2013 NetworkDiscoveryviaConstrainedT

• (Davidson et al., 2013) ⇒ Ian Davidson, Sean Gilpin, Owen Carmichael, and Peter Walker. (2013). “Network Discovery via Constrained Tensor Analysis of FMRI Data.” In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-2174-7 doi:10.1145/2487575.2487619

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222013%22+Network+Discovery+via+Constrained+Tensor+Analysis+of+FMRI+Data
• http://dl.acm.org/citation.cfm?id=2487575.2487619&preflayout=flat#citedby

Quotes

Author Keywords

• Cosntraints; data mining; fmri; tensor decomposition

Abstract

We pose the problem of network discovery which involves simplifying spatio-temporal data into cohesive regions (nodes) and relationships between those regions (edges). Such problems naturally exist in fMRI scans of human subjects. These scans consist of activations of thousands of voxels over time with the aim to simplify them into the underlying cognitive network being used. We propose supervised and semi-supervised variations of this problem and postulate a constrained tensor decomposition formulation and a corresponding alternating least squares solver that is easy to implement. We show this formulation works well in controlled experiments where supervision is incomplete, superfluous and noisy and is able to recover the underlying ground truth network. We then show that for real fMRI data our approach can reproduce well known results in neurology regarding the default mode network in resting-state healthy and Alzheimer affected individuals. Finally, we show that the reconstruction error of the decomposition provides a useful measure of the network strength and is useful at predicting key cognitive scores both by itself and with clinical information.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2013 NetworkDiscoveryviaConstrainedT	Ian Davidson Sean Gilpin Owen Carmichael Peter Walker			Network Discovery via Constrained Tensor Analysis of FMRI Data				10.1145/2487575.2487619		2013