Open AccessDetection of physiological noise in resting state fMRI using machine learning
Author(s) -
Ash Tom,
Suckling John,
Walter Martin,
Ooi Cinly,
Tempelmann Claus,
Carpenter Adrian,
Williams Guy
Publication year - 2013
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.21487
Subject(s) - noise (video) , correlation , resting state fmri , support vector machine , communication noise , pattern recognition (psychology) , computer science , artificial intelligence , fourier transform , noise reduction , pearson product moment correlation coefficient , phase (matter) , time point , statistics , image (mathematics) , mathematics , psychology , neuroscience , physics , mathematical analysis , linguistics , philosophy , geometry , quantum mechanics , acoustics
We present a technique for predicting cardiac and respiratory phase on a time point by time point basis, from fMRI image data. These predictions have utility in attempts to detrend effects of the physiological cycles from fMRI image data. We demonstrate the technique both in the case where it can be trained on a subject's own data, and when it cannot. The prediction scheme uses a multiclass support vector machine algorithm. Predictions are demonstrated to have a close fit to recorded physiological phase, with median Pearson correlation scores between recorded and predicted values of 0.99 for the best case scenario (cardiac cycle trained on a subject's own data) down to 0.83 for the worst case scenario (respiratory predictions trained on group data), as compared to random chance correlation score of 0.70. When predictions were used with RETROICOR—a popular physiological noise removal tool—the effects are compared to using recorded phase values. Using Fourier transforms and seed based correlation analysis, RETROICOR is shown to produce similar effects whether recorded physiological phase values are used, or they are predicted using this technique. This was seen by similar levels of noise reduction noise in the same regions of the Fourier spectra, and changes in seed based correlation scores in similar regions of the brain. This technique has a use in situations where data from direct monitoring of the cardiac and respiratory cycles are incomplete or absent, but researchers still wish to reduce this source of noise in the image data. Hum Brain Mapp , 2013. © 2011 Wiley Periodicals, Inc.