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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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.
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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