MRI-based diagnostic biomarkers for early onset pediatric multiple sclerosis

• Main Authors: Martin Weygandt, Hannah-Maria Hummel, Katharina Schregel, Kerstin Ritter, Carsten Allefeld, Esther Dommes, Peter Huppke, John­Dylan Haynes, Jens Wuerfel, Jutta Gärtner
• Format: Article
• Language: English
• Published: Elsevier 2015-01-01
• Series: NeuroImage: Clinical
• Subjects: Pediatric multiple sclerosis; Early onset pediatric multiple sclerosis; Biomarkers; Diagnostic information
• Online Access: http://www.sciencedirect.com/science/article/pii/S221315821400093X

Currently, it is unclear whether pediatric multiple sclerosis (PMS) is a pathoetiologically homogeneous disease phenotype due to clinical and epidemiological differences between early and late onset PMS (EOPMS and LOPMS). Consequently, the question was raised whether diagnostic guidelines need to be complemented by specific EOPMS markers. To search for such markers, we analyzed cerebral MRI images acquired with standard protocols using computer-based classification techniques. Specifically, we applied classification algorithms to gray (GM) and white matter (WM) tissue probability parameters of small brain regions derived from T2-weighted MRI images of EOPMS patients (onset <12 years), LOPMS patients (onset ≥12 years), and healthy controls (HC). This was done for PMS subgroups matched for disease duration and participant age independently. As expected, maximal diagnostic information for distinguishing PMS patients and HC was found in a periventricular WM area containing lesions (87.1% accuracy, p < 2.2 × 10−5). MRI-based biomarkers specific for EOPMS were identified in prefrontal cortex. Specifically, a coordinate in middle frontal gyrus contained maximal diagnostic information (77.3%, p = 1.8 × 10−4). Taken together, we were able to identify biomarkers reflecting pathognomonic processes specific for MS patients with very early onset. Especially GM involvement in the separation between PMS subgroups suggests that conventional MRI contains a richer set of diagnostically informative features than previously assumed.