In human neuroscience, studies of cognition are rarely grounded in non-task-evoked, ‘spontaneous’ neural activity. Indeed, studies of spontaneous activity tend to focus predominantly on intrinsic neural patterns (for example, resting-state networks). Taking a ‘representation-rich’ approach bridges the gap between cognition and resting-state communities: this approach relies on decoding task-related representations from spontaneous neural activity, allowing quantification of the representational content and rich dynamics of such activity. For example, if we know the neural representation of an episodic memory, we can decode its subsequent replay during rest. We argue that such an approach advances cognitive research beyond a focus on immediate task demand and provides insight into the functional relevance of the intrinsic neural pattern (for example, the default mode network). This in turn enables a greater integration between human and animal neuroscience, facilitating experimental testing of theoretical accounts of intrinsic activity, and opening new avenues of research in psychiatry.
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The authors thank E. Wimmer, C. Higgins and P. Schwartenbeck for helpful discussions and fruitful collaborations regarding the work and ideas presented in this Review. This work was supported by the Fundamental Research Funds for the Central Universities (to Y.L.), a Wellcome Trust Investigator Award (098362/Z/12/Z to R.J.D.), a UCL Welcome PhD Fellowship for Clinicians (102186/B/13/Z to M.M.N.), a Wellcome Trust Senior Research Fellowship (104765/Z/14/Z to T.B.), a Principal Research Fellowship (219525/Z/19/Z to T.B.), a James S. McDonnell Foundation Award (JSMF220020372 to T.B.), an Independent Research Group Grant from the Max Planck Society (M.TN.A.BILD0004 to N.W.S.) and a Starting Grant from the European Union (ERC-2019-StG REPLAY-852669 to N.W.S.). M.M.N. is a predoctoral fellow of the International Max Planck Research School on Computational Methods in Psychiatry and Ageing Research (https://www.mps-ucl-centre.mpg.de/en/comp2psych). The Max Planck UCL Centre for Computational Psychiatry and Ageing Research is supported by UCL and the Max Planck Society. The Wellcome Centre for Human Neuroimaging is supported by core funding from the Wellcome Trust (203147/Z/16/Z). The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust (203139/Z/16/Z).
The authors declare no competing interests.
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An experiment designed to manipulate an assumed cognitive process.
Period without explicit task demand (for example, during rest).
- Functional connectivity
Temporal dependency of neuronal activation (for example, correlation) between anatomically separated brain regions.
The strength of a signal in a given frequency band.
Reading out task-related information from neural activity.
- Pairwise multivoxel correlation
Correlation between all pairs of voxels of interest using the entire time course of the functional MRI signal.
- Representational similarity analysis
Measure of the similarity of neural activity among different conditions.
- Multivoxel patterns
Neural activity profile of multiple voxels in the brain.
- Transition matrix
A matrix that stores the probability of transition from state s to state s′.
Independent variables in a regression model.
- Multivariate decision boundary
A region of a problem space where the output label of a classifier is ambiguous.
- Charles Bonnet syndrome
A condition where visual hallucinations occur as a result of vision loss.
- Resting states
The states when an explicit task is not being performed.
- Brownian diffusive spatial trajectories
Trajectories whose movement is random in space.
- Superdiffusive dynamics
Random movement but with sudden jumps.
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Liu, Y., Nour, M.M., Schuck, N.W. et al. Decoding cognition from spontaneous neural activity. Nat Rev Neurosci 23, 204–214 (2022). https://doi.org/10.1038/s41583-022-00570-z
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