Characterizing multiple sclerosis disease progression using a combined structural and functional connectivity metric

Purpose: A combined resting state functional connectivity MRI (fcMRI) and diffusion tensor imaging (DTI) metric called structural and functional connectivity index (SFCI) was recently proposed for tracking disease status and progression in multiple sclerosis (MS). The metric combines fcMRI and transverse diffusivity (TD) along different functional pathways involved in principle symptomatic domains of MS. In a longitudinal study of patients with MS receiving the same MS therapy, initial worsening of transcallosal (TC) motor and frontoparietal (FP) cognitive networks, as measured by fcMRI and DTI over the first year was followed by stabilization in the second year of follow-up. In this study we have (i) probed relationships between individual and composite neurological measures of MS with SFCI and its individual components along TC motor and FP cognitive pathways and (ii) compared sensitivity of SFCI to treatment-induced longitudinal changes with each individual imaging measure. Methods: Twenty five patients with MS (15 female, age 42 +/- 8 y) participated in this study and were scanned at 3 T whole body MRI scanner with diffusion tensor imaging (DTI) and resting-state functional connectivity MRI (fcMRI) scan protocol at baseline and 6, 12, 18 and 24 months after starting fingolimod. fcMRI and TD along TC and FP pathways were combined to form structural and functional connectivity index (SFCI) at each time point. Correlations between individual/combined neurological measures and individual imaging components/SFCI at baseline and were evaluated and compared. In addition, efficacies of individual and combined imaging metrics in tracking network integrity were compared. Results: Individual TD along the TC pathway was significantly inversely correlated with all individual/composite neurological scores. There were moderate correlations of TC and FP components of SFCI with most of the neurological scores, and the pathway-combined SFCI was significantly correlated with all neurological scores. Trend-level increases of both TC and FP fcMRI were observed during the second year of follow-up, both TC and FP TD increased significantly in the first year and then stabilized during the second year. A trend toward a decrease in combined imaging metrics along TC and FP were observed during the first year, followed by a trend toward an increase in these metrics during the second year, while a significant decrease in SFCI during the first year followed by a significant increase during the second year was observed. Conclusions: SFCI was more effective in tracking network integrity/disease progression than individual pathway-specific components, which supports its use as an imaging marker for MS disease status and progression.