title: Adaptive smoothing based on Gaussian processes regression increases the sensitivity and specificity of fMRI data.
abstract: Temporal and spatial filtering of fMRI data is often used to improve statistical power. However, conventional methods, such as smoothing with fixed-width Gaussian filters, remove fine-scale structure in the data, necessitating a tradeoff between sensitivity and specificity. Specifically, smoothing may increase sensitivity (reduce noise and increase statistical power) but at the cost loss of specificity in that fine-scale structure in neural activity patterns is lost. Here, we propose an alternative smoothing method based on Gaussian processes (GP) regression for single subjects fMRI experiments. This method adapts the level of smoothing on a voxel by voxel basis according to the characteristics of the local neural activity patterns. GP-based fMRI analysis has been heretofore impractical owing to computational demands. Here, we demonstrate a new implementation of GP that makes it possible to handle the massive data dimensionality of the typical fMRI experiment. We demonstrate how GP can be used as a drop-in replacement to conventional preprocessing steps for temporal and spatial smoothing in a standard fMRI pipeline. We present simulated and experimental results that show the increased sensitivity and specificity compared to conventional smoothing strategies. Hum Brain Mapp 38:1438-1459, 2017. © 2016 Wiley Periodicals, Inc.
authors.affiliation: Department of Neurology, Washington University in Saint Louis, School of Medicine, Saint Louis, Missouri.|Neurobiology Department, Weizmann Institute of Science, Rehovot, 7610001, Israel.
authors.forename: Francesca
authors.identifier: 0000-0002-4371-6995
authors.initials: F
authors.lastname: Strappini
authors.affiliation: Preston M. Green Department of Electrical and Systems Engineering, Washington University in Saint Louis, Saint Louis, Missouri.|Department of Electrical Engineering, Technion Israel Institute of Technology, Haifa, 3200003, Israel.
authors.forename: Elad
authors.initials: E
authors.lastname: Gilboa
authors.affiliation: Cognitive and Motor Rehabilitation Unit, Santa Lucia Foundation, Rome, 00179, Italy.|Department of Motor, Human and Health Sciences, University of Rome "Foro Italico,", Rome, 00194, Italy.
authors.forename: Sabrina
authors.initials: S
authors.lastname: Pitzalis
authors.affiliation: Department of Psychology, Washington University in Saint Louis, School of Medicine, Saint Louis, Missouri.|Department of Radiology, University of Minnesota Twin Cities, Minneapolis, Minnesota.
authors.forename: Kendrick
authors.initials: K
authors.lastname: Kay
authors.affiliation: Department of Radiology, Washington University in Saint Louis, School of Medicine, Saint Louis, Missouri.
authors.forename: Mark
authors.initials: M
authors.lastname: McAvoy
authors.affiliation: Preston M. Green Department of Electrical and Systems Engineering, Washington University in Saint Louis, Saint Louis, Missouri.
authors.forename: Arye
authors.initials: A
authors.lastname: Nehorai
authors.affiliation: Department of Neurology, Washington University in Saint Louis, School of Medicine, Saint Louis, Missouri.|Department of Radiology, Washington University in Saint Louis, School of Medicine, Saint Louis, Missouri.
authors.forename: Abraham Z
authors.initials: AZ
authors.lastname: Snyder
mesh_terms: D001921:Brain / Q000000981:diagnostic imaging*; D001931:Brain Mapping; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D015233:Models, Statistical; D016011:Normal Distribution; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
keywords: Gaussian processes regression; classification; denoising; early visual areas; fMRI smoothing; multivoxel pattern analysis; retinotopic mapping; searchlight; visual cortex