Reconciliation of Criticality and a Hierarchy of Timescales in the Brain
Poster No:
1580
Submission Type:
Abstract Submission
Authors:
Leonardo Gollo1
Institutions:
1Monash University, Melbourne, VIC
First Author:
Introduction:
The brain operates as a complex multiscale system, with numerous models and theories proposed to explain the diverse behaviors observed experimentally. Two prominent frameworks for understanding multiscale phenomena in the brain are criticality [1] and the hierarchy of timescales [2]. The hierarchy of timescales suggests that brain regions exhibit interrelated hierarchical dynamics that correspond to their organization within hierarchy of structural brain networks [2,3]. Meanwhile, the concept of criticality proposes that the brain operates near a critical point to leverage computational advantages associated with this state [1].
Methods:
Both frameworks are supported by evidence from studies spanning various spatiotemporal scales, including electrophysiology [4,5], magnetoencephalography (MEG) [6,7], and functional magnetic resonance imaging (fMRI) [8,9]. However, research on brain criticality has largely focused on identifying phase transitions under the assumption that brain dynamics can be captured by a single control parameter. This approach tends to overlook regional heterogeneity, which enables the brain to operate across a wide range of timescales, from rapid neural responses to external stimuli to slower cognitive functions.
Results:
A synthesis of these two frameworks emerges when considering that different brain regions are at varying distances from criticality. Specifically, regions higher in the timescale hierarchy, with slower dynamics, tend to operate closer to criticality [10]. This conceptual integration allows for the coexistence of both critical and subcritical dynamics, offering a natural explanation for the brain's hierarchical organization of timescales.
Conclusions:
This perspective provides novel insights into the neurophysiological mechanisms supporting the brain's temporal dynamics and its ability to balance computational flexibility and stability across multiple scales.
Modeling and Analysis Methods:
Methods Development 1
Other Methods 2
Keywords:
Computational Neuroscience
Systems
Other - Connectomics
1|2Indicates the priority used for review
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Please indicate below if your study was a "resting state" or "task-activation” study.
Resting state
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Healthy subjects
Was this research conducted in the United States?
No
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel?
NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Yes
Were any animal research approved by the relevant IACUC or other animal research panel?
NOTE: Any animal studies without IACUC approval will be automatically rejected.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
Computational modeling
For human MRI, what field strength scanner do you use?
3.0T
Provide references using APA citation style.
[1] Cocchi, L., Gollo, L. L., Zalesky, A., & Breakspear, M. (2017). Criticality in the brain: A synthesis of neurobiology, models and cognition. Progress in neurobiology, 158, 132-152.
[2] Kiebel, S. J., Daunizeau, J., & Friston, K. J. (2008). A hierarchy of time-scales and the brain. PLoS computational biology, 4(11), e1000209.
[3] Gollo, L. L., Zalesky, A., Hutchison, R. M., Van Den Heuvel, M., & Breakspear, M. (2015). Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1668), 20140165.
[4] Murray, J. D., Bernacchia, A., Freedman, D. J., Romo, R., Wallis, J. D., Cai, X., ... & Wang, X. J. (2014). A hierarchy of intrinsic timescales across primate cortex. Nature neuroscience, 17(12), 1661-1663.
[5] Beggs, J. M., & Plenz, D. (2003). Neuronal avalanches in neocortical circuits. Journal of neuroscience, 23(35), 11167-11177.
[6] Shriki, O., Alstott, J., Carver, F., Holroyd, T., Henson, R. N., Smith, M. L., ... & Plenz, D. (2013). Neuronal avalanches in the resting MEG of the human brain. Journal of Neuroscience, 33(16), 7079-7090.
[7] Golesorkhi, M., Gomez-Pilar, J., Tumati, S., Fraser, M., & Northoff, G. (2021). Temporal hierarchy of intrinsic neural timescales converges with spatial core-periphery organization. Communications biology, 4(1), 277.
[8] Tagliazucchi, E., Balenzuela, P., Fraiman, D., & Chialvo, D. R. (2012). Criticality in large-scale brain fMRI dynamics unveiled by a novel point process analysis. Frontiers in physiology, 3, 15.
[9] Raut, R. V., Snyder, A. Z., & Raichle, M. E. (2020). Hierarchical dynamics as a macroscopic organizing principle of the human brain. Proceedings of the National Academy of Sciences, 117(34), 20890-20897.
[10] Harris, B., Gollo, L. L., & Fulcher, B. D. (2024). Tracking the distance to criticality in systems with unknown noise. Physical Review X, 14(3), 031021.
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