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.

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.

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.

(I’ll be teaching couples dance again at AmWeek 2012!)

Eric Black and Diane Zingale with many other organizers created an amazing AmWeek 2011. Visit the website and fb page and join us for AmWeek 2012! See Photos.

Mary Wesley and I attended morning calling session for squares with Will Mentor and contra with Erik Weburg. Will gave me the opportunity to mimic his freeform squares from the first night. Erik suggested that I be a little more assertive in my calling (rather than saying things in a question-sounding way).

In the afternoons, Karina Wilson and I lead couples dance workshops with the moving music of Crowfoot (Jaige, Adam, and Nickolas). The first day was waltz and moves from uncrossed-hands position, including the cuddle. The second day we did Scandinavian dance, including the Snoa and the Hambo. The last day we continued waltz with moves from crossed-hands position, including cape and skaters. In an additional “waltz-swap” session, dancers “traded licks”, which is such a great idea at dance camps with so many talented dancers.

Dance teaching handouts: waltz hambo

I got picked to call the techno contra, too. What a great surprise! I called a medley of 3 dances to music that Katie Hepp brought to camp, and Will djed (clicked “play”) from his Mac. Because the music is loud and it’s a medley, I found it helped to call most of the calls all the way through each turn of the dance, only dropping calls for “hook” moves, and having additional prompts like, “something new” or “listen up”, before transitioning to the next dance in the medley. It was more challenging to call because I was behind the speakers and couldn’t hear the music as well as I can with a live band; a monitor with the same mix as the floor would be helpful to hear both the music and my voice.

And some exciting news for me from the BACDS newsletter Winter 2012 about “American Dance & Music Week 2012″:

“Couple dancing including waltzes and other styles will be taught by returning teacher Erik Erhardt. Erik was very well received last July, and we’ve asked him to come back and do it again.”

To be invited back is the biggest complement!

The previous year, 2010, Joyce Miller and Joyce Cooper made sure we were well taken care of! Joyce Cooper, especially, went way out of her way to have a van at the airport parking lot for me and my five wonderful Santa Fe girls. Then, on the way home, Eric Black saved us by giving us (me, Karina, Laurel, Lauren, Mia, Zoe, Chelsea, Tank, Andrew) an emergency place to slumber and threw in an extra “Get out of Hell, free” card, which I hope I don’t have to use with him again… Eric and Diane’s thoughtful caring really makes life wonderful for those around them.

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

A1 ———–
(4) Pass the ocean
(4) Balance the wave
(8) Partner swing
A2 ———–
(4) Gents forward into a long wavy line of gents
(4) Gents balance the wave
(8) Gents allemande right 1-1/2 WHILE Ladies orbit CCW to cross the set (keeping Gents to their left)
B1 ———–
(8) Neighbor Left-hand gypsy
(8) Neighbor Right-shoulder swing
B2 ———–
(8) Gents chain, Gents pull by left, Lady courtesy turn partner Gent
(8) Long lines, forward and back on Left diagonal

Notes: B2 – Teach the courtesy turn first. Ladies will turn their Gent partner in place. Gent puts his right hand in front of the lady and his left hand on his left hip. Lady takes his hands in hers and courtesy turn him once around to feel that scooping motion.
A1 – Pass the ocean has Ladies turn an allemande left 1/4 while Gents pass through and turn a quarter right to take right hands with their partner.
B1 – Left-hand gypsy has left hand at your waist, palm facing your own hip, and linked palm-to-palm with your neighbor as you gypsy. Turn this into a swing by pulling the joined left hand up into an allemande position, put right hand on partner’s right shoulder, and reverse body momentum to swing in the usual direction.
B2 – Long lines towards same-gender new neighbor, back on left diagonal to end across from new opposite-gender neighbor.

Other Notes: Written for Noah Segal 10/25/2011. Posted today (11/22) for his birthday!

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).

# following example from http://learnr.wordpress.com/2009/03/29/ggplot2_marimekko_mosaic_chart/
setwd("F:\\Dropbox\\UNM\\seminar\\DailyLobo\\20111103_SuicideStatistics")

################################################################################
df <- data.frame(
          M = c(
            "Cut/Pierce",       # "C/P",
            "Firearms",         # "F",
            "Poisoning",        # "P",
            "Suffocation",      # "S",
            "Other/Unspecified")# "O/U")       # Method
        , S = c(4,191,60,73,13)     # Successful
        , U = c(229,16,1097,23,91)  # Unsuccessful
      )

df$T  <- df$S+df$U;                 # Total across method
#df$pS <- df$S/sum(df$S);            # prop S
#df$pU <- df$U/sum(df$U);            # prop U
df$Successful <- 100*round(df$S/df$T, digits = 2);     # prop S by M
df$Unsuccessful <- 100*round(df$U/df$T, digits = 2);   # prop S by M
df$pT  <- 100*round(df$T/sum(df$T), digits = 2);       # prop total
df <- df[order(-df$S),];  # sort so largest method is first

# proportions on x-axis
df$xmax <- cumsum(df$pT);
df$xmin <- df$xmax - df$pT;

#Data looks like this before the long-format conversion:
df

library(ggplot2)
dfm <- melt(df, id = c("M", "T", "pT", "S", "U", "xmin", "xmax"))
dfm

#Now we need to determine how the columns are stacked and where to position the text labels.

#Calculate ymin and ymax:
dfm1 <- ddply(dfm , .(M), transform, ymax = cumsum(value))
dfm1 <- ddply(dfm1, .(M), transform, ymin = ymax - value)
n <- dim(dfm1)[1];
dfm1$F <- as.vector(t(matrix(c(dfm1$S[seq(1,n-1,by=2)],dfm1$U[seq(2,n,by=2)]),ncol=2)))

# Positioning of text:
dfm1$xtext <- with(dfm1, xmin + (xmax - xmin)/2)
dfm1$ytext <- with(dfm1, ymin + (ymax - ymin)/2)

# Finally, we are ready to start the plotting process:
p <- ggplot(dfm1, aes(ymin = ymin, ymax = ymax, xmin = xmin, xmax = xmax, fill = variable))

# Use grey border to distinguish between the segments:
p1 <- p + geom_rect(colour = I("grey"))

# The explanation of different fill colours will be included in the text label of Segment A using the ifelse function.
p2 <- p1 + geom_text(aes(x = xtext, y = ytext,
      label = ifelse(M == df$M[1],
                     paste(variable, "\n", value, "%\n", F, sep = ""),
                     paste(value, "%\n", F, sep = "")))
      , size = 3.5)

# The maximum y-axes value is 100 (as in 100%), and to add the segment description above each column I manually specify the text position.
p3 <- p2 + geom_text(aes(x = xtext, y = 107, label = paste(M,"\n",pT,"%\n",T,sep="")), size = 4)
#p3

# Some last-minute changes to the default formatting: remove axis labels, legend and gridlines.
p4 <- p3 + theme_bw() + labs(x = "Percent Attempts by Method", y = "Percent Success by Method",
     fill = NULL) + opts(legend.position = "none",
     panel.grid.major = theme_line(colour = NA),
     panel.grid.minor = theme_line(colour = NA))
p4

pdf("fromR.pdf")
p4
dev.off()

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.

Dear friends: I am thrilled to say hello to you as the new CDSS Youth Intern. As I settle into the position I will have lots to tell you about my ideas and projects. Look for my voice in the CDSS blog and updates on the “Youth Desk” soon. For right now I am tickled to have a brand new e-mail address: mary@cdss.org. Use it! I rely on you to be my eyes on the world of song and dance that we all share. Let me know what’s happening in your community: What are your challenges? What’s working well? What are you really excited about? The more I hear from you, the more energy and direction I will have to support your endeavors. I can’t wait to chat with you! See you on the dance floor. — Mary

I’m excited for Mary because she is passionate about dancing and dance leadership and I am confident she has the skills and drive to continue to make great things happen (for youth) in the dance community.  She’s here to support us, so help her help us by sending her an email and letting her know our dancing dreams.  She can help make them come true…

Artist: Katherine Sanden
Music: Eri-Eri-Eri-Erik (lyrics), March 2010

Interview (June 2010) with Katherine Sanden about music and song writing.  Kat studied mathematics at Princeton University.  She now tutors in mathematics, teaches music and piano, and writes sublime rhymes and beats.

For contra dance weekend Stellar Days & Nights, in Buena Vista, Colorado, February 18-21, 2010, I drove up with Richard, Laurel, and Karina Wilson, Lauren Lamont, and Della O’Keefe. During the silent auction CDSS‘s Max Newman and I got into a fierce bidding war over a custom song written and performed by Katherine Sanden. I had every intention to win, and when the bell rang, I had. In the spirit of the “new sincerity” I requested a “monster ego explosion” (after all, how many chances will I have for someone to write a song about ME!?). What I got was much, much more! I still flush with embarrassment each time I hear it. Quality headphones are recommended for a dynamic experience of the full audial range. Everyone needs a steamy power jam — lucky me!