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Catherine Orr

Bio: Catherine Orr is an academic researcher from University of Vermont. The author has contributed to research in topics: Cannabis & Population. The author has an hindex of 21, co-authored 39 publications receiving 1844 citations. Previous affiliations of Catherine Orr include Swinburne University of Technology & University of Melbourne.

Papers
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Journal ArticleDOI
TL;DR: An overview of the imaging procedures of the ABCD study is provided, the basis for their selection and preliminary quality assurance and results that provide evidence for the feasibility and age-appropriateness of procedures and generalizability of findings to the existent literature are provided.

1,114 citations

Journal ArticleDOI
14 Aug 2014-Nature
TL;DR: By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention.
Abstract: Many factors have been proposed as contributors to risk of alcohol abuse, but quantifying their influence has been difficult; here a longitudinal study of a large sample of adolescents and machine learning are used to generate models of predictors of current and future alcohol abuse, assessing the relative contribution of many factors, including life history, individual personality differences, brain structure and genotype. Many factors have been identified as contributors to risk of alcohol abuse but their relative importance has been difficult to quantify. Robert Whelan et al. constructed models of current and future adolescent binge drinking using data from the IMAGEN project, a study of risk-taking behaviour in more than 2,000 teenagers recruited at age 14 from the United Kingdom, Ireland, France and Germany. The authors used machine learning to generate models of predictors of current and future alcohol abuse, assessing the contribution of many factors including life history, individual personality differences, brain structure and genotype. A key finding of the study was that personality factors were, surprisingly, not particularly useful predictors of future alcohol misuse. In contrast, neurodevelopmental immaturity, certain structural and functional indicators in the brain, sexual experience and prenatal alcohol exposure were associated with current and future binge drinking. A comprehensive account of the causes of alcohol misuse must accommodate individual differences in biology, psychology and environment, and must disentangle cause and effect. Animal models1 can demonstrate the effects of neurotoxic substances; however, they provide limited insight into the psycho-social and higher cognitive factors involved in the initiation of substance use and progression to misuse. One can search for pre-existing risk factors by testing for endophenotypic biomarkers2 in non-using relatives; however, these relatives may have personality or neural resilience factors that protect them from developing dependence3. A longitudinal study has potential to identify predictors of adolescent substance misuse, particularly if it can incorporate a wide range of potential causal factors, both proximal and distal, and their influence on numerous social, psychological and biological mechanisms4. Here we apply machine learning to a wide range of data from a large sample of adolescents (n = 692) to generate models of current and future adolescent alcohol misuse that incorporate brain structure and function, individual personality and cognitive differences, environmental factors (including gestational cigarette and alcohol exposure), life experiences, and candidate genes. These models were accurate and generalized to novel data, and point to life experiences, neurobiological differences and personality as important antecedents of binge drinking. By identifying the vulnerability factors underlying individual differences in alcohol misuse, these models shed light on the aetiology of alcohol misuse and suggest targets for prevention.

357 citations

Journal ArticleDOI
TL;DR: The results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.
Abstract: Objective: Although lower brain volume has been routinely observed in individuals with substance dependence compared with nondependent control subjects, the brain regions exhibiting lower volume have not been consistent across studies. In addition, it is not clear whether a common set of regions are involved in substance dependence regardless of the substance used or whether some brain volume effects are substance specific. Resolution of these issues may contribute to the identification of clinically relevant imaging biomarkers. Using pooled data from 14 countries, the authors sought to identify general and substance-specific associations between dependence and regional brain volumes. Method: Brain structure was examined in a mega-analysis of previously published data pooled from 23 laboratories, including 3,240 individuals, 2,140 of whom had substance dependence on one of five substances: alcohol, nicotine, cocaine, methamphetamine, or cannabis. Subcortical volume and cortical thickness in regions defined by FreeSurfer were compared with nondependent control subjects when all sampled substance categories were combined, as well as separately, while controlling for age, sex, imaging site, and total intracranial volume. Because of extensive associations with alcohol dependence, a secondary contrast was also performed for dependence on all substances except alcohol. An optimized split-half strategy was used to assess the reliability of the findings. Results: Lower volume or thickness was observed in many brain regions in individuals with substance dependence. The greatest effects were associated with alcohol use disorder. A set of affected regions related to dependence in general, regardless of the substance, included the insula and the medial orbitofrontal cortex. Furthermore, a support vector machine multivariate classification of regional brain volumes successfully classified individuals with substance dependence on alcohol or nicotine relative to nondependent control subjects. Conclusions: The results indicate that dependence on a range of different substances shares a common neural substrate and that differential patterns of regional volume could serve as useful biomarkers of dependence on alcohol and nicotine.

176 citations

Journal ArticleDOI
TL;DR: FMRI BOLD data from 56 healthy participants who had previously been administered the Error Awareness Task was re-analyzed and suggested that error awareness is associated with error-related dACC activity but that the role of this activity is probably best understood in relation to the activity in other regions.
Abstract: Research examining the neural mechanisms associated with error awareness has consistently identified dorsal anterior cingulate activity (ACC) as necessary but not predictive of conscious error detection. Two recent studies (Steinhauser and Yeung, 2010; Wessel et al. 2011) have found a contrary pattern of greater dorsal ACC activity (in the form of the error-related negativity) during detected errors, but suggested that the greater activity may instead reflect task influences (e.g., response conflict, error probability) and or individual variability (e.g., statistical power). We re-analyzed fMRI BOLD data from 56 healthy participants who had previously been administered the Error Awareness Task, a motor Go/No-go response inhibition task in which subjects make errors of commission of which they are aware (Aware errors), or unaware (Unaware errors). Consistent with previous data, the activity in a number of cortical regions was predictive of error awareness, including bilateral inferior parietal and insula cortices, however in contrast to previous studies, including our own smaller sample studies using the same task, error-related dorsal ACC activity was significantly greater during aware errors when compared to unaware errors. While the significantly faster RT for aware errors (compared to unaware) was consistent with the hypothesis of higher response conflict increasing ACC activity, we could find no relationship between dorsal ACC activity and the error RT difference. The data suggests that individual variability in error awareness is associated with error-related dorsal ACC activity, and therefore this region may be important to conscious error detection, but it remains unclear what task and individual factors influence error awareness.

82 citations

Journal ArticleDOI
TL;DR: The reduced interhemispheric RSFC in adolescent cannabis users complements previous reports of white matter deficits associated with cannabis use and suggests that altered intrinsic connectivity may be characteristic of adolescent cannabis dependence.
Abstract: Background: Cannabis is the most commonly used illicit drug in adolescence. Heavy use is associated with deficits on a broad range of cognitive functions and heavy use during adolescence may impact development of gray and white matter. Objectives: To examine differences in intrinsic brain activity and connectivity associated with cannabis dependence in adolescence using whole-brain voxelwise approaches. Methods: Adolescents admitted to a drug-treatment facility for cannabis dependence (n = 17) and age-matched controls (n = 18) were compared on a measure of oscillations in the low-frequency blood oxygen level-dependent signal at rest (the fractional amplitude of low-frequency fluctuations fALFF, 0.01–0.1 Hz) and interhemispheric resting-state functional connectivity (RSFC) using voxel-mirrored homotopic connectivity. Results: The cannabis-dependent population showed increased fALFF activity compared to the control group in right hemisphere regions including the superior parietal gyrus, superior fro...

73 citations


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01 Jan 2016
TL;DR: This application applied longitudinal data analysis modeling change and event occurrence will help people to enjoy a good book with a cup of coffee in the afternoon instead of facing with some infectious virus inside their computer.
Abstract: Thank you very much for downloading applied longitudinal data analysis modeling change and event occurrence. As you may know, people have look hundreds times for their favorite novels like this applied longitudinal data analysis modeling change and event occurrence, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some infectious virus inside their computer.

2,102 citations

Journal ArticleDOI
TL;DR: It is proposed that principles and techniques from the field of machine learning can help psychology become a more predictive science and an increased focus on prediction, rather than explanation, can ultimately lead to greater understanding of behavior.
Abstract: Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology’s near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research ques...

1,026 citations

Journal ArticleDOI
TL;DR: It is demonstrated that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention, and predicts a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents.
Abstract: Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person's overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention--symptoms of attention deficit hyperactivity disorder--from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.

801 citations

Journal ArticleDOI
TL;DR: This work reviews recent advances in data driven and theory driven Computational psychiatry, with an emphasis on clinical applications, and highlights the utility of combining them.
Abstract: Translating advances in neuroscience into benefits for patients with mental illness presents enormous challenges because it involves both the most complex organ, the brain, and its interaction with a similarly complex environment. Dealing with such complexities demands powerful techniques. Computational psychiatry combines multiple levels and types of computation with multiple types of data in an effort to improve understanding, prediction and treatment of mental illness. Computational psychiatry, broadly defined, encompasses two complementary approaches: data driven and theory driven. Data-driven approaches apply machine-learning methods to high-dimensional data to improve classification of disease, predict treatment outcomes or improve treatment selection. These approaches are generally agnostic as to the underlying mechanisms. Theory-driven approaches, in contrast, use models that instantiate prior knowledge of, or explicit hypotheses about, such mechanisms, possibly at multiple levels of analysis and abstraction. We review recent advances in both approaches, with an emphasis on clinical applications, and highlight the utility of combining them.

672 citations

Journal ArticleDOI
TL;DR: To inform the political discourse with scientific evidence, the literature was reviewed to identify what is known and not known about the effects of cannabis use on human behavior, including cognition, motivation, and psychosis.
Abstract: With a political debate about the potential risks and benefits of cannabis use as a backdrop, the wave of legalization and liberalization initiatives continues to spread. Four states (Colorado, Washington, Oregon, and Alaska) and the District of Columbia have passed laws that legalized cannabis for recreational use by adults, and 23 others plus the District of Columbia now regulate cannabis use for medical purposes. These policy changes could trigger a broad range of unintended consequences, with profound and lasting implications for the health and social systems in our country. Cannabis use is emerging as one among many interacting factors that can affect brain development and mental function. To inform the political discourse with scientific evidence, the literature was reviewed to identify what is known and not known about the effects of cannabis use on human behavior, including cognition, motivation, and psychosis.

618 citations