Improving the signal detection accuracy of functional Magnetic Resonance Imaging

A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pa...

Full description

Saved in:
Bibliographic Details
Published in:NeuroImage (Orlando, Fla.) Fla.), 2018-08, Vol.176, p.92-109
Main Authors: Janssen, Niels, Hernández-Cabrera, Juan A., Foronda, Laura Ezama
Format: Article
Language:English
Subjects:
Accuracy
Brain mapping
Brain slice preparation
Data processing
Detection accuracy
FIR basis functions
fMRI BOLD
Functional magnetic resonance imaging
Neuroimaging
NMR
Nuclear magnetic resonance
Random variables
Slice-based fMRI
Statistical analysis
Statistical methods
Statistical modeling
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A major drawback of functional Magnetic Resonance Imaging (fMRI) concerns the lack of detection accuracy of the measured signal. Although this limitation stems in part from the neuro-vascular nature of the fMRI signal, it also reflects particular methodological decisions in the fMRI data analysis pathway. Here we show that the signal detection accuracy of fMRI is affected by the specific way in which whole-brain volumes are created from individually acquired brain slices, and by the method of statistically extracting signals from the sampled data. To address these limitations, we propose a new framework for fMRI data analysis. The new framework creates whole-brain volumes from individual brain slices that are all acquired at the same point in time relative to a presented stimulus. These whole-brain volumes contain minimal temporal distortions, and are available at a high temporal resolution. In addition, statistical signal extraction occurred on the basis of a non-standard time point-by-time point approach. We evaluated the detection accuracy of the extracted signal in the standard and new framework with simulated and real-world fMRI data. The new slice-based data-analytic framework yields greatly improved signal detection accuracy of fMRI signals.
ISSN:1053-8119
1095-9572
DOI:10.1016/j.neuroimage.2018.01.076