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Abstract #2469

Neural correlates of fluid intelligence identified by empirical neural network-based brain states

Robert Englert1, Balint Kincses2, Giuseppe Gallitto2, Raviteja Kotikalapudi1, Kevin Hoffschlag1, and Tamas Spisak1
1Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Germany, Essen, Germany, 2Department of Neurology, University Hospital Essen, Germany, Essen, Germany


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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