A group model for stable multi-subject ICA on fMRI datasets

Spatial Independent Component Analysis (ICA) is an increasingly used data-driven method to analyze functional Magnetic Resonance Imaging (fMRI) data. To date, it has been used to extract sets of mutually correlated brain regions without prior information on the time course of these regions. Some of these sets of regions, interpreted as functional networks, have recently been used to provide markers of brain diseases and open the road to paradigm-free population comparisons. Such group studies raise the question of modeling subject variability within ICA: how can the patterns representative of a group be modeled and estimated via ICA for reliable inter-group comparisons? In this paper, we propose a hierarchical model for patterns in multi-subject fMRI datasets, akin to mixed-effect group models used in linear-model-based analysis. We introduce an estimation procedure, CanICA (Canonical ICA), based on i) probabilistic dimension reduction of the individual data, ii) canonical correlation analysis to identify a data subspace common to the group iii) ICA-based pattern extraction. In addition, we introduce a procedure based on cross-validation to quantify the stability of ICA patterns at the level of the group. We compare our method with state-of-the-art multi-subject fMRI ICA methods and show that the features extracted using our procedure are more reproducible at the group level on two datasets of 12 healthy controls: a resting-state and a functional localizer study

A blind deconvolution approach to recover effective connectivity brain networks from resting state fMRI data

A great improvement to the insight on brain function that we can get from fMRI data can come from effective connectivity analysis, in which the flow of information between even remote brain regions is inferred by the parameters of a predictive dynamical model. As opposed to biologically inspired models, some techniques as Granger causality (GC) are purely data-driven and rely on statistical prediction and temporal precedence. While powerful and widely applicable, this approach could suffer from two main limitations when applied to BOLD fMRI data: confounding effect of hemodynamic response function (HRF) and conditioning to a large number of variables in presence of short time series. For task-related fMRI, neural population dynamics can be captured by modeling signal dynamics with explicit exogenous inputs; for resting-state fMRI on the other hand, the absence of explicit inputs makes this task more difficult, unless relying on some specific prior physiological hypothesis. In order to overcome these issues and to allow a more general approach, here we present a simple and novel blind-deconvolution technique for BOLD-fMRI signal. Coming to the second limitation, a fully multivariate conditioning with short and noisy data leads to computational problems due to overfitting. Furthermore, conceptual issues arise in presence of redundancy. We thus apply partial conditioning to a limited subset of variables in the framework of information theory, as recently proposed. Mixing these two improvements we compare the differences between BOLD and deconvolved BOLD level effective networks and draw some conclusions

Permutation Inference for Canonical Correlation Analysis

Canonical correlation analysis (CCA) has become a key tool for population neuroimaging, allowing investigation of associations between many imaging and non-imaging measurements. As other variables are often a source of variability not of direct interest, previous work has used CCA on residuals from a model that removes these effects, then proceeded directly to permutation inference. We show that such a simple permutation test leads to inflated error rates. The reason is that residualisation introduces dependencies among the observations that violate the exchangeability assumption. Even in the absence of nuisance variables, however, a simple permutation test for CCA also leads to excess error rates for all canonical correlations other than the first. The reason is that a simple permutation scheme does not ignore the variability already explained by previous canonical variables. Here we propose solutions for both problems: in the case of nuisance variables, we show that transforming the residuals to a lower dimensional basis where exchangeability holds results in a valid permutation test; for more general cases, with or without nuisance variables, we propose estimating the canonical correlations in a stepwise manner, removing at each iteration the variance already explained, while dealing with different number of variables in both sides. We also discuss how to address the multiplicity of tests, proposing an admissible test that is not conservative, and provide a complete algorithm for permutation inference for CCA.Comment: 49 pages, 2 figures, 10 tables, 3 algorithms, 119 reference

Changes in Hemodynamic Responses in Chronic Stroke Survivors Do Not Affect fMRI Signal Detection in a Block Experimental Design

The use of canonical functions to model BOLD-fMRI data in people post-stroke may lead to inaccurate descriptions of task-related brain activity. The purpose of this study was to determine whether the spatiotemporal profile of hemodynamic responses (HDRs) obtained from stroke survivors during an event-related experiment could be used to develop individualized HDR functions that would enhance BOLD-fMRI signal detection in block experiments. Our long term goal was to use this information to develop individualized HDR functions for stroke survivors that could be used to analyze brain activity associated with locomotor-like movements. We also aimed to examine the reproducibility of HDRs obtained across two scan sessions in order to determine whether data from a single event-related session could be used to analyze block data obtained in subsequent sessions. Results indicate that the spatiotemporal profile of HDRs measured with BOLD-fMRI in stroke survivors was not the same as that observed in individuals without stroke. We observed small between-group differences in the rates of rise and decline of HDRs that were more apparent in individuals with cortical as compared to subcortical stroke. There were no differences in the peak or time to peak of HDRs in people with and without stroke. Of interest, differences in HDRs were not as substantial as expected from previous reports and were not large enough to necessitate the use of individualized HDR functions to obtain valid measures of movement-related brain activity. We conclude that all strokes do not affect the spatiotemporal characteristics of HDRs in such a way as to produce inaccurate representations of brain activity as measured by BOLD-fMRI. However, care should be taken to identify individuals whose BOLD-fMRI data may not provide an accurate representation of underlying brain activation when canonical models are used. Examination of HDRs need not be done for each scan session, as our data suggest that the characteristics of HDRs in stroke survivors are reproducible across days