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Related Concept Videos

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Basics of Multivariate Analysis in Neuroimaging Data
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Decoding cognitive concepts from neuroimaging data using multivariate pattern analysis.

Sarah Alizadeh1, Hamidreza Jamalabadi1, Monika Schönauer2

  • 1Medical Psychology and Behavioral Neurobiology, University of Tübingen, Silcherstr. 5, 72076 Tübingen, Germany; Bernstein Center for Computational Neuroscience, Ludwig-Maximilians-Universität München, Großhadernerstr. 2, 82152 Planegg-Martinsried, Germany; IMPRS for Cognitive and Systems Neuroscience, University of Tübingen, Österbergerstr. 3, 72074 Tübingen, Germany; Department of Psychiatry, Division for Translational Psychiatry, University of Tübingen, Calwerstr. 14, 72076 Tübingen, Germany.

Neuroimage
|August 3, 2017
PubMed
Summary
This summary is machine-generated.

Multivariate pattern analysis (MVPA) can be confounded by stimulus differences. We developed a method to separate stimulus and concept processing in brain data, revealing distinct temporal and spatial patterns.

Keywords:
Concept-response curveMultivariate pattern analysisNeuroimagingPermutation statisticsStimulus-related confounds

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Area of Science:

  • Cognitive Neuroscience
  • Machine Learning in Neuroscience
  • Brain-Computer Interfaces

Background:

  • Multivariate pattern analysis (MVPA) is increasingly used in life sciences but faces challenges with complex data.
  • High sensitivity of MVPA to stimulus differences can confound the decoding of cognitive concepts.
  • Distinguishing between stimulus-related and concept-related neural processing is experimentally difficult.

Purpose of the Study:

  • To develop and validate a novel method for disentangling stimulus-specific and concept-specific neural information using MVPA.
  • To quantify the relative contributions of lower-order stimulus processing and higher-order concept processing to decoding performance.
  • To analyze the temporal dynamics and spatial distribution of concept-related versus stimulus-related neural activity.

Main Methods:

  • Proposed a method utilizing concept-unrelated grouping factors and blocked permutation tests.
  • Gradually manipulated the proportion of concept-related information while keeping stimulus-related factors constant.
  • Generated a 'concept-response curve' to visualize the interplay between stimulus and concept information.
  • Applied the method to three EEG datasets for decoding concepts (digits/letters, faces/houses, animals/fruits) at the single-trial level.

Main Results:

  • Exemplar-specific stimulus differences can lead to above-chance classification accuracy without conceptual information.
  • Concept-response curves effectively separated stimulus-related from concept-related neural processing.
  • Perceptual information was decoded earlier than conceptual information for digits and letters.
  • Concept-related neural activity showed wider spatial distribution and later frontal cortex involvement compared to stimulus-level predictive sites.

Conclusions:

  • The proposed method successfully differentiates stimulus-driven from concept-driven neural processing.
  • Concept-response curves provide valuable insights into the temporal course and spatial signatures of cognitive processes.
  • MVPA can be refined to better understand neural mechanisms underlying complex cognition by accounting for stimulus confounds.