Comparison of Detrending Methods for Optimal fMRI Preprocessing

Tanabe, Jody; Miller, David E.; Tregellas, Jason R.; Freedman, Robert; Meyer, François G.

doi:10.1006/nimg.2002.1053

Cited by 112 publications

(73 citation statements)

References 30 publications

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“…In addition, replicating previous findings, we demonstrate that the choice of temporal detrending polynomial order is a dominant effect for fixed preprocessing pipelines. It is known that detrending method has a significant impact on fMRI results, in both univariate [Tanabe et al, 2002] and multivariate frameworks [LaConte et al, 2005;Shaw et al, 2003]. Fixed-pipeline performance is also dependant on whether highest detrending order is even or odd, with the former offering better (R, P).…”

Section: Discussionmentioning

confidence: 99%

“…the preprocessing pipeline), or that the default settings in established software packages give near-optimal results. In recent years, though, it has been repeatedly shown that both standard and non-standard data preprocessing choices may have significant effects (negative or positive) on the quality of extracted results (e.g., [Della-Maggiore et al, 2002;Kay et al, 2007;Morgan et al, 2007;Murphy et al, 2009;Poline et al, 2006;Sarty, 2007;Strother et al, 2004;Tanabe et al, 2002;Zhang et al, 2009]. To draw reliable conclusions from fMRI results, it is thus necessary to evaluate the interactions of preprocessing and data analysis choices in a rigorous, systematic manner.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, Jones et al [2008] have shown that the effectiveness of PNC depends on its order in the preprocessing pipeline. Low-order temporal detrending has also been found to significantly affect results, depending on choice of detrending basis [Kay et al, 2007;Tanabe et al, 2002] and interactions with other parameters, such as spatial smoothing [Shaw et al, 2003]. A number of studies have also investigated basis decomposition techniques using Principal Component Analysis (PCA) and Independent Component Analysis (ICA) for data denoising [e.g.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimizing preprocessing and analysis pipelines for single‐subject fMRI. I. Standard temporal motion and physiological noise correction methods

Churchill

Oder

Abdi

et al. 2011

Human Brain Mapping

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Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747-771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89-95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or "pipeline") in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was CIHR Author Manuscript CIHR Author Manuscript CIHR Author Manuscriptdemonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimizing preprocessing and analysis pipelines for single‐subject fMRI. I. Standard temporal motion and physiological noise correction methods

Churchill

Oder

Abdi

et al. 2011

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…Subsequent preprocessing of the rs-fMRI magnitude data included the following steps: (1) retrospective motion correction; (2) slice-timing correction; (3) detrending up to the 3 rd order spline (Tanabe et al, 2002); (4) regression of six motion parameters; and (5) spatial smoothing using a Gaussian kernel. These was performed using AFNI (National Institutes of Health, Bethesda, Maryland; http://afni.nimh.nih.gov/afni) and the FEAT package in FSL (FMRIB, Oxford University; http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FEAT).…”

Section: Bold Data Preprocessingmentioning

confidence: 99%

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

2016

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In conventional neuroimaging, cerebrovascular reactivity (CVR) is quantified primarily using the blood-oxygenation level-dependent (BOLD) functional MRI (fMRI) signal, specifically, as the BOLD response to intravascular carbon dioxide (CO 2 ) modulations, in units of [%ΔBOLD/ mmHg]. While this method has achieved wide appeal and clinical translation, the tolerability of CO 2 -related tasks amongst patients and the elderly remains a challenge in more routine and largescale applications. In this work, we propose an improved method to quantify CVR by exploiting intrinsic fluctuations in CO 2 and corresponding changes in the resting-state BOLD signal (rsqCVR). Our rs-qCVR approach requires simultaneous monitoring of PETCO 2 , cardiac pulsation and respiratory volume. In 16 healthy adults, we compare our quantitative CVR estimation technique to the prospective CO 2 -targeting based CVR quantification approach (qCVR, the "standard"). We also compare our rs-CVR to non-quantitative alternatives including the restingstate fluctuation amplitude (RSFA), amplitude of low-frequency fluctuation (ALFF) and globalsignal regression. When all subjects were pooled, only RSFA and ALFF were significantly associated with qCVR. However, for characterizing regional CVR variations within each subject, only the PETCO 2 -based rs-qCVR measure is strongly associated with standard qCVR in 100% of the subjects (p<=0.1). In contrast, for the more qualitative CVR measures, significant withinsubject association with qCVR was only achieved in 50-70% of the subjects. Our work establishes the feasibility of extracting quantitative CVR maps using rs-fMRI, opening the possibility of mapping functional connectivity and qCVR simultaneously.

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“…Fluctuations of non-neuronal origin and fMRI data preprocessing Non-neuronal physiological fluctuations in fMRI studies and scanner instabilities which may impact the signals are real concerns (Glover et al, 2000;Tanabe et al, 2002;Wise et al, 2004;Birn et al, 2006;Lund et al, 2006). Blood Oxygenated Level Dependent (BOLD) fMRI, as is most commonly used in recent years, in essence captures the change in presence of deoxyhemoglobin brought on by activation in regions of the brain.…”

Section: Statistical Interpretationsmentioning

confidence: 99%

Evaluation and calibration of functional network modeling methods based on known anatomical connections

et al. 2013

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1-Abstract EnglishRecent studies have identified large scale brain networks based on the spatio-temporal structure of spontaneous fluctuations in resting-state fMRI data. It is expected that functional connectivity based on resting-state data is reflective of-but not identical to the underlying anatomical connectivity.However, which functional connectivity analysis methods reliably predict the network structure remains unclear. Here we tested and compared network connectivity analysis methods by applying them to fMRI restingstate time-series obtained from the human visual cortex. The methods evaluated here are those previously tested against simulated data in Smith et al. (Neuroimage, 2011).To this end, we defined regions within retinotopic visual areas V1, V2, and V3 according to their eccentricity in the visual field, delineating central, intermediate, and peripheral eccentricity regions of interest (ROIs). These ROIs served as nodes in the models we study. We based our evaluation -anatomical connectivity in the monkey visual cortex. For each evaluated method, we computed the fractional rate of detecting connections known to -th percentile of the 4 distribution of interaction magnitudes of those connections not expected to exist.Under optimal conditions, including session duration of 68 minutes, a relatively small network consisting of 9 nodes and artifact-free regression of the global effect, each of the top methods predicted the expected connections with 75%-83% c-sensitivity. Partial Correlation performed best (PCorr; 83%), followed by Regularized Inverse Covariance (ICOV; 79%), Bayesian Network methods (BayesNet; 77%), Correlation (75%), andGeneral Synchronization measures (75%). With decreased session duration, these top methods saw decreases in c-sensitivities, achieving 66%-78% and 60%-70% for 34 and 17 minute sessions, respectively. With a short resting-state fMRI scan of 8.5 minutes (TR = 2s), none of the methods predicted the real network well, with ICOV (53%) and PCorr (51%) performing best. With increased complexity of the network from 9 to 36 nodes, multivariate methods including PCorr and BayesNet saw a decrease in performance. However, this decrease became small when using data from a long (68 minutes) session. Artifact-free regression of the global effect significantly increased the c-sensitivity of all top-performing methods. In an overall evaluation across all tests we performed, PCorr, ICOV and BayesNet set themselves somewhat above all other methods. 5We propose that data-based calibration based on known anatomical connections be integrated into future network studies, in order to maximize sensitivity and reduce false positives. Studies have also demonstrated changes in fMRI-measured brain activity when the subjects are in a state of rest. Importantly, it has been shown that these spontaneous (i.e. without any external stimulation)fluctuations in fMRI signals demonstrate functional connectivity over large parts of the human brain (Xiong et al., 1999). These large parts of the brain correspo...

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Comparison of Detrending Methods for Optimal fMRI Preprocessing

Cited by 112 publications

References 30 publications

Optimizing preprocessing and analysis pipelines for single‐subject fMRI. I. Standard temporal motion and physiological noise correction methods

Optimizing preprocessing and analysis pipelines for single‐subject fMRI. I. Standard temporal motion and physiological noise correction methods

Quantitative mapping of cerebrovascular reactivity using resting-state BOLD fMRI: Validation in healthy adults

Evaluation and calibration of functional network modeling methods based on known anatomical connections

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