Trends in Cognitive Sciences
ReviewThe Rediscovery of Slowness: Exploring the Timing of Cognition
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,
References (78)
Functional connectivity dynamics: modeling the switching behavior of the resting state
Neuroimage
(2015)- et al.
A default mode of brain function: a brief history of an evolving idea
Neuroimage
(2007) - et al.
Does the brain have a baseline? Why we should be resisting a rest
Neuroimage
(2007) Resting brains never rest: computational insights into potential cognitive architectures
Trends Neurosci.
(2013)Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
Neuroimage
(2002)An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
Neuroimage
(2006)- et al.
Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI
J. Magn. Reson. B
(1996) MR connectomics: principles and challenges
J. Neurosci. Methods
(2010)- et al.
Time-frequency dynamics of resting-state brain connectivity measured with fMRI
Neuroimage
(2010) - et al.
The metastable brain
Neuron
(2014)
Exploring the network dynamics underlying brain activity during rest
Prog. Neurobiol.
Role of local network oscillations in resting-state functional connectivity
Neuroimage
Exploring mechanisms of spontaneous functional connectivity in MEG: How delayed network interactions lead to structured amplitude envelopes of band-pass filtered oscillations
Neuroimage
Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders
Neuron
The functional neuroanatomy of the evolving parent–infant relationship
Prog. Neurobiol.
Pleasure systems in the brain
Neuron
Fledgling pathoconnectomics of psychiatric disorders
Trends Cogn. Sci.
Die Entdeckung der Langsamkeit (transl. ‘The Discovery of Slowness’)
Individual differences in reasoning: implications for the rationality debate?
Behav. Brain Sci.
Attention and cognitive control
Judgment under uncertainty: heuristics and biases
Science
Thinking, Fast and Slow
The brainweb: phase synchronization and large-scale integration
Nat. Rev. Neurosci.
Functional architectures and structured flows on manifolds: a dynamical framework for motor behavior
Psychol. Rev.
Time scale hierarchies in the functional organization of complex behaviors
PLoS Comput. Biol.
Building neurocognitive networks with a distributed functional architecture
Adv. Exp. Med. Biol.
On the nature of seizure dynamics
Brain
Complex processes from dynamical architectures with time-scale hierarchy
PLoS ONE
Tracking whole-brain connectivity dynamics in the resting state
Cereb. Cortex
Toward discovery science of human brain function
Proc. Natl. Acad. Sci. U.S.A.
A default mode of brain function
Proc. Natl. Acad. Sci. U.S.A.
Investigations into resting-state connectivity using independent component analysis
Philos. Trans. R. Soc. Lond. B: Biol. Sci.
Spontaneous low-frequency BOLD signal fluctuations: an fMRI investigation of the resting-state default mode of brain function hypothesis
Hum. Brain Mapp.
Functional connectivity in the motor cortex of resting human brain using echo-planar MRI
Magn. Reson. Med.
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Proc. Natl. Acad. Sci. U.S.A.
Complex brain networks: graph theoretical analysis of structural and functional systems
Nat. Rev. Neurosci.
Intrinsic functional architecture in the anaesthetized monkey brain
Nature
Functional connectivity of the macaque brain across stimulus and arousal states
J. Neurosci.
The variable discharge of cortical neurons: implications for connectivity, computation, and information coding
J. Neurosci.
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