Update: both papers have been published, simulation toolbox and inter-subject variability. Elena Allen and I recently submitted two papers that detail a simulation toolbox for fMRI data (SimTB) and capturing inter-subject variability with group independent component analysis (ICA) using simulations. It’s been an exciting and interesting project because we can at last generate interesting and complex datasets to use as a “ground truth” to compare estimation and processing techniques.  We’ve learned a lot about the limits of some methods, as well as their robustness.  The papers will be submitted next week.  For those with MATLAB, it’s available at http://mialab.mrn.org/software. SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability EB Erhardt, EA Allen, Y Wei, T Eichele, VD Calhoun. (2011)

We introduce SimTB, a MATLAB toolbox designed to simulate functional magnetic resonance imaging (fMRI) datasets under a model of spatiotemporal separability. The toolbox meets the increasing need of the fMRI community to more comprehensively understand the effects of complex processing strategies by providing a ground truth that estimation methods may be compared against. SimTB captures the fundamental structure of real data, but data generation is fully parameterized and fully controlled by the user, allowing for accurate and precise comparisons. The toolbox offers a wealth of options regarding the number and configuration of spatial sources, implementation of experimental paradigms, inclusion of tissue-specific properties, addition of noise and head movement, and much more. A straightforward data generation method and short computation time (3-10 seconds for each dataset) allow a practitioner to simulate and analyze many datasets to potentially understand a problem from many angles. Beginning MATLAB users can use the SimTB graphical user interface (GUI) to design and execute simulations while experienced users can write batch scripts to automate and customize this process. The toolbox is freely available at http://mialab.mrn.org/software together with sample scripts and tutorials.

Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study EA Allen, EB Erhardt, Y Wei, T Eichele, VD Calhoun. (2011)

A key challenge in functional neuroimaging is the meaningful combination of results across subjects. Even in a sample of healthy participants, brain morphology and functional organization exhibit considerable variability, such that no two individuals have the same neural activation at the same location in response to the same stimulus. This inter-subject variability limits inferences at the group-level as average activation patterns may fail to represent the patterns seen in individuals. A promising approach to multi-subject analysis is group independent components analysis (GICA), which identities group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specied. However, a comprehensive understanding of the performance of GICA under realistic conditions of inter-subject variability is lacking. In this study we use simulated functional magnetic resonance imaging (fMRI) data to determine the capabilities and limitations of GICA under conditions of spatial, temporal, and amplitude variability. Simulations, generated with the SimTB toolbox, address questions that commonly arise in GICA studies, such as: (1) How well can individual subject activations be estimated and when will spatial variability preclude estimation? (2) Why does component splitting occur and how is it aected by model order? (3) How should we analyze component features to maximize sensitivity to intersubject differences? Overall, our results indicate an excellent capability of GICA to capture between-subject differences and we make a number of recommendations regarding analytic choices for application to functional imaging data.

[caption id="" align="alignnone" width="480" caption="SimTB flowchart for simulation of fMRI data"][/caption]
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In this paper we’ve put together a cohort of so-called “healthy normal controls” in the largest single group independent component analysis (ICA) forward model. Included in our analysis approach is a statistical methodology for performing mancova when the number of fMRI brain voxels (roughly V=60K) to test for activation differences over demographic variables is much larger than the number of subjects (N=603).  This massive project involved researchers nearly spanning our Medical Image Analysis Laboratory (MIALab), but many other investigators at the Mind Research Network (MRN).  It is a great example of what makes collaboration fun, challenging, and productive (thank you Elena, Eswar, Bill, Judith, Martin, and Srinivas). A baseline for the multivariate comparison of resting state networks http://www.frontiersin.org/systems_neuroscience/10.3389/fnsys.2011.00002/abstract Citation:Allen EA, Erhardt EB, Damaraju E, Gruner W, Segall JM, Silva RF, Havlicek M, Rachakonda S, Fries J, Kalyanam R, Michael AM, Caprihan A, Turner JA, Eichele T, Adelsheim S, Bryan AD, Bustillo J, Clark VP, Feldstein Ewing SW, Filbey F, Ford CC, Hutchison K, Jung RE, Kiehl KA, Kodituwakku P, Komesu YM, Mayer AR, Pearlson GD, Phillips JP, Sadek JR, Stevens M, Teuscher U, Thoma RJ and Calhoun VD (2011). A baseline for the multivariate comparison of resting state networks. Front. Syst. Neurosci. 5:2. doi: 10.3389/fnsys.2011.00002 Received: 16 Jun 2010; Accepted: 03 Jan 2011; Published online: 04 Feb 2011. Abstract As the size of functional and structural MRI datasets expands, it becomes increasingly important to establish a baseline from which diagnostic relevance may be determined, a processing strategy that efficiently prepares data for analysis, and a statistical approach that identifies important effects in a manner that is both robust and reproducible. In this paper, we introduce a multivariate analytic approach that optimizes sensitivity and reduces unnecessary testing. We demonstrate the utility of this mega-analytic approach by identifying the effects of age and gender on the resting state networks of 603 healthy adolescents and adults (mean age: 23.4 years, range: 12 to 71 years). Data were collected on the same scanner, preprocessed using an automated analysis pipeline based in SPM, and studied using group independent component analysis. Resting state networks were identified and evaluated in terms of three primary outcome measures: time course spectral power, spatial map intensity, and functional network connectivity. Results revealed robust effects of age on all three outcome measures, largely indicating decreases in network coherence and connectivity with increasing age. Gender effects were of smaller magnitude but suggested stronger intra-network connectivity in females and more inter-network connectivity in males, particularly with regard to sensorimotor networks. These findings, along with the analysis approach and statistical framework described here, provide a useful baseline for future investigations of brain networks in health and disease.
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Erhardt, EB, Rachakonda, S, Bedrick, EJ, Allen, EA, Adali, T, and Calhoun, VD, Comparison of multi-subject ICA methods for analysis of fMRI data. Human Brain Mapping, n/a. doi: 10.1002/hbm.21170 This is my first postdoc paper at the Mind Research Network.
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