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Activity flow over resting-state networks shapes cognitive task activations

Nature Neuroscience volume 19, pages 1718–1726 (2016)Cite this article

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Abstract

Resting-state functional connectivity (FC) has helped reveal the intrinsic network organization of the human brain, yet its relevance to cognitive task activations has been unclear. Uncertainty remains despite evidence that resting-state FC patterns are highly similar to cognitive task activation patterns. Identifying the distributed processes that shape localized cognitive task activations may help reveal why resting-state FC is so strongly related to cognitive task activations. We found that estimating task-evoked activity flow (the spread of activation amplitudes) over resting-state FC networks allowed prediction of cognitive task activations in a large-scale neural network model. Applying this insight to empirical functional MRI data, we found that cognitive task activations can be predicted in held-out brain regions (and held-out individuals) via estimated activity flow over resting-state FC networks. This suggests that task-evoked activity flow over intrinsic networks is a large-scale mechanism explaining the relevance of resting-state FC to cognitive task activations.

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Figure 1: Activity flow mapping over resting-state FC networks allows prediction of held-out task activations.
Figure 2: Activity flow mapping predicts cognitive task activations with empirical fMRI data.
Figure 3: Using multiple regression to estimate resting-state FC increases prediction accuracy.
Figure 4: Predicting voxelwise activation patterns.
Figure 5: Illustration of activity flow mapping of single region.

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Acknowledgements

We thank B. Biswal, M. Dixon, T. Braver, S. Petersen and J. Power for helpful conversations during preparation of this manuscript. Data were provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: D. Van Essen and K. Ugurbil; 1U54MH091657) funded by the 16 NIH Institutes and Centers that support the NIH Blueprint for Neuroscience Research; and by the McDonnell Center for Systems Neuroscience at Washington University. M.W.C. was supported by the US National Institutes of Health under award K99-R00 MH096801. D.S.B. acknowledges support from the John D. and Catherine T. MacArthur Foundation, the Army Research Laboratory and the Army Research Office through contract numbers W911NF-10-2-0022 and W911NF-14-1-0679, the National Institute of Mental Health (2-R01-DC-009209-11), the National Institute of Child Health and Human Development (1R01HD086888-01), the Office of Naval Research and the National Science Foundation (#BCS-1441502, #BCS-1430087, and #PHY-1554488). The content is solely the responsibility of the authors and does not necessarily represent the official views of any of the funding agencies.

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Authors and Affiliations

  1. Center for Molecular and Behavioral Neuroscience, Rutgers University, Newark, New Jersey, USA

    Michael W Cole, Takuya Ito & Douglas H Schultz

  2. Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Danielle S Bassett

  3. Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA

    Danielle S Bassett

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  1. Michael W Cole
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Contributions

M.W.C. conceived of the study, developed the activity flow mapping algorithm, developed the computational model, developed the multiple-regression functional connectivity approach, performed the analyses and wrote the manuscript. T.I. developed the computational model and assisted with writing the manuscript. D.S.B. provided feedback on the activity flow mapping algorithm and assisted with writing the manuscript. D.H.S. assisted with writing the manuscript.

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Correspondence to Michael W Cole.

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Integrated supplementary information

Supplementary Figure 1 Isolating and predicting task-specific activations using Pearson correlation FC

A) The activation patterns for all 7 tasks are shown (right) relative to rest baseline. Resting-state FC using Pearson correlation was used to predict activation patterns across the 7 tasks (mean activity amplitude of each region for each task). These activation patterns were highly similar to one another, reducing the ability to make inferences at the level of individual tasks. The across-task average activity flow prediction r-value was 0.54. B) We illustrate a framework involving the decomposition of task activation patterns with the motor task as an example. Whole-brain task activation patterns are shown to be composed of a task-general activation pattern common across tasks (the first principal component of the other 6 tasks) and a task-specific activation pattern (the task activation vector with the task-general activation vector regressed out). Each task-specific pattern is equivalent in some ways to a general linear model contrast of that task’s activations versus the activation patterns of the other tasks. Note the task-specific increase in the motor/tactile network (cyan arrow) consistent with the motor task. See Fig. 2C for the result of this procedure for all 7 tasks.

Supplementary Figure 2 Activity-flow-based predictions depend on an accurate FC architecture

We randomized which region’s FC was used for each region’s prediction. A) An example of what happens to the activation prediction matrix when resting-state multiple regression FC is randomized: an across-task average activity flow prediction r-value of 0.002. B) The distribution of Pearson correlation r-values between predicted and actual activity patterns over 10,000 permutations of FC. The highest r-value was 0.024. Resting-state multiple regression FC was used along with task-specific activation patterns.

Supplementary Figure 3 Predicted-to-actual activation pattern similarities by network

A) The correlations between predicted and actual activation patterns for each network are shown separately for each task. Some networks showed negative correlations, such as the motor/tactile (mouth) network. B) The predicted and actual activations are shown for the motor/tactile (mouth) network across all tasks (7 tasks X 5 regions = 25 values) on the left (r=0.91). However, the correlation was negative (r=-0.69) for that network when focusing on task 3, the language task (right side). The red ellipse in the left plot indicates where the language task activations are located, indicating that they are fairly accurate relative to the across-task range of this network’s activations.

Supplementary Figure 4 Predicting voxelwise activation patterns for all seven tasks

Predicted and actual task-specific activation patterns are shown for all 7 tasks. These results are based on principal components multiple regression FC with task-specific activation patterns. Maps correspond to the emotion task (A), gambling task (B), language task (C), motor task (D), relational reasoning task (E), social task (F), and the N-back working memory task (G).

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Cole, M., Ito, T., Bassett, D. et al. Activity flow over resting-state networks shapes cognitive task activations. Nat Neurosci 19, 1718–1726 (2016). https://doi.org/10.1038/nn.4406

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