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Structural brain network constrained neuroimaging marker identification for predicting cognitive functions
- Source :
- Lecture Notes in Computer Science ISBN: 9783642388675, IPMI
- Publication Year :
- 2014
-
Abstract
- Neuroimaging markers have been widely used to predict the cognitive functions relevant to the progression of Alzheimer's disease (AD). Most previous studies identify the imaging markers without considering the brain structural correlations between neuroimaging measures. However, many neuroimaging markers interrelate and work together to reveal the cognitive functions, such that these relevant markers should be selected together as the phenotypic markers. To solve this problem, in this paper, we propose a novel network constrained feature selection (NCFS) model to identify the neuroimaging markers guided by the structural brain network, which is constructed by the sparse representation method such that the interrelations between neuroimaging features are encoded into probabilities. Our new methods are evaluated by the MRI and AV45-PET data from ADNI-GO and ADNI-2 (Alzheimer's Disease Neuroimaging Initiative). In all cognitive function prediction tasks, our new NCFS method outperforms other state-of-the-art regression approaches. Meanwhile, we show that the new method can select the correlated imaging markers, which are ignored by the competing approaches.
- Subjects :
- Pathology
medicine.medical_specialty
Brain Mapping
Imaging genetics
Computer science
Reproducibility of Results
Feature selection
Cognition
Computational biology
Sparse approximation
medicine.disease
Image Enhancement
Brain mapping
Sensitivity and Specificity
Article
Neuroimaging
Alzheimer Disease
Image Interpretation, Computer-Assisted
medicine
Humans
Identification (biology)
Alzheimer's disease
Nerve Net
Cognition Disorders
Algorithms
Subjects
Details
- ISBN :
- 978-3-642-38867-5
- ISSN :
- 10112499
- ISBNs :
- 9783642388675
- Volume :
- 23
- Database :
- OpenAIRE
- Journal :
- Information processing in medical imaging : proceedings of the ... conference
- Accession number :
- edsair.doi.dedup.....56ecc9baec8a550eb3f7cdea6e20c0a5