1,721,227 research outputs found
sj-pdf-1-cmx-10.1177_10775595211060050 – Supplemental Material for The Enduring Importance of Parenting: Caregiving Quality and Fear-Potentiated Startle in Emerging Adults With a Child Maltreatment History
No full textSupplemental Material, sj-pdf-1-cmx-10.1177_10775595211060050 for The Enduring Importance of Parenting: Caregiving Quality and Fear-Potentiated Startle in Emerging Adults With a Child Maltreatment History by Alexandra D. W. Sullivan, Zoe M. F. Brier, Alison C. Legrand, Katherine van Stolk-Cooke, Tanja Jovanovic, Seth D. Norrholm, Hugh Garavan, Rex Forehand, and Matthew Price in Child Maltreatment</p
Identifying predictive brain structure features in alcohol dependent subjects
No full text3:00 PM3:00pm-5:00pmGraduateIdentifying predictive brain structure features in alcohol dependent subjects Sage Hahn, Nicholas Allgaier, Scott Mackey, Hugh Garavan Typical neuroimaging analysis tends to remain bounded in exploring linear effects between features and/or groupings of features. The objective of this study was to explore the merit of machine learning techniques, generally capable of modeling more complex nonlinear effects, in predicting alcohol dependence from measurements of brain structure, previously shown possible by (Mackey et al. 2018). Of particular interest was in isolating subsets of features responsible for accurate cross validated predictions. A dataset of 911 individuals, 640 diagnosed as alcohol dependent, was collected by the Enhancing Neuro-Imaging Genetics Through Meta-Analysis (ENIGMA) Addiction Working Group (Mackey et al. 2016). Freesurfer 5.3 was utilized to process each patients structural weighted T1 MRI scan extracting volume measurements for 7 bilateral subcortical regions and measurements corresponding to thickness and surface volume for 34 bilateral cortical regions. Measurements were then residualized according to age, sex, intra cranial volume and study site. In order to reliably determine classifier performance repeated (n=50) random 3-fold stratified cross validation (CV) was employed, where specifically a support vector machine (SVM) with a radial basis function kernel was trained and evaluated on all available measurements, with SVM parameters chosen from a randomized parameter search (n=100) using further nested 3-fold CV (Suykens et al. 1999). Baseline SVM performance when trained on all 150 available measurements achieved an average area under the receiver operating characteristic curve (ROC AUC) of .779 +- .027, in comparison to when trained on only the 14 subcortical volume measurements with a ROC AUC .626 +- .032, 64 measures of surface area with a ROC AUC .605 +- .030, and 64 measures of average thickness with a ROC AUC .780 +- .027. These results suggest that only measurements corresponding to average thickness contribute to classifier performance, though notably there still remains 2^68 - 1 (1.8 * 10^19) possible combinations of features potentially responsible. A multi-objective evolutionary search algorithm was designed with the goal of finding both the smallest set of useful thickness measurements possible as well as the most predictive. Outputted feature sets from the search were then thresholded, retaining only sets of features with a ROC AUC > .77 under the previously introduced evaluation methodology. After 4 searches, 28 separate groupings of 11 to 18 features met this criteria. These sets were then analyzed for predictive importance with the assumption that a particular features importance is directly related to the fraction of feature sets in which it appears. Two regions in particular, the right posterior cingulate cortex and right middle temporal gyrus appeared in 90+% of sets, along with a total of 10 features occurring in over 50% out of 45 which appeared at least once. The ability of a machine learning classifier to predict alcohol dependence from measurements of cortical thickness alone represents an encouraging result towards the development of dependence related neuroimaging biomarkers. Likewise, efforts towards isolating the specific sets of thickness measurements responsible move closer towards that goal. Future experiments will run additional evolutionary searches as well as seek to replicate classifier performance on unseen datasets. Mackey, Scott, et al. "Mega-Analysis of Gray Matter Volume in Substance Dependence: General and Substance-Specific Regional Effects." American Journal of Psychiatry (2018): appi-ajp. Mackey, Scott, et al. "Genetic imaging consortium for addiction medicine: From neuroimaging to genes." Progress in brain research. Vol. 224. Elsevier, 2016. 203-223. Suykens, Johan AK, and Joos Vandewalle. "Least squares support vector machine classifiers." Neural processing letters9.3 (1999): 293-300.University of Vermon
Biobehavioral Predictors Of Cannabis Use In Adolescence
No full textCannabis use initiated during adolescence may precipitate lasting consequences on the brain and behavioral health of the individual. However, research on the risk factors for cannabis use during adolescence has been largely cross-sectional in design. Despite the few prospective studies, even less is known about the neurobiological predictors. This dissertation improves on the extant literature by leveraging a large longitudinal study to uncover the predictors of cannabis use in adolescent samples collected prior to exposure. All data were drawn from the IMAGEN study and contained a large sample of adolescents studied at age 14 (N=2,224), and followed up at age 16 and 19. Participants were richly characterized using psychosocial questionnaires, structural and functional MRI, and genetic measurements. Two hypothesis-driven studies focused on amygdala reactivity and two data-driven studies across the feature domains were completed to characterize cannabis use in adolescence. The first study was cross-sectional and identified bilateral amygdala hyperactivity to angry faces in a sample reporting cannabis use by age 14 (n=70). The second study determined this amygdala effect was predictive of cannabis use by studying a sample of cannabis-naïve participants at age 14 who then used cannabis by age 19 (n=525). A dose-response relationship was observed such that heavy cannabis users exhibited higher amygdala reactivity. Exploratory analyses suggested amygdala reactivity decreased from age 14 to 19 within the cannabis sample, although statistical significance was not found. In the third study, data-driven machine learning analyses predicted cannabis initiation by age 16 separately for males (n=207) and females (n=158) using data from all feature domains. These analyses identified a sparse set of shared psychosocial predictors, whereas the identified brain predictors exhibited sex- and drug-specificity. Additional analyses predicted initiation by age 19 and identified a sparse set of psychosocial predictors for females only (n=145). The final study improved on drug-specificity by performing differential prediction analyses between matched samples of participants who initiated cannabis+binge drinking vs. binge drinking only by age 16 (males n=178; females n=148). A sparse subset of psychosocial predictors identified in the third study was reproduced, and novel brain predictors were identified. Those analyses were unique as they compared two machine learning algorithms, namely regularized logistic regression and random forest analyses. These studies substantiated the use of both hypothesis- and data-driven prediction analyses applied to large longitudinal datasets. They also addressed common issues related to human addiction research by examining sex-differences and drug-specificity. Critically, these studies uncovered predictors of use in samples collected prior to cannabis-exposure. The identified predictors are therefore disentangled from consequences of use. Results from all studies inform etiological mechanisms influencing cannabis use in adolescence. These findings can also be used to stratify risk in vulnerable adolescents and inform targets for interventions designed to curb use.PsychologyDoctor of Philosophy (PhD
Inhibitory Control Efficiency In Successful Weight Loss Participants
No full textEating unhealthy foods and eating past satiety are inappropriate behaviors that promote obesity. The ability to effectively inhibit an inappropriate behavior is a key component of cognitive restraint and its impairment has been previously linked to obesity. In this study, a Go/No-Go fMRI task was completed by a cohort of adult women that had experienced initial weight loss followed by various levels of weight regain or continued weight loss. Region of interest fMRI analysis revealed that greater total weight loss was significantly related to decreasing activation in the right inferior frontal gyrus and the right superior frontal gyrus. These results suggest that as weight loss increases fewer cognitive resources are needed in order to maintain levels of inhibitory control. This cognitive efficiency, though only partially supported by better task performance, is supported by greater exercise. An analysis of resting state patterns of correlation between task-activated regions revealed a significant correlation between the right inferior frontal gyrus and the left middle temporal gyrus. The strength of this relationship was significantly correlated with increasing total weight loss and continued weight loss over time. Cognitive restraint was also associated with this fronto-temporal correlation and provides support for cognitive efficiency. Right inferior frontal gyrus was also correlated with left inferior frontal gyrus and this relationship was positively correlated with initial weight loss suggesting that fewer neurocognitive resources were required by those who were able to achieve greater initial weight loss.NeuroscienceMaster of Science (MS
The Impact of Perinatal Maternal Social Support on Infant Temperament During the COVID-19 Pandemic
No full textThe effect of maternal state on infant development in utero has been an important topic of research; however, maternal social support has not been well defined as a factor in infant outcomes. The COVID-19 pandemic provided an opportunity to study widespread changes in social support in perinatal women. In an effort to better understand the relationship between perinatal maternal social support and infant temperament and development, this study analyzed data from the COVID-19 and Perinatal Experiences (COPE) study, including ten measures of social support from the Medical Outcomes Study (MOS) Social Support Survey and COPE-IS survey, three measures of infant temperament from the Infant Behavior Questionnaire-Revised, and five measures of infant development from the Ages and Stages Questionnaire, Third Edition. The main hypothesis was that increased maternal social support during the perinatal period would be associated with a decrease in infant negative affect at twelve months of age. Linear regressions were run predicting infant negative affect from maternal social support, as well as predicting other measures of infant temperament and development. Maternal social network support and a specific measure of emotional/informational support significantly predicted infant negative affect approximately one year later. Additionally, the relationship appeared to be specific to social support measured early in the perinatal period. No other relationships were found between maternal social support and infant temperament or development. Overall, these findings promote perinatal maternal social support as an important factor in the development of infant temperament (specifically negative affect), with emotional support appearing to have the greatest individual influence.The full contents of this thesis are available only in the Honors College office.Neuroscienc
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
A Computational Model of Prediction Error for Substance Abuse
No full textOHBM 2014 posterhttps://www.humanbrainmapping.org/i4a/pages/index.cfm?pageID=3565joint work with Robert Whelan (now at Trinity College Dublin) and Hugh Garavan of University of Vermont and the Imagen Consortium (https://imagen-europe.com/)see the following for related work:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5684700/</div
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
- …
