Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation

Liang Sun, Rinkal Patel, Jun Liu, Kewei Chen, Teresa Wu, Jing Li, Eric Reiman, Jieping Ye

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

33 Citations (Scopus)

Abstract

Effective diagnosis of Alzheimer's disease (AD), the most common type of dementia in elderly patients, is of primary importance in biomedical research. Recent studies have demonstrated that AD is closely related to the structure change of the brain network, i.e., the connectivity among different brain regions. The connectivity patterns will provide useful imaging-based biomarkers to distinguish Normal Controls (NC), patients with Mild Cognitive Impairment (MCI), and patients with AD. In this paper, we investigate the sparse inverse covariance estimation technique for identifying the connectivity among different brain regions. In particular, a novel algorithm based on the block coordinate descent approach is proposed for the direct estimation of the inverse covariance matrix. One appealing feature of the proposed algorithm is that it allows the user feedback(e.g., prior domain knowledge) to be incorporated into the estimation process, while the connectivity patterns can be discovered automatically. We apply the proposed algorithm to a collection of FDG-PET images from 232 NC, MCI, and AD subjects. Our experimental results demonstrate that the proposed algorithm is promising in revealing the brain region connectivity differences among these groups.

Original languageEnglish (US)
Title of host publicationProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Pages1335-1343
Number of pages9
DOIs
StatePublished - 2009
Event15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09 - Paris, France
Duration: Jun 28 2009Jul 1 2009

Other

Other15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
CountryFrance
CityParis
Period6/28/097/1/09

Fingerprint

Brain
Biomarkers
Covariance matrix
Feedback
Imaging techniques

Keywords

  • Alzheimer's disease
  • Brain network
  • FDGPET
  • Neuroimaging
  • Sparse inverse covariance estimation

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this

Sun, L., Patel, R., Liu, J., Chen, K., Wu, T., Li, J., ... Ye, J. (2009). Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1335-1343) https://doi.org/10.1145/1557019.1557162

Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation. / Sun, Liang; Patel, Rinkal; Liu, Jun; Chen, Kewei; Wu, Teresa; Li, Jing; Reiman, Eric; Ye, Jieping.

Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009. p. 1335-1343.

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

Sun, L, Patel, R, Liu, J, Chen, K, Wu, T, Li, J, Reiman, E & Ye, J 2009, Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation. in Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. pp. 1335-1343, 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09, Paris, France, 6/28/09. https://doi.org/10.1145/1557019.1557162
Sun L, Patel R, Liu J, Chen K, Wu T, Li J et al. Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009. p. 1335-1343 https://doi.org/10.1145/1557019.1557162
Sun, Liang ; Patel, Rinkal ; Liu, Jun ; Chen, Kewei ; Wu, Teresa ; Li, Jing ; Reiman, Eric ; Ye, Jieping. / Mining brain region connectivity for alzheimer's disease study via sparse inverse covariance estimation. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2009. pp. 1335-1343
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