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Data mining in brain imaging

Department of Computer Science, Dartmouth Experimental Visualization Laboratory, Dartmouth College, Hanover, New Hampshire, USA, vasilis{at}cis.temple.edu

James Ford

Department of Computer Science, Dartmouth Experimental Visualization Laboratory, Dartmouth College, Hanover, New Hampshire, USA

Li Shen

Department of Computer Science, Dartmouth Experimental Visualization Laboratory, Dartmouth College, Hanover, New Hampshire, USA

Fillia Makedon

Department of Computer Science, Dartmouth Experimental Visualization Laboratory, Dartmouth College, Hanover, New Hampshire, USA

Andrew Saykin

Brain Imaging Laboratory, Departments of Psychiatry and Radiology, Dartmouth Medical School, Dartmouth Hitchcock Medical Center, Lebanon, New Hampshire, USA

Data mining in brain imaging is proving to be an effective methodology for disease prognosis and prevention. This, together with the rapid accumulation of massive heterogeneous data sets, motivates the need for efficient methods that filter, clarify, assess, correlate and cluster brain-related information. Here, we present data mining methods that have been or could be employed in the analysis of brain images. These methods address two types of brain imaging data: structural and functional. We introduce statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data. We consider several applications of these methods, such as the analysis of task-activation, lesion-deficit, and structure morphological variability; the development of probabilistic atlases; and tumour analysis. We include examples of applications to real brain data. Several data mining issues, such as that of method validation or verification, are also discussed.

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