FIG. 1.

Illustration of the proposed framework. (a) Resting-state functional magnetic resonance imaging data. Here, the input dataset includes 210 subjects from the most recently updated ADNI database until September 2013. (b) Dictionary matrix. (c) Coefficient weight matrix. (d) Ten identified RSNs using ICA templates. (e) Other dictionary atoms. (f) Using dictionary matrix and identified RSNs we constructed six types of features. They are spatial overlapping rate, functional connectivity within RSNs, functional connectivity within dictionary, entropy of functional connectivity, entropy of component distribution within RSNs, and common dictionary distribution. (g) Correlation-based feature selection and support vector machine classifier. ADNI, Alzheimer's Disease Neuroimaging Initiative; ICA, independent-component analysis; RSN, resting-state network. Color images available online at www.liebertpub.com/brain