We propose a novel method which considers the functional connectome as an already-trained, empirical continuous Hopfield Network, to extract brain states from a population connectome to analyze the dynamics of the so-called attractor states on the subject level. We apply our method to the Human Connectome Project dataset, and we could show, that the mean activation of the participants during different states is a significant predictor of fluid intelligence.
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