Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.

Journal: NeuroImage
Published Date: Aug 15, 2016

Abstract

Functional MRI (fMRI) is a popular and important tool for noninvasive mapping of neural activity. As fMRI measures the hemodynamic response, the resulting activation maps do not perfectly reflect the underlying neural activity. The purpose of this work was to design a data-driven model to improve the spatial accuracy of fMRI maps in the rat olfactory bulb. This system is an ideal choice for this investigation since the bulb circuit is well characterized, allowing for an accurate definition of activity patterns in order to train the model. We generated models for both cerebral blood volume weighted (CBVw) and blood oxygen level dependent (BOLD) fMRI data. The results indicate that the spatial accuracy of the activation maps is either significantly improved or at worst not significantly different when using the learned models compared to a conventional general linear model approach, particularly for BOLD images and activity patterns involving deep layers of the bulb. Furthermore, the activation maps computed by CBVw and BOLD data show increased agreement when using the learned models, lending more confidence to their accuracy. The models presented here could have an immediate impact on studies of the olfactory bulb, but perhaps more importantly, demonstrate the potential for similar flexible, data-driven models to improve the quality of activation maps calculated using fMRI data.

Authors

  • Matthew C Murphy
    Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: murphymc@pitt.edu.
  • Alexander J Poplawsky
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Alberto L Vazquez
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  • Kevin C Chan
    Department of Ophthalmology, New York University Grossman School of Medicine, NYU Langone Health, New York University, New York, NY, United States.
  • Seong-Gi Kim
    Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, Republic of Korea; Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea; Department of Biological Sciences, Sungkyunkwan University, Suwon, Republic of Korea.
  • Mitsuhiro Fukuda
    Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.

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