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Inter-subject pattern analysis for multivariate group analysis of functional neuroimaging. A unifying formalization.

Qi Wang1, Thierry Artières2, Sylvain Takerkart3

  • 1Institut de Neurosciences de la Timone UMR 7289 Aix-Marseille Université, CNRS Faculté de Médecine, 27 boulevard Jean Moulin, Marseille 13005, France; Laboratoire d'Informatique et Systèmes UMR 7020 Aix-Marseille Université, CNRS, Ecole Centrale de Marseille Faculté des Sciences, 163 avenue de Luminy, Case 901, Marseille 13009, France.

Computer Methods and Programs in Biomedicine
|September 28, 2020
PubMed
Summary

This study formalizes inter-subject pattern analysis for functional neuroimaging population studies. Our framework improves brain decoding by treating it as a multi-source transductive transfer learning problem, enhancing generalization across subjects.

Keywords:
Functional neuroimagingMachine learningNeuroinformaticsPopulation studies

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Area of Science:

  • Neuroimaging
  • Machine Learning
  • Medical Imaging

Background:

  • Population studies in medical imaging require invariant features despite individual differences.
  • Inter-subject pattern analysis (ISPA) is promising for identifying population-level neural coding principles in functional neuroimaging.
  • A formal definition for ISPA is lacking, hindering its widespread adoption.

Purpose of the Study:

  • To provide the first principled formalization of inter-subject pattern analysis (ISPA) for multivariate group analysis in functional neuroimaging.
  • To frame ISPA within well-defined machine learning settings, enabling broader algorithm application.
  • To assess the framework's relevance through model comparisons in brain decoding experiments.

Main Methods:

  • Proposed ISPA as a multi-source transductive transfer learning problem.
  • Conducted two inter-subject brain decoding experiments using magneto-encephalography (16 subjects) and functional magnetic resonance imaging (100 subjects) open datasets.
  • Compared brain decoding models utilizing the proposed formalization against those that do not.

Main Results:

  • A brain decoder using subject-by-subject standardization within the proposed framework outperformed state-of-the-art models with different standardization schemes.
  • Experiments confirmed the difficulty of generalizing brain decoders to new participants compared to new data from existing participants, highlighting a 'transfer gap'.
  • The multi-source and transductive aspects of the formalization were shown to be beneficial.

Conclusions:

  • This work presents the first formalization of ISPA as a multi-source transductive transfer learning problem.
  • Proof-of-concept experiments on diverse functional neuroimaging datasets demonstrate the framework's added value.
  • The formalization aims to popularize ISPA in neuroimaging population studies and stimulate future methodological advancements.