Control mechanisms for brain state transitions - new tools
Final Report Abstract
This project aimed to extend and adapt methods from the field of network control theory to establish a framework in which brain state transitions derived from real fMRI experiments could be studied to investigate how the brain controls dynamic transitions between experimentally defined task states covering a range of cognitive and emotive functions. In a first step, we have successfully adapted and extended a fine-grained multimodal brain atlas covering cortical and subcortical brain areas to study brain structure and function simultaneously. In a second step, we established a framework for studying brain state transitions during working memory. Our results show i) that regions in frontoparietal areas act as universal controllers for generally facilitating brain state transition, b) transitions into cognitively more demanding state require more control energy than opposite transitions, c) the effort to control brain state transitions is critically, but differentially modulated by dopamine D1 and D2 receptor functioning and d) such control properties are altered in schizophrenia patients, who show a more diversified pattern of suboptimal transitions. In a third step, we applied the newly established framework to study brain transitions between multiple tasks. Here, we could identify brain regions in the attention network as critical drivers of brain state transitions independent of specific cognitive or emotive domains. In summary, this project has successfully established a framework for studying transitions between experimentally defined brain states that can be applied across several fMRI tasks.
Publications
- (2019). Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia. bioRxiv preprint
Braun U., Harneit A., Pergola G., Menara T., Schaefer A., Betzel R.F., Zang Z., Schweiger J.I., Zhang X., Schwarz K., Chen J., Blasi G., Bertolino A., Durstewitz D., Pasqualetti F., Schwarz E., Meyer-Lindenberg A., Basset D.S., & Tost H.
(See online at https://doi.org/10.1101/679670) - (2019). Generative network models identify biological mechanisms of altered structural brain connectivity in schizophrenia. bioRxiv, preprint
Zhang X., Braun U., Harneit A., Zang Z., Geiger L.S., Betzel R.F., Chen J., Schweiger J., Schwarz K., Reinwald J.R., Fritze S., Witt S., Rietschel M., Nöthen M.M., Degenhardt F., Schwarz E., Hirjak D., Meyer-Lindenberg A., Bassett D.S., & Tost H.
(See online at https://doi.org/10.1101/604322) - (2020). Data-Driven Approaches to Neuroimaging Analysis to Enhance Psychiatric Diagnosis and Therapy. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging
Zhang X., Braun U., Tost H., & Bassett D.S.
(See online at https://doi.org/10.1016/j.bpsc.2019.12.015)
