Review
The Rediscovery of Slowness: Exploring the Timing of Cognition

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The dynamics of the human brain exhibits ‘slowness’ during spontaneous activity and task-based cognition.

Whole-brain computational modeling can account for the mechanisms underlying this slowness in terms of maximal metastability of the dynamical system.

A better understanding of the balance between fast and slow brain processing could lead to fundamental new insights into the brain in health and disease.

Slowness of thought is not necessarily a handicap but could be a signature of optimal brain function. Emerging evidence shows that neuroanatomical and dynamical constraints of the human brain shape its functionality in optimal ways, characterized by slowness during task-based cognition in the context of spontaneous resting-state activity. This activity can be described mechanistically by whole-brain computational modeling that relates directly to optimality in the context of theories arguing for metastability in the brain. We discuss the role for optimal processing of information in the context of cognitive, task-related activity, and propose that combining multi-modal neuroimaging and explicit whole-brain models focused on the timing of functional dynamics can help to uncover fundamental rules of brain function in health and disease.

Section snippets

Rediscovering Slowness

In his thoughtful and beautifully written book, Die Entdeckung der Langsamkeit (transl. ‘The Discovery of Slowness’) [1], the German author Sten Nadolny meditates over the ultimate reasons for the success and failure of the English polar researcher John Franklin (1786–1847). Franklin was much celebrated during the Victorian era but perished on his third expedition to discover the mythical Northwest Passage, supposed to link the Atlantic and the Pacific Oceans above the northern coast of the

Characterizing Functional and Structural Brain Connectivity

Over the past decade there has been an explosion of empirical and theoretical interest in the spontaneous or intrinsic activity of the brain [14]. Initially, neuroimaging experiments assumed that this spontaneous activity was mainly noise and could be used simply as a control for discovering task-related activity 15, 16. However, careful experiments using a wide variety of neuroimaging techniques have found that spontaneous activity is highly structured in time and space 17, 18, 19.

Uncovering the Spatiotemporal Dynamics of Brain Activity

Studies have demonstrated that the correlations among brain regions, both within and between networks, evolve over time 35, 36, 37. Important insights recently emerged from observations of fluctuations of the FC when calculated over relatively short sliding windows [12]. Further progress was made in quantifying the spatiotemporal structure of these fluctuations by introducing a careful dynamic analysis characterizing the FC dynamics (FCD) and explaining its nature based on large-scale network

Whole-Brain Computational Modeling

One powerful way to investigate and model the non-stationary dynamics of whole-brain activity is to use whole-brain computational modeling (Box 1), which takes into account the interplay and mutual entrainment of local dynamics using the underlying structural connections, and results in global whole-brain dynamics (Figure 3A). Indeed, whole-brain models have been shown to be able to explain the spatial aspects of the resting FC by fitting the spatial correlation as extracted by the grand

The Resting Brain Is Maximally Metastable and Slow

Evidence from a wide range of experiments suggests that the brain is metastable [39] (Box 2). Further empirical evidence has recently come from whole-brain computational models with oscillatory local nodes using the Kuramoto model 50, 56. These simulations fitting the model to MEG data recorded over milliseconds importantly show that the brain is not only metastable, but maximally metastable, which implies the possibility for the widest exploration of the dynamical repertoire of the network.

Beyond Resting: The Brain Is Slow During Stimuli/Task Processing

One must question the importance of slowness in the resting brain when it could be argued that cognition is more about engaging with tasks and stimuli than what is done at rest [16]. From the very start, neuroimaging experiments have – albeit mostly involuntarily – explored the link between spontaneous and task-related activity by pervasive use of subtraction paradigms. Nevertheless, it is only with the rise of resting-state paradigms that a better understanding has arisen of the link between

Concluding Remarks and Future Perspectives

Slowness is not obviously a desirable quality in a ruthless natural world built primarily on survival; even so, the relative evolutionary success of humans is evidence that a degree of slowness of cognition combined with fast reactions can be highly adaptive. The flexibility of human cognition for goal-directed behavior relies on our ability to build coherent, distributed dynamical states by segregating and integrating information on multiple timescales [65].

As an example, think about how

Acknowledgments

M.L.K. is supported by European Research Council (ERC) Consolidator Grant: CAREGIVING (615539). G.D. is supported by ERC Advanced Grant: DYSTRUCTURE (295129) and by the Spanish Research Project PSI2013-42091-P. In addition, the authors acknowledge the support of the German Ministry of Education and Research (Bernstein Focus State Dependencies of Learning 01GQ0971-5) to P.R., the James S. McDonnell Foundation (Brain Network Recovery Group JSMF22002082) to A.R.M., G.D., P.R., and V.J., the

Glossary

Attractor
a set to which a dynamical system evolves after a sufficiently long time. Points close to the attractor remain close, even under small perturbations.
Bifurcation
an abrupt qualitative change in the system's dynamics when one or more parameter pass through critical values, for instance loss of stability and the appearance of sustained oscillations.
Connectome
the complete description of the structural connections between elements of a nervous system.
Criticality
at the brink of a bifurcation,

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