Limits...
Analysis of ageing-associated grey matter volume in patients with multiple sclerosis shows excess atrophy in subcortical regions

View Article: PubMed Central - PubMed

ABSTRACT

Age of onset in multiple sclerosis (MS) exerts an influence on the course of disease. This study examined whether global and regional brain volumes differed between “younger” and “older” onset MS subjects who were matched for short disease duration, mean 1.9 years and burden as measured by the MS Severity Score and relapses.

21 younger-onset MS subjects (age 30.4 ± 3.2 years) were compared with 17 older-onset (age 48.7 ± 3.3 years) as well as age-matched controls (n = 31, 31.9 ± 3.5 years and n = 21, 47.3 ± 4.0 years). All subjects underwent 3D volumetric T1 and T2-FLAIR imaging. White matter (WM) and grey matter (GM) lesions were outlined manually. Lesions were filled prior to tissue and structural segmentation to reduce classification errors.

Volume loss versus control was predominantly in the subcortical GM, at > 13% loss. Younger and older-onset MS subjects had similar, strong excess loss in the putamen, thalamus, and nucleus accumbens. No excess loss was detected in the amygdala or pallidum. The hippocampus and caudate showed significant excess loss in the younger group (p < 0.001) and a strong trend in the older-onset group.

These results provide a potential imaging correlate of published neuropsychological studies that reported the association of younger age at disease onset with impaired cognitive performance, including decreased working memory.

No MeSH data available.


Image analysis workflow. The image analysis workflow for extracting measures of scaled tissue volumes from the input T1-weighted MPRAGE scans of MS patients (left) and healthy controls (right). Blue boxes signify 3D image volumes, whilst green boxes signify processing steps. The final numerical outputs are colored in purple.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC5121150&req=5

f0005: Image analysis workflow. The image analysis workflow for extracting measures of scaled tissue volumes from the input T1-weighted MPRAGE scans of MS patients (left) and healthy controls (right). Blue boxes signify 3D image volumes, whilst green boxes signify processing steps. The final numerical outputs are colored in purple.

Mentions: Images were processed following the analysis workflow depicted in Fig. 1. Each subject's T2-FLAIR volume was co-registered to their MPRAGE using the rigid-body transformation of FLIRT (Jenkinson et al., 2002). To improve the subsequent tissue segmentations (Popescu et al., 2012), the MNI152 template was aligned using the affine registration of FLIRT (Jenkinson et al., 2002) to the same MPRAGE. This transform was then applied to a rectangular mask covering the MNI152 template, and the transformed mask applied to the MPRAGE to remove excess neck that can corrupt brain extraction tools (Popescu et al., 2012). After masking, the anatomical scans were segmented using additional FSL tools: (i) SIENAX (Smith et al., 2002) for scaled tissue volumes of WM, GM, and cortical GM, normalized for subject head size; and (ii) FIRST (Patenaude et al., 2011) for subcortical grey matter volumes of the putamen, caudate, thalamus, hippocampus, amygdala, accumbens, and pallidum. Subcortical GM structures were multiplied by the SIENAX volume-correction factor to normalize the volumes across subjects. For the MS patients, additional processing steps were performed. WM and GM lesions were filled (Battaglini et al., 2012) using the manually defined lesion masks before the tissue segmentations. After segmentation, correct assignment of WM and GM lesions to WM and GM respectively was checked by masking the partial volume estimates inside the lesion masks. Finally, the recommended brain extraction tool (BET) parameters (of B and f = 0.1) for bias field estimation with SIENAX in MS subjects (Popescu et al., 2012) were used.


Analysis of ageing-associated grey matter volume in patients with multiple sclerosis shows excess atrophy in subcortical regions
Image analysis workflow. The image analysis workflow for extracting measures of scaled tissue volumes from the input T1-weighted MPRAGE scans of MS patients (left) and healthy controls (right). Blue boxes signify 3D image volumes, whilst green boxes signify processing steps. The final numerical outputs are colored in purple.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5121150&req=5

f0005: Image analysis workflow. The image analysis workflow for extracting measures of scaled tissue volumes from the input T1-weighted MPRAGE scans of MS patients (left) and healthy controls (right). Blue boxes signify 3D image volumes, whilst green boxes signify processing steps. The final numerical outputs are colored in purple.
Mentions: Images were processed following the analysis workflow depicted in Fig. 1. Each subject's T2-FLAIR volume was co-registered to their MPRAGE using the rigid-body transformation of FLIRT (Jenkinson et al., 2002). To improve the subsequent tissue segmentations (Popescu et al., 2012), the MNI152 template was aligned using the affine registration of FLIRT (Jenkinson et al., 2002) to the same MPRAGE. This transform was then applied to a rectangular mask covering the MNI152 template, and the transformed mask applied to the MPRAGE to remove excess neck that can corrupt brain extraction tools (Popescu et al., 2012). After masking, the anatomical scans were segmented using additional FSL tools: (i) SIENAX (Smith et al., 2002) for scaled tissue volumes of WM, GM, and cortical GM, normalized for subject head size; and (ii) FIRST (Patenaude et al., 2011) for subcortical grey matter volumes of the putamen, caudate, thalamus, hippocampus, amygdala, accumbens, and pallidum. Subcortical GM structures were multiplied by the SIENAX volume-correction factor to normalize the volumes across subjects. For the MS patients, additional processing steps were performed. WM and GM lesions were filled (Battaglini et al., 2012) using the manually defined lesion masks before the tissue segmentations. After segmentation, correct assignment of WM and GM lesions to WM and GM respectively was checked by masking the partial volume estimates inside the lesion masks. Finally, the recommended brain extraction tool (BET) parameters (of B and f = 0.1) for bias field estimation with SIENAX in MS subjects (Popescu et al., 2012) were used.

View Article: PubMed Central - PubMed

ABSTRACT

Age of onset in multiple sclerosis (MS) exerts an influence on the course of disease. This study examined whether global and regional brain volumes differed between “younger” and “older” onset MS subjects who were matched for short disease duration, mean 1.9 years and burden as measured by the MS Severity Score and relapses.

21 younger-onset MS subjects (age 30.4 ± 3.2 years) were compared with 17 older-onset (age 48.7 ± 3.3 years) as well as age-matched controls (n = 31, 31.9 ± 3.5 years and n = 21, 47.3 ± 4.0 years). All subjects underwent 3D volumetric T1 and T2-FLAIR imaging. White matter (WM) and grey matter (GM) lesions were outlined manually. Lesions were filled prior to tissue and structural segmentation to reduce classification errors.

Volume loss versus control was predominantly in the subcortical GM, at > 13% loss. Younger and older-onset MS subjects had similar, strong excess loss in the putamen, thalamus, and nucleus accumbens. No excess loss was detected in the amygdala or pallidum. The hippocampus and caudate showed significant excess loss in the younger group (p < 0.001) and a strong trend in the older-onset group.

These results provide a potential imaging correlate of published neuropsychological studies that reported the association of younger age at disease onset with impaired cognitive performance, including decreased working memory.

No MeSH data available.