Revisiting multi-subject random effects in fMRI: Advocating prevalence estimation (original) (raw)
Related papers
Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses
Neuroimage, 2007
The aim of group fMRI studies is to relate contrasts of tasks or stimuli to regional brain activity increases. These studies typically involve 10 to 16 subjects. The average regional activity statistical significance is assessed using the subject to subject variability of the effect (random effects analyses). Because of the relatively small number of subjects included, the sensitivity and reliability of these analyses is questionable and hard to investigate. In this work, we use a very large number of subject (more than 80) to investigate this issue. We take advantage of this large cohort to study the statistical properties of the inter-subject activity and focus on the notion of reproducibility by bootstrapping. We asked simple but important methodological questions: Is there, from the point of view of reliability, an optimal statistical threshold for activity maps? How many subjects should be included in group studies? What method should be preferred for inference? Our results suggest that i) optimal thresholds can indeed be found, and are rather lower than usual corrected for multiple comparison thresholds, ii) 20 subjects or more should be included in functional neuroimaging studies in order to have sufficient reliability, iii) non-parametric significance assessment should be preferred to parametric methods, iv) cluster-level thresholding is more reliable than voxel-based thresholding, and v) mixed effects tests are much more reliable than random effects tests. Moreover, our study shows that inter-subject variability plays a prominent role in the relatively low sensitivity and reliability of group studies.
Impact of the joint detection-estimation approach on random effects group studies in FMRI
2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2011
Inter-subject analysis of functional Magnetic Resonance Imaging (fMRI) data relies on single intra-subject studies, which are usually conducted using a massively univariate approach. In this paper, we investigate the impact of an improved intra-subject analysis on group studies. basically the joint detection-estimation (JDE) framework [1-3] where an explicit characterization of the Hemodynamic Response Function (HRF) is performed at a regional scale and a stimulus-specific adaptive spatial correlation model enables the detection of activation clusters at voxel level. For the group statistics, we conducted several Random effect analyses (RFX) which relied either on the General Linear Model (GLM), or on the JDE analyses, or even on an intermediate approach named Spatially Adaptive GLM (SAGLM). Our comparative study perfomed during a fast-event related paradigm involves 18 subjects and illustrates the regionspecific differences between the GLM, SAGLM and JDE analyses in terms of statistical sensitivity. On different contrasts of interest, spatial regularization is shown to have a beneficial impact on the statistical sensitivity. Also, by studying the spatial variability of the HRF, we demonstrate that the JDE framework provides more robust detection performance in cognitive regions due to the higher hemodynamic variability in these areas.
Dealing with spatial normalization errors in fMRI group inference using hierarchical modeling
2008
An important challenge in neuroimaging multi-subject studies is to take into account that different brains cannot be aligned perfectly. To this end, we extend the classical mass univariate model for group analysis to incorporate uncertainty on localization by introducing, for each subject, a spatial "jitter" variable to be marginalized out. We derive a Bayes factor to test for the mean population effect's sign in each voxel of a search volume, and discuss a Gibbs sampler to compute it. This Bayes factor, which generalizes the classical t-statistic, may be combined with a permutation test in order to control the frequentist false positive rate. Results on both simulated and experimental data suggest that this test may outperform conventional mass univariate tests in terms of detection power, while limiting the problem of overestimating the size of activity clusters.
A general statistical analysis for fMRI data
NeuroImage, 2000
We propose a method for the statistical analysis of fMRI data that seeks a compromise between efficiency, generality, validity, simplicity, and execution speed. The main differences between this analysis and previous ones are: a simple bias reduction and regularization for voxel-wise autoregressive model parameters; the combination of effects and their estimated standard deviations across different runs/sessions/ subjects via a hierarchical random effects analysis using the EM algorithm; overcoming the problem of a small number of runs/session/subjects using a regularized variance ratio to increase the degrees of freedom.
IEEE Transactions on Medical Imaging, 2007
Group studies of functional MRI datasets are usually based on the computation of the mean signal across subjects at each voxel (Random Effects Analyses), assuming that all subjects have been set in the same anatomical space (normalization). Although this approach allows for a correct specificity (rate of false detections), it is not very efficient, for three reasons: i) its underlying hypotheses, perfect coregistration of the individual datasets and normality of the measured signal at the group level, are frequently violated ; ii) the group size is small in general, so that asymptotic approximations on the parameters distributions do not hold ; iii) the large size of the images requires some conservative strategies to control the false detection rate, at the risk of increasing the number of false negatives.
NeuroImage, 2012
In this paper we investigate the use of classical fMRI Random Effect (RFX) group statistics when analyzing a very large cohort and the possible improvement brought from anatomical information. Using 1326 subjects from the IMAGEN study, we first give a global picture of the evolution of the group effect t-value from a simple face-watching contrast with increasing cohort size. We obtain a wide activated pattern, far from being limited to the reasonably expected brain areas, illustrating the difference between statistical significance and practical significance. This motivates us to inject tissue-probability information into the group estimation, we model the BOLD contrast using a matter-weighted mixture of Gaussians and compare it to the common, single-Gaussian model. In both cases, the model parameters are estimated per-voxel for one subgroup, and the likelihood of both models is computed on a second, separate subgroup to reflect model generalization capacity. Various group sizes are...
Variability in fMRI: An examination of intersession differences
Neuroimage, 2000
The results from a single functional magnetic resonance imaging session are typically reported as indicative of the subject's functional neuroanatomy. Underlying this interpretation is the implicit assumption that there are no responses specific to that particular session, i.e., that the potential variability of response between sessions is negligible. The present study sought to examine this assumption empirically. A total of 99 sessions, comprising 33 repeats of simple motor, visual, and cognitive paradigms, were collected over a period of 2 months on a single male subject. For each paradigm, the inclusion of session-by-condition interactions explained a significant amount of error variance (P < 0.05 corrected for multiple comparisons) over a model assuming a common activation magnitude across all sessions. However, many of those voxels displaying significant session-by-condition interactions were not seen in a multisession fixed-effects analysis of the same data set; i.e., they were not activated on average across all sessions. Most voxels that were both significantly variable and activated on average across all sessions did not survive a randomeffects analysis (modeling between-session variance). We interpret our results as demonstrating that correct inference about subject responses to activation tasks can be derived through the use of a statistical model which accounts for both within-and between-session variance, combined with an appropriately large session sample size. If researchers have access to only a single session from a single subject, erroneous conclusions are a possibility, in that responses specific to this single session may be claimed to be typical responses for this subject.
Assessing study-specific regional variations in fMRI signal
2001
In this paper, we present a post hoc method for identifying regions where functional MRI data are subject to signal reduction that may compromise sensitivity to activations. The motivation for developing this technique derives from recent language studies that showed responses in the inferior temporal lobe that could be detected by PET but not by fMRI. Reduced signal is due mostly to susceptibility artifacts and can be identified by comparing the T2* images (which are subject to susceptibility artifacts) with T2 images (which are not). However, T2 images are not usually acquired in fMRI studies. Therefore, we propose that areas with reduced signal can be identified by comparing T2* images that are corrected for nonuniformity with the original uncorrected images. The technique provides a voxel-wise characterization of signal reduction that pertains to the particular data that enter into a statistical model. It requires only the functional data and can thus be applied post hoc and without any additional scans.