Connectivity-informed Sparse Classifiers for fMRI Brain Decoding

• Published in: 2012 International Workshop on Pattern Recognition in NeuroImaging pp. 101 - 104
• Main Authors: Ng, B., Siless, V., Varoquaux, G., Poline, J-B, Thirion, B., Abugharbieh, R.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.07.2012
• Subjects: Accuracy; Brain; connectivity; Decoding; Diffusion tensor imaging; DTI; Face; fMRI; sparse classifiers; Support vector machines; Tensile stress
• ISBN: 1467321826; 9781467321822
• DOI: 10.1109/PRNI.2012.11

In recent years, sparse regularization has become a dominant means for handling the curse of dimensionality in functional magnetic resonance imaging (fMRI) based brain decoding problems. Enforcing sparsity alone, however, neglects the interactions between connected brain areas. Methods that additionally impose spatial smoothness would account for local but not long-range interactions. In this paper, we propose incorporating connectivity into sparse classifier learning so that both local and long-range connections can be jointly modeled. On real data, we demonstrate that integrating connectivity information inferred from diffusion tensor imaging (DTI) data provides higher classification accuracy and more interpretable classifier weight patterns than standard classifiers. Our results thus illustrate the benefits of adding neurologically-relevant priors in fMRI brain decoding.