ArticleJournal of neuroscience methods2014
Application of fused lasso logistic regression to the study of corpus callosum thickness in early Alzheimer's disease.
Article in Journal of neuroscience methods, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Corpus Callosum Segmentation on Structural MRI Using Multi-atlas Deformable Registration.Neuroinformatics · 2026Article
- What matters most? Identifying key resource gain and loss factors affecting nurses' work engagement and job burnout in digital healthcare contexts.BMC nursing · 2025Article
- Targeted co-expression networks for the study of traits.Scientific reports · 2024Article
- Stabilized COre gene and Pathway Election uncovers pan-cancer shared pathways and a cancer-specific driver.Science advances · 2022Article
- Revisit to functional data analysis of sleeping energy expenditure.Journal of applied statistics · 2022Article
- Folded concave penalized learning of high-dimensional MRI data in Parkinson's disease.Journal of neuroscience methods · 2021Article
- Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks.Quantitative imaging in medicine and surgery · 2018Article
- Predicting progression from mild cognitive impairment to Alzheimer's disease using longitudinal callosal atrophy.Alzheimer's & dementia (Amsterdam, Netherlands) · 2016Article
- Corpus callosum atrophy rate in mild cognitive impairment and prodromal Alzheimer's disease.Journal of Alzheimer's disease : JAD · 2015Article
- Software pipeline for midsagittal corpus callosum thickness profile processing : automated segmentation, manual editor, thickness profile generator, group-wise statistical comparison and results display.Neuroinformatics · 2014Article
- FastSurfer-CC: A robust, accurate, and comprehensive framework for corpus callosum morphometry.Imaging neuroscience (Cambridge, Mass.)Article
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Authors and funding
5 authors.
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Abstract
We propose a fused lasso logistic regression to analyze callosal thickness profiles. The fused lasso regression imposes penalties on both the l1-norm of the model coefficients and their successive differences, and finds only a small number of non-zero coefficients which are locally constant. An iterative method of solving logistic regression with fused lasso regularization is proposed to make this a practical procedure. In this study we analyzed callosal thickness profiles sampled at 100 equal intervals between the rostrum and the splenium. The method was applied to corpora callosa of elderly normal controls (NCs) and patients with very mild or mild Alzheimer's disease (AD) from the Open Access Series of Imaging Studies (OASIS) database. We found specific locations in the genu and splenium of AD patients that are proportionally thinner than those of NCs. Callosal thickness in these regions combined with the Mini Mental State Examination scores differentiated AD from NC with 84% accuracy.
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