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MRI pattern recognition in multiple sclerosis normal-appearing brain areas.

Weygandt M, Hackmack K, Pfüller C, Bellmann-Strobl J, Paul F, Zipp F, Haynes JD - PLoS ONE (2011)

Bottom Line: We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas.This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes.Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin, Charité - University Medicine, Berlin, Germany. martin.weygandt@bccn-berlin.de

ABSTRACT

Objective: Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing-remitting type) in lesioned areas, areas of normal-appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques.

Methods: A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information.

Results: Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10(-13)). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10(-7)). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10(-10)).

Interpretation: We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.

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Related in: MedlinePlus

Overview of data processing.For details please see text and Material S1. MNI, Montreal Neurological Institute; NAGM, normal­appearing grey matter; NAWM, normal-appearing white matter; NABT, normal-appearing brain tissue.
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pone-0021138-g001: Overview of data processing.For details please see text and Material S1. MNI, Montreal Neurological Institute; NAGM, normal­appearing grey matter; NAWM, normal-appearing white matter; NABT, normal-appearing brain tissue.

Mentions: We continued by regressing out the variance contained in tissue intensities that could be explained by the local deformation parameters determined during spatial normalization. This step was performed to rule out that classification could rely on systematic intensity differences between groups induced by the spatial transformation (e.g. due to correction of thalamic atrophy in patients only). These data are referred to as ‘corrected data’. Then, we conducted between-subject z-transformation of the corrected and uncorrected data to account for intensity variations e.g. due to coil loadings. Finally, corrected and uncorrected data (voxel resolution: 2×2×2 mm) were restricted to the search space of each analysis defined by the group masks described above and entered the analyses. See Figure 1 and Material S1 for further details.


MRI pattern recognition in multiple sclerosis normal-appearing brain areas.

Weygandt M, Hackmack K, Pfüller C, Bellmann-Strobl J, Paul F, Zipp F, Haynes JD - PLoS ONE (2011)

Overview of data processing.For details please see text and Material S1. MNI, Montreal Neurological Institute; NAGM, normal­appearing grey matter; NAWM, normal-appearing white matter; NABT, normal-appearing brain tissue.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3117878&req=5

pone-0021138-g001: Overview of data processing.For details please see text and Material S1. MNI, Montreal Neurological Institute; NAGM, normal­appearing grey matter; NAWM, normal-appearing white matter; NABT, normal-appearing brain tissue.
Mentions: We continued by regressing out the variance contained in tissue intensities that could be explained by the local deformation parameters determined during spatial normalization. This step was performed to rule out that classification could rely on systematic intensity differences between groups induced by the spatial transformation (e.g. due to correction of thalamic atrophy in patients only). These data are referred to as ‘corrected data’. Then, we conducted between-subject z-transformation of the corrected and uncorrected data to account for intensity variations e.g. due to coil loadings. Finally, corrected and uncorrected data (voxel resolution: 2×2×2 mm) were restricted to the search space of each analysis defined by the group masks described above and entered the analyses. See Figure 1 and Material S1 for further details.

Bottom Line: We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas.This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes.Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.

View Article: PubMed Central - PubMed

Affiliation: Bernstein Center for Computational Neuroscience Berlin, Charité - University Medicine, Berlin, Germany. martin.weygandt@bccn-berlin.de

ABSTRACT

Objective: Here, we use pattern-classification to investigate diagnostic information for multiple sclerosis (MS; relapsing-remitting type) in lesioned areas, areas of normal-appearing grey matter (NAGM), and normal-appearing white matter (NAWM) as measured by standard MR techniques.

Methods: A lesion mapping was carried out by an experienced neurologist for Turbo Inversion Recovery Magnitude (TIRM) images of individual subjects. Combining this mapping with templates from a neuroanatomic atlas, the TIRM images were segmented into three areas of homogenous tissue types (Lesions, NAGM, and NAWM) after spatial standardization. For each area, a linear Support Vector Machine algorithm was used in multiple local classification analyses to determine the diagnostic accuracy in separating MS patients from healthy controls based on voxel tissue intensity patterns extracted from small spherical subregions of these larger areas. To control for covariates, we also excluded group-specific biases in deformation fields as a potential source of information.

Results: Among regions containing lesions a posterior parietal WM area was maximally informative about the clinical status (96% accuracy, p<10(-13)). Cerebellar regions were maximally informative among NAGM areas (84% accuracy, p<10(-7)). A posterior brain region was maximally informative among NAWM areas (91% accuracy, p<10(-10)).

Interpretation: We identified regions indicating MS in lesioned, but also NAGM, and NAWM areas. This complements the current perception that standard MR techniques mainly capture macroscopic tissue variations due to focal lesion processes. Compared to current diagnostic guidelines for MS that define areas of diagnostic information with moderate spatial specificity, we identified hotspots of MS associated tissue alterations with high specificity defined on a millimeter scale.

Show MeSH
Related in: MedlinePlus