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Mind the drift - improving sensitivity to fMRI pattern information by accounting for temporal pattern drift

Arjen Alink, Alexander Walther, Alexandra Krugliak, Jasper J.F. van den Bosch, Nikolaus Kriegeskorte
doi: https://doi.org/10.1101/032391
Arjen Alink
1MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, Cambridge, United Kingdom
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Alexander Walther
1MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, Cambridge, United Kingdom
2Institute of Cognitive Neuroscience, University College London, Alexandra House, 17-19 Queen Square, London WC1N 3AR, United Kingdom
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Alexandra Krugliak
4Centre for Computational Neuroscience and Cognitive Robotics, School of Psychology, University of Birmingham, Birmingham B15 2TT, United Kingdom
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Jasper J.F. van den Bosch
3Institute for Learning and Brain Sciences, University of Washington, Seattle, WA, USA
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Nikolaus Kriegeskorte
1MRC Cognition and Brain Sciences Unit, 15 Chaucer Road, CB2 7EF, Cambridge, United Kingdom
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Abstract

Analyzing functional magnetic resonance imaging (fMRI) pattern similarity is becoming increasingly popular because it allows one to relate distributed patterns of voxel activity to continuous perceptual and cognitive states of the human brain. Here we show that fMRI pattern similarity estimates are severely affected by temporal pattern drifts in fMRI data – even after voxel-wise detrending. For this particular dataset, the drift effect obscures orientation information as measured by fMRI pattern dissimilarities. We demonstrate that orientation information can be recovered using three different methods: 1. Regressing out the drift component through linear modeling; 2. Computing representational distances between conditions measured in independent imaging runs; 3. Crossvalidation of pattern distance estimates. One possible source of temporal pattern drift could be random walk like fluctuations — physiological or scanner related — occurring within single voxel timecourses. This explanation is consistent with voxel-wise detrending not alleviating pattern drift effects. In addition, this would explain why crossvalidated pattern distances are robust to temporal drift because a random walk process is expected to give rise to non-replicable drift directions. Given these findings, we recommend that future fMRI studies take pattern drift into account when analyzing pattern similarity as this can greatly enhance the sensitivity to experimental effects of interest.

Footnotes

  • ↵arjen.alink{at}mrc-cbu.cam.ac.uk

  • alexander.walther{at}mrc-cbu.cam.ac.uk

  • axk263{at}bham.ac.uk

  • jasperb{at}uw.edu

  • nikolaus.kriegeskorte{at}mrc-cbu.cam.ac.uk

  • * Shared first authorship

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-NC-ND 4.0 International license.
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Posted December 04, 2015.
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Mind the drift - improving sensitivity to fMRI pattern information by accounting for temporal pattern drift
Arjen Alink, Alexander Walther, Alexandra Krugliak, Jasper J.F. van den Bosch, Nikolaus Kriegeskorte
bioRxiv 032391; doi: https://doi.org/10.1101/032391
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Mind the drift - improving sensitivity to fMRI pattern information by accounting for temporal pattern drift
Arjen Alink, Alexander Walther, Alexandra Krugliak, Jasper J.F. van den Bosch, Nikolaus Kriegeskorte
bioRxiv 032391; doi: https://doi.org/10.1101/032391

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