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.
Jaime Ide (Yale University)
I am currently Associate Research Scientist at Yale University, working with Prof. Chiang-shan Ray Li, MD., PhD. Previously, I was Assistant Director at LCNeuro and Research Assistant Professor in the Department of Biomedical Engineering at Stony Brook University, and Assistant Professor at Federal University of Sao Paulo (Brazil). I got my Bachelor in Mechatronic (Robotics) Engineering, and Ph.D. in Engineering (Probabilistic Graphical Models and Computational Statistics, supervisor Professor Fabio G. Cozman) from University of Sao Paulo, Brazil. I received my training in MRI in the Department of Radiology at University of Pennsylvania, as Biomedical Postdoctoral Researcher (2007-2008), and worked with Bayesian methods applied to neuroimaging. Continuing the training in fMRI, I was Postdoctoral Associate at Yale University (2008-2010), under supervision of Prof. Li in exciting projects including investigations of functional connectivity (Granger causality, regression models, Bayesian networks) of brain areas involved in cognitive control. Broadly, I am interested in addiction and cognitive control research, with particular focus on the application of machine learning and pattern recognition techniques and theoretical modeling of decision-making, as well as predictive modeling.