title: A probabilistic model of functional brain connectivity network for discovering novel biomarkers.
abstract: Graph theoretical analyses of functional brain connectivity networks have been limited to a static view of brain activities over the entire timeseries. In this paper, we propose a new probabilistic model of the functional brain connectivity network, the strong-edge model, which incorporates the temporal fluctuation of neurodynamics. We also introduce a systematic approach to identifying biomarkers based on network characteristics that quantitatively describe the organization of the brain network. The evaluation results of the proposed strong-edge network model is quite promising. The biomarkers derived from the strong-edge model have achieved much higher prediction accuracy of 89% (ROCAUC: 0.96) in distinguishing depression subjects from healthy controls in comparison with the conventional network model (accuracy: 76%, ROC-AUC: 0.87). These novel biomarkers have the high potential of being applied clinically in diagnosing neurological and psychiatric brain diseases with noninvasive neuroimaging technologies.
authors.affiliation: University of Arkansas for Medical Sciences, Little Rock, AR;
authors.forename: Jiang
authors.initials: J
authors.lastname: Bian
authors.forename: Mengjun
authors.initials: M
authors.lastname: Xie
authors.forename: Umit
authors.initials: U
authors.lastname: Topaloglu
authors.forename: Josh M
authors.initials: JM
authors.lastname: Cisler