Publications by authors named "Hung-Wen Yeh"

78 Publications

Sex differences in postnatal weight gain trajectories of extremely preterm newborns.

J Perinatol 2021 May 25. Epub 2021 May 25.

Division of Health Services and Outcomes Research, Children's Mercy-Kansas City, Kansas City, MO, USA.

Objective: Both postnatal growth and sex play a crucial role in long-term outcomes of extremely preterm newborns (EPNs), but the relationship between sex and postnatal growth is not clear. This study aims to assess sex differences in weight trajectories.

Study Design: Weight data in the first 200 days of life from 4327 EPNs were used for generalized additive mixed modeling. We considered gestational age and sex as fixed-effects, and included random intercepts and random slopes for postnatal age. We assessed interactions between fixed-effects and postnatal age.

Results: Male EPNs had higher predicted weight trajectories than females. Weight z-score trajectories decreased in both sexes before term-equivalent age comparably, but females showed faster increases afterward. Although weight gain velocity was comparable between both sexes, weight gain velocity in male EPNs was lower compared to the corresponding reference values from the 2013 Fenton growth charts, which explained slower z-score rises.

Conclusion: Sex disparity exists in postnatal weight gain trajectories of EPNs after reaching the term-equivalent age.
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http://dx.doi.org/10.1038/s41372-021-01099-2DOI Listing
May 2021

Neurocognitive Empowerment for Addiction Treatment (NEAT): study protocol for a randomized controlled trial.

Trials 2021 May 7;22(1):330. Epub 2021 May 7.

Laureate Institute for Brain Research, 6655 South Yale Ave., Tulsa, OK, 74136, USA.

Background: Neurocognitive deficits (NCDs) and associated meta-cognition difficulties associated with chronic substance use often delay the learning and change process necessary for addiction recovery and relapse prevention. However, very few cognitive remediation programs have been developed to target NCDs and meta-cognition for substance users. The study described herein aims to investigate the efficacy of a multi-component neurocognitive rehabilitation and awareness program termed "Neurocognitive Empowerment for Addiction Treatment" (NEAT). NEAT is a fully manualized, cartoon-based intervention involving psychoeducation, cognitive practice, and compensatory strategies relevant across 10 major cognitive domains, including aspects of attention, memory, executive functions, and decision-making.

Method/design: In a single-blind randomized controlled trial (RCT), 80 female opioid and/or methamphetamine users will be recruited from an addiction recovery program providing an alternative to incarceration for women with substance use-related offenses. Eight groups of 9-12 participants will be randomized into NEAT or treatment-as-usual (TAU). NEAT involves 14 90-min sessions, delivered twice weekly. The primary outcome is change in self-reported drug craving from before to after intervention using Obsessive Compulsive Drug Use Scale. Secondary and exploratory outcomes include additional psychological, neurocognitive, and structural and functional neuroimaging measures. Clinical measures will be performed at five time points (pre- and post-intervention, 3-, 6-, and 12-month follow-up); neuroimaging measures will be completed at pre- and post-intervention.

Discussion: The present RCT is the first study to examine the efficacy of an adjunctive neurocognitive rehabilitation and awareness program for addiction. Results from this study will provide initial information concerning potential clinical efficacy of the treatment, as well as delineate neural mechanisms potentially targeted by this novel intervention.

Trial Registration: ClinicalTrials.gov NCT03922646 . Registered on 22 April 2019.
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http://dx.doi.org/10.1186/s13063-021-05268-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8106153PMC
May 2021

Extracurricular Activities, Screen Media Activity, and Sleep May Be Modifiable Factors Related to Children's Cognitive Functioning: Evidence From the ABCD Study.

Child Dev 2021 Apr 26. Epub 2021 Apr 26.

Laureate Institute for Brain Research.

This study used a machine learning framework in conjunction with a large battery of measures from 9,718 school-age children (ages 9-11) from the Adolescent Brain Cognitive Development (ABCD) Study to identify factors associated with fluid cognitive functioning (FCF), or the capacity to learn, solve problems, and adapt to novel situations. The identified algorithm explained 14.74% of the variance in FCF, replicating previously reported socioeconomic and mental health contributors to FCF, and adding novel and potentially modifiable contributors, including extracurricular involvement, screen media activity, and sleep duration. Pragmatic interventions targeting these contributors may enhance cognitive performance and protect against their negative impact on FCF in children.
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http://dx.doi.org/10.1111/cdev.13578DOI Listing
April 2021

Failure to Identify Robust Latent Variables of Positive or Negative Valence Processing Across Units of Analysis.

Biol Psychiatry Cogn Neurosci Neuroimaging 2021 05 3;6(5):518-526. Epub 2021 Mar 3.

Department of Psychiatry, University of California San Diego, La Jolla, California; Laureate Institute for Brain Research, Tulsa, Oklahoma.

Background: The heterogeneous nature of mood and anxiety disorders highlights a need for dimensionally based descriptions of psychopathology that inform better classification and treatment approaches. Following the Research Domain Criteria approach, this investigation sought to derive constructs assessing positive and negative valence domains across multiple units of analysis.

Methods: Adults with clinically impairing mood and anxiety symptoms (N = 225) completed comprehensive assessments across several units of analysis. Self-report assessments included nine questionnaires that assess mood and anxiety symptoms and traits reflecting the negative and positive valence systems. Behavioral assessments included emotional reactivity and distress tolerance tasks, during which skin conductance and heart rate were measured. Neuroimaging assessments included fear conditioning and a reward processing task. The latent variable structure underlying these measures was explored using sparse Bayesian group factor analysis.

Results: Group factor analysis identified 11 latent variables explaining 31.2% of the variance across tasks, none of which loaded across units of analysis or tasks. Instead, variance was best explained by individual latent variables for each unit of analysis within each task. Post hoc analyses 1) showed associations with small effect sizes between latent variables that were derived separately from functional magnetic resonance imaging and self-report data and 2) showed that some latent variables are not directly related to individual valence system constructs.

Conclusions: The lack of latent structure across units of analysis highlights challenges of the Research Domain Criteria approach and suggests that while dimensional analyses work well to reveal within-task features, more targeted approaches are needed to reveal latent cross-modal relationships that could illuminate psychopathology.
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http://dx.doi.org/10.1016/j.bpsc.2020.12.005DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8113074PMC
May 2021

Latent Variables Quantifying Neighborhood Characteristics and Their Associations with Poor Mental Health.

Int J Environ Res Public Health 2021 01 29;18(3). Epub 2021 Jan 29.

Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA.

Neighborhood characteristics can have profound impacts on resident mental health, but the wide variability in methodologies used across studies makes it difficult to reach a consensus as to the implications of these impacts. The aim of this study was to simplify the assessment of neighborhood influence on mental health. We used a factor analysis approach to reduce the multi-dimensional assessment of a neighborhood using census tracts and demographic data available from the American Community Survey (ACS). Multivariate quantitative characterization of the neighborhood was derived by performing a factor analysis on the 2011-2015 ACS data. The utility of the latent variables was examined by determining the association of these factors with poor mental health measures from the 500 Cities Project 2014-2015 data (2017 release). A five-factor model provided the best fit for the data. Each factor represents a complex multi-dimensional construct. However, based on heuristics and for simplicity we refer to them as (1) Affluence, (2) Singletons in Tract, (3) African Americans in Tract, (4) Seniors in Tract, and (5) Hispanics or Latinos in Tract. African Americans in Tract (with loadings showing larger numbers of people who are black, single moms, and unemployed along with fewer people who are white) and Affluence (with loadings showing higher income, education, and home value) were strongly associated with poor mental health (R2=0.67, R2=0.83). These findings demonstrate the utility of this factor model for future research focused on the relationship between neighborhood characteristics and resident mental health.
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http://dx.doi.org/10.3390/ijerph18031202DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7908478PMC
January 2021

Latent variables for region of interest activation during the monetary incentive delay task.

Neuroimage 2021 04 24;230:117796. Epub 2021 Jan 24.

Laureate Institute for Brain Research, 6655 South Yale Avenue, Tulsa, OK 74136, USA; Department of Community Medicine, Oxley Health Sciences, University of Tulsa, 800 South Tucker Drive, Tulsa, OK 74104, USA.

Background: The Monetary Incentive Delay task (MID) has been used extensively to probe anticipatory reward processes. However, individual differences evident during this task may relate to other constructs such as general arousal or valence processing (i.e., anticipation of negative versus positive outcomes). This investigation used a latent variable approach to parse activation patterns during the MID within a transdiagnostic clinical sample.

Methods: Participants were drawn from the first 500 individuals recruited for the Tulsa-1000 (T1000), a naturalistic longitudinal study of 1000 participants aged 18-55 (n = 476 with MID data). We employed a multiview latent analysis method, group factor analysis, to characterize factors within and across variable sets consisting of: (1) region of interest (ROI)-based blood oxygenation level-dependent (BOLD) contrasts during reward and loss anticipation; and (2) self-report measures of positive and negative valence and related constructs.

Results: Three factors comprised of ROI indicators emerged to accounted for >43% of variance and loaded on variables representing: (1) general arousal or general activation; (2) valence, with dissociable responses to anticipation of win versus loss; and (3) region-specific activation, with dissociable activation in salience versus perceptual brain networks. Two additional factors were comprised of self-report variables, which appeared to represent arousal and valence.

Conclusions: Results indicate that multiview techniques to identify latent variables offer a novel approach for differentiating brain activation patterns during task engagement. Such approaches may offer insight into neural processing patterns through dimension reduction, be useful for probing individual differences, and aid in the development of optimal explanatory or predictive frameworks.
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http://dx.doi.org/10.1016/j.neuroimage.2021.117796DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8110176PMC
April 2021

Behavioral activation therapy for depression is associated with a reduction in the concentration of circulating quinolinic acid.

Psychol Med 2020 Nov 25:1-10. Epub 2020 Nov 25.

Laureate Institute for Brain Research, Tulsa, OK, USA.

Background: An inflammation-induced imbalance in the kynurenine pathway (KP) has been reported in major depressive disorder but the utility of these metabolites as predictive or therapeutic biomarkers of behavioral activation (BA) therapy is unknown.

Methods: Serum samples were provided by 56 depressed individuals before BA therapy and 29 of these individuals also provided samples after 10 weeks of therapy to measure cytokines and KP metabolites. The PROMIS Depression Scale (PROMIS-D) and the Sheehan Disability Scale were administered weekly and the Beck depression inventory was administered pre- and post-therapy. Data were analyzed with linear mixed-effect, general linear, and logistic regression models. The primary outcome for the biomarker analyses was the ratio of kynurenic acid to quinolinic acid (KynA/QA).

Results: BA decreased depression and disability scores (p's < 0.001, Cohen's d's > 0.5). KynA/QA significantly increased at post-therapy relative to baseline (p < 0.001, d = 2.2), an effect driven by a decrease in QA post-therapy (p < 0.001, uncorrected, d = 3.39). A trend towards a decrease in the ratio of kynurenine to tryptophan (KYN/TRP) was also observed (p = 0.054, uncorrected, d = 0.78). The change in KynA/QA was nominally associated with the magnitude of change in PROMIS-D scores (p = 0.074, Cohen's f2 = 0.054). Baseline KynA/QA did not predict response to BA therapy.

Conclusion: The current findings together with previous research show that electronconvulsive therapy, escitalopram, and ketamine decrease concentrations of the neurotoxin, QA, raise the possibility that a common therapeutic mechanism underlies diverse forms of anti-depressant treatment but future controlled studies are needed to test this hypothesis.
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http://dx.doi.org/10.1017/S0033291720004389DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8144244PMC
November 2020

Reduced Environmental Stimulation in Anorexia Nervosa: An Early-Phase Clinical Trial.

Front Psychol 2020 6;11:567499. Epub 2020 Oct 6.

Laureate Institute for Brain Research (LIBR), Tulsa, OK, United States.

Reduced Environmental Stimulation Therapy (REST) alters the balance of sensory input to the nervous system by systematically attenuating sensory signals from visual, auditory, thermal, tactile, vestibular, and proprioceptive channels. Previous research from our group has shown that REST floatation acutely reduces anxiety and blood pressure (BP) while simultaneously heightening interoceptive awareness in clinically anxious populations. Anorexia nervosa (AN) is an eating disorder characterized by elevated anxiety, distorted body representation, and abnormal interoception, raising the question of whether REST might positively impact these symptoms. However, this approach has never been studied in eating disorders, and it is unknown whether exposure to floatation REST might worsen AN symptoms. To examine these possibilities, we conducted an open-label study to investigate the safety and tolerability of REST in AN. We also explored the acute impact of REST on BP, affective symptoms, body image disturbance, and interoception. Twenty-one partially weight-restored AN outpatients completed a protocol involving four sequential sessions of REST: reclining in a zero-gravity chair, floating in an open pool, and two sessions of floating in an enclosed pool. All sessions were 90 min, approximately 1 week apart. We measured orthostatic BP before and immediately after each session (primary outcome), in addition to collecting BP readings every 10 min during the session using a wireless waterproof system as a secondary outcome measure. Each participant's affective state, awareness of interoceptive sensations, and body image were assessed before and after every session (exploratory outcomes). There was no evidence of orthostatic hypotension following floating, and no adverse events (primary outcome). Secondary analyses revealed that REST induced statistically significant reductions in BP ( < 0.001; Cohen's , 0.2-0.5), anxiety ( < 0.001; Cohen's , >1) and negative affect ( < 0.01; Cohen's , >0.5), heightened awareness of cardiorespiratory ( < 0.01; Cohen's , 0.2-0.5) but not gastrointestinal sensations, and reduced body image dissatisfaction ( < 0.001; Cohen's , >0.5). The findings from this initial trial suggest that individuals with AN can safely tolerate the physical effects of REST floatation. Future randomized controlled trials will need to investigate whether these initial observations of improved anxiety, interoception, and body image disturbance occur in acutely ill AN populations.

Clinical Trial Registration: ClinicalTrials.gov; Identifier: NCT02801084 (April 01, 2016).
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http://dx.doi.org/10.3389/fpsyg.2020.567499DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573249PMC
October 2020

What is the relationship between body mass index and eating disorder symptomatology in professional female fashion models?

Psychiatry Res 2020 11 2;293:113358. Epub 2020 Aug 2.

University of Nottingham, Nottingham, UK.

Low body mass index (BMI<18/18.5) is utilized as a mandated cutoff for professional fashion model employment, based on assumptions that low BMI indicates eating disorder pathology. No previous studies have examined the association between experimenter-measured BMI and eating disorder symptomatology in professional fashion models. We measured BMI and Eating Disorder Examination Questionnaire (EDE-Q) responses in United Kingdom (UK) professional fashion models, and nonmodels. Characteristics were compared using robust standardized mean difference (rSMD) obtained via probability of superiority. Associations between BMI and eating disorder symptomatology were examined using robust regression, controlling for age. Models exhibited lower BMI but higher fat-percentage and muscle mass. On the EDE-Q, models had higher Restraint, Global, Eating, and Weight Concerns, and similar Shape Concern scores compared to nonmodels. BMI was positively associated with eating disorder symptoms in both groups, and all but one of the eight models with clinically significant EDE-Q level had ≥18.5 measured BMI. Lower BMI was not indicative of worse eating disorder symptomatology in models or nonmodels. Thus, using a low BMI cutoff (<18.5) may not be an appropriate single index of health for detecting elevated eating disorder symptoms in models. Different policies to protect models' health should be considered.
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http://dx.doi.org/10.1016/j.psychres.2020.113358DOI Listing
November 2020

Postnatal growth in extremely low birth weight newborns: nature or nurture?

J Perinatol 2021 Mar 17;41(3):648-649. Epub 2020 Jul 17.

Department of Pediatrics, University of Missouri-Kansas City, Kansas City, MO, USA.

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http://dx.doi.org/10.1038/s41372-020-0737-7DOI Listing
March 2021

Modeling interactions between brain function, diet adherence behaviors, and weight loss success.

Obes Sci Pract 2020 Jun 25;6(3):282-292. Epub 2020 Feb 25.

Department of Biostatistics University of Kansas Medical Center Kansas City Kansas.

Introduction: Obesity is linked to altered activation in reward and control brain circuitry; however, the associated brain activity related to successful or unsuccessful weight loss (WL) is unclear.

Methods: Adults with obesity (N = 75) completed a baseline functional magnetic resonance imaging (fMRI) scan before entering a WL intervention (ie,3-month diet and physical activity [PA] program). We conducted an exploratory analysis to identify the contributions of baseline brain activation, adherence behavior patterns, and the associated connections to WL at the conclusion of a 3-month WL intervention. Food cue-reactivity brain regions were functionally identified using fMRI to index brain activation to food vs nonfood cues. Food consumption, PA, and class attendance were collected weekly during the 3-month intervention.

Results: The left middle frontal gyrus (L-MFG, BA 46) and right middle frontal gyrus (R-MFG; BA 9) were positively activated when viewing food compared with nonfood images. Structural equation modeling with bootstrapping was used to investigate a hypothesized path model and revealed the following significant paths: (1) attendance to 3-month WL, (2) R-MFG to attendance, and (3) indirect effects of R-MFG through attendance on WL.

Conclusion: Findings suggest that brain activation to appetitive food cues predicts future WL through mediating session attendance, diet, and PA. This study contributes to the growing evidence of the importance of food cue reactivity and self-regulation brain regions and their impact on WL outcomes.
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http://dx.doi.org/10.1002/osp4.403DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7278911PMC
June 2020

Web-Based Graphic Representation of the Life Course of Mental Health: Cross-Sectional Study Across the Spectrum of Mood, Anxiety, Eating, and Substance Use Disorders.

JMIR Ment Health 2020 Jan 28;7(1):e16919. Epub 2020 Jan 28.

Laureate Institute for Brain Research, Tulsa, OK, United States.

Background: Although patient history is essential for informing mental health assessment, diagnosis, and prognosis, there is a dearth of standardized instruments measuring time-dependent factors relevant to psychiatric disorders. Previous research has demonstrated the potential utility of graphical representations, termed life charts, for depicting the complexity of the course of mental illness. However, the implementation of these assessments is limited by the exclusive focus on specific mental illnesses (ie, bipolar disorder) and the lack of intuitive graphical interfaces for data collection and visualization.

Objective: This study aimed to develop and test the utility of the Tulsa Life Chart (TLC) as a Web-based, structured approach for obtaining and graphically representing historical information on psychosocial and mental health events relevant across a spectrum of psychiatric disorders.

Methods: The TLC interview was completed at baseline by 499 participants of the Tulsa 1000, a longitudinal study of individuals with depressive, anxiety, substance use, or eating disorders and healthy comparisons (HCs). All data were entered electronically, and a 1-page electronic and interactive graphical representation was developed using the Google Visualization Application Programming Interface. For 8 distinct life epochs (periods of approximately 5-10 years), the TLC assessed the following factors: school attendance, hobbies, jobs, social support, substance use, mental health treatment, family structure changes, negative and positive events, and epoch and event-related mood ratings. We used generalized linear mixed models (GLMMs) to evaluate trajectories of each domain over time and by sex, age, and diagnosis, using case examples and Web-based interactive graphs to visualize data.

Results: GLMM analyses revealed main or interaction effects of epoch and diagnosis for all domains. Epoch by diagnosis interactions were identified for mood ratings and the number of negative-versus-positive events (all P values <.001), with all psychiatric groups reporting worse mood and greater negative-versus-positive events than HCs. These differences were most robust at different epochs, depending on diagnosis. There were also diagnosis and epoch main effects for substance use, mental health treatment received, social support, and hobbies (P<.001). User experience ratings (each on a 1-5 scale) revealed that participants found the TLC pleasant to complete (mean 3.07, SD 1.26) and useful for understanding their mental health (mean 3.07, SD 1.26), and that they were likely to recommend it to others (mean 3.42, SD 0.85).

Conclusions: The TLC provides a structured, Web-based transdiagnostic assessment of psychosocial history relevant for the diagnosis and treatment of psychiatric disorders. Interactive, 1-page graphical representations of the TLC allow for the efficient communication of historical life information that would be useful for clinicians, patients, and family members.
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http://dx.doi.org/10.2196/16919DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7013650PMC
January 2020

Consensus features nested cross-validation.

Bioinformatics 2020 05;36(10):3093-3098

Tandy School of Computer Science, University of Tulsa, Tulsa, OK, USA.

Summary: Feature selection can improve the accuracy of machine-learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy-preserving noise mechanism to identify features that are stable between training and holdout sets.We develop consensus nested cross-validation (cnCV) that combines the idea of feature stability from differential privacy with nCV. Feature selection is applied in each inner fold and the consensus of top features across folds is used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by cross-validation, differential privacy and private evaporative cooling (pEC). We also compare these methods using real RNA-seq data from a study of major depressive disorder.The cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification.

Availability And Implementation: Code available at https://github.com/insilico/cncv.

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa046DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7776094PMC
May 2020

A pragmatic clinical trial examining the impact of a resilience program on college student mental health.

Depress Anxiety 2020 03 4;37(3):202-213. Epub 2019 Nov 4.

Laureate Institute for Brain Research, Tulsa, Oklahoma.

Background: One in three college students experience significant depression or anxiety interfering with daily functioning. Resilience programs that can be administered to all students offer an opportunity for addressing this public health problem. The current study objective was to assess the benefit of a brief, universal resilience program for first-year college students.

Method: First-year students at a private, midwestern university participated. This trial used a pragmatic design, delivering the intervention within university-identified orientation courses and was not randomized. The four-session resilience program included goal-building, mindfulness, and resilience skills. The comparison was orientation-as-usual. Primary outcomes included PROMIS® Depression and Anxiety and Connor-Davidson Resilience Scale. Secondary and exploratory outcomes included the Perceived Stress Scale, Emotion Regulation, and Cognitive Behavioral Therapy (CBT) Skills Questionnaires, and Freiburg Mindfulness Inventory. Time by treatment interactions at post-training and semester-end were examined using linear mixed models.

Results: Analysis included 252 students, 126 who completed resilience programming and a matched comparison sample. Resilience programming did not relate to improvements in depression at post-training (CI: -2.53 to 1.02; p = .404, d =-0.08), but did at semester-end (95% CI: -4.27 to -0.72; p = .006, d = -0.25) and improvements in perceived stress were observed at post-training (CI: -3.31 to -0.44; p = .011, d = -0.24) and semester-end (CI: -3.30 to -0.41; p = .013, d = -0.24). Emotion regulation, mindfulness, and CBT skills increased, with CBT skills mediating clinical improvements.

Conclusions: Universal implementation of a brief, resilience intervention may be effective for improving college student mental health.
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http://dx.doi.org/10.1002/da.22969DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054149PMC
March 2020

Increasing access to smoking cessation treatment among Latino smokers using case management.

J Smok Cessat 2019 Sep 11;14(3):168-175. Epub 2019 Mar 11.

Department of Cancer Prevention and Control, Hackensack University Medical Center, Hackensack, NJ, 07047; USA.

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http://dx.doi.org/10.1017/jsc.2019.1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6822981PMC
September 2019

Exposure to placental insufficiency alters postnatal growth trajectory in extremely low birth weight infants.

J Dev Orig Health Dis 2019 Oct 4:1-8. Epub 2019 Oct 4.

Department of Pediatrics, University of Kansas Medical Center, Kansas City, KS, USA.

Growth in the immediate postnatal period for extremely low birth weight (ELBW, birth weight < 1000 g) infants is an important topic in neonatal medicine. The goal is to ensure adequate postnatal growth and to minimize complications resulting from suboptimal growth. Past efforts have focused on postnatal nutrition as well as on minimizing comorbidities. It has not been systematically assessed whether antenatal factors play a role in postnatal growth. In this report, we conducted a retrospective study on 91 maternal-neonatal pairs. We prospectively collected maternal and neonatal demographic data, neonatal nutrition in the first 7 days of life and after enteral nutrition is fully established, comorbidity data, as well as weight data from birth to 50 weeks corrected gestational age. We developed a linear mixed-effects model to examine the role of placental insufficiency, as defined by fetal Doppler studies, in postnatal weight z-score trajectory over time in the ELBW population. We relied on Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) for model selection. Interestingly, the selected model included a quadratic term of time and a placental insufficiency-by-time interaction term. In a covariate analysis, AIC and BIC both favored a model that included calories intake in the first 7 days of life and the total duration of antibiotics as fixed-effects, but not their interaction terms with time. Overall, we demonstrated for the first time that placental insufficiency, an antenatal factor, is a major determinant of postnatal weight trajectory in the ELBW population. Prospective studies are warranted to confirm our findings.
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http://dx.doi.org/10.1017/S2040174419000564DOI Listing
October 2019

Machine Learning and Brain Imaging: Opportunities and Challenges.

Trends Neurosci 2019 10 31;42(10):659-661. Epub 2019 Jul 31.

Laureate Institute for Brain Research, Tulsa, OK, USA; Health Services and Outcomes Research, Children's Mercy Hospital, Kansas City, MO, USA.

Machine learning approaches may provide ways to link brain activation patterns to behavior at an individual-subject level. Using a comparative performance analysis, Jollans and colleagues (Neuroimage, 2019) highlight in a recent paper key considerations when applying machine learning algorithms to neuroimaging data.
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http://dx.doi.org/10.1016/j.tins.2019.07.007DOI Listing
October 2019

Machine Learning Analysis of the Relationships Between Gray Matter Volume and Childhood Trauma in a Transdiagnostic Community-Based Sample.

Biol Psychiatry Cogn Neurosci Neuroimaging 2019 08 13;4(8):734-742. Epub 2019 Mar 13.

Laureate Institute for Brain Research, Tulsa, Oklahoma; Department of Community Medicine, University of Tulsa, Tulsa, Oklahoma; Stephenson School of Biomedical Engineering, University of Oklahoma, Norman, Oklahoma.

Background: Childhood trauma is a significant risk factor for adult psychopathology. Previous investigations have implicated childhood trauma-related structural changes in anterior cingulate, dorsolateral prefrontal and orbitofrontal cortex, and hippocampus. Using a large transdiagnostic community sample, the goal of this investigation was to differentially associate regional gray matter (GM) volume with childhood trauma severity specifically, distinct from adult psychopathology.

Methods: A total of 577 non-treatment-seeking adults (n = 207 men) completed diagnostic, childhood trauma, and structural magnetic resonance imaging assessments with regional GM volume estimated using FreeSurfer. Elastic net analysis was conducted in a nested cross-validation framework, with GM volumes, adult psychopathology, age, education, sex, and magnetic resonance imaging coil type as potential predictors for childhood trauma severity.

Results: Elastic net identified age, education, sex, medical condition, adult psychopathology, and 13 GM regions as predictors of childhood trauma severity. GM regions identified included right caudate; left pallidum; bilateral insula and cingulate sulcus; left superior, inferior, and orbital frontal regions; and regions within temporal and parietal lobes and cerebellum.

Conclusions: Results from this large, transdiagnostic sample implicate GM volume in regions central to current neurobiological theories of trauma (e.g., prefrontal cortex) as well as additional regions involved in reward, interoceptive, attentional, and sensory processing (e.g., striatal, insula, and parietal/occipital cortices). Future longitudinal studies examining the functional impact of structural changes in this broader network of regions are needed to clarify the role each may play in longer-term outcomes following trauma.
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http://dx.doi.org/10.1016/j.bpsc.2019.03.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688962PMC
August 2019

Neighborhood affluence is not associated with positive and negative valence processing in adults with mood and anxiety disorders: A Bayesian inference approach.

Neuroimage Clin 2019 1;22:101738. Epub 2019 Mar 1.

Laureate Institute for Brain Research, Tulsa, OK, United States of America. Electronic address:

Survey-based studies show that neighborhood disadvantage is associated with community reported mental health problems. However, fewer studies have examined whether neighborhood characteristics have measurable impact on mental health status of individuals in general and whether neighborhood characteristics impact positive/negative valence processing at both behavioral and brain levels. This study addressed these questions by investigating effects of census-based neighborhood affluence on self-reported symptoms, brain functions, and structures associated with positive/negative valence processing in a sample of individuals with mood and anxiety disorders (n = 262). Employing a Bayesian inference approach, our investigation demonstrates that neighborhood affluence fails to be associated with positive/negative valence processing measured across multiple modalities, with the only effects of neighborhood affluence identified in trait anxiety scores. These findings highlight that while community-based relationships between neighborhood characteristics and mental health problems are strong, it is much less clear that these characteristics have a measurable impact on the individual.
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http://dx.doi.org/10.1016/j.nicl.2019.101738DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6416773PMC
December 2019

Physical characteristics not psychological state or trait characteristics predict motion during resting state fMRI.

Sci Rep 2019 01 23;9(1):419. Epub 2019 Jan 23.

Laureate Institute for Brain Research (LIBR), Tulsa, OK, USA.

Head motion (HM) during fMRI acquisition can significantly affect measures of brain activity or connectivity even after correction with preprocessing methods. Moreover, any systematic relationship between HM and variables of interest can introduce systematic bias. There is a large and growing interest in identifying neural biomarkers for psychiatric disorders using resting state fMRI (rsfMRI). However, the relationship between HM and different psychiatric symptoms domains is not well understood. The aim of this investigation was to determine whether psychiatric symptoms and other characteristics of the individual predict HM during rsfMRI. A sample of n = 464 participants (174 male) from the Tulsa1000, a naturalistic longitudinal study recruiting subjects with different levels of severity in mood/anxiety/substance use disorders based on the dimensional NIMH Research Domain Criteria framework was used for this study. Based on a machine learning (ML) pipeline with nested cross-validation to avoid overfitting, the stacked model with 15 anthropometric (like body mass index, BMI) and demographic (age and sex) variables identifies BMI and weight as the most important variables and explained 10.9 percent of the HM variance (95% CI: 9.9-11.8). In comparison ML models with 105 self-report measures for state and trait psychological characteristics identified nicotine and alcohol use variables as well as impulsivity inhibitory control variables but explain only 5 percent of HM variance (95% CI: 3.5-6.4). A combined ML model using all 120 variables did not perform significantly better than the model using only 15 physical variables (combined model 95% confidence interval: 10.2-12.4). Taken together, after considering physical variables, state or trait psychological characteristics do not provide additional power to predict motion during rsfMRI.
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http://dx.doi.org/10.1038/s41598-018-36699-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6344520PMC
January 2019

A Nonlinear Simulation Framework Supports Adjusting for Age When Analyzing BrainAGE.

Front Aging Neurosci 2018 24;10:317. Epub 2018 Oct 24.

Laureate Institute for Brain Research, Tulsa, OK, United States.

Several imaging modalities, including T1-weighted structural imaging, diffusion tensor imaging, and functional MRI can show chronological age related changes. Employing machine learning algorithms, an individual's imaging data can predict their age with reasonable accuracy. While details vary according to modality, the general strategy is to: (1) extract image-related features, (2) build a model on a training set that uses those features to predict an individual's age, (3) validate the model on a test dataset, producing a predicted age for each individual, (4) define the "Brain Age Gap Estimate" (BrainAGE) as the difference between an individual's predicted age and his/her chronological age, (5) estimate the relationship between BrainAGE and other variables of interest, and (6) make inferences about those variables and accelerated or delayed brain aging. For example, a group of individuals with overall positive BrainAGE may show signs of accelerated aging in other variables as well. There is inevitably an overestimation of the age of younger individuals and an underestimation of the age of older individuals due to "regression to the mean." The correlation between chronological age and BrainAGE may significantly impact the relationship between BrainAGE and other variables of interest when they are also related to age. In this study, we examine the detectability of variable effects under different assumptions. We use empirical results from two separate datasets [training = 475 healthy volunteers, aged 18-60 years (259 female); testing = 489 participants including people with mood/anxiety, substance use, eating disorders and healthy controls, aged 18-56 years (312 female)] to inform simulation parameter selection. Outcomes in simulated and empirical data strongly support the proposal that models incorporating BrainAGE should include chronological age as a covariate. We propose either including age as a covariate in step 5 of the above framework, or employing a multistep procedure where age is regressed on BrainAGE prior to step 5, producing BrainAGE Residualized (BrainAGER) scores.
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http://dx.doi.org/10.3389/fnagi.2018.00317DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6208001PMC
October 2018

Quantitative clinical outcomes of therapy for head and neck lymphedema.

Adv Radiat Oncol 2018 Jul-Sep;3(3):366-371. Epub 2018 Apr 27.

Department of Radiation Oncology, The University of Kansas Health System, Kansas City, Kansas.

Purpose: Head and neck surgery and radiation cause tissue fibrosis that leads to functional limitations and lymphedema. The objective of this study was to determine whether lymphedema therapy after surgery and radiation for head and neck cancer decreases neck circumference, increases cervical range of motion, and improves pain scores.

Methods And Materials: A retrospective review of all patients with squamous cell carcinoma of the oral cavity, oropharynx, or larynx who were treated with high-dose radiation therapy at a single center between 2011 and 2012 was performed. Patients received definitive or postoperative radiation for squamous cell carcinoma of the oral cavity, oropharynx, or larynx. Patients were referred to a single, certified, lymphedema therapist with specialty training in head and neck cancer after completion of radiation treatment and healing of acute toxicity (typically 1-3 months). Patients underwent at least 3 months of manual lymphatic decongestion and skilled fibrotic techniques. Circumferential neck measurements and cervical range of motion were measured clinically at 1, 3, 6, 9, and 12 months after completion of radiation therapy. Pain scores were also recorded.

Results: Thirty-four consecutive patients were eligible and underwent a median of 6 months of lymphedema therapy (Range, 3-12 months). Clinically measured total neck circumference decreased in all patients with 1 month of treatment. Cervical rotation increased by 30.2% on the left and 27.9% on the right at 1 month and continued to improve up to 44.6% and 55.3%, respectively, at 12 months. Patients undergoing therapy had improved pain scores from 4.3 at baseline to 2.0 after 1 month.

Conclusions: Lymphedema therapy is associated with objective improvements in range of motion, neck circumference, and pain scores in the majority of patients.
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http://dx.doi.org/10.1016/j.adro.2018.04.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6128036PMC
April 2018

Predicting Age From Brain EEG Signals-A Machine Learning Approach.

Front Aging Neurosci 2018 2;10:184. Epub 2018 Jul 2.

Laureate Institute for Brain Research, Tulsa, OK, United States.

The brain age gap estimate (BrainAGE) is the difference between the estimated age and the individual chronological age. BrainAGE was studied primarily using MRI techniques. EEG signals in combination with machine learning (ML) approaches were not commonly used for the human age prediction, and BrainAGE. We investigated whether age-related changes are affecting brain EEG signals, and whether we can predict the chronological age and obtain BrainAGE estimates using a rigorous ML framework with a novel and extensive EEG features extraction. EEG data were obtained from 468 healthy, mood/anxiety, eating and substance use disorder participants (297 females) from the Tulsa-1000, a naturalistic longitudinal study based on Research Domain Criteria framework. Five sets of preprocessed EEG features across channels and frequency bands were used with different ML methods to predict age. Using a nested-cross-validation (NCV) approach and stack-ensemble learning from EEG features, the predicted age was estimated. The important features and their spatial distributions were deduced. The stack-ensemble age prediction model achieved = 0.37 (0.06), Mean Absolute Error (MAE) = 6.87(0.69) and RMSE = 8.46(0.59) in years. The age and predicted age correlation was = 0.6. The feature importance revealed that age predictors are spread out across different feature types. The NCV approach produced a reliable age estimation, with features consistent behavior across different folds. Our rigorous ML framework and extensive EEG signal features allow a reliable estimation of chronological age, and BrainAGE. This general framework can be extended to test EEG association with and to predict/study other physiological relevant responses.
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http://dx.doi.org/10.3389/fnagi.2018.00184DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6036180PMC
July 2018

Effect of Ibuprofen on BrainAGE: A Randomized, Placebo-Controlled, Dose-Response Exploratory Study.

Biol Psychiatry Cogn Neurosci Neuroimaging 2018 10 23;3(10):836-843. Epub 2018 Jun 23.

Laureate Institute for Brain Research, University of Tulsa, Tulsa, Oklahoma. Electronic address:

Background: The age of a person's brain can be estimated from structural brain images using an aggregate measure of variation in morphology across the whole brain. The brain age gap estimation (BrainAGE) score is computed as the difference between kernel-estimated brain age and chronological age. In this exploratory study, we investigated the application of the BrainAGE measure to identify potential novel effects of pharmacological agents on brain morphology.

Methods: Twenty healthy participants (23-47 years of age) completed three structural magnetic resonance imaging scans 45 minutes after administration of placebo or 200 or 600 mg of ibuprofen in a double-blind, crossover study. An externally derived BrainAGE model from a sample of 480 healthy participants was used to examine the acute effect of ibuprofen on temporary neuroanatomical changes in healthy individuals.

Results: The BrainAGE model produced age prediction for each participant with a mean absolute error of 6.7 years between the estimated and chronological age. The intraclass correlation coefficient for BrainAGE was 0.96. Relative to placebo, 200 and 600 mg of ibuprofen significantly decreased BrainAGE by 1.18 and 1.15 years, respectively (p < .05). The trained BrainAGE model identified the medial prefrontal cortex to be the strongest age predictor.

Conclusions: BrainAGE is a potentially useful construct to examine neurological effects of therapeutic drugs. Ibuprofen temporarily reduces BrainAGE by approximately 1 year, which is likely due to its acute anti-inflammatory effects.
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http://dx.doi.org/10.1016/j.bpsc.2018.05.002DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6510235PMC
October 2018

Brain Response to Non-Painful Mechanical Stimulus to Lumbar Spine.

Brain Sci 2018 Mar 1;8(3). Epub 2018 Mar 1.

Department of Physical Therapy and Rehabilitation Science, University of Kansas Medical Center, Kansas City, KS 66160, USA.

Pressure application to the lumbar spine is an important assessment and treatment method of low back pain. However, few studies have characterized brain activation patterns in response to mechanical pressure. The objective of this study was to map brain activation associated with various levels of mechanical pressure to the lumbar spine in healthy subjects. Fifteen healthy subjects underwent functional magnetic resonance imaging (fMRI) scanning while mechanical pressure was applied to their lumbar spine with a custom-made magnetic resonance imaging (MRI)-compatible pressure device. Each subject received three levels of pressure (low/medium/high) based on subjective ratings determined prior to the scan using a block design (pressure/rest). Pressure rating was assessed with an 11-point scale (0 = no touch; 10 = max pain-free pressure). Brain activation differences between pressure levels and rest were analyzed. Subjective pressure ratings were significantly different across pressure levels ( < 0.05). The overall brain activation pattern was not different across pressure levels (all > 0.05). However, the overall effect of pressure versus rest showed significant decreases in brain activation in response to the mechanical stimulus in regions associated with somatosensory processing including the precentral gyri, left hippocampus, left precuneus, left medial frontal gyrus, and left posterior cingulate. There was increase in brain activation in the right inferior parietal lobule and left cerebellum. This study offers insight into the neural mechanisms that may relate to manual mobilization intervention used for managing low back pain.
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http://dx.doi.org/10.3390/brainsci8030041DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870359PMC
March 2018

Examining the short-term anxiolytic and antidepressant effect of Floatation-REST.

PLoS One 2018 2;13(2):e0190292. Epub 2018 Feb 2.

Laureate Institute for Brain Research, Tulsa, Oklahoma, United States of America.

Floatation-REST (Reduced Environmental Stimulation Therapy) reduces sensory input to the nervous system through the act of floating supine in a pool of water saturated with Epsom salt. The float experience is calibrated so that sensory signals from visual, auditory, olfactory, gustatory, thermal, tactile, vestibular, gravitational and proprioceptive channels are minimized, as is most movement and speech. This open-label study aimed to examine whether Floatation-REST would attenuate symptoms of anxiety, stress, and depression in a clinical sample. Fifty participants were recruited across a spectrum of anxiety and stress-related disorders (posttraumatic stress, generalized anxiety, panic, agoraphobia, and social anxiety), most (n = 46) with comorbid unipolar depression. Measures of self-reported affect were collected immediately before and after a 1-hour float session, with the primary outcome measure being the pre- to post-float change score on the Spielberger State Anxiety Inventory. Irrespective of diagnosis, Floatation-REST substantially reduced state anxiety (estimated Cohen's d > 2). Moreover, participants reported significant reductions in stress, muscle tension, pain, depression and negative affect, accompanied by a significant improvement in mood characterized by increases in serenity, relaxation, happiness and overall well-being (p < .0001 for all variables). In reference to a group of 30 non-anxious participants, the effects were found to be more robust in the anxious sample and approaching non-anxious levels during the post-float period. Further analysis revealed that the most severely anxious participants reported the largest effects. Overall, the procedure was well-tolerated, with no major safety concerns stemming from this single session. The findings from this initial study need to be replicated in larger controlled trials, but suggest that Floatation-REST may be a promising technique for transiently reducing the suffering in those with anxiety and depression.

Trial Registration: ClinicalTrials.gov NCT03051074.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0190292PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796691PMC
March 2018

Tulsa 1000: a naturalistic study protocol for multilevel assessment and outcome prediction in a large psychiatric sample.

BMJ Open 2018 01 24;8(1):e016620. Epub 2018 Jan 24.

Laureate Institute for Brain Research, Tulsa, Oklahoma, USA.

Introduction: Although neuroscience has made tremendous progress towards understanding the basic neural circuitry underlying important processes such as attention, memory and emotion, little progress has been made in applying these insights to psychiatric populations to make clinically meaningful treatment predictions. The overall aim of the Tulsa 1000 (T-1000) study is to use the NIMH Research Domain Criteria framework in order to establish a robust and reliable dimensional set of variables that quantifies the positive and negative valence, cognition and arousal domains, including interoception, to generate clinically useful treatment predictions.

Methods And Analysis: The T-1000 is a naturalistic study that will recruit, assess and longitudinally follow 1000 participants, including healthy controls and treatment-seeking individuals with mood, anxiety, substance use and eating disorders. Each participant will undergo interview, behavioural, biomarker and neuroimaging assessments over the course of 1 year. The study goal is to determine how disorders of affect, substance use and eating behaviour organise across different levels of analysis (molecules, genes, cells, neural circuits, physiology, behaviour and self-report) to predict symptom severity, treatment outcome and long-term prognosis. The data will be used to generate computational models based on Bayesian statistics. The final end point of this multilevel latent variable analysis will be standardised assessments that can be developed into clinical tools to help clinicians predict outcomes and select the best intervention for each individual, thereby reducing the burden of mental disorders, and taking psychiatry a step closer towards personalised medicine.

Ethics And Dissemination: Ethical approval was obtained from Western Institutional Review Board screening protocol #20101611. The dissemination plan includes informing health professionals of results for clinical practice, submitting results to journals for peer-reviewed publication, presenting results at national and international conferences and making the dataset available to researchers and mental health professionals.

Trial Registration Number: NCT02450240; Pre-results.
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http://dx.doi.org/10.1136/bmjopen-2017-016620DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5786129PMC
January 2018

Randomized trial of vitamin D3 to prevent worsening of musculoskeletal symptoms in women with breast cancer receiving adjuvant letrozole. The VITAL trial.

Breast Cancer Res Treat 2017 Nov 2;166(2):491-500. Epub 2017 Aug 2.

Department of Internal Medicine, University of Kansas Medical Center, Kansas City, KS, USA.

Purpose: Aromatase inhibitor-associated musculoskeletal symptoms (AIMSS) frequently occur in women being treated for breast cancer. Prior studies suggest high prevalence of vitamin D deficiency in breast cancer patients with musculoskeletal (MS) pain. We conducted a randomized, placebo-controlled trial to determine if 30,000 IU vitamin D3 per week (VitD3) would prevent worsening of AIMSS in women starting adjuvant letrozole for breast cancer.

Methods: Women with stage I-III breast cancer starting adjuvant letrozole and 25(OH)D level ≤40 ng/ml were eligible. All subjects received standard daily supplement of 1200 mg calcium and 600 IU vitamin D3 and were randomized to 30,000 IU oral VitD3/week or placebo. Pain, disability, fatigue, quality of life, 25(OH)D levels, and hand grip strength were assessed at baseline, 12, and 24 weeks. The primary endpoint was incidence of an AIMSS event.

Results: Median age of the 160 subjects (80/arm) was 61. Median 25OHD (ng/ml) was 25 at baseline, 32 at 12 weeks, and 31 at 24 weeks in the placebo arm and 22, 53, and 57 in the VitD3 arm. There were no serious adverse events. At week 24, 51% of women assigned to placebo had a protocol defined AIMSS event (worsening of joint pain using a categorical pain intensity scale (CPIS), disability from joint pain using HAQ-II, or discontinuation of letrozole due to MS symptoms) vs. 37% of women assigned to VitD3 (p = 0.069). When the brief pain inventory (BPI) was used instead of CPIS, the difference was statistically significant: 56 vs. 39% (p = 0.024).

Conclusions: Although 30,000 IU/week of oral vitamin D3 is safe and effective in achieving adequate vitamin D levels, it was not associated with a decrease in AIMSS events based on the primary endpoint. Post-hoc analysis using a different tool suggests potential benefit of vitamin D3 in reducing AIMSS.
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http://dx.doi.org/10.1007/s10549-017-4429-8DOI Listing
November 2017

Statistical differences in the white matter tracts in subjects with depression by using different skeletonized voxel-wise analysis approaches and DTI fitting procedures.

Brain Res 2017 Aug 16;1669:131-140. Epub 2017 Jun 16.

Center for Social and Affective Neuroscience, Department of Clinical and Experimental Medicine, Linköping, Sweden. Electronic address:

Major depressive disorder (MDD) is one of the most significant contributors to the global burden of illness. Diffusion tensor imaging (DTI) is a procedure that has been used in several studies to characterize abnormalities in white matter (WM) microstructural integrity in MDD. These studies, however, have provided divergent findings, potentially due to the large variety of methodological alternatives available in conducting DTI research. In order to determine the importance of different approaches to coregistration of DTI-derived metrics to a standard space, we compared results from two different skeletonized voxel-wise analysis approaches: the standard TBBS pipeline and the Advanced Normalization Tools (ANTs) approach incorporating a symmetric image normalization (SyN) algorithm and a group-wise template (ANTs TBSS). We also assessed effects of applying twelve different fitting procedures for the diffusion tensor. For our dataset, lower fractional anisotropy (FA) and axial diffusivity (AD) in depressed subjects compared with healthy controls were found for both methods and for all fitting procedures. No group differences were found for radial and mean diffusivity indices. Importantly, for the AD metric, the normalization methods and fitting procedures showed reliable differences, both in the volume and in the number of significant between-groups difference clusters detected. Additionally, a significant voxel-based correlation, in the left inferior fronto-occipital fasciculus, between AD and self-reported stress was found only for one of the normalization procedure (ANTs TBSS). In conclusion, the sensitivity to detect group-level effects on DTI metrics might depend on the DTI normalization and/or tensor fitting procedures used.
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http://dx.doi.org/10.1016/j.brainres.2017.06.013DOI Listing
August 2017

Structural Brain Imaging in People With Low Back Pain.

Spine (Phila Pa 1976) 2017 May;42(10):726-732

Department of Physical Therapy and Rehabilitation Science, University of Kansas Medical Center, Kansas City, KS.

Study Design: Cross-sectional study.

Objective: The aim of this study was to determine whether low back pain (subacute and chronic) is related to differences in brain volume.

Summary Of Background Data: Inconsistent findings have been reported about the effect of chronic low back pain on brain volume, and the effect of subacute low back pain on brain volume has not been sufficiently investigated.

Methods: A total of 130 participants were included (23 subacute and 68 chronic low back pain; 39 healthy controls). The main outcome measure was whole and regional brain volume. Clinical outcome measures included pain duration, pain intensity, fear avoidance belief questionnaire, Oswestry Disability Index, and Beck's Depression Inventory.

Results: Decrease in brain volume in several regions was observed in chronic low back pain when compared with health subjects; however, after correcting for multiple comparisons, no significant differences were detected between any of the three groups in whole-brain volume. Regionally, we detected less gray matter volume in two voxels in the middle frontal gyrus in chronic low back pain participants compared with healthy controls. None of the clinical outcome measures were correlated with brain volume measurements.

Conclusion: Low back pain (subacute or chronic) is not related to significant differences in brain volume after correction for multiple comparisons. The effect size was too small to detect possible subtle changes unless much larger sample sizes are examined, or it is possible that low back pain does not affect brain volume.

Level Of Evidence: 5.
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http://dx.doi.org/10.1097/BRS.0000000000001915DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5425308PMC
May 2017