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Prediction of Memory Impairment with MRI Data: A Longitudinal Study of Alzheimer's Disease
- Source :
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016 ISBN: 9783319467191, MICCAI (1)
- Publication Year :
- 2017
-
Abstract
- Alzheimer’s Disease (AD), a severe type of neurodegenerative disorder with progressive impairment of learning and memory, has threatened the health of millions of people. How to recognize AD at early stage is crucial. Multiple models have been presented to predict cognitive impairments by means of neuroimaging data. However, traditional models did not employ the valuable longitudinal information along the progression of the disease. In this paper, we proposed a novel longitudinal feature learning model to simultaneously uncover the interrelations among different cognitive measures at different time points and utilize such interrelated structures to enhance the learning of associations between imaging features and prediction tasks. Moreover, we adopted Schatten p-norm to identify the interrelation structures existing in the low-rank subspace. Empirical results on the ADNI cohort demonstrated promising performance of our model.
- Subjects :
- 0301 basic medicine
Longitudinal study
Disease
Sensitivity and Specificity
Article
03 medical and health sciences
0302 clinical medicine
Neuroimaging
Alzheimer Disease
medicine
Memory impairment
Humans
Cognitive Dysfunction
Longitudinal Studies
Memory Disorders
Reproducibility of Results
Cognition
medicine.disease
Magnetic Resonance Imaging
030104 developmental biology
Cohort
Disease Progression
Regression Analysis
Alzheimer's disease
Psychology
Feature learning
030217 neurology & neurosurgery
Algorithms
Cognitive psychology
Subjects
Details
- ISBN :
- 978-3-319-46719-1
- ISBNs :
- 9783319467191
- Volume :
- 9900
- Database :
- OpenAIRE
- Journal :
- Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
- Accession number :
- edsair.doi.dedup.....42224c9cb730f92c4df8a13061b266a9