title: Computational approaches to fMRI analysis.
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.
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.|Department of Psychology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Jonathan D
authors.initials: JD
authors.lastname: Cohen
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.|Department of Psychology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Nathaniel
authors.identifier: http://orcid.org/0000-0001-5029-1430
authors.initials: N
authors.lastname: Daw
authors.affiliation: Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
authors.forename: Barbara
authors.initials: B
authors.lastname: Engelhardt
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.|Department of Psychology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Uri
authors.initials: U
authors.lastname: Hasson
authors.affiliation: Department of Computer Science, Princeton University, Princeton, New Jersey, USA.
authors.forename: Kai
authors.initials: K
authors.lastname: Li
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.|Department of Psychology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Yael
authors.identifier: http://orcid.org/0000-0002-0259-8371
authors.initials: Y
authors.lastname: Niv
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.|Department of Psychology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Kenneth A
authors.initials: KA
authors.lastname: Norman
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.|Department of Psychology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Jonathan
authors.initials: J
authors.lastname: Pillow
authors.affiliation: Department of Electrical Engineering, Princeton University, Princeton, New Jersey, USA.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Ramadge
authors.affiliation: Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.|Department of Psychology, Princeton University, Princeton, New Jersey, USA.
authors.forename: Nicholas B
authors.initials: NB
authors.lastname: Turk-Browne
authors.affiliation: Intel Labs, Intel Corporation, Santa Clara, California, USA.
authors.forename: Theodore L
authors.initials: TL
authors.lastname: Willke
mesh_terms: D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods; D003071:Cognition / Q000502:physiology*; D006801:Humans; D007858:Learning / Q000502:physiology*; D008279:Magnetic Resonance Imaging / Q000379:methods; D008959:Models, Neurological