ArticleJournal of the American Statistical Association2021
Brain Regions Identified as Being Associated with Verbal Reasoning through the Use of Imaging Regression via Internal Variation.
Article in Journal of the American Statistical Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- TENSOR QUANTILE REGRESSION WITH LOW-RANK TENSOR TRAIN ESTIMATION.The annals of applied statistics · 2024Article
- Multi-task Learning with High-Dimensional Noisy Images.Journal of the American Statistical Association · 2024Article
- Imaging mediation analysis for longitudinal outcomes: a case study of childhood brain tumor survivorship.Statistics and its interface · 2024Article
- An Insight into developmental changes in reasoning skills among Indian Preschoolers: A cross-sectional study using a story-based approach.F1000Research · 2023Article
- TENSOR QUANTILE REGRESSION WITH APPLICATION TO ASSOCIATION BETWEEN NEUROIMAGES AND HUMAN INTELLIGENCE.The annals of applied statistics · 2021Article
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Authors and funding
3 authors.
Funding
Abstract
Brain-imaging data have been increasingly used to understand intellectual disabilities. Despite significant progress in biomedical research, the mechanisms for most of the intellectual disabilities remain unknown. Finding the underlying neurological mechanisms has been proved difficult, especially in children due to the rapid development of their brains. We investigate verbal reasoning, which is a reliable measure of individuals' general intellectual abilities, and develop a class of high-order imaging regression models to identify brain subregions which might be associated with this specific intellectual ability. A key novelty of our method is to take advantage of spatial brain structures, and specifically the piecewise smooth nature of most imaging coefficients in the form of high-order tensors. Our approach provides an effective and urgently needed method for identifying brain subregions potentially underlying certain intellectual disabilities. The idea behind our approach is a carefully constructed concept called Internal Variation (IV). The IV employs tensor decomposition and provides a computationally feasible substitution for Total Variation (TV), which has been considered in the literature to deal with similar problems but is problematic in high order tensor regression. Before applying our method to analyze the real data, we conduct comprehensive simulation studies to demonstrate the validity of our method in imaging signal identification. Then, we present our results from the analysis of a dataset based on the Philadelphia Neurodevelopmental Cohort for which we preprocessed the data including re-orienting, bias-field correcting, extracting, normalizing and registering the magnetic resonance images from 978 individuals. Our analysis identified a subregion across the cingulate cortex and the corpus callosum as being associated with individuals' verbal reasoning ability, which, to the best of our knowledge, is a novel region that has not been reported in the literature. This finding is useful in further investigation of functional mechansims for verbal reasoning.
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