Abstract
Dialectical Behavior Therapy (DBT) is an evidence-based treatment for borderline personality disorder that has been widely disseminated to many outpatient treatment settings. Many practitioners depend on third-party payers to fund treatment delivery. DBT requires additional clinical services not often included in outpatient therapy, including a weekly skills group led by 2 clinicians, and the requirement for clinicians to attend weekly consultation team and provide intersession contact for coaching. Standard outpatient insurance rates for individual and group sessions do not provide adequate reimbursement for the additional services of DBT. This paper describes how two DBT team leaders collaborated to obtain improved reimbursement for their programs. The two teams met with insurers, educated them about DBT, and showed outcomes from their programs to achieve large increases in reimbursement rates. The paper includes client outcome data from both programs.

12:00-12:15 open waltzing (warm-up/beginner lesson)
12:15-1:15 lesson (learn)
1:15-2:00 open waltzing (open dancing/practice)

$5/person (suggested)
Gary Diggs and Erik Erhardt, instructors
Join the Facebook group

Cross-step waltz Albuquerque is a new dance group meeting every Sunday 12-2pm at Lloyd Shaw Dance Center in Albuquerque organized and taught by Gary Diggs and me, Erik Erhardt.  I’d heard about the west coast cross-step phenomenon, danced the follow role with a couple California leads at dance camps, and dreamily watched YouTube videos over the previous couple years.  At last, my moment arrived with the arrival of Richard Powers and Angela Amarillas from Stanford University to Albuquerque to teach a cross-step waltz workshop the weekend of October 12th, 2012, hosted by the Albuquerque International Folk Dance Foundation.  Cross-step immediately became my favorite dance form!  Soon after, Gary approached me to co-teach a class with him.  Because Gary knows all the fancy moves and I connect well with beginners, we play well together and offer something for everyone.  We welcome beginners and experienced dancers, alike.  Each class is structured with a fifteen-minute warm-up/beginner lesson, a one-hour lesson, and forty five-minutes of open dancing/practice/one-on-one touch-up.

For a lovely online preview, search for “Richard Powers Cross-Step Waltz” to find his social dance page including an uncut three-minute video sequence with many of flowing variations and a generous description of the dance, its history, and hundreds of variations (in active development).  Next, of course, come cross-step with us (especially gents)!

Erik Erhardt teaches couples dance and calls contra and English country dance in New Mexico and around the country.
1/28/2013

Tonight KOB-TV4 aired the NM Law Enforcement Academy “Police cadet test scores under investigation” story on the Eyewitness News 4 at 10 P.M., for which I gave a short interview to Gadi Schwartz using a plot I created from the test score data.

I gave the information and interview out of a personal desire to be helpful and was not acting on the University’s behalf. I did not speculate on the cause for the outlying class’s scores. I value the men and women who risk their lives daily serving our communities.

Before

After

R code to create the “after” plot follows.

# Erik B. Erhardt
# 4/28/2012

# Recreating this plot as a Marimekko mosaic chart
# NM Registered Voters - Party by Age Line Chart (Oct 2008)
# http://rpinc.com/wb/media/reports/Party%20by%20age%20line%20chart%20-%202008-10.pdf

# Census population sizes
# NM population numbers
# http://factfinder2.census.gov/faces/tableservices/jsf/pages/productview.xhtml?pid=DEC_10_SF1_QTP1&prodType=table

ages <- c("15-19", "20-24", "25-29", "30-34", "35-39", "40-44", "45-49", "50-54",
          "55-59", "60-64", "65-69", "70-74", "75-79", "80-84", "85-89", "90-99")
pop.ages <- c(149861,142370,139678,127567,123303,125220,144839,147170,
              136799,120137, 87890, 65904, 50230, 36238, 21622, 10371)

age <- seq(18,99)
pop <- c(rep(pop.ages/5,each=5)[c(4:75)], rep(pop.ages[length(pop.ages)]/10,10))
pop.prop <- pop/sum(pop)

# datathief http://rpinc.com/wb/media/reports/Party%20by%20age%20line%20chart%20-%202008-10.pdf

dem <- c(0.46,0.46,0.45,0.44,0.40,0.41,0.42,0.43,0.43,0.44,0.46
        ,0.46,0.46,0.46,0.47,0.48,0.48,0.48,0.48,0.48,0.48,0.48
        ,0.48,0.48,0.49,0.49,0.49,0.49,0.49,0.49,0.49,0.50,0.50
        ,0.51,0.52,0.53,0.52,0.53,0.54,0.55,0.55,0.55,0.55,0.54
        ,0.54,0.55,0.54,0.54,0.53,0.55,0.55,0.55,0.55,0.55,0.55
        ,0.56,0.56,0.56,0.56,0.56,0.56,0.56,0.58,0.58,0.57,0.57
        ,0.57,0.57,0.56,0.58,0.56,0.58,0.58,0.58,0.59,0.59,0.61
        ,0.59,0.62,0.61,0.62,0.60)

rep <- c(0.24,0.25,0.26,0.28,0.28,0.27,0.27,0.27,0.27,0.27,0.26
        ,0.27,0.27,0.28,0.28,0.28,0.29,0.30,0.31,0.31,0.32,0.32
        ,0.33,0.33,0.33,0.33,0.34,0.34,0.34,0.35,0.35,0.35,0.34
        ,0.34,0.34,0.33,0.33,0.33,0.32,0.31,0.31,0.31,0.31,0.32
        ,0.33,0.32,0.34,0.34,0.35,0.34,0.35,0.35,0.35
        ,0.35,0.35,0.35,0.35,0.36,0.36,0.36,0.36,0.35,0.34,0.34
        ,0.35,0.35,0.34,0.34,0.36,0.35,0.36,0.35,0.34,0.35,0.34
        ,0.34,0.33,0.34,0.33,0.32,0.30,0.31)

dts  <- c(0.26,0.25,0.25,0.24,0.30,0.29,0.28
        ,0.28,0.26,0.26,0.24,0.24,0.23,0.23,0.21,0.20,0.19,0.19
        ,0.18,0.18,0.17,0.17,0.16,0.16,0.15,0.15,0.14,0.15,0.14
        ,0.14,0.13,0.13,0.13,0.12,0.12,0.12,0.12,0.12,0.12,0.12
        ,0.12,0.12,0.12,0.12,0.11,0.11,0.10,0.10,0.11,0.10,0.09
        ,0.09,0.09,0.08,0.08,0.08,0.08,0.07,0.07,0.07,0.07,0.07
        ,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.07,0.06,0.06
        ,0.07,0.07,0.07,0.06,0.06,0.04,0.06,0.05,0.06)

other <- c(0.05,0.05,0.05,0.05,0.03,0.03,0.04,0.04,0.04,0.04,0.04
          ,0.04,0.04,0.04,0.04,0.04,0.04,0.03,0.03,0.03,0.03,0.03
          ,0.03,0.03,0.03,0.03,0.03,0.02,0.03,0.03,0.03,0.03,0.03
          ,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03,0.03
          ,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02
          ,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.02,0.01
          ,0.02,0.02,0.01,0.01,0.02,0.01,0.01,0.02,0.01,0.02,0.01
          ,0.01,0.01,0.00,0.01,0.02)

all <- data.frame(dem, rep, dts, other)
rowSums(all)

# correct rounding errors from datathief
for (i in 1:length(age)) {
  all[i,] <- all[i,]/sum(all[i,]);
}
rowSums(all)

## getting data list above
# x <- scan()
# [datathief numbers]
#
# round(matrix(x,ncol=2,byrow=TRUE)[,2],2)
# plot(round(matrix(x,ncol=2,byrow=TRUE)[,1],0))

# following example from http://learnr.wordpress.com/2009/03/29/ggplot2_marimekko_mosaic_chart/

################################################################################
df <- data.frame(
          segment = age
        , segpct = pop.prop * 100
        , Other = all$other * 100
        , DTS  = all$dts    * 100
        , Rep = all$rep     * 100
        , Dem = all$dem     * 100
      )

df$xmax <- cumsum(df$segpct)
df$xmin <- df$xmax - df$segpct
df$segpct <- NULL

library(ggplot2)
library(reshape)

dfm <- melt(df, id = c("segment", "xmin", "xmax"))

dfm1 <- ddply(dfm , .(segment), transform, ymax = cumsum(value))
dfm1 <- ddply(dfm1, .(segment), transform, ymin = ymax - value)

dfm1$xtext <- with(dfm1, xmin + (xmax - xmin)/2)
dfm1$ytext <- with(dfm1, ymin + (ymax - ymin)/2)

dfm1$segmentlabel <- rep("",length(dfm1$segment))
ss <- ((dfm1$segment %% 5)==0); # every 5 years, display age
dfm1$segmentlabel[ss] <- dfm1$segment[ss]
dfm1$segmentlabel[(dfm1$segment==18)] <- "age"

p <- ggplot(dfm1, aes(ymin = ymin, ymax = ymax, xmin = xmin, xmax = xmax, fill = variable))

p <- p + geom_rect(colour = I("grey"), alpha=0.75, size=.01) +
      xlab("Percentage age distribution") +
      ylab("Percent registered voter for party by age") +
      labs(title="NM Registered Voters - Party by Age (Oct 2008)")

p <- p + geom_text(aes(x = xtext, y = ytext,
     label = ifelse(segment == 20, paste(variable), " ")), size = 3.5)

p <- p + geom_text(aes(x = xtext, y = -3, label = paste(dfm1$segmentlabel)), size = 3)
p

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.

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

Abstract
Bayesian inference about small areas is of considerable current interest, and simultaneous intervals for the parameters for the areas are needed because these parameters are correlated. This is not usually pursued because with many areas the problem becomes difficult. We describe a method for finding simultaneous credible intervals for a relatively large number of parameters, each corresponding to a single area. Our method is model based, it uses a hierarchical Bayesian model, and it starts with either the 100(1-alpha)% (e.g., alpha=0.05 for 95%) credible interval or highest posterior density (HPD) interval for each area. As in the construction of the HPD interval, our method is the result of the solution of two simultaneous equations, an equation that accounts for the probability content, 100(1-alpha)% of all the intervals combined, and an equation that contains an optimality condition like the “equal ordinates” condition in the HPD interval. We compare our method with one based on a nonparametric method, which as expected under a parametric model, does not perform as well as ours, but is a good competitor. We illustrate our method and compare it with the nonparametric method using an example on disease mapping which utilizes a standard Poisson regression model.

Abstract:

Stable isotope sourcing is used to estimate proportional contributions of sources to a mixture, such as in the analysis of animal diets and plant nutrient use. Statistical methods for inference on the diet proportions using stable isotopes have focused on the linear mixing model. Existing frequentist methods provide inferences when the diet proportion vector can be uniquely solved for in terms of the isotope ratios. Bayesian methods apply for arbitrary numbers of isotopes and diet sources but existing models are somewhat limited as they assume that trophic fractionation or discrimination is estimated without error or that isotope ratios are uncorrelated. We present a Bayesian model for the estimation of mean diet that accounts for uncertainty in source means and discrimination and allows correlated isotope ratios. This model is easily extended to allow the diet proportion vector to depend on covariates, such as time. Two data sets are used to illustrate the methodology. Code is available for selected analyses.

A better Hambo workshop

Chuck Gordon invited me to teach Hambo with Heather Carmichael at Pinewoods, Swing into Summer. The two one-hour sessions, and the support of several dance angels (experienced hambo-ers), absolutely helped the success of the workshop. My experience is that a single one-hour workshop (or even 1:15) isn’t quite enough to get the dance into the feet of the dancers.  But one hour to get solid on the components and their synthesis (Dreyfus model levels 2 or 3, advanced beginner or competency), a night of rest and maybe practicing the turn in free moments, then a second day of dancing over and over with many dancers gives enough time and thought to ascend to level 4 (proficiency).  I think even two 45-minute session are preferable to one 1:30 workshop or even a single 2-hour workshop.

From the experience, I still feel engaged, joyful, and inspired because of the direct and special way I could connect in the community, watch the dancers grow in the two hours, and facilitate the mutual nurturing and compassion between the dancers as many went from “what’s hambo?” to, “let’s dance”!

Heather and I thank Emily Troll, Mary Lea, and Julie Vallimont who played a series of lovely tunes as we all got to dance as a mixer for a solid 40 minutes of the second hour — my most successful hambo workshop!

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.