical activations and the severity of structural subcortical changes were found to be strongly correlated (Filippi and Rocca, 2004b). The individual patients' ability to recruit functionally related cortical areas, in the presence of similar amounts of tissue damage, might, therefore, contribute to a different long-term clinical outcome. It is worthy to mention that the actual performance of the predictive variables we identified would also need to be properly validated in another patient cohort. However, albeit such a proper validation would represent the ideal approach, this is unlikely to occur, at least in the upcoming 5–10 years, when different and large enough cohorts followed up for long periods of time with non-cMRI techniques will be available. While our findings also call for additional studies to investigate whether other prognostic factors might be disclosed by larger sample sizes, additional paraclinical tools and longer follow-up periods, we believe that the present results are valuable per se in providing insights about the potential mechanisms underlying multiple sclerosis evolution at different disease stages.


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