2009 MiningBrainRegionConnectivityfo

• (Sun et al., 2009) ⇒ Liang Sun, Rinkal Patel, Jun Liu, Kewei Chen, Teresa Wu, Jing Li, Eric Reiman, and Jieping Ye. (2009). “Mining Brain Region Connectivity for Alzheimer's Disease Study via Sparse Inverse Covariance Estimation.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557162

Subject Headings:

Notes

• Categories and Subject Descriptors H.2.8 Database Management: Database Applications - Data Mining; J.3 Life and Medical Sciences: Health, Medical Information Systems
• General Terms: Algorithm

Cited By

• http://scholar.google.com/scholar?q=%22Mining+brain+region+connectivity+for+alzheimer's+disease+study+via+sparse+inverse+covariance+estimation%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557162&preflayout=flat#citedby

Quotes

Author Keywords

Brain Network, Alzheimer’s Disease, Neuroimaging, FDGPET, Sparse Inverse Covariance Estimation

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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 MiningBrainRegionConnectivityfo	Jieping Ye Liang Sun Rinkal Patel Jun Liu Kewei Chen Teresa Wu Jing Li Eric Reiman			Mining Brain Region Connectivity for Alzheimer's Disease Study via Sparse Inverse Covariance Estimation		KDD-2009 Proceedings		10.1145/1557019.1557162		2009