Dataset Information

Prospective active marker motion correction improves statistical power in BOLD fMRI.

ABSTRACT: Group level statistical maps of blood oxygenation level dependent (BOLD) signals acquired using functional magnetic resonance imaging (fMRI) have become a basic measurement for much of systems, cognitive and social neuroscience. A challenge in making inferences from these statistical maps is the noise and potential confounds that arise from the head motion that occurs within and between acquisition volumes. This motion results in the scan plane being misaligned during acquisition, ultimately leading to reduced statistical power when maps are constructed at the group level. In most cases, an attempt is made to correct for this motion through the use of retrospective analysis methods. In this paper, we use a prospective active marker motion correction (PRAMMO) system that uses radio frequency markers for real-time tracking of motion, enabling on-line slice plane correction. We show that the statistical power of the activation maps is substantially increased using PRAMMO compared to conventional retrospective correction. Analysis of our results indicates that the PRAMMO acquisition reduces the variance without decreasing the signal component of the BOLD (beta). Using PRAMMO could thus improve the overall statistical power of fMRI based BOLD measurements, leading to stronger inferences of the nature of processing in the human brain.

SUBMITTER: Muraskin J

PROVIDER: S-EPMC3638742 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

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Prospective active marker motion correction improves statistical power in BOLD fMRI.

Muraskin Jordan J Ooi Melvyn B MB Goldman Robin I RI Krueger Sascha S Thomas William J WJ Sajda Paul P Brown Truman R TR

NeuroImage 20121205

Group level statistical maps of blood oxygenation level dependent (BOLD) signals acquired using functional magnetic resonance imaging (fMRI) have become a basic measurement for much of systems, cognitive and social neuroscience. A challenge in making inferences from these statistical maps is the noise and potential confounds that arise from the head motion that occurs within and between acquisition volumes. This motion results in the scan plane being misaligned during acquisition, ultimately lea ...[more]

PMID: 23220430

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Project description:The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Project description:Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Dataset Information

Prospective active marker motion correction improves statistical power in BOLD fMRI.

Publications

Prospective active marker motion correction improves statistical power in BOLD fMRI.

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