Person-Specific Methods for Characterizing the Course and Temporal Dynamics of Concussion Symptomatology: A Pilot Study.

Better characterization of acute concussion symptomatology is needed in order to advance clinical and scientific understanding of persistent concussion symptoms. This paper aims to illustrate a novel framework for conceptualizing, collecting, and analyzing concussion symptom data. To that end, we describe the temporal and structural dynamics of acute concussion symptoms at the individual-patient level. Ten recently concussion adolescents and young adults completed 20 days of ecological momentary assessment (EMA) of post-concussion symptoms. Follow-up assessments were completed at 3 months post-injury. Network modeling revealed marked heterogeneity across participants. In the overall sample, temporal patterns explained the most variance in light sensitivity (48%) and the least variance in vomiting (5%). About half of the participants had symptom networks that were sparse after controlling for temporal variation. The other individualized symptom networks were densely interconnected clusters of symptoms. Networks were highly idiosyncratic in nature, yet emotional symptoms (nervousness, emotional, sadness), cognitive symptoms (mental fogginess, slowness), and symptoms of hyperacusis (sensitivity to light, sensitivity to noise) tended to cluster together across participants. Person-specific analytic techniques revealed a number of idiosyncratic features of post-concussion symptomatology. We propose applying this framework to future research to better understand individual differences in concussion recovery

Statistical inferences of Rs;k = Pr(Xk-s+1:k \u3e Y ) for general class of exponentiated inverted exponential distribution with progressively type-II censored samples with uniformly distributed random removal

The problem of statistical inference of the reliability parameter Pr(Xk-s+1:k \u3e Y ) of an s-out-of-k : G system with strength components X1,X2,…,Xk subjected to a common stress Y when X and Y are independent two-parameter general class of exponentiated inverted exponential (GCEIE) progressively type-II right censored data with uniformly random removal random variables, are discussed. We use p-value as a basis for hypothesis testing. There are no exact or approximate inferential procedures for reliability of a multicomponent stress-strength model from the GCEIE based on the progressively type-II right censored data with random or fixed removals available in the literature. Simulation studies and real-world data analyses are given to illustrate the proposed procedures. The size of the test, adjusted and unadjusted power of the test, coverage probability and expected confidence lengths of the confidence interval, and biases of the estimator are also discussed

Using network analysis for the prediction of treatment dropout in patients with mood and anxiety disorders: a methodological proof-of-concept study

There are large health, societal, and economic costs associated with attrition from psychological services. The recently emerged, innovative statistical tool of complex network analysis was used in the present proof-of-concept study to improve the prediction of attrition. Fifty-eight patients undergoing psychological treatment for mood or anxiety disorders were assessed using Ecological Momentary Assessments four times a day for two weeks before treatment (3,248 measurements). Multilevel vector autoregressive models were employed to compute dynamic symptom networks. Intake variables and network parameters (centrality measures) were used as predictors for dropout using machine-learning algorithms. Networks for patients differed significantly between completers and dropouts. Among intake variables, initial impairment and sex predicted dropout explaining 6% of the variance. The network analysis identified four additional predictors: Expected force of being excited, outstrength of experiencing social support, betweenness of feeling nervous, and instrength of being active. The final model with the two intake and four network variables explained 32% of variance in dropout and identified 47 out of 58 patients correctly. The findings indicate that patients’ dynamic network structures may improve the prediction of dropout. When implemented in routine care, such prediction models could identify patients at risk for attrition and inform personalized treatment recommendations.This work was supported by the German Research Foundation National Institute (DFG, Grant nos. LU 660/8-1 and LU 660/10-1 to W. Lutz). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the manuscript. The corresponding author had access to all data in the study and had final responsibility for the decision to submit for publication. Dr. Hofmann receives financial support from the Alexander von Humboldt Foundation (as part of the Humboldt Prize), NIH/NCCIH (R01AT007257), NIH/NIMH (R01MH099021, U01MH108168), and the James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative. (LU 660/8-1 - German Research Foundation National Institute (DFG); LU 660/10-1 - German Research Foundation National Institute (DFG); Alexander von Humboldt Foundation; R01AT007257 - NIH/NCCIH; R01MH099021 - NIH/NIMH; U01MH108168 - NIH/NIMH; James S. McDonnell Foundation 21st Century Science Initiative in Understanding Human Cognition - Special Initiative)Accepted manuscrip

Methods for High Dimensional Analysis, Multiple Testing, and Visual Exploration

My thesis work focuses on aiding the practical implementation of advanced statistical methods. Chapter 2 concerns the common practice of visual exploratory data analysis, and the extent to which humans can visually detect statistical significance from plots. We find that human accuracy in detecting significance was initially poor, but improved with practice. Chapter 3 aids the implementation of bootstrap principal component analysis, by providing significant computational improvements. In a dataset of brain magnetic resonance images, the proposed method can reduce bootstrap standard error computation times from approximately 4 days to 47 minutes. Chapter 4 proposes an approximate optimization technique for adaptive clinical trials, aimed at lowering the expected sample size or expected duration of a trial

A new method reveals microtubule minus ends throughout the meiotic spindle

Anastral meiotic spindles are thought to be organized differently from astral mitotic spindles, but the field lacks the basic structural information required to describe and model them, including the location of microtubule-nucleating sites and minus ends. We measured the distributions of oriented microtubules in metaphase anastral spindles in Xenopus laevis extracts by fluorescence speckle microscopy and cross-correlation analysis. We localized plus ends by tubulin incorporation and combined this with the orientation data to infer the localization of minus ends. We found that minus ends are localized throughout the spindle, sparsely at the equator and at higher concentrations near the poles. Based on these data, we propose a model for maintenance of the metaphase steady-state that depends on continuous nucleation of microtubules near chromatin, followed by sorting and outward transport of stabilized minus ends, and, eventually, their loss near poles

Folk Classification and Factor Rotations:Whales, Sharks, and the Problems With the Hierarchical Taxonomy of Psychopathology (HiTOP)

The Hierarchical Taxonomy of Psychopathology (HiTOP) uses factor analysis to group self-reported symptoms of mental illness (i.e., like goes with like). It is hailed as a significant improvement over other diagnostic taxonomies. However, the purported advantages and fundamental assumptions of HiTOP have received little, if any, scientific scrutiny. We critically evaluated five fundamental claims about HiTOP. We conclude that HiTOP does not demonstrate a high degree of verisimilitude and has the potential to hinder progress on understanding the etiology of psychopathology. It does not lend itself to theory building or taxonomic evolution, and it cannot account for multifinality, equifinality, or developmental and etiological processes. In its current form, HiTOP is not ready to use in clinical settings and may result in algorithmic bias against underrepresented groups. We recommend a bifurcation strategy moving forward in which the Diagnostic and Statistical Manual of Mental Disorders is used in clinical settings while researchers focus on developing a falsifiable theory-based classification system

Structure and belonging: Pathways to success for underrepresented minority and women PhD students in STEM fields

The advancement of underrepresented minority and women PhD students to elite postdoctoral and faculty positions in the STEM fields continues to lag that of majority males, despite decades of efforts to mitigate bias and increase opportunities for students from diverse backgrounds. In 2015, the National Science Foundation Alliance for Graduate Education and the Professoriate (NSF AGEP) California Alliance (Berkeley, Caltech, Stanford, UCLA) conducted a wide-ranging survey of graduate students across the mathematical, physical, engineering, and computer sciences in order to identify levers to improve the success of PhD students, and, in time, improve diversity in STEM leadership positions, especially the professoriate. The survey data were interpreted via path analysis, a method that identifies significant relationships, both direct and indirect, among various factors and outcomes of interest. We investigated two important outcomes: publication rates, which largely determine a new PhD student’s competitiveness in the academic marketplace, and subjective well-being. Women and minority students who perceived that they were well-prepared for their graduate courses and accepted by their colleagues (faculty and fellow students), and who experienced well-articulated and structured PhD programs, were most likely to publish at rates comparable to their male majority peers. Women PhD students experienced significantly higher levels of distress than their male peers, both majority and minority, while both women and minority student distress levels were mitigated by clearly-articulated expectations, perceiving that they were well-prepared for graduate level courses, and feeling accepted by their colleagues. It is unclear whether higher levels of distress in women students is related directly to their experiences in their STEM PhD programs. The findings suggest that mitigating factors that negatively affect diversity should not, in principle, require the investment of large resources, but rather requires attention to the local culture and structure of individual STEM PhD programs

The Hierarchical Taxonomy of Psychopathology (HiTOP) Is Not an Improvement Over the DSM

DeYoung and colleagues did not sufficiently address three fundamental flaws with HiTOP. First, HiTOP was created using a simple structure factor analytic approach, which does not adequately represent the dimensional space of the symptoms of psychopathology. Consequently, HiTOP is not the empirical structure of psychopathology. Second, factor analysis and dimensional ratings do not fix the problems inherent to descriptive (folk) classification; self-reported symptoms are still the basis upon which clinical judgments about people are made. Finally, HiTOP is not ready to use in real-world clinical settings. There is currently no empirical evidence demonstrating that clinicians who use HiTOP have better clinical outcomes than those who use the DSM. In sum, HiTOP is a factor analytic variation of the DSM that does not get us closer to a more valid and useful taxonomy