Erik Barry Erhardt, PhD

A morphometric analysis of Actaea racemosa L. (Ranunculaceae)
Z. Gardner, L. Lueck, E.B. Erhardt, L.E. Craker
Journal of Medicinally Active Plants
http://scholarworks.umass.edu/cgi/preview.cgi?article=1008&context=jmap

Abstract
Actaea racemosa L. (syn. Cimicifuga racemosa [L.] Nutt.), Ranunculaceae, commonly known as black cohosh, is an herbaceous, perennial, medicinal plant native to the deciduous woodlands of eastern North America. Historical texts and current sales data indicate the continued popularity of this plant as an herbal remedy for over 175 years. Much of the present supply of A. racemosa is harvested from the wild. Diversity within and between populations of the species has not been well characterized. The purpose of this study was to assess the morphological variation of A. racemosa and identify patterns of variation at the population and species levels. A total of twentysix populations representative of a significant portion of the natural range of the species were surveyed and plant material was collected for the morphological analysis of 37 leaflet, flower, and whole plant characteristics. In total, 511 leaflet samples and 83 flower samples were examined. Several of the populations surveyed had sets of relatively unique characteristics (large leaflet measurements, tall leaves and flowers, and a large number of stamen) and Tukey-Kramer multiple comparisons revealed significant differences between specific populations for 20 different characteristics. However, no unique phenotype was found. Considerable morphological plasticity was noted in the apices of the staminodia. Cluster analyses showed that the morphological variation within populations is not smaller than between population and that this variation in not influenced by their geographic distribution.

Research, Statistics

We got one! Research Allocation Committee (RAC) Grants are for supporting new research or creative works. The RAC is particularly supportive of projects that may lead to outside funding and/or larger related projects.

PIs: Erik Erhardt and David Hanson
Title: “Frequentist (bootstrap) and Bayesian modeling of (photo)respiration in plants”
Amount: $3982.63, RAC 12-04
Use: To hire statistics graduate student, Mohammad Hattab, to implement and develop modeling that I did last summer in Switzerland.

Purpose:
We are requesting $3982.63 to develop statistical models to estimate (photo)respiration in plants, accounting for sources of uncertainty and prior information. Because current models provide estimates without meaningful assessments of uncertainty, our model will have broad application in understanding photosynthetic pathways and carbon usage in plants, clarifying the precision of our knowledge, conditional on what is already believed. This modeling is an important step towards developing more comprehensive models of photosynthetic parameters. Support from the Resource Allocation Committee will allow us to: (1) develop frequentist (bootstrap) and Bayesian models to analyze existing experimental data, providing inferences on the set of parameters related in the model; (2) design experiments and acquire additional data to distinguish and estimate respiration and photorespiration under a set of scientifically relevant conditions; (3) conduct validations using pre-existing data and estimates; (4) publish our model with results; and (5) develop grant proposals to apply this model more broadly.

Research, stable isotopes, Statistics

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.

MIND, Research

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
Available online 8 December 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)
Keywords: simulation; fMRI; group analysis

SimTB flowchart for simulation of fMRI data

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

MIND, Research

This article was printed in the Daily Lobo on 11/10/2011.

Presenting information in a way that clearly answers interesting questions is challenging. Every plot has an implicit question (hypothesis) that it helps you answer. Therefore, it is important to align a visual display of information with the intended interesting question(s). Collaboration or consultation with a statistician can clarify interesting questions and lead to answers through appropriate data analysis (visit UNM’s free statistics consulting clinic, www.stat.unm.edu/~clinic).

Suicide was the topic of the front cover story in the Daily Lobo on Thurs, Nov 3rd. With the story, two pie charts displayed average annual proportions of “successful” and “unsuccessful” suicides by method in NM. The “successful” pie chart answers this statement of conditional probability (their implied question): “given a successful suicide, what percentage used certain methods?” A question I consider more interesting reverses the conditioning (my question): “given an attempted suicide with a certain method, what percentage were successful?” Furthermore, I want to know the overall frequency and percentage of each method attempted. How can we present the information in a way that simultaneously answers these questions?

The Suicide Prevention Resource Center (SPRC.org) maintains national and state suicide fact sheets, last updated September 2008, describing “deaths by suicide, estimated hospitalized attempts, and data on medical costs, work loss costs, gender, race/ethnicity, age, and method of suicide.” The pie charts in Thursday’s Daily Lobo were reproductions of those found on the NM fact sheet. From their NM summaries, below is the SPRC table for estimated mean frequencies by method for “successful” and “unsuccessful” suicides.

Their question and pie charts (below) consider percentages down columns. When the data are reduced to row percentages for “successful” and “unsuccessful” attempts separately, you lose the relative frequency of attempts. The percentage of firearms “successes” (56%), for example, depends on all the other “successful” attempts. Because proportions for “successful” and “unsuccessful” attempts are separate, you can’t learn about how successful firearm attempts are.

Original pie charts of proportions of method conditional on attempt "success", which doesn't ask/answer the interesting/relavant question.

It is critical to consider the temporal process: a person first chooses a method, then makes an attempt, and is either “successful” or not. The data display and questions should follow these temporal steps. The pie chart displays ignore this process.

My question and plot (below) considers the temporal process of attempting suicide, considering percentages across rows, including row total information. First, the relative use of various methods is clear; almost two-thirds of attempts are by poisoning, and firearm and cut/pierce are each just above one in ten. However, though attempts by firearms (12%) and cut/pierce (13%) are relatively rare, the “success” rates are extremely different (92% versus 2%)! The plot has been sorted by the numbers of “successes” to emphasize the relative risk of the methods in terms of lives, information which is lost in the pie charts. Also, the area of each box is relative to the frequency in each box. The Agora Crisis Center (505-277-3013, 9am-midnight, every day) plays a critical role in our community, and our education as individuals around these issues can save someone. Using statistics and visualization to tell and understand the important story in the data can lead to improvements in strategies and resource allocation for treatment and prevention.

Improved visualization has relative use of methods across the horizontal and proportion of successes along the vertical. Area is proportional to people.

R code follows to produce plot above (with modest post-production necessary).
Read more…

Research, Statistics

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.
(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.

MIND, Research

Applying for an academic job is serious work.  I ended up lucky (though, luck favors the prepared (Louis Pasteur)).  I received two job offers this season and took my first-choice job.  But I worked hard to get those offers.  I kept a detailed CV my entire student career (starting as a BA student, not waiting until job season to start), wrote an extensive teaching dossier for the 20 courses I’ve taught and ugrad tutoring experience, and developed a research statement as that vision became clearer to me.  Clearly, self-investment and personal excellence are the most important ingredients.  Next is to find people who want to hire you.

Two sites and one magazine basically covers the bases for statistics.

1.  If you’re a statistics student, you’re already a member of the ASA, right?  If so, the back of the AmStat News magazine has many jobs listed.

http://magazine.amstat.org/

2. Many jobs are posted at the American Statistical Association (ASA) jobs website.   Subscribe to their feed in your RSS reader:

http://jobs.amstat.org/search/results/index.cfm?SN=25&ss=1&display=rss

While I have had my CV posted on the site for years, I’ve never received any contact because of it.  I think the more direct approach of networking or replying to specific jobs is more effective.

3. The University of Florida statistics website lists many jobs, too.  My impression is that this site is even more comprehensive than jobs.amstat sometimes.

http://www.stat.ufl.edu/vlib/Index.html

I recommend being subscribed to the jobs.amstat.org in your RSS reader, because then most of the jobs will come to you.  You can follow-up at the UFlorida website to make sure you’re not missing anything.  Start looking in Sept/Oct and work on cover letters through Nov/Dec for the Dec/Jan/Feb deadlines.  Ask for letters of recommendation early (maybe even late summer while your professors are not busy with the semester).  Ask your advisor to look over your CV, cover letter, and other submission materials (scan a pdf of your unofficial transcript).  They’ve reviewed many applications hiring in their department before and will have good advice.  Send your application materials (all in pdf format — not doc!) as soon as you are ready to help yours be near the top of their review pile.  And while your application is in the hands of many hiring committees, try not to sweat — you’ve done all you can and it’s largely out of your control until they ask for an interview (or send you a form rejection letter, or never respond to you at all).  Feel free to send a follow-up email to request status if it’s a week or so after their self-predicted decision deadline, if it will help calm your nerves, but try not to hassle them.  It’s a very challenging market and positions regularly get 80-300 applications, so everything you can do to rise to the top of that deep stack can make the difference between getting a toe in the door and the alternative.

Interviewing is next step.  Here are some pages with questions to prepare for.  Write your questions down just as you’d say them and practice saying them aloud, maybe to a friend who will listen.  You want to clarify your answers to yourself and get them to flow smoothly out of your mouth.
10 tough interview questions
General advice

The job talk is the last step.
You’re a grown up, use Mac’s iWork Keynote — it’s the best presentation software available.
BBP was a great resource, provided you can ignore all the MSPP BS.  First five slides.  Template. Video.
Matt Might’s presentation tips and job hunt advice.
CS Berkeley

Negotiating for your salary, start-up, teaching reduction, and more — ask your advisor for advice.  If you have a second offer, all of this becomes much, much easier!

Research, Statistics

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.

MIND, Research

Together with David Hanson, I developed R package tdllicor which reads TDL and Licor files, aligns them, and calculates quantities of interest with bootstrap intervals.  It is currently private as it is specialized and not of general interest.  It has already been important for a number of conference publications and is used for active research:

Conference Publications

DT Pater, EB Erhardt, and DT Hanson. Photorespiratory and respiratory carbon
isotope fractionation in leaves. In Proceedings of the Biophysical Society 55th
Annual Meeting, Baltimore, MD, Mar 2010. Biophysical Society.

DT Pater, EB Erhardt, and DT Hanson. Isotopic signature of photorespiration.
In Joint Annual Meetings of the American Society of Plant Biologists and the
Canadian Society of Plant Physiologists, Montreal, CA, August 2010.

Research, Statistics

Working with Aaftab Jain, we developed a estimator for total number of bird and bat carcasses at a windfarm called “mortest” and implemented it as an R package.  We are interested in estimating c, the total number of carcasses (mortalities) in a period (year). The total number of carcasses is the sum of carcasses over size classes, c = sum_s=1^S c_s. If carcasses are retained (that is, not scavenged) and searcher efficiency is perfect (every carcass is found) and every tower is searched, then each c_s would be counted perfectly. Yet, carcass scavenging by predators and searchers overlooking carcasses are a reality, making observed counts an underestimate. Furthermore, tower sampling rather than censusing is a cost-saving convenience. Our estimator of total mortality, c, weighs the estimates from different search intervals and adjusts the observed counts for scavenging, search efficiency, searchable area of each tower, and proportion of towers searched, accounting for uncertainty in these estimates using a bootstrap.

The software was written by Erik Erhardt and is currently private.  Contact Aaftab Jain <aaftabj+gmail.com> for more information for using the software.

Research, Statistics