title: Improved spatial accuracy of functional maps in the rat olfactory bulb using supervised machine learning approach.
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.affiliation: 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.
authors.forename: Matthew C
authors.initials: MC
authors.lastname: Murphy
authors.affiliation: Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
authors.forename: Alexander J
authors.initials: AJ
authors.lastname: Poplawsky
authors.affiliation: Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
authors.forename: Alberto L
authors.initials: AL
authors.lastname: Vazquez
authors.affiliation: Department of Ophthalmology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
authors.forename: Kevin C
authors.initials: KC
authors.lastname: Chan
authors.affiliation: 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.
authors.forename: Seong-Gi
authors.initials: SG
authors.lastname: Kim
authors.affiliation: Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
authors.forename: Mitsuhiro
authors.initials: M
authors.lastname: Fukuda
mesh_terms: D000465:Algorithms; D000818:Animals; D001931:Brain Mapping / Q000379:methods*; D004558:Electric Stimulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009830:Olfactory Bulb / Q000502:physiology*; D010363:Pattern Recognition, Automated / Q000379:methods; D051381:Rats; D017207:Rats, Sprague-Dawley; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012903:Smell / Q000502:physiology*; D062211:Spatio-Temporal Analysis; D000069553:Supervised Machine Learning