Group Replicator Dynamics: A Novel Group-Wise Evolutionary Approach for Sparse Brain Network Detection

• Published in: IEEE transactions on medical imaging Vol. 31; no. 3; pp. 576 - 585
• Main Authors: Ng, B., McKeown, M. J., Abugharbieh, R.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2012; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain; Brain - physiology; Brain - physiopathology; Brain modeling; Case-Control Studies; Convergence; Correlation; Databases, Factual; Entropy; Functional connectivity; functional magnetic resonance imaging (fMRI); group analysis; Humans; Image Processing, Computer-Assisted; inter-subject variability; Magnetic Resonance Imaging - methods; Models, Neurological; Optimization; Parkinson Disease - physiopathology; Principal Component Analysis; Principal components analysis; replicator dynamics; Reproducibility of Results; sparse principal component analysis (PCA); Studies; Vectors
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2011.2173699

Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, "group replicator dynamics" (GRD), for detecting sparse functional brain networks that are common across a group of subjects. We extend the replicator dynamics (RD) approach, which we show to be a solution of the nonnegative sparse principal component analysis problem, by integrating group information into each subject's RD process. Our proposed strategy effectively coaxes all subjects' networks to evolve towards the common network of the group. This results in sparse networks comprising the same brain regions across subjects yet with subject-specific weightings of the identified brain regions. Thus, in contrast to traditional averaging approaches, GRD enables inter-subject variability to be modeled, which facilitates statistical group inference. Quantitative validation of GRD on synthetic data demonstrated superior network detection performance over standard methods. When applied to real fMRI data, GRD detected task-specific networks that conform well to prior neuroscience knowledge.