Category Archives: MIND

Paper published: On network derivation, classification, and visualization: a response to Habeck and Moeller

For the second issue of Brain Connectivity, a new journal, we were invited to provide a response to a “controversial article” about issues of analysis and interpretation in fMRI.  In a fun paper, Elena, Eswar, Vince, and I provide our perspective and some better practices to continue the dialogue. On network derivation, classification, and visualization: a response to Habeck and Moeller Erik B. Erhardt, Elena A. Allen, Eswar Damaraju, Vince D. Calhoun. Brain Connectivity 1(2), 2011. Abstract In the decade and a half since Biswal’s fortuitous discovery of spontaneous correlations in functional imaging data, the field of functional connectivity (FC) has seen exponential growth resulting in the identification of widely-replicated intrinsic networks and the innovation of novel analytic methods with the promise of diagnostic application.  As such a young field undergoing rapid change, we have yet to converge upon a desired and needed set of standards.  In this issue, Habeck and Moeller begin a dialogue for developing best practices by providing four criticisms with respect to FC estimation methods, interpretation of FC networks, assessment of FC network features in classifying sub-populations, and network visualization.  Here, we respond to Habeck and Moeller and provide our own perspective on the concerns raised in the hope that the neuroimaging field will benefit from this discussion.
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A simulation toolbox for fMRI data: SimTB

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 a ected 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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Paper published: A baseline for the multivariate comparison of resting state networks

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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Visions

A few important areas of focus, reflecting what I’m doing and where I’m going.

Professional

Statistics for Stable Isotope applications

My vision is to be the recognized leader of statistical methods in stable isotope sourcing.  This will be accomplished through publishing papers from my dissertation work, collaborations leading to publications on methodological extensions, and giving talks in university departments and at courses and conferences.

Postdoctoral fellowship at the Mind Research Network

At the MRN my vision is to be an exceptional statistician, a valuable member of Vince Calhoun’s team, and an expert on statistical methods applying to ICA and fMRI.  This will be accomplished with thorough discussions and detailed answers to statistical inquiries, active curiosity about others’ work and how I may contribute, and careful study of existing ICA models and sound application of statistical principles. My career goals at the MRN are to develop a broad and deep knowledge of the methods for analysis of fMRI data in particular, and brain imaging data in general, to publish carefully developed extensions in well-written papers, and make contributions to others’ work.  This will be accomplished by dissecting the modeling details from published work and uncovering further details by contacting the authors, appealing to theoretical results and experimental confirmation before publicizing new methods, and helping others consider their methods, results, and interpretations.

Personal

Dance

My vision is to contribute more to the Albuquerque contra dance community and bring dance to more people, especially youth.  This will be accomplished by making opportunities for new callers, writing and calling dances, leading and participating in workshops, helping make more dance and music opportunities to bring the community together, outreach efforts to introduce dance to more people, and always collaborating with our vibrant New Mexico dance community to make it happen.
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RA at MIND Institute begins

On Jan 20th, 2009, I joined the Medical Image Analysis Laboratory (MIALab) as a research assistant (RA) at the MIND institute at UNM.  This position will transition to a 2-3 year postdoc upon the completion of my PhD this May. Vince Calhoun, Edward Bedrick (my stat advisor), Jeremy Bockholt, and I compose the Biostatistics & NeuroInformatics Core (STATNI) on UNM’s Center of Biomedical Research Excellence (COBRE) grant funded by the National Center for Research Resources (NCRR), a part of the National Institutes of Health (NIH).  STATNI serves as a centralized resource for biostatistical consulting for a number of scientific projects. My role will be in the development of statistical methods and programming numerical and statistical methods to address the aims of the projects.  Specifically, the development of a Bayesian ICA model for fMRI data. There are five aims to the project that will ultimately extend the ability to incorporate prior information to move beyond the semi-blind ICA approach. [from the project summary] First, we will extend our semi-blind ICA (sbICA) framework to provide a general framework for incorporating prior information from multiple spatial and temporal sources. In the second aim we will focus upon statistical inference and develop a framework for integrating the relevant functional components. In the third aim, we will validate the algorithms in aims 1 and 2, including using fMRI data collected on multiple days from a variety of paradigms. In this aim we develop a decision mechanism for selecting the best combination of methods given a particular problem. For the fourth aim, we will apply our methods to data collected during four well-studied paradigms in healthy controls and patients with schizophrenia. Our final aim involves the continuing development of our GIFT toolbox, and incorporation of the above algorithms, constraint selection mechanisms, and visual interfaces into the software. The successful completion of this research will provide a powerful set of tools for the research community to increase the sensitivity and specificity of BOLD analysis methods by drawing upon the strengths of both model-based and data-driven approaches. These tools will also provide a way to study the inter-relationship among functional networks in a flexible manner. This is an ideal position for me because the modeling is similar to work I have done in my dissertation, I continue to work closely with my advisor, Ed, who I continue to learn so much from, I get to learn and work with Vince who has many ideas and is very prolific, and all of this in the hot area of fMRI.  I also have family and friends in Albuquerque who I want to stay close with for a little longer and this position allows me to stay put.
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