Combined permutation test and mixed‐effect model for group average analysis in fMRI

Mériaux, Sébastien; Roche, Alexis; Dehaene‐Lambertz, Ghislaine; Thirion, Bertrand; Poline, Jean‐Baptiste

doi:10.1002/hbm.20251

Cited by 34 publications

(30 citation statements)

References 25 publications

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“…Then we systematically compare the results of surface-and volume-based statistical analysis and provide results on the dierence in sensitivity of the two approaches for dierent tests, for a given control of the type I error. More specically, we use for the comparison mixed-and random-eects inference at the voxel and at the cluster level [25] and assess their reproducibility with bootstrap.…”

Section: An Empirical Comparison Of Volume-and Surface-based Alignmenmentioning

confidence: 99%

An empirical comparison of surface-based and volume-based group studies in neuroimaging

et al. 2012

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Thirion. An empirical comparison of surface-based and volume-based group studies in neuroimaging. NeuroImage, Elsevier, 2012, 63 (3) Being able to detect reliably functional activity in a population of subjects is crucial in human brain mapping, both for the understanding of cognitive functions in normal subjects and for the analysis of patient data. The usual approach proceeds by normalizing brain volumes to a common three-dimensional template. However, a large part of the data acquired in fMRI aims at localizing cortical activity, and methods working on the cortical surface may provide better inter-subject registration than the standard procedures that process the data in the volume. Nevertheless, few assessments of the performance of surface-based (2D) versus volume-based (3D) procedures have been shown so far, mostly because inter-subject cortical surface maps are not easily obtained. In this paper we present a systematic comparison of 2D versus 3D group-level inference procedures, by using cluster-level and voxel-level statistics assessed by permutation, in random eects (RFX) and mixed-eects analyses (MFX). We consider dierent schemes to perform meaningful comparisons between thresholded statistical maps in the volume and on the cortical surface. We nd that surface-based multi-subject statistical analyses are generally more sensitive than their volume-based counterpart, in the sense that they detect slightly denser networks of regions when performing peak-level detection; this eect is less clear for cluster-level inference and is reduced by smoothing. Surface-based inference also increases the reliability of the activation maps.

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Section: An Empirical Comparison Of Volume-and Surface-based Alignmenmentioning

confidence: 99%

An empirical comparison of surface-based and volume-based group studies in neuroimaging

et al. 2012

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“…A promising approach is to apply multivariate analyses (that simultaneously process all brain pixels) of perfusion-based pharmacological and functional MRI data, often in a data-driven fashion . The existence of a potential hierarchical brain response to drugs, from regional CBF changes to neurotransmitter system to whole-brain CBF changes to repeated measurements within the same subject, may call for more advanced statistical analysis methods and clinical trial design such as multilevel mixed effects model in conjunction with within-subject crossover design to improve the sensitivity of ASL-based phMRI (Mériaux et al, 2006).…”

Section: Arterial Spin Labeling Perfusion Mri: Challenges For Phmrimentioning

confidence: 99%

Potentials and Challenges for Arterial Spin Labeling in Pharmacological Magnetic Resonance Imaging

Wang

Chen

Fernández‐Seara

et al. 2011

J Pharmacol Exp Ther

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Pharmacological magnetic resonance imaging (phMRI) is increasingly being used in drug discovery and development to speed the translation from the laboratory to the clinic. The two primary methods in phMRI include blood-oxygen-level-dependent (BOLD) contrast and arterial spin-labeled (ASL) perfusion MRI. BOLD contrast has been widely applied in existing phMRI studies. However, because of the lack of absolute quantification and poor reproducibility over time scales longer than hours or across scanning sessions, BOLD fMRI may not be suitable to track oral and other long-term drug effects on baseline brain function. As an alternative method, ASL provides noninvasive, absolute quantification of cerebral blood flow both at rest and during task activation. ASL perfusion measurements have been shown to be highly reproducible over minutes and hours to days and weeks. These two characteristics make ASL an ideal tool for phMRI for studying both intravenous and oral drug action as well as understanding drug effects on baseline brain function and brain activation to cognitive or sensory processing. When ASL is combined with BOLD fMRI, drug-induced changes in cerebral metabolic rate of oxygen may also be inferred. Representative phMRI studies using ASL perfusion MRI on caffeine, remifentanil, and metoclopramide (dopamine antagonist) are reviewed here, with an emphasis on the methodologies used to control for potentially confounding vascular and systemic effects. Both the potentials and limitations of using ASL as an imaging marker of drug action are discussed.

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“…This means that potentially other statistics may provide more sensitive results in a nonparametric setting. Also, nonparametric testing refers here to the computation of the distribution under the null hypothesis using permutations [22]. Due to lack of space, we only report the t-score computations to test Assumptions (5):…”

Section: Group-level Statistical Analysismentioning

confidence: 99%

Multifractal analysis of Resting State Networks in functional MRI

Ciuciu¹,

Varoquaux

Abry

et al. 2011

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

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It has been know for at least one decade [1] that functional MRI time series display long-memory properties, such as power-law scaling in the frequency spectrum. Concomitantly, multivariate modelfree analysis of spatial patterns , such as spatial Independent Component Analysis (sICA) [2], has been successfully used to segment from spontaneous activity Resting-State Networks (RSN) that correspond to known brain function. As recent neuroscientific studies suggest a link between spectral properties of brain activity and cognitive processes [3], a burning question emerges: can temporal scaling properties offer new markers of brain states encoded in these large scale networks? In this paper, we combine two recent methodologies: group-level canonical ICA for multi-subject segmentation of brain network, and wavelet leader-based multifractal formalism for the analysis of RSN scaling properties. We identify the brain networks that elicit self-similarity or multifractality and explore which spectral properties correspond specifically to known functionallyrelevant processes in spontaneous activity.

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Combined permutation test and mixed‐effect model for group average analysis in fMRI

Cited by 34 publications

References 25 publications

An empirical comparison of surface-based and volume-based group studies in neuroimaging

An empirical comparison of surface-based and volume-based group studies in neuroimaging

Potentials and Challenges for Arterial Spin Labeling in Pharmacological Magnetic Resonance Imaging

Multifractal analysis of Resting State Networks in functional MRI

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