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Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity

Erica L. Busch, Lukas Slipski, Ma Feilong, J. Swaroop Guntupalli, Matteo Visconti di Oleggio Castello, Jeremy F. Huckins, View ORCID ProfileSamuel A. Nastase, M. Ida Gobbini, Tor D. Wager, James V. Haxby
doi: https://doi.org/10.1101/2020.11.25.398883
Erica L. Busch
1Department of Psychology, Yale University, New Haven, CT, USA
2Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
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  • For correspondence: James.V.Haxby@dartmouth.edu Erica.Busch@yale.edu Lukas.Slipski.gr@dartmouth.edu
Lukas Slipski
2Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
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  • For correspondence: James.V.Haxby@dartmouth.edu Erica.Busch@yale.edu Lukas.Slipski.gr@dartmouth.edu
Ma Feilong
2Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
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J. Swaroop Guntupalli
3Vicarious AI, Union City, CA, USA
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Matteo Visconti di Oleggio Castello
4Helen Wills Neuroscience Institute, University of California, Berkeley
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Jeremy F. Huckins
2Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
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Samuel A. Nastase
5Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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  • ORCID record for Samuel A. Nastase
M. Ida Gobbini
6Department of Experimental, Diagnostic, and Specialty Medicine, Medical School, University of Bologna, Italy
7Cognitive Science Program, Dartmouth College, Hanover, NH, USA
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Tor D. Wager
2Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
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James V. Haxby
2Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
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  • For correspondence: James.V.Haxby@dartmouth.edu Erica.Busch@yale.edu Lukas.Slipski.gr@dartmouth.edu
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Abstract

Shared information content is represented across brains in idiosyncratic functional topographies. Hyperalignment addresses these idiosyncrasies by using neural responses to project individuals’ brain data into a common model space while maintaining the geometric relationships between distinct activity patterns. The dimensions of this common model can encode any kind of functional profiles shared across individuals, such as cortical response profiles collected during a common time-locked stimulus presentation (e.g. movie viewing) or functional connectivity profiles. Performing hyperalignment with either response-based or connectivity-based input data derives transformations to project individuals’ neural data from anatomical space into the common model such that functional information is optimally aligned across brains. Previously, only response or connectivity profiles were used in the derivation of these transformations. In this study, we used three separate data sets collected while participants watched feature films to derive transformations representing both response-based and connectivity-based information with a single algorithm. Our new method, hybrid hyperalignment, aligns response-based information as well as or better than response hyperalignment while simultaneously aligning connectivity-based information better than connectivity hyperalignment, all in one information space. These results suggest that a single common information space could encode both shared cortical response and functional connectivity profiles across individuals.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • http://www.pymvpa.org/

  • https://openneuro.org/datasets/ds003017/versions/1.0.2

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted November 27, 2020.
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Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity
Erica L. Busch, Lukas Slipski, Ma Feilong, J. Swaroop Guntupalli, Matteo Visconti di Oleggio Castello, Jeremy F. Huckins, Samuel A. Nastase, M. Ida Gobbini, Tor D. Wager, James V. Haxby
bioRxiv 2020.11.25.398883; doi: https://doi.org/10.1101/2020.11.25.398883
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Hybrid Hyperalignment: A single high-dimensional model of shared information embedded in cortical patterns of response and functional connectivity
Erica L. Busch, Lukas Slipski, Ma Feilong, J. Swaroop Guntupalli, Matteo Visconti di Oleggio Castello, Jeremy F. Huckins, Samuel A. Nastase, M. Ida Gobbini, Tor D. Wager, James V. Haxby
bioRxiv 2020.11.25.398883; doi: https://doi.org/10.1101/2020.11.25.398883

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