Theoretical and Computational Neuroscience
Author: Juan Ignacio Piccinini | email: piccijuan@gmail.com
Juan Ignacio Piccinini 1°, Yonatan Sanz Perl 2°, Enzo Tagliazucchi 1°
1° Buenos Aires Physics Institute and Physics Department, University of Buenos Aires, Buenos Aires, Argentina & National Scientific and Technical Research Council (CONICET), CABA, Buenos Aires, Argentina
2° Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Barcelona, Spain
Human behavior and cognitive function correlate with complex patterns of spatio-temporal brain dynamics, which can be simulated using computational models with different degrees of biophysical realism. We used a data-driven optimization algorithm to determine and classify the types of local dynamics that enable the reproduction of different observables derived from functional magnetic resonance recordings. The phase space analysis of the resulting equations revealed a predominance of stable spiral attractors, which optimized the similarity to the empirical data in terms of the synchronization, metastability, and functional connectivity dynamics. For stable limit cycles, departures from harmonic oscillations improved the fit in terms of functional connectivity dynamics. Eigenvalue analyses showed that proximity to a bifurcation improved the accuracy of the simulation for wakefulness, while deep sleep was associated with increased stability. Our results provide testable predictions that constrain the landscape of suitable biophysical models, while supporting noise-driven dynamics close to a bifurcation as a canonical mechanism underlying the complex fluctuations that characterize endogenous brain activity.