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Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer's Disease Prediction
- Source :
- IEEE Trans Med Imaging
- Publication Year :
- 2019
-
Abstract
- Alzheimer’s disease (AD) is a serious neurodegenerative condition that affects millions of individuals across the world. As the average age of individuals in the United States and the world increases, the prevalence of AD will continue to grow. To address this public health problem, the research community has developed computational approaches to sift through various aspects of clinical data and uncover their insights, among which one of the most challenging problem is to determine the biological mechanisms that cause AD to develop. To study this problem, in this paper we present a novel Joint Multi-Modal Longitudinal Regression and Classification method and show how it can be used to identify the cognitive status of the participants in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort and the underlying biological mechanisms. By intelligently combining clinical data of various modalities ( i.e. , genetic information and brain scans) using a variety of regularizations that can identify AD-relevant biomarkers, we perform the regression and classification tasks simultaneously. Because the proposed objective is a non-smooth optimization problem that is difficult to solve in general, we derive an efficient iterative algorithm and rigorously prove its convergence. To validate our new method in predicting the cognitive scores of patients and their clinical diagnosis, we conduct comprehensive experiments on the ADNI cohort. Our promising results demonstrate the benefits and flexibility of the proposed method. We anticipate that our new method is of interest to clinical communities beyond AD research and have open-sourced the code of our method online. 1 1 The code package for the proposed Joint Multi-Modal Longitudinal Regression and Classification model have been made publicly available online at https://github.com/minds-mines/jmmlrc .
- Subjects :
- Computer science
Neuroimaging
Disease
Machine learning
computer.software_genre
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
0302 clinical medicine
Alzheimer Disease
Image Interpretation, Computer-Assisted
Humans
Electrical and Electronic Engineering
Radiological and Ultrasound Technology
business.industry
Flexibility (personality)
Brain
Cognition
Regression
Computer Science Applications
Cohort
Biomarker (medicine)
Artificial intelligence
business
computer
Software
Algorithms
Biomarkers
Subjects
Details
- ISSN :
- 1558254X
- Volume :
- 39
- Issue :
- 6
- Database :
- OpenAIRE
- Journal :
- IEEE transactions on medical imaging
- Accession number :
- edsair.doi.dedup.....40b592b82e240a92fbd816e9172c6a84