Dataset Information

The genetics of white matter lesions.

ABSTRACT: White matter lesions (WMLs), commonly seen as hyperintensities on T2-weighted MRI scans of healthy elderly individuals, are considered to be related to small vessel disease in the brain, and are often associated with subtle cognitive and functional impairments. WMLs also show a strong correlation with a wide range of neurodegenerative and neuropsychiatric disorders. Although a number of vascular risk factors for WMLs have been identified, genetic factors are also important with twin and family studies reporting high heritability. Mutations in several genes have been described that lead to monogenic disorders manifesting WMLs, such as Fabry disease and CADASIL. Because most individuals with WMLs do not have Mendelian disorders, most of the focus has been on single nucleotide polymorphisms as genetic risk markers for WMLs, either directly or through their interactions with other genes or medical risk factors. Candidate genes examined to date include those involved in cholesterol regulation and atherosclerosis, hypertension, neuronal repair, homocysteine levels, and oxidative stress pathways. In addition, although there have been a few genome-wide linkage studies, only one genome-wide association study has been performed. The majority of the genetic findings need independent replication, and studies need to be extended to other candidate genes. Collaborative efforts to examine genome-wide associations in large samples of both sexes of a broad age range using longitudinal studies are necessary. The identification of individuals genetically at risk of developing white matter lesions will have important implications for recognizing the etiology of WMLs and thereby developing clinical intervention strategies for their prevention.

SUBMITTER: Assareh A

PROVIDER: S-EPMC6493881 | biostudies-literature | 2011 Oct

REPOSITORIES: biostudies-literature

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The genetics of white matter lesions.

Assareh Arezoo A Mather Karen A KA Schofield Peter R PR Kwok John B J JB Sachdev Perminder S PS

CNS neuroscience & therapeutics 20100707 5

White matter lesions (WMLs), commonly seen as hyperintensities on T2-weighted MRI scans of healthy elderly individuals, are considered to be related to small vessel disease in the brain, and are often associated with subtle cognitive and functional impairments. WMLs also show a strong correlation with a wide range of neurodegenerative and neuropsychiatric disorders. Although a number of vascular risk factors for WMLs have been identified, genetic factors are also important with twin and family s ...[more]

PMID: 21951372

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Project description:Background: White matter lesions (WML) have been proved to be significantly associated with many brain diseases. Precise evaluation of burden of WML at early stage could provide insights in the prognosis and assist in intervention. However, acute ischemic lesions (AIL) exhibit hyperintensities on FLAIR images either, and are detected by diffusion weighted imaging (DWI). It is challenging to identify and segment WML in the patients with WML and AIL. Convolutional neural network (CNN) based architecture has been validated as an efficient tool for automatic segmentation. This study aimed to evaluate the performance of U-net in evaluation of WML in the patients with WML and AIL. Methods: A total of 208 cases from Chinese Atherosclerosis Risk Evaluation (CARE II) study were recruited in the present study. All subjects underwent imaging of FLAIR and DWI on 3.0 Tesla scanners. The contours of WML delineated by the observer and its scores rated by the observer were considered as gold standard. Among all 208 cases, 108 were randomly selected as train set, and the remaining 100 cases were used as test set. The performance of lesion segmentation toolbox (LST) and three U-net models were evaluated on three levels: pixel, lesion, and subject levels. The performance of all methods in WML identification and segmentation was also evaluated among the cases with different lesion volumes and between the cases with and without AIL. Results: All U-net models outperformed LST on pixel, lesion, and subject levels, while no differences were found among three U-net models. All segmentation methods performed best in the cases with WML volume (WMLV) > 20 ml but worst in those with WMLV < 5 ml. In addition, all methods showed similar performance between the cases with and without AIL. The scores determined by U-net exhibited a strong correlation with the gold standard (all Spearman correlation coefficients >0.89, ICCs >0.88, p-values <0.001). Conclusion: U-net performs well on identification and segmentation of WML in the patients with WML and AIL. The performance of U-net is validated by a dataset of multicenter study. Our results indicate that U-net has an advantage in assessing the burden of WML in the patients suffered from both WML and AIL.

Dataset Information

The genetics of white matter lesions.

Publications

The genetics of white matter lesions.

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