B0340
Title: Classification model with weighted regularization to improve the reproducibility of neuroimaging signature selection
Authors: Fengqing Zhang - Drexel University (United States) [presenting]
Abstract: Machine learning (ML) has been extensively applied in brain imaging studies to aid the diagnosis of psychiatric disorders and the selection of potential biomarkers. Due to the high dimensionality of imaging data and heterogeneous subtypes of psychiatric disorders, the reproducibility of ML results in brain imaging studies has drawn increasing attention. The reproducibility in brain imaging has been primarily examined in terms of prediction accuracy. However, achieving high prediction accuracy and discovering relevant features are two separate but related goals. An important yet under-investigated problem is the reproducibility of feature selection in brain imaging studies. A new metric is proposed to quantify the reproducibility of neuroimaging feature selection via bootstrapping. The reproducibility index (R-index) is estimated for each feature as the reciprocal coefficient of variation of absolute mean difference across a larger number of bootstrap samples. The R-index in regularized classification models is then integrated as penalty weight. Reproducible features with a larger R-index are assigned smaller penalty weights and thus are more likely to be selected by the proposed models. Both simulated and multimodal neuroimaging data are used to examine the performance of our proposed models.
