Browsing by Author "Moore, Jason"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    Class-Balanced Deep Learning with Adaptive Vector Scaling Loss for Dementia Stage Detection
    (Springer, 2024) Tong, Boning; Zhou, Zhuoping; Tarzanagh, Davoud Ataee; Hou, Bojian; Saykin, Andrew J.; Moore, Jason; Ritchie, Marylyn; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Alzheimer’s disease (AD) leads to irreversible cognitive decline, with Mild Cognitive Impairment (MCI) as its prodromal stage. Early detection of AD and related dementia is crucial for timely treatment and slowing disease progression. However, classifying cognitive normal (CN), MCI, and AD subjects using machine learning models faces class imbalance, necessitating the use of balanced accuracy as a suitable metric. To enhance model performance and balanced accuracy, we introduce a novel method called VS-Opt-Net. This approach incorporates the recently developed vector-scaling (VS) loss into a machine learning pipeline named STREAMLINE. Moreover, it employs Bayesian optimization for hyperparameter learning of both the model and loss function. VS-Opt-Net not only amplifies the contribution of minority examples in proportion to the imbalance level but also addresses the challenge of generalization in training deep networks. In our empirical study, we use MRI-based brain regional measurements as features to conduct the CN vs MCI and AD vs MCI binary classifications. We compare the balanced accuracy of our model with other machine learning models and deep neural network loss functions that also employ class-balanced strategies. Our findings demonstrate that after hyperparameter optimization, the deep neural network using the VS loss function substantially improves balanced accuracy. It also surpasses other models in performance on the AD dataset. Moreover, our feature importance analysis highlights VS-Opt-Net’s ability to elucidate biomarker differences across dementia stages.
  • Thumbnail Image
    Item
    Network-Based Genome Wide Study of Hippocampal Imaging Phenotype In Alzheimer's Disease To Identify Functional Interaction Modules
    (IEEE, 2017) Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon; Moore, Jason; Saykin, Andrew; Shen, Li; Radiology and Imaging Sciences, School of Medicine
    Identification of functional modules from biological network is a promising approach to enhance the statistical power of genome-wide association study (GWAS) and improve biological interpretation for complex diseases. The precise functions of genes are highly relevant to tissue context, while a majority of module identification studies are based on tissue-free biological networks that lacks phenotypic specificity. In this study, we propose a module identification method that maps the GWAS results of an imaging phenotype onto the corresponding tissue-specific functional interaction network by applying a machine learning framework. Ridge regression and support vector machine (SVM) models are constructed to re-prioritize GWAS results, followed by exploring hippocampus-relevant modules based on top predictions using GWAS top findings. We also propose a GWAS top-neighbor-based module identification approach and compare it with Ridge and SVM based approaches. Modules conserving both tissue specificity and GWAS discoveries are identified, showing the promise of the proposal method for providing insight into the mechanism of complex diseases.
  • Thumbnail Image
    Item
    Structured sparse CCA for brain imaging genetics via graph OSCAR
    (Biomed Central, 2016) Du, Lei; Huang, Heng; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon; Inlow, Mark; Moore, Jason; Saykin, Andrew J.; Shen, Li; Department of Radiology and Imaging Sciences, IU School of Medicine
    Recently, structured sparse canonical correlation analysis (SCCA) has received increased attention in brain imaging genetics studies. It can identify bi-multivariate imaging genetic associations as well as select relevant features with desired structure information. These SCCA methods either use the fused lasso regularizer to induce the smoothness between ordered features, or use the signed pairwise difference which is dependent on the estimated sign of sample correlation. Besides, several other structured SCCA models use the group lasso or graph fused lasso to encourage group structure, but they require the structure/group information provided in advance which sometimes is not available.