Category Archives: MIND

Sabbatical

I’m enjoying Sabbatical during the Fall 2017 – Spring 2018 academic year.  My Sabbatical Leave Plan includes visiting both UC Irvine and the Mind Research Network to:
  • learn more about Bayesian graphical models,
  • learn more about Hamiltonian Monte Carlo (HMC),
  • learn about their statistical and computational implementations, and
  • apply both to extend current models in the application to fMRI brain imaging data.
  • Continue UNM 100-level statistics and mathematics education initiatives to understand factors influencing student success and find strategies to increase success.

... more

Paper published: Multidimensional frequency domain analysis of full-volume fMRI reveals significant effects of age, gender and mental illness on the spatiotemporal organization of resting-state brain activity

Multidimensional frequency domain analysis of full-volume fMRI reveals significant effects of age, gender and mental illness on the spatiotemporal organization of resting-state brain activity Miller, RL, EB Erhardt, EA Allen, AM Michael, JA Turner, J Bustillo, JM Ford, DH Mathalon, TGM van Erp, S Potkin, A Preda, G Pearlson, and VD Calhoun (2015). Frontiers in Neuroscience. 9(203) pdf, 1–19. Online: June 16, 2015 http://journal.frontiersin.org/article/10.3389/fnins.2015.00203/abstract doi: 10.3389/fnins.2015.00203 Abstract Clinical research employing functional magnetic resonance imaging (fMRI) is often conducted within the connectionist paradigm, focusing on patterns of connectivity between voxels, regions of interest (ROIs) or spatially distributed functional networks. Connectivity-based analyses are concerned with pairwise correlations of the temporal activation associated with restrictions of the whole-brain hemodynamic signal to locations of a priori interest. There is a more abstract question however that such spatially granular correlation-based approaches do not elucidate: Are the broad spatiotemporal organizing principles of brains in certain populations distinguishable from those of others? Global patterns (in space and time) of hemodynamic activation are rarely scrutinized for features that might characterize complex psychiatric conditions, aging effects or gender—among other variables of potential interest to researchers. We introduce a canonical, transparent technique for characterizing the role in overall brain activation of spatially scaled periodic patterns with given temporal recurrence rates. A core feature of our technique is the spatiotemporal spectral profile (STSP), a readily interpretable 2D reduction of the native four-dimensional brain × time frequency domain that is still “big enough” to capture important group differences in globally patterned brain activation. Its power to distinguish populations of interest is demonstrated on a large balanced multi-site resting fMRI dataset with nearly equal numbers of schizophrenia patients and healthy controls. Our analysis reveals striking differences in the spatiotemporal organization of brain activity that correlate with the presence of diagnosed schizophrenia, as well as with gender and age. To the best of our knowledge, this is the first demonstration that a 4D frequency domain analysis of full volume fMRI data exposes clinically or demographically relevant differences in resting-state brain function.
... more

Paper published: Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia

Assessing dynamic brain graphs of time-varying connectivity in fMRI data: application to healthy controls and patients with schizophrenia Yu, Q, EB Erhardt, J Sui, Y Du, H He, D Hjelm, M Cetin, S Rachakonda, R Miller, G Pearlson, and VD Calhoun NeuroImage 107, pdf supp, pp. 345–355. Online: 15 February 2015 http://www.sciencedirect.com/science/article/pii/S105381191401012X DOI: 10.1016/j.neuroimage.2014.12.020 Abstract Graph theory-based analysis has been widely employed in brain imaging studies, and altered topological properties of brain connectivity have emerged as important features of mental diseases such as schizophrenia. However, most previous studies have focused on graph metrics of stationary brain graphs, ignoring that brain connectivity exhibits fluctuations over time. Here we develop a new framework for accessing dynamic graph properties of time-varying functional brain connectivity in resting-state fMRI data and apply it to healthy controls (HCs) and patients with schizophrenia (SZs). Specifically, nodes of brain graphs are defined by intrinsic connectivity networks (ICNs) identified by group independent component analysis (ICA). Dynamic graph metrics of the time-varying brain connectivity estimated by the correlation of sliding time-windowed ICA time courses of ICNs are calculated. First- and second-level connectivity states are detected based on the correlation of nodal connectivity strength between time-varying brain graphs. Our results indicate that SZs show decreased variance in the dynamic graph metrics. Consistent with prior stationary functional brain connectivity works, graph measures of identified first-level connectivity states show lower values in SZs. In addition, more first-level connectivity states are disassociated with the second-level connectivity state which resembles the stationary connectivity pattern computed by the entire scan. Collectively, the findings provide new evidence about altered dynamic brain graphs in schizophrenia, which may underscore the abnormal brain performance in this mental illness.
... more

Paper Published: Tracking whole-brain connectivity dynamics in the resting-state

Tracking Whole-Brain Connectivity Dynamics in the Resting State Elena A. Allen, Eswar Damaraju, Sergey M. Plis, Erik B. Erhardt, Tom Eichele, and Vince D. Calhoun Cerebral Cortex Received: July 24, 2012 Accepted: October 15, 2012 Online: November 11, 2012 http://cercor.oxfordjournals.org/content/early/2012/11/09/cercor.bhs352.abstract doi: 10.1093/cercor/bhs352 Abstract Spontaneous fluctuations are a hallmark of recordings of neural signals, emergent over time scales spanning milliseconds and tens of minutes. However, investigations of intrinsic brain organization based on resting-state functional magnetic resonance imaging have largely not taken into account the presence and potential of temporal variability, as most current approaches to examine functional connectivity (FC) implicitly assume that relationships are constant throughout the length of the recording. In this work, we describe an approach to assess whole-brain FC dynamics based on spatial independent component analysis, sliding time window correlation, and k-means clustering of windowed correlation matrices. The method is applied to resting-state data from a large sample (n = 405) of young adults. Our analysis of FC variability highlights particularly flexible connections between regions in lateral parietal and cingulate cortex, and argues against a labeling scheme where such regions are treated as separate and antagonistic entities. Additionally, clustering analysis reveals unanticipated FC states that in part diverge strongly from stationary connectivity patterns and challenge current descriptions of interactions between large-scale networks. Temporal trends in the occurrence of different FC states motivate theories regarding their functional roles and relationships with vigilance/arousal. Overall, we suggest that the study of time-varying aspects of FC can unveil flexibility in the functional coordination between different neural systems, and that the exploitation of these dynamics in further investigations may improve our understanding of behavioral shifts and adaptive processes.
... more

Invited talks: Neuroimaging and Statistics at Wright State University, Dayton, OH

I just returned from a fun event-filled couple days at Wright State Univeristy in Dayton, Ohio, visiting statistician Harry Khamis.  Harry invited me to give two talks on Friday, November 2nd, 2012: one in Statistics and a second in Neuroscience, arranged by Thomas N. Hangartner.  Harry was the model host; I always felt taken care of, my needs met. I was excited to meet two people from my talks who could use the methods I presented. Prof Nasser H Kashou develops models for HRF functions, which the SimTB might be helpful for. Prof Yvonne Vadeboncoeur uses stable isotopes to study freshwater ecosystems, and we had some exciting discussion about collaborative opportunities. The links to the papers the talks draw on are at the bottom. My morning neuroimaging talk (10:15) in the Department of Biomedical, Industrial & Human Factors Engineering (BIE) included two-and-one-half topics: SimTB, subject variability with GICA, and a little data visualization.
Title Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study Abstract 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 component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. 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 affected 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. mialab.mrn.org/software/simtb
My afternoon statistics talk (3:00) in the Department of Mathematics and Statistics to a packed room (they had to bring in additional chairs!) included work that extends my published stable isotope sourcing work.
Title An extended Bayesian stable isotope mixing model for trophic level inference Abstract You are what and where you eat on the food web. We developed an extended Bayesian mixing model to jointly infer organic matter utilization and isotopic enrichment of organic matter sources in order to infer the trophic levels of several numerically abundant fish species (consumers) present in Apalachicola Bay, FL, USA. Bayesian methods apply for arbitrary numbers of isotopes and diet sources but existing models are somewhat limited as they assume that trophic fractionation is estimated without error or that isotope ratios are uncorrelated. The model uses stable isotope ratios of carbon, nitrogen, and sulfur, isotopic fractionations, elemental concentrations, elemental assimilation efficiencies, as well as prior information (expert opinion) to inform the diet and trophic level parameters. The model appropriately accounts for uncertainly and prior information at all levels of the analysis.
Neuroscience talk Summary of both SimTB papers. SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability Erik B. Erhardt, Elena A. Allen, Yonghua Wei, Tom Eichele, Vince D. Calhoun NeuroImage 59 (2012), pp. 4160-4167 http://www.sciencedirect.com/science/article/pii/S105381191101370X Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study Elena A. Allen, Erik B. Erhardt, Yonghua Wei, Tom Eichele, Vince D. Calhoun NeuroImage http://www.sciencedirect.com/science/article/pii/S1053811911011712 Data visualization in the neurosciences: overcoming the curse of dimensionality Elena A. Allen, Erik B. Erhardt, Vince D. Calhoun Neuron www.cell.com/neuron/retrieve/pii/S089662731200428X Statistics talk A Bayesian framework for stable isotope mixing models Erik B. Erhardt and Edward J. Bedrick Environmental and Ecological Statistics http://www.springerlink.com/content/vg4v62j8717671p3/ Bio Erik Barry Erhardt, PhD, is an Assistant Professor of Statistics at the University of New Mexico Department of Mathematics and Statistics, where he serves as Director of the statistics consulting clinic. His research interests include Bayesian and frequentist statistical methods for stable isotope sourcing and brain imaging. Erik is a Howard Hughes Medical Institute Interfaces Scholar collaborating in interdisciplinary research and consulting. StatAcumen.com
... more

Paper published: Data visualization in the neurosciences: overcoming the curse of dimensionality

Data visualization in the neurosciences: overcoming the curse of dimensionality
Elena A. Allen, Erik B. Erhardt, Vince D. Calhoun Neuron Accepted 7 May 2012 Online 24 May 2012 doi:10.1016/j.neuron.2012.05.001 www.cell.com/neuron/retrieve/pii/S089662731200428X Abstract: In publications, presentations, and popular media, scientific results are predominantly communicated through graphs. But are these figures clear and honest, or misleading? We examine current practices in data visualization and discuss improvements, advocating design choices which reveal data rather than hide it.
... more

Paper published: Correspondence between Structure and Function in the Human Brain at Rest

Correspondence between Structure and Function in the Human Brain at Rest Judith Maxine Segall, Elena A Allen, Rex E Jung, Erik B Erhardt, Sunil Kumar Arja, Kent A Kiehl, Vince D Calhoun Frontiers in Neuroinformatics Accepted 12 Mar 2012, 6:10 www.frontiersin.org/neuroinformatics/10.3389/fninf.2012.00010/abstract Abstract: To further the understanding of basic and complex cognitive functions of the human brain, multidisciplinary neuroimaging research has explored both functional and structural connectivity. For structural connectivity, the most prevalent method has been diffusion weighted imaging, which measures the connections of large white matter bundles. Recently, functional connectivity has been measured using resting-state fMRI (rs-fMRI). Surprisingly, few studies have examined structural gray matter, which supports the BOLD response. The overall aim of this study is to explore how gray matter (GM) structure corresponds to function. A cohort of 603 healthy participants was scanned on the same 3T scanner at the Mind Research Network to investigate the spatial correlations between structure and function. This was done by applying spatial independent component analysis (ICA) to GMD maps, to delineate structural components based on the covariation of GMD between regions, and to rs-fMRIdata, to discover spatial patterns with common temporal features. Decomposed structural and functional components were then compared by spatial correlation. The basal ganglia network showed the highest structural to rs-functional component correlation (r=0.59). Our remaining results generally show correspondence between one structural network and several functional networks. We also studied relationships between the weights of different structural components and found networks in frontal and parietal regions showing covariation across subjects. We also identified the precuneus as a hub for in structural network correlations. In addition, we analyzed relationships between component weights and age, concluding that age has an effect on structural components.
... more

Paper published: Modular organization of functional network connectivity

Modular organization of functional network connectivity in healthy controls and patients with schizophrenia during the resting state Qingbao Yu, Sergey M Plis, Erik B Erhardt, Elena A Allen, Jing Sui, Kent A Kiehl, Godfrey Pearlson, Vince D Calhoun 2011 Frontiers in Systems Neuroscience Abstract Neuroimaging studies have shown that functional brain networks composed from select regions of interest (ROIs) have a modular community structure. However, the organization of functional network connectivity (FNC), comprising a purely data-driven network built from spatially independent brain components, is not yet clear. The aim of this study is to explore the modular organization of FNC in both healthy controls (HCs) and patients with schizophrenia (SZs). Resting state functional magnetic resonance imaging (R-fMRI) data of HCs and SZs were decomposed into independent components (ICs) by group independent component analysis (ICA). Then weighted brain networks (in which nodes are brain components) were built based on correlations between ICA time courses. Clustering coefficients and connectivity strength of the networks were computed. A dynamic branch cutting algorithm was used to identify modules of the FNC in HCs and SZs. Results show stronger connectivity strength and higher clustering coefficient in HCs with more and smaller modules in SZs. In addition, HCs and SZs had some different hubs. Our findings demonstrate altered modular architecture of the FNC in schizophrenia and provide insights into abnormal topological organization of intrinsic brain networks in this mental illness.
... more

Paper published: SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability

Our paper detailing our simulation toolbox (SimTB) has been published in NeuroImage. SimTB, a simulation toolbox for fMRI data under a model of spatiotemporal separability Erik B. Erhardt, Elena A. Allen, Yonghua Wei, Tom Eichele, Vince D. Calhoun NeuroImage 59 (2012), pp. 4160-4167 Received at Elsevier: 29 Nov 2011, Available online 8 Dec 2011, published online 5 Jan 2012 ISSN 1053-8119, 10.1016/j.neuroimage.2011.11.088 (http://dx.doi.org/10.1016/j.neuroimage.2011.11.088) http://www.sciencedirect.com/science/article/pii/S105381191101370X Keywords: simulation; fMRI; group analysis [caption id="" align="alignnone" width="480" caption="SimTB flowchart for simulation of fMRI data"][/caption] Abstract 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. Keyword: simulation; fMRI; group analysis
... more

Paper published: Capturing inter-subject variability with group independent component analysis of fMRI data: a simulation study

Our paper using our simulation toolbox (SimTB) detailing what can be learned about multi-subject ICA on fMRI data has been published in NeuroImage. Capturing inter-subject variability with group independent component analysis of fMRI data: A simulation study Elena A. Allen, Erik B. Erhardt, Yonghua Wei, Tom Eichele, Vince D. Calhoun NeuroImage, Available online 14 October 2011, ISSN 1053-8119, 10.1016/j.neuroimage.2011.10.010. Volume 59, Issue 4, 15 February 2012, Pages 4141–4159 (http://www.sciencedirect.com/science/article/pii/S1053811911011712) Keywords: fMRI; Inter-subject variability; Group ICA; Multi-subject; Model order; Simulations Abstract 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 component analysis (GICA), which identifies group components and reconstructs activations at the individual level. GICA has gained considerable popularity, particularly in studies where temporal response models cannot be specified. 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 affected 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.
... more