Biology subjects

Paulus, M. P.

Publications and source records attributed to Paulus, M. P..

23 records · Page 2Linked to original sources

bioRxiv2020

It is Never as Good the Second Time Around: Brain Areas Involved in Salience Processing Habituate During Repeated Drug Cue Exposure in Methamphetamine and Opioid Users

IntroductionThe brain response to drug-related cues is an important marker in addiction-medicine, however, the temporal dynamics of this response in repeated exposure to the cues are not well known yet. In an fMRI drug cue-reactivity task, the presence of rapid habituation or sensitization was investigated by modeling time and its interaction with condition (drug>neutral) using an initial discovery-sample. Replication of this temporal response was tested in two other clinical populations. MethodsSixty-five male participants (35.8{+/-}8.4 years-old) with methamphetamine use disorder (MUD) were recruited as the discovery-sample. A linear mixed effects model was used to identify areas with a time-by-condition interaction in the discovery-sample. Replication of these effects was tested in two other samples (29 female with MUD and 22 male with opioid use disorder). The second replication-sample was re-tested within two weeks. ResultsIn the discovery-sample, clusters within the VMPFC, amygdala and ventral striatum showed both significant condition and condition-by-time interaction with a habituation response for the drug-related cues but not neutral cues. The estimates for the main effects and interactions were generally consistent between the discovery and replication-samples across all clusters. The re-test data showed consistent lack of drug>neutral and habituation response within all selected clusters in the second cue-exposure session. ConclusionsVMPFC, amygdala and ventral striatum show a habituation in response to drug-related cues which is consistent among different clinical populations. Habituation in response in the first session of cue-exposure and lack of reactivity in the second session of exposure provide foundations for development of cue-desensitization interventions.

neuroscience↗

bioRxiv2020

Consensus Features Nested Cross-Validation

MotivationFeature 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. MethodsWe develop consensus nested CV (cnCV) that combines the idea of feature stability from differential privacy with nested CV. Feature selection is applied in each inner fold and the consensus of top features across folds is a 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 CV, differential privacy, and private Evaporative Cooling (pEC). We also compare these methods using real RNA-Seq data from a study of major depressive disorder. ResultsThe 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. AvailabilityCode available at https://github.com/insilico/cncv. Contactbrett.mckinney@utulsa.edu Supplementary information:

bioinformatics↗

bioRxiv2019

LIBR Methamphetamine and Opioid Cue Database (LIBR MOCD): Development and Validation

IntroductionDrug cue reactivity (DCR) is widely used in experimental settings for both assessment and intervention. There is no validated database of pictorial cues available for methamphetamine and opioids.\n\nMethods360 images in three-groups (methamphetamine, opioid and neutral (control)) matched for their content (objects, hands, faces and actions) were selected in an initial development phase. 28 participants with a history of both methamphetamine and opioid use (37.1 {+/-} 8.11 years old, 12 female) with over six months of abstinence were asked to rate images for craving, valence, arousal, typicality and relatedness.\n\nResultsAll drug images were differentiated from neutral images. Drug related images received higher arousal and lower valence ratings compared to neutral images (craving (0-100) for neutral (11.5{+/-}21.9), opioid (87.7{+/-}18.5), and methamphetamine (88{+/-}18), arousal (1-9) for neutral (2.4{+/-}1.9), opioid (4.6{+/-}2.7), and methamphetamine (4.6{+/-}2.6), and valence (1-9) for neutral (4.8{+/-}1.3), opioid (4.4{+/-}1.9), and methamphetamine (4.4{+/-}1.8)). There is no difference between methamphetamine and opioid images in craving, arousal and valence. There is a significant positive relationship between the amount of time that participants spent on drug-related images and the craving they reported for the image. Every 10 points of craving were associated with an increased response time of 383millisecond. Three image sets were automatically selected for equivalent fMRI tasks (methamphetamine and opioids) from the database (tasks are available at github).\n\nConclusionLIBR MOCD provides a resource of validated images/tasks for future DCR studies. Additionally, researchers can select several sets of unique but equivalent images based-on their psychological/physical characteristics for multiple assessments/interventions.

neuroscience↗

bioRxiv2019

Dimensions of neighborhood tracts and their associations with mental health problems

ObjectiveNeighborhood characteristics can have profound effects on resident health. The aim of this study was to use an unsupervised learning approach to reduce the multi-dimensional assessment of a neighborhood using American Community Survey (ACS) data to simplify the assessment of neighborhood influence on health.\n\nMethodMultivariate 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 2017 release.\n\nResultsA five-factor model provided the best fit for the data and the latent factors quantified the following characteristics of the census tract: (1) affluence, (2) proportion of singletons in neighborhood, (3) proportion of African-Americans in neighborhood, (4) proportion of seniors in neighborhood, and (5) proportion of noncitizens in neighborhood. African-Americans (R2 = 0.67) in neighborhood and Affluence (R2 = 0.83) were strongly associated with poor mental health.\n\nConclusionsThese findings indicate the importance of this factor model in future research focused on the relationship between neighborhood characteristics and resident health.

neuroscience↗

bioRxiv2018

A nonlinear simulation framework supports adjusting for age when analyzing BrainAGE.

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 individuals 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 individuals 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 individuals predicted age and his/her chronological age, and 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.

bioinformatics↗