title: A Model of Representational Spaces in Human Cortex.
abstract: Current models of the functional architecture of human cortex emphasize areas that capture coarse-scale features of cortical topography but provide no account for population responses that encode information in fine-scale patterns of activity. Here, we present a linear model of shared representational spaces in human cortex that captures fine-scale distinctions among population responses with response-tuning basis functions that are common across brains and models cortical patterns of neural responses with individual-specific topographic basis functions. We derive a common model space for the whole cortex using a new algorithm, searchlight hyperalignment, and complex, dynamic stimuli that provide a broad sampling of visual, auditory, and social percepts. The model aligns representations across brains in occipital, temporal, parietal, and prefrontal cortices, as shown by between-subject multivariate pattern classification and intersubject correlation of representational geometry, indicating that structural principles for shared neural representations apply across widely divergent domains of information. The model provides a rigorous account for individual variability of well-known coarse-scale topographies, such as retinotopy and category selectivity, and goes further to account for fine-scale patterns that are multiplexed with coarse-scale topographies and carry finer distinctions.
authors.affiliation: Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
authors.forename: J Swaroop
authors.initials: JS
authors.lastname: Guntupalli
authors.affiliation: Department of Psychology, University of Magdeburg, Magdeburg 39106, Germany.
authors.forename: Michael
authors.initials: M
authors.lastname: Hanke
authors.affiliation: Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
authors.forename: Yaroslav O
authors.initials: YO
authors.lastname: Halchenko
authors.affiliation: Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.
authors.forename: Andrew C
authors.initials: AC
authors.lastname: Connolly
authors.affiliation: Department of Electrical Engineering, Princeton University, Princeton, NJ 08544, USA.
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Ramadge
authors.affiliation: Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH 03755, USA.|Center for Mind/Brain Sciences (CIMeC), University of Trento, Rovereto, Trentino 38068, Italy.
authors.forename: James V
authors.initials: JV
authors.lastname: Haxby
mesh_terms: D000465:Algorithms; D001307:Auditory Perception / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D002540:Cerebral Cortex / Q000000981:diagnostic imaging / Q000502:physiology*; D005260:Female; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008959:Models, Neurological; D009483:Neuropsychological Tests; D014796:Visual Perception / Q000502:physiology*; D055815:Young Adult
keywords: functional magnetic resonance imaging (fMRI); hyperalignment; multivariate pattern analysis (MVPA); neural decoding; representational similarity analysis (RSA)