| Autor: |
Wrosch JK; Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany. Jana.Wrosch@UK-Erlangen.de., Einem VV; Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany.; Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany., Breininger K; Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany., Dahlmanns M; Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany., Maier A; Pattern Recognition Lab, Department of Computer Science, Friedrich-Alexander University of Erlangen-Nürnberg, 91058, Erlangen, Germany., Kornhuber J; Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany., Groemer TW; Department of Psychiatry and Psychotherapy, Friedrich-Alexander University of Erlangen-Nuremberg, 91054, Erlangen, Germany. |
| Abstrakt: |
Analyzing the connectivity of neuronal networks, based on functional brain imaging data, has yielded new insight into brain circuitry, bringing functional and effective networks into the focus of interest for understanding complex neurological and psychiatric disorders. However, the analysis of network changes, based on the activity of individual neurons, is hindered by the lack of suitable meaningful and reproducible methodologies. Here, we used calcium imaging, statistical spike time analysis and a powerful classification model to reconstruct effective networks of primary rat hippocampal neurons in vitro. This method enables the calculation of network parameters, such as propagation probability, path length, and clustering behavior through the measurement of synaptic activity at the single-cell level, thus providing a fuller understanding of how changes at single synapses translate to an entire population of neurons. We demonstrate that our methodology can detect the known effects of drug-induced neuronal inactivity and can be used to investigate the extensive rewiring processes affecting population-wide connectivity patterns after periods of induced neuronal inactivity. |
