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MRI-based diagnostic biomarkers for early onset pediatric multiple sclerosis.

Weygandt M, Hummel HM, Schregel K, Ritter K, Allefeld C, Dommes E, Huppke P, Haynes JD, Wuerfel J, Gärtner J - Neuroimage Clin (2014)

Bottom Line: 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)).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.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin, Charité - Universitätsmedizin, Berlin, Germany ; NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Germany.

ABSTRACT
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.

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Related in: MedlinePlus

Preprocessing of MRI data. Starting with the second segmentation run, preprocessing was performed separately for each of the pairs of groups. The figure shows two exemplary subjects belonging to the early onset PMS vs. later onset PMS pair. EOPMS, early onset pediatric MS; LOPMS, later onset pediatric MS; T2w, T2-weighted; z-Trsfo, z-transformation.
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fig1: Preprocessing of MRI data. Starting with the second segmentation run, preprocessing was performed separately for each of the pairs of groups. The figure shows two exemplary subjects belonging to the early onset PMS vs. later onset PMS pair. EOPMS, early onset pediatric MS; LOPMS, later onset pediatric MS; T2w, T2-weighted; z-Trsfo, z-transformation.

Mentions: In a final step, we computed a within-subject z-transformation of the masked, resampled, and modulated GM and WM tissue probability maps for each subject to account for potential between group differences of overall brain volume. The resulting z-transformed, masked, resampled and modulated tissue probability maps then entered the classification analyses (see below). Please see Fig. 1 for an overview on preprocessing steps conducted.


MRI-based diagnostic biomarkers for early onset pediatric multiple sclerosis.

Weygandt M, Hummel HM, Schregel K, Ritter K, Allefeld C, Dommes E, Huppke P, Haynes JD, Wuerfel J, Gärtner J - Neuroimage Clin (2014)

Preprocessing of MRI data. Starting with the second segmentation run, preprocessing was performed separately for each of the pairs of groups. The figure shows two exemplary subjects belonging to the early onset PMS vs. later onset PMS pair. EOPMS, early onset pediatric MS; LOPMS, later onset pediatric MS; T2w, T2-weighted; z-Trsfo, z-transformation.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4310929&req=5

fig1: Preprocessing of MRI data. Starting with the second segmentation run, preprocessing was performed separately for each of the pairs of groups. The figure shows two exemplary subjects belonging to the early onset PMS vs. later onset PMS pair. EOPMS, early onset pediatric MS; LOPMS, later onset pediatric MS; T2w, T2-weighted; z-Trsfo, z-transformation.
Mentions: In a final step, we computed a within-subject z-transformation of the masked, resampled, and modulated GM and WM tissue probability maps for each subject to account for potential between group differences of overall brain volume. The resulting z-transformed, masked, resampled and modulated tissue probability maps then entered the classification analyses (see below). Please see Fig. 1 for an overview on preprocessing steps conducted.

Bottom Line: 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)).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.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin, Charité - Universitätsmedizin, Berlin, Germany ; NeuroCure Clinical Research Center, Charité - Universitätsmedizin Berlin, Germany.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus