Structural Analysis of fMRI Data Revisited: Improving the Sensitivity and Reliability of fMRI Group Studies (original) (raw)

Surface-based versus volume-based fMRI group analysis: a case study

2009

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 3D 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 3D. 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 effects (RFX) and mixed-effects analyses (MFX). We find that, using a voxel-level thresholding, and to some extent, cluster-level thresholding, the surface-based approach generally detects more, but smaller active regions than the corresponding volume-based approach for both RFX and MFX procedures, and that surface-based supra-threshold regions are more reproducible by bootstrap.

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.

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.

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.

The Statistical Analysis of Multi-Voxel Patterns in Functional Imaging

PLoS ONE, 2013

The goal of multi-voxel pattern analysis (MVPA) in BOLD imaging is to determine whether patterns of activation across multiple voxels change with experimental conditions. MVPA is a powerful technique, its use is rapidly growing, but it poses serious statistical challenges. For instance, it is well-known that the slow nature of the BOLD response can lead to greatly exaggerated performance estimates. Methods are available to avoid this overestimation, and we present those here in tutorial fashion. We go on to show that, even with these methods, standard tests of significance such as Students' T and the binomial tests are invalid in typical MRI experiments. Only a carefully constructed permutation test correctly assesses statistical significance. Furthermore, our simulations show that performance estimates increase with both temporal as well as spatial signal correlations among multiple voxels. This dependence implies that a comparison of MVPA performance between areas, between subjects, or even between BOLD signals that have been preprocessed in different ways needs great care.

Revisiting multi-subject random effects in fMRI: Advocating prevalence estimation

NeuroImage, 2014

to generalize findings from the study group to the whole population. Generalizing findings is obviously harder than detecting activation in the study group since in order to be significant, an activation has to be larger than the inter-subject variability. Indeed, detected regions are smaller when using random effect analysis versus fixed effects. The statistical assumptions behind the classic random effects model are that the effect in each location is normally distributed over subjects, and "activation" refers to a non-null mean effect. We argue this model is unrealistic compared to the true population variability, where, due to functional plasticity and registration anomalies, at each brain location some of the subjects are active and some are not. We propose a finite-Gaussian-mixture-random-effect. A model that amortizes between-subject spatial disagreement and quantifies it using the "prevalence" of activation at each location. This measure has several desirable properties:

Finding landmarks in the functional brain: detection and use for group characterization

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2005

FMRI group studies are usually based on stereotactic spatial normalization and present voxel by voxel average activity across subjects. This technique does not in general adequately model the inter subject spatial variability. In this work, we propose to identify functional landmarks that are reliable across subjects with subject specific Talairach coordinates that are similar -but not exactly identical- between subjects. We call these Brain Functional Landmarks (BFLs), and define them based on cross-validation techniques using 38 subjects. We explore a dataset acquired while subjects were involved in several cognitive and sensori-motor processes, and show that this representation allows to classify subjects into sub-groups on the basis of their BFL activity.

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.

Group-wise FMRI activation detection on corresponding cortical landmarks

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 2013

Group-wise activation detection in task-based fMRI has been widely used because of its robustness to noises and statistical power to deal with variability of individual brains. However, current group-wise fMRI activation detection methods typically rely on the spatial alignment established by coregistration of individual brains' fMRI images into the same template space, which has difficulty in dealing with the remarkable anatomic variation of different brains. As a consequence, the resulted misalignment among multiple brains could substantially degrade the accuracy and specificity of group-wise fMRI activation detection. To address these challenges, this paper presents a novel methodology to detect group-wise fMRI activation based on a publicly released dense map of DTI-derived structural cortical landmarks, which possess intrinsic correspondences across individuals and populations. The basic idea here is that a first-level general linear model (GLM) analysis is performed on fMR...