Exploring high-order functional interactions via structurally-weighted LASSO models

Dajiang Zhu, Xiang Li, Xi Jiang, Hanbo Chen, Dinggang Shen, Tianming Liu

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

1 Citation (Scopus)

Abstract

A major objective of brain science research is to model and quantify functional interaction patterns among neural networks, in the sense that meaningful interaction patterns reflect the working mechanisms of neural systems and represent their relationships with the external world. Most current research approaches in the neuroimaging field, however, focus on pair-wise functional/effective connectivity and are thus unable to handle high-order, network-scale functional interactions. In this paper, we propose a novel structurally-weighted LASSO (SW-LASSO) regression model to represent the functional interaction among multiple regions of interests (ROIs) based on resting state fMRI (rsfMRI) data. In particular, the structural connectivity constraints derived from diffusion tenor imaging (DTI) data are used to guide the selection of the weights, thus adaptively adjusting the penalty levels of different coefficients which correspond to different ROIs. The robustness and accuracy of our models are evaluated and demonstrated via a series of carefully designed experiments. In an application example, the generated regression graphs show different assortative mixing patterns between Mild Cognitive Impairment (MCI) patients and normal controls (NC). Our results indicate that the proposed model has promising potential to enable the construction of high-order functional networks and their applications in clinical datasets.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages13-24
Number of pages12
Volume7917 LNCS
DOIs
Publication statusPublished - 2013 Jul 12
Externally publishedYes
Event23rd International Conference on Information Processing in Medical Imaging, IPMI 2013 - Asilomar, CA, United States
Duration: 2013 Jun 282013 Jul 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7917 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other23rd International Conference on Information Processing in Medical Imaging, IPMI 2013
CountryUnited States
CityAsilomar, CA
Period13/6/2813/7/3

Fingerprint

Higher Order
Region of Interest
Interaction
Connectivity
Neuroimaging
Functional Magnetic Resonance Imaging
Model
Penalty
Brain
Regression Model
Quantify
Regression
Imaging
Neural Networks
Robustness
Neural networks
Imaging techniques
Series
Coefficient
Graph in graph theory

Keywords

  • High-order functional interaction
  • LASSO

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Zhu, D., Li, X., Jiang, X., Chen, H., Shen, D., & Liu, T. (2013). Exploring high-order functional interactions via structurally-weighted LASSO models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7917 LNCS, pp. 13-24). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7917 LNCS). https://doi.org/10.1007/978-3-642-38868-2_2

Exploring high-order functional interactions via structurally-weighted LASSO models. / Zhu, Dajiang; Li, Xiang; Jiang, Xi; Chen, Hanbo; Shen, Dinggang; Liu, Tianming.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7917 LNCS 2013. p. 13-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7917 LNCS).

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

Zhu, D, Li, X, Jiang, X, Chen, H, Shen, D & Liu, T 2013, Exploring high-order functional interactions via structurally-weighted LASSO models. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7917 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7917 LNCS, pp. 13-24, 23rd International Conference on Information Processing in Medical Imaging, IPMI 2013, Asilomar, CA, United States, 13/6/28. https://doi.org/10.1007/978-3-642-38868-2_2
Zhu D, Li X, Jiang X, Chen H, Shen D, Liu T. Exploring high-order functional interactions via structurally-weighted LASSO models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7917 LNCS. 2013. p. 13-24. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-38868-2_2
Zhu, Dajiang ; Li, Xiang ; Jiang, Xi ; Chen, Hanbo ; Shen, Dinggang ; Liu, Tianming. / Exploring high-order functional interactions via structurally-weighted LASSO models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7917 LNCS 2013. pp. 13-24 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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