Surface-based versus volume-based fMRI group analysis: a case study (original) (raw)
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Selection of a Model of Cerebral Activity for fMRI Group Data Analysis
2010
This thesis is dedicated to the statistical analysis of multi-subject fMRI data, with the purpose of identifying bain structures involved in certain cognitive or sensori-motor tasks, in a reproducible way across subjects. To overcome certain limitations of standard voxel-based testing methods, as implemented in the Statistical Parametric Mapping (SPM) software, we introduce a Bayesian model selection approach to this problem, meaning that the most probable model of cerebral activity given the data is selected from a pre-defined collection of possible models.
Parcellation Schemes and Statistical Tests to Detect Active Regions on the Cortical Surface
Proceedings of COMPSTAT'2010, 2010
Activation detection in functional Magnetic Resonance Imaging (fMRI) datasets is usually performed by thresholding activation maps in the brain volume or, better, on the cortical surface. However, basing the analysis on a site-by-site statistical decision may be detrimental both to the interpretation of the results and to the sensitivity of the analysis, because a perfect point-to-point correspondence of brain surfaces from multiple subjects cannot be guaranteed in practice. In this paper, we propose a new approach that first defines anatomical regions such as cortical gyri outlined on the cortical surface, and then segments these regions into functionally homogeneous structures using a parcellation procedure that includes an explicit between-subject variability model, i.e. random effects. We show that random effects inference can be performed in this framework. Our procedure allows an exact control of the specificity using permutation techniques, and we show that the sensitivity of this approach is higher than the sensitivity of voxel-or cluster-level random effects tests performed on the cortical surface.
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.
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.
Dealing with the shortcomings of spatial normalization: Multi-subject parcellation of fMRI datasets
Human Brain Mapping, 2006
The analysis of functional magnetic resonance imaging (fMRI) data recorded on several subjects resorts to the so-called spatial normalization in a common reference space. This normalization is usually carried out on a voxel-by-voxel basis, assuming that after coregistration of the functional images with an anatomical template image in the Talairach reference system, a correct voxel-based inference can be carried out across subjects. Shortcomings of such approaches are often dealt with by spatially smoothing the data to increase the overlap between subject-specific activated regions. This procedure, however, cannot adapt to each anatomo-functional subject configuration. We introduce a novel technique for intra-subject parcellation based on spectral clustering that delineates homogeneous and connected regions. We also propose a hierarchical method to derive group parcels that are spatially coherent across subjects and functionally homogeneous. We show that we can obtain groups (or cliques) of parcels that well summarize inter-subject activations. We also show that the spatial relaxation embedded in our procedure improves the sensitivity of random-effect analysis. Hum Brain Mapp, 2005. © 2005 Wiley-Liss, Inc.
The efficiency of fMRI region of interest analysis methods for detecting group differences
Journal of Neuroscience Methods, 2014
Background-Using a standard space brain template is an efficient way of determining regionof-interest (ROI) boundaries for functional magnetic resonance imaging (fMRI) data analyses. However, ROIs based on landmarks on subject-specific (i.e., native space) brain surfaces are anatomically accurate and probably best reflect the regional blood oxygen level dependent (BOLD) response for the individual. Unfortunately, accurate native space ROIs are often timeintensive to delineate even when using automated methods. New Method-We compared analyses of group differences when using standard versus native space ROIs using both volume and surface-based analyses. Collegiate and military-veteran participants completed a button press task and a digit-symbol verification task during fMRI acquisition. Data were analyzed within ROIs representing left and right motor and prefrontal cortices, in native and standard space. Volume and surface-based analysis results were also compared using both functional (i.e., percent signal change) and structural (i.e., voxel or node count) approaches. Results and Comparison with Existing Method(s)-Results suggest that transformation into standard space can affect the outcome of structural and functional analyses (inflating/ minimizing differences, based on cortical geography), and these transformations can affect conclusions regarding group differences with volumetric data. Conclusions-Caution is advised when applying standard space ROIs to volumetric fMRI data. However, volumetric analyses show group differences and are appropriate in circumstances when time is limited. Surface-based analyses using functional ROIs generated the greatest group
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.
Multivariate fMRI Analysis Using Optimally-discriminative Voxel-based Analysis
2012 Second International Workshop on Pattern Recognition in NeuroImaging, 2012
This significantly extends Multi-Voxel Pattern Analysis (MVPA) methods, such as the Searchlight and related methods, by building on an approach that was recently proposed for structural brain images, and was named Optimally-Discriminative Voxel-Based Analysis (ODVBA), which uses machine learning models to determine the optimal anisotropic filtering of images that enhances group differences. Precise spatial maps of activation are computed by tallying the weights of each voxel to all of the neighborhood in which it belongs, and significance maps are obtained via permutation testing. We adapt this idea to both single and multi-subject fMRI analysis. Both simulated data and real data from 12 adolescent subjects who completed a standard working memory task demonstrated the use of ODVBA in fMRI improves accuracy and spatial specificity of activation detection over Searchlight.