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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping
Published on: September 25, 2019
Individual prediction and classification of cognitive impairment in patients with white matter lesions based on gray
Jinfang Wang1,2, Cui Zhao3,4, Jing Wei3,4
1Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, Center of Stroke, Beijing Institute for Brain Disorders, Beijing Key Laboratory of Translational Medicine for Cerebrovascular Disease, Beijing, China.
Background:
Vascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer's disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).
Methods:
A total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.
Results:
Multi-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.
Conclusions:
Based on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.
