A Neuroimaging Feature Extraction Model for Imaging Genetics with Application to Alzheimer's Disease

Chunfei Li, Chen Fang, Malek Adjouadi, Mercedes Cabrerizo, Armando Barreto, Jean Andrian, Ranjan Duara, David Loewenstein

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

Neuroimaging is an important research platform that can be very useful for eliciting new understanding on the complicated pathogenesis between genetics and disease phenotypes. Due to the extremely high dimensionality of image and genetic data, and considering the potential joint effect of genetic variants, multivariate techniques have been examined to detect Alzheimers disease (AD) related genetic variants expressed through single-nucleotide polymorphisms (SNPs). However, the image features used in support of those methods are not immediately related to the disease, and the detected genetic markers may not be related to AD. In this study, we propose an ensemble model based framework for firstly extracting 50 region-based image features whose values are predicted by base learners trained on raw neuroimaging morphological variables. This task is followed by performing sparse Partial Least Squares regression (sPLS) method on the extracted 50 AD related image features and pre-selected 1508 SNPs to detect the significant SNPs associated with the extracted image features. Instead of modeling a direct link between genetic variants and disease label, we captured disease information indirectly.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages15-20
Number of pages6
ISBN (Electronic)9781538613245
DOIs
StatePublished - Jul 1 2017
Event17th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2017 - Herndon, United States
Duration: Oct 23 2017Oct 25 2017

Publication series

NameProceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017
Volume2018-January

Other

Other17th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2017
CountryUnited States
CityHerndon
Period10/23/1710/25/17

Keywords

  • Alzheimers disease
  • Genome-wide association studies
  • feature extraction
  • genetics
  • neuroimaging
  • sparse partial least squares regression

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Biomedical Engineering
  • Modeling and Simulation
  • Health Informatics

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  • Cite this

    Li, C., Fang, C., Adjouadi, M., Cabrerizo, M., Barreto, A., Andrian, J., Duara, R., & Loewenstein, D. (2017). A Neuroimaging Feature Extraction Model for Imaging Genetics with Application to Alzheimer's Disease. In Proceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017 (pp. 15-20). (Proceedings - 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering, BIBE 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE.2017.00-85