Detrended Partial Cross Correlation for Brain Connectivity Analysis

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Ide, Jaime S. ; Cappabianco, Fabio A. ; Faria, Fabio A. ; Li, Chiang-shan R. ; Guyon, I ; Luxburg, UV ; Bengio, S ; Wallach, H ; Fergus, R ; Vishwanathan, S ; Garnett, R

• Tipo de documento: Artigo Científico
• Fonte: ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017); v. 30, p. 9-pg., 2017-01-01.

Resumo

Brain connectivity analysis is a critical component of ongoing human connectome projects to decipher the healthy and diseased brain. Recent work has highlighted the power-law (multi-time scale) properties of brain signals; however, there remains a lack of methods to specifically quantify short- vs. long- time range brain connections. In this paper, using detrended partial cross-correlation analysis (DPCCA), we propose a novel functional connectivity measure to delineate brain interactions at multiple time scales, while controlling for covariates. We use a rich simulated fMRI dataset to validate the proposed method, and apply it to a real fMRI dataset in a cocaine dependence prediction task. We show that, compared to extant methods, the DPCCA-based approach not only distinguishes short and long memory functional connectivity but also improves feature extraction and enhances classification accuracy. Together, this paper contributes broadly to new computational methodologies in understanding neural information processing. (AU)

• Processo FAPESP: 16/21591-5 - Desenvolvimento de métodos robustos para delineamento de bordas em imagens utilizando grafos
• Beneficiário: Fábio Augusto Menocci Cappabianco
• Modalidade de apoio: Auxílio à Pesquisa - Regular