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Encoding the local connectivity patterns of fMRI for cognitive task and state classification

  • Itir Onal Ertugrul
  • Mete Ozay
  • Fatos T. Yarman Vural
Original Research

Abstract

In this work, we propose a novel framework to encode the local connectivity patterns of brain, using Fisher vectors (FV), vector of locally aggregated descriptors (VLAD) and bag-of-words (BoW) methods. We first obtain local descriptors, called mesh arc descriptors (MADs) from fMRI data, by forming local meshes around anatomical regions, and estimating their relationship within a neighborhood. Then, we extract a dictionary of relationships, called brain connectivity dictionary by fitting a generative Gaussian mixture model (GMM) to a set of MADs, and selecting codewords at the mean of each component of the mixture. Codewords represent connectivity patterns among anatomical regions. We also encode MADs by VLAD and BoW methods using k-Means clustering. We classify cognitive tasks using the Human Connectome Project (HCP) task fMRI dataset and cognitive states using the Emotional Memory Retrieval (EMR). We train support vector machines (SVMs) using the encoded MADs. Results demonstrate that, FV encoding of MADs can be successfully employed for classification of cognitive tasks, and outperform VLAD and BoW representations. Moreover, we identify the significant Gaussians in mixture models by computing energy of their corresponding FV parts, and analyze their effect on classification accuracy. Finally, we suggest a new method to visualize the codewords of the learned brain connectivity dictionary.

Keywords

fMRI Brain decoding Fisher vector encoding Mesh arc descriptors 

Notes

Acknowledgments

This work was completed when Itir Onal Ertugrul was with the Department of Computer Engineering, METU.

Funding

This work was supported by CREST, JST, Grant Number JPMJCR14D1, the ImPACT Program of the Council for Science, Technology, and Innovation (Cabinet Office, Government of Japan) and TUBITAK Project No 116E091. Itir Onal Ertugrul was supported by TUBITAK 2211E.

Compliance with Ethical Standards

Conflict of interests

Itir Onal Ertugrul, Mete Ozay and Fatos T. Yarman Vural declare that they have no conflicts of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors. Data used in this study were previously collected.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.Graduate School of Information SciencesTohoku UniversitySendaiJapan
  3. 3.Department of Computer EngineeringMiddle East Technical UniversityAnkaraTurkey

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