Discriminative self-representation sparse regression for neuroimaging-based alzheimer's disease diagnosis

Classification of Mild Cognitive Impairment With Multimodal Data Using Both Labeled and Unlabeled Samples

Mild Cognitive Impairment (MCI) is a preclinical stage of Alzheimer's Disease (AD) and is clinical heterogeneity. The classification of MCI is crucial for the early diagnosis and treatment of AD. In this study, we investigated the potential of using both labeled and unlabeled samples from the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort to classify MCI through the multimodal co-training method. We utilized both structural magnetic resonance imaging (sMRI) data and genotype data of 364 MCI samples including 228 labeled and 136 unlabeled MCI samples from the ADNI-1 cohort. First, the selected quantitative trait (QT) features from sMRI data and SNP features from genotype data were used to build two initial classifiers on 228 labeled MCI samples. Then, the co-training method was implemented to obtain new labeled samples from 136 unlabeled MCI samples. Finally, the random forest algorithm was used to obtain a combined classifier to classify MCI patients in the independent ADNI-2 dataset. The experimental results showed that our proposed framework obtains an accuracy of 85.50 percent and an AUC of 0.825 for MCI classification, respectively, which showed that the combined utilization of sMRI and SNP data through the co-training method could significantly improve the performances of MCI classification.

Longitudinal score prediction for Alzheimer's disease based on ensemble correntropy and spatial-temporal constraint

Neuroimaging data has been widely used to predict clinical scores for automatic diagnosis of Alzheimer's disease (AD). For accurate clinical score prediction, one of the major challenges is high feature dimension of the imaging data. To address this issue, this paper presents an effective framework using a novel feature selection model via sparse learning. In contrast to previous approaches focusing on a single time point, this framework uses information at multiple time points. Specifically, a regularized correntropy with the spatial-temporal constraint is used to reduce the adverse effect of noise and outliers, and promote consistent and robust selection of features by exploring data characteristics. Furthermore, ensemble learning of support vector regression (SVR) is exploited to accurately predict AD scores based on the selected features. The proposed approach is extensively evaluated on the Alzheimer's disease neuroimaging initiative (ADNI) dataset. Our experiments demonstrate that the proposed approach not only achieves promising regression accuracy, but also successfully recognizes disease-related biomarkers.

Mapping the contribution and strategic distribution patterns of neuroimaging features of small vessel disease in poststroke cognitive impairment

OBJECTIVES

Individual neuroimaging features of small vessel disease (SVD) have been reported to influence poststroke cognition. This study aimed to investigate the joint contribution and strategic distribution patterns of multiple types of SVD imaging features in poststroke cognitive impairment.

METHODS

We studied 145 first-ever ischaemic stroke patients with MRI and Montreal Cognitive Assessment (MoCA) examined at baseline. The local burdens of acute ischaemic lesion (AIL), white matter hyperintensity, lacune, enlarged perivascular space and cross-sectional atrophy were quantified and entered into support vector regression (SVR) models to associate with the global and domain scores of MoCA. The SVR models were optimised with feature selection through 10-fold cross-validations. The contribution of SVD features to MoCA scores was measured by the prediction accuracy in the corresponding SVR model after optimisation.

RESULTS

The combination of the neuroimaging features of SVD contributed much more to the MoCA deficits on top of AILs compared with individual SVD features, and the cognitive impact of different individual SVD features was generally similar. As identified by the optimal SVR models, the important SVD-affected regions were mainly located in the basal ganglia and white matter around it, although the specific regions varied for MoCA and its domains.

CONCLUSIONS

Multiple types of SVD neuroimaging features jointly had a significant impact on global and domain cognitive functionings after stroke on top of AILs. The map of strategic cognitive-relevant regions of SVD features may help clinicians to understand their complementary impact on poststroke cognition.