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Computational approaches to fMRI analysis

Nature Neuroscience volume 20, pages 304–313 (2017)Cite this article

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Abstract

Analysis methods in cognitive neuroscience have not always matched the richness of fMRI data. Early methods focused on estimating neural activity within individual voxels or regions, averaged over trials or blocks and modeled separately in each participant. This approach mostly neglected the distributed nature of neural representations over voxels, the continuous dynamics of neural activity during tasks, the statistical benefits of performing joint inference over multiple participants and the value of using predictive models to constrain analysis. Several recent exploratory and theory-driven methods have begun to pursue these opportunities. These methods highlight the importance of computational techniques in fMRI analysis, especially machine learning, algorithmic optimization and parallel computing. Adoption of these techniques is enabling a new generation of experiments and analyses that could transform our understanding of some of the most complex—and distinctly human—signals in the brain: acts of cognition such as thoughts, intentions and memories.

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Figure 1: Types of MVPA.
Figure 2: Uses of real-time fMRI.
Figure 3: Schematic of model-based fMRI.
Figure 4: Process for SRM.
Figure 5: Comparison of SRM to other multisubject approaches.
Figure 6: Functional interactions within and between participants.
Figure 7: Real-time cloud system architecture.

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Acknowledgements

We thank C. Chen, M. Regev, Y. Wang, and H. Zhang for assistance and the members of our labs at Princeton University and Intel Labs for their numerous invaluable contributions to the work described herein. This work was made possible by support from Intel Corporation, the John Templeton Foundation, NIH grants R01 EY021755 and R01 MH069456, and NSF grant MRI BCS1229597. The opinions expressed in this publication do not necessarily reflect the views of these agencies.

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Authors and Affiliations

  1. Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA

    Jonathan D Cohen, Nathaniel Daw, Uri Hasson, Yael Niv, Kenneth A Norman, Jonathan Pillow & Nicholas B Turk-Browne

  2. Department of Psychology, Princeton University, Princeton, New Jersey, USA

    Jonathan D Cohen, Nathaniel Daw, Uri Hasson, Yael Niv, Kenneth A Norman, Jonathan Pillow & Nicholas B Turk-Browne

  3. Department of Computer Science, Princeton University, Princeton, New Jersey, USA

    Barbara Engelhardt & Kai Li

  4. Department of Electrical Engineering, Princeton University, Princeton, New Jersey, USA

    Peter J Ramadge

  5. Intel Labs, Intel Corporation, Santa Clara, California, USA.,

    Theodore L Willke

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Contributions

All authors helped conceive the manuscript and wrote one or more sections. N.B.T.-B. edited the manuscript. N.B.T.-B. revised the manuscript with input from J.D.C., K.A.N. and P.J.R. Author order was determined alphabetically.

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Correspondence to Nicholas B Turk-Browne.

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All authors receive research support from Intel Corporation. T.L.W. is employed by Intel Corporation.

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Cohen, J., Daw, N., Engelhardt, B. et al. Computational approaches to fMRI analysis. Nat Neurosci 20, 304–313 (2017). https://doi.org/10.1038/nn.4499

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