Advanced MRI unravels the nature of tissue alterations in early multiple sclerosis. (3rd June 2014) - Bonnier, Guillaume; Roche, Alexis; Romascano, David; Simioni, Samanta; Meskaldji, Djalel; Rotzinger, David; Lin, Ying‐Chia; Menegaz, Gloria; Schluep, Myriam; Du Pasquier, Renaud; Sumpf, Tilman Johannes; Frahm, Jens; et al.
Abstract Introduction In patients with multiple sclerosis (MS), conventional magnetic resonance imaging (MRI) provides only limited insights into the nature of brain damage with modest clinic‐radiological correlation. In this study, we applied recent advances in MRI techniques to study brain microstructural alterations in early relapsing‐remitting MS (RRMS) patients with minor deficits. Further, we investigated the potential use of advanced MRI to predict functional performances in these patients. Methods Brain relaxometry (T1, T2, T2*) and magnetization transfer MRI were performed at 3T in 36 RRMS patients and 18 healthy controls (HC). Multicontrast analysis was used to assess for microstructural alterations in normal‐appearing (NA) tissue and lesions. A generalized linear model was computed to predict clinical performance in patients using multicontrast MRI data, conventional MRI measures as well as demographic and behavioral data as covariates. Results Quantitative T2 and T2* relaxometry were significantly increased in temporal normal‐appearing white matter (NAWM) of patients compared to HC, indicating subtle microedema (P = 0.03 and 0.004). Furthermore, significant T1 and magnetization transfer ratio (MTR) variations in lesions (mean T1 z‐score: 4.42 and mean MTR z‐score: −4.09) suggested substantial tissue loss. Combinations of multicontrast and conventional MRI data significantly predicted cognitive fatigue (P = 0.01, Adj‐R2 = 0.4), attention (P = 0.0005, Adj‐R2 = 0.6), and disability (P = 0.03, Adj‐R2 = 0.4). Conclusion Advanced MRI techniques at 3T, unraveled the nature of brain tissue damage in early MS and substantially improved clinical–radiological correlations in patients with minor deficits, as compared to conventional measures of disease.