Individual prediction and classification of cognitive impairment in patients with white matter lesions based on gray matter volume.

Original Article Page 1 of 18 Individual prediction and classification of cognitive impairment in patients with white matter lesions based on gray matter volume Jinfang Wang1,2#, Cui Zhao3,4#, Jing Wei3,4, Chunlin Li3,4, Xu Zhang3,4, Ying Liang3,4, Yumei Zhang1 1 Department 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; 2Department of Neurology, General Hospital of The Yang Tze River Shipping, Wuhan Brain Hospital, Wuhan, China; 3School of Biomedical Engineering, Capital Medical University, Beijing, China; 4Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing, China Contributions: (I) Conception and design: Y Liang, Y Zhang; (II) Administrative support: Y Liang, Y Zhang; (III) Provision of study materials or patients: J Wang; (IV) Collection and assembly of data: J Wang; (V) Data analysis and interpretation: C Zhao, C Li, J Wei, X Zhang; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors. # These authors contributed equally to this work. Correspondence to: Yumei Zhang. Department 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, 100053, China. Email: ; Ying Liang. School of Biomedical Engineering, Capital Medical University, Beijing 100069, China. Email: . 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 (crossvalidation 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. Keywords: Mild cognitive impairment; dementia; aging; machine learning; white matter lesions Submitted Jul 08, 2021. Accepted for publication Nov 26, 2021. doi: 10.21037/atm-21-3571 View this article at: https://dx.doi.org/10.21037/atm-21-3571 © Annals of Translational Medicine. All rights reserved. Ann Transl Med 2022;10(5):246 | https://dx.doi.org/10.21037/atm-21-3571 Page 2 of 18 Introduction White matter lesions (WMLs), also referred to as agerelated white matter hyperintensities on T2 weighted images, are prevalent in the elderly, especially in individuals with cardiovascular risk factors. Mounting evidence indicates that WMLs contribute to cognitive dysfunction in multiple domains, especially executive function, processing speed, and memory (1-3). Evidence has also shown that WMLs contribute to a spectrum of vascular mild cognitive impairments (VaMCI) (4) and may even predict Alzheimer’s disease (AD) at least a decade before the clinical stage of the disease, independently of AD pathology biomarkers (5). Although many studies have observed an association between the severity of WMLs and cognitive dysfunction, there have been no consistent conclusions. Structural magnetic resonance imaging (MRI) studies have found that older adults with WMLs have a significantly reduced gray matter (GM) volume and cortical thickness (6-8). The cortical alterations caused by WMLs may lead to cognitive decline and future dementia (9,10). Our previous study found that WMLs caused changes in GM density (1). Therefore, it may be possible to predict the severity of cognitive impairment based on the properties of the GM in older people with WMLs. As the diagnosis of cognitive function is primarily based on neuropsychological assessment, it is easy for diagnostic errors to occur when there is a lack of cooperation due to a patient’s educational level or serious cognitive impairment. Automatically determining the severity of cognitive impairment could facilitate the formulation of an appropriate clinical diagnosis and treatment plan. In the last decade, machine-learning methods have provided the opportunity to perform quantitative predictions of individual clinical assessments and disease classifications (11) and have been proposed as an aid in the early diagnosis of dementia (12). One of the advantages of machine learning approaches is that they can analyze many variables simultaneously and observe inherent patterns in the data (13). In addition to this, machine learning algorithms are also sensitive to the subtle, spatially distributed differences in brain MRI which have great promise in deriving individualized neuroimaging features of brain anatomy and providing an ideal framework to investigate psychiatric disorders (14). Relevance vector regression (RVR), a multivariate machine learning technique (15), has been used to quantitatively predict variables of interest in several neuroimaging studies © Annals of Translational Medicine. All rights reserved. Wang et al. MCI classification in patients with WMLs (16-18). Support vector machine (SVM) and Gaussian process classification (GPC) models have achieved high accuracy in AD classification, even with relatively small trainin (...truncated)