Bi-Threshold frequent subgraph mining for Alzheimer disease risk assessment

Fei Gao, Jing Li, Teresa Wu, Kewei Chen, Xiaonan Liu, Leslie Baxter, Richard John Caselli

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

Abstract

An emerging trend in AD research is brain network development including graphic metrics and graph mining techniques. To construct a brain structural network, Diffusion Tensor Imaging (DTI) in conjunction with T1 weighted Magnetic Resonance Imaging (MRI) can be used to isolate brain regions as nodes, white matter tracts as the edge, and the density of the tracts as the weight to the edge. To study such network, its sub-network is often obtained by excluding unrelated nodes or edges. Existing research has heavily relied on domain knowledge or single-Thresholding individual subject based network metrics to identify the sub network. In this research, we develop a bi-Threshold frequent subgraph mining method (BT-FSG) to automatically filter out less important edges in responding to the clinical questions. Using this method, we are able to discover a subgraph of human brain network that can significantly reveal the difference between cognitively unimpaired APOE-4 carriers and noncarriers based on the correlations between the age vs. network local metric and age vs. network or global metric. This can potentially become a brain network marker for evaluating the AD risks for preclinical individuals.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2018
Subtitle of host publicationImaging Informatics for Healthcare, Research, and Applications
PublisherSPIE
Volume10579
ISBN (Electronic)9781510616479
DOIs
StatePublished - Jan 1 2018
EventMedical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications - Houston, United States
Duration: Feb 13 2018Feb 15 2018

Other

OtherMedical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications
CountryUnited States
CityHouston
Period2/13/182/15/18

Fingerprint

risk assessment
Risk assessment
Brain
Alzheimer Disease
thresholds
brain
Research
Diffusion tensor imaging
Diffusion Tensor Imaging
Magnetic Resonance Imaging
Weights and Measures
markers
magnetic resonance
emerging
tensors
trends
filters

Keywords

  • AD
  • APOE
  • Brain network
  • Frequent subgraph mining

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Cite this

Gao, F., Li, J., Wu, T., Chen, K., Liu, X., Baxter, L., & Caselli, R. J. (2018). Bi-Threshold frequent subgraph mining for Alzheimer disease risk assessment. In Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications (Vol. 10579). [105790C] SPIE. https://doi.org/10.1117/12.2293773

Bi-Threshold frequent subgraph mining for Alzheimer disease risk assessment. / Gao, Fei; Li, Jing; Wu, Teresa; Chen, Kewei; Liu, Xiaonan; Baxter, Leslie; Caselli, Richard John.

Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. Vol. 10579 SPIE, 2018. 105790C.

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

Gao, F, Li, J, Wu, T, Chen, K, Liu, X, Baxter, L & Caselli, RJ 2018, Bi-Threshold frequent subgraph mining for Alzheimer disease risk assessment. in Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. vol. 10579, 105790C, SPIE, Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications, Houston, United States, 2/13/18. https://doi.org/10.1117/12.2293773
Gao F, Li J, Wu T, Chen K, Liu X, Baxter L et al. Bi-Threshold frequent subgraph mining for Alzheimer disease risk assessment. In Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. Vol. 10579. SPIE. 2018. 105790C https://doi.org/10.1117/12.2293773
Gao, Fei ; Li, Jing ; Wu, Teresa ; Chen, Kewei ; Liu, Xiaonan ; Baxter, Leslie ; Caselli, Richard John. / Bi-Threshold frequent subgraph mining for Alzheimer disease risk assessment. Medical Imaging 2018: Imaging Informatics for Healthcare, Research, and Applications. Vol. 10579 SPIE, 2018.
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