Showing papers by "Russell A. Poldrack published in 2009"

Striatal Dopamine D2/D3 Receptor Availability Is Reduced in Methamphetamine Dependence and Is Linked to Impulsivity

Abstract: While methamphetamine addiction has been associated with both impulsivity and striatal dopamine D(2)/D(3) receptor deficits, human studies have not directly linked the latter two entities. We therefore compared methamphetamine-dependent and healthy control subjects using the Barratt Impulsiveness Scale (version 11, BIS-11) and positron emission tomography with [(18)F]fallypride to measure striatal dopamine D(2)/D(3) receptor availability. The methamphetamine-dependent subjects reported recent use of the drug 3.3 g per week, and a history of using methamphetamine, on average, for 12.5 years. They had higher scores than healthy control subjects on all BIS-11 impulsiveness subscales (p < 0.001). Volume-of-interest analysis found lower striatal D(2)/D(3) receptor availability in methamphetamine-dependent than in healthy control subjects (p < 0.01) and a negative relationship between impulsiveness and striatal D(2)/D(3) receptor availability in the caudate nucleus and nucleus accumbens that reached statistical significance in methamphetamine-dependent subjects. Combining data from both groups, voxelwise analysis indicated that impulsiveness was related to D(2)/D(3) receptor availability in left caudate nucleus and right lateral putamen/claustrum (p < 0.05, determined by threshold-free cluster enhancement). In separate group analyses, correlations involving the head and body of the caudate and the putamen of methamphetamine-dependent subjects and the lateral putamen/claustrum of control subjects were observed at a weaker threshold (p < 0.12 corrected). The findings suggest that low striatal D(2)/D(3) receptor availability may mediate impulsive temperament and thereby influence addiction.

Decoding the Large-Scale Structure of Brain Function by Classifying Mental States Across Individuals

Abstract: Brain-imaging research has largely focused on localizing patterns of activity related to specific mental processes, but recent work has shown that mental states can be identified from neuroimaging data using statistical classifiers. We investigated whether this approach could be extended to predict the mental state of an individual using a statistical classifier trained on other individuals, and whether the information gained in doing so could provide new insights into how mental processes are organized in the brain. Using a variety of classifier techniques, we achieved cross-validated classification accuracy of 80% across individuals (chance = 13%). Using a neural network classifier, we recovered a low-dimensional representation common to all the cognitive-perceptual tasks in our data set, and we used an ontology of cognitive processes to determine the cognitive concepts most related to each dimension. These results revealed a small organized set of large-scale networks that map cognitive processes across a highly diverse set of mental tasks, suggesting a novel way to characterize the neural basis of cognition.

Prospect Theory and the Brain

Abstract: Publisher Summary This chapter highlights the behavioral and neuroscience work on the prospect theory and the neuroscience of behavioral decision-making. Several applications of prospect theory from neuroeconomics to decision analysis to behavioral finance require individual assessment of value and weighting functions. In order to measure the shape of the value and weighting functions exhibited by participants in the laboratory, one must first discuss how these functions can be formally modeled. The field of neuroeconomics is providing a rapidly increasing amount of data regarding the phenomena that lie at the heart of prospect theory, such as framing effects and loss aversion. It is clear that the demonstrations of neural correlates of several of the fundamental behavioral phenomena underlying prospect theory (loss aversion, framing effects, and probability weighting distortions) provide strong evidence to even the most entrenched rational choice theorists that these anomalies are real. The data have also started to provide more direct evidence regarding specific claims of the theory.

Independence in ROI analysis: where is the voodoo?

Abstract: We discuss the effects of non-independence on region of interest (ROI) analysis of functional magnetic resonance imaging data, which has recently been raised in a prominent article by Vul et al. We outline the problem of non-independence, and use a previously published dataset to examine the effects of non-independence. These analyses show that very strong correlations (exceeding 0.8) can occur even when the ROI is completely independent of the data being analyzed, suggesting that the claims of Vul et al. regarding the implausibility of these high correlations are incorrect. We conclude with some recommendations to help limit the potential problems caused by non-independence.

Tools of the Trade Independence in ROI analysis: where is the voodoo?

Abstract: We discuss the effects of non-independence on region of interest (ROI) analysis of functional magnetic resonance imaging data, which has recently been raised in a prominent article by Vul et al. We outline the problem of non-independence, and use a previously published dataset to examine the effects of non-independence. These analyses show that very strong correlations (exceeding 0.8) can occur even when the ROI is completely independent of the data being analyzed, suggesting that the claims of Vul et al. regarding the implausibility of these high correlations are incorrect. We conclude with some recommendations to help limit the potential problems caused by non-independence. Rarely does a methodological review paper evoke the kind of frenzy that occurred when the paper on 'Voodoo correlations in social neuroscience' by Vul et al. (in press) was released as a preprint. 1 The blogosphere was soon abuzz with discussions of its implications, and authors on the 'red list' scrambled to write rejoinders to the piece and defend their methods and previous findings to editors and funding agencies. The discussion of this issue even reached the pages of Newsweek (Begley 2009), which reflects just how important functional magnetic resonance imaging (fMRI) has become due to its prevalence in the media. In this article, we summarize the arguments of Vul et al. and discuss the strengths and weaknesses of several strategies to address the problem that their paper raises. We then evaluate the impact of using non-independent region of interest (ROI) analysis, using a published dataset that had originally included such analyses. We find that the bias due to using non-independent analysis is relatively small and does not invalidate the claims of the paper, and certainly does not support the dramatic label of 'voodoo'. We note up front that this does not necessarily imply that the same holds for other papers that have used non-independent analyses. We hope that others will also apply some of the methods discussed here in order to determine the degree of bias due to non-independence.

CNTRICS Final Task Selection: Executive Control

Abstract: The third meeting of the Cognitive Neuroscience Treatment Research to Improve Cognition in Schizophrenia (CNTRICS) was focused on selecting promising measures for each of the cognitive constructs selected in the first CNTRICS meeting. In the domain of executive control, the 2 constructs of interest were “rule generation and selection” and “dynamic adjustments in control.” CNTRICS received 4 task nominations for each of these constructs, and the breakout group for executive control evaluated the degree to which each of these tasks met prespecified criteria. For rule generation and selection, the breakout group for executive control recommended the intradimensional/extradimensional shift task and the switching Stroop for translation for use in clinical trial contexts in schizophrenia research. For dynamic adjustments in control, the breakout group recommended conflict and error adaptation in the Stroop and the stop signal task for translation for use in clinical trials. This article describes the ways in which each of these tasks met the criteria used by the breakout group to recommend tasks for further development.

Cognitive ontologies for neuropsychiatric phenomics research.

Abstract: Now that genome-wide association studies (GWAS) are dominating the landscape of genetic research on neuropsychiatric syndromes, investigators are being faced with complexity on an unprecedented scale. It is now clear that phenomics, the systematic study of phenotypes on a genome-wide scale, comprises a rate-limiting step on the road to genomic discovery. To gain traction on the myriad paths leading from genomic variation to syndromal manifestations, informatics strategies must be deployed to navigate increasingly broad domains of knowledge and help researchers find the most important signals. The success of the Gene Ontology project suggests the potential benefits of developing schemata to represent higher levels of phenotypic expression. Challenges in cognitive ontology development include the lack of formal definitions of key concepts and relations among entities, the inconsistent use of terminology across investigators and time, and the fact that relations among cognitive concepts are not likely to be well represented by simple hierarchical "tree" structures. Because cognitive concept labels are labile, there is a need to represent empirical findings at the cognitive test indicator level. This level of description has greater consistency, and benefits from operational definitions of its concepts and relations to quantitative data. Considering cognitive test indicators as the foundation of cognitive ontologies carries several implications, including the likely utility of cognitive task taxonomies. The concept of cognitive "test speciation" is introduced to mark the evolution of paradigms sufficiently unique that their results cannot be "mated" productively with others in meta-analysis. Several projects have been initiated to develop cognitive ontologies at the Consortium for Neuropsychiatric Phenomics (www.phenomics.ucla.edu), in the hope that these ultimately will enable more effective collaboration, and facilitate connections of information about cognitive phenotypes to other levels of biological knowledge. Several free web applications are available already to support examination and visualisation of cognitive concepts in the literature (PubGraph, PubAtlas, PubBrain) and to aid collaborative development of cognitive ontologies (Phenowiki and the Cognitive Atlas). It is hoped that these tools will help formalise inference about cognitive concepts in behavioural and neuroimaging studies, and facilitate discovery of the genetic bases of both healthy cognition and cognitive disorders.

Introduction: A Brief History of Neuroeconomics

Abstract: Publisher Summary This chapter explores historical facts associated with neuroeconomics The birth of Economics is often traced back to Adam Smith's publication The Wealth of Nations in 1776 With this publication began the classical period of economic theory Smith described a number of phenomena critical for understanding choice behavior and the aggregation of choices into market activity These were, in essence, psychological insights They were relatively ad hoc rules that explained how features of the environment influenced the behavior of a nation of consumers and producers Despite these impressive accomplishments, “neuroeconomics” is at best a decade old and has yet to demonstrate a critical role in neuroscience, psychology, or economics Indeed, scholars within neuroeconomics are still debating whether neuroscientific data will provide theory for economists or whether economic theory will provide structure for neuroscience We hope that both the goals will be accomplished, but the exact form of this contribution is not yet clear However, there are also skeptical voices For example, Pareto and Friedman came up with the arguments that economics is only about choices that still lives in the form of fundamentalist critique, whereas Gul and Pesendorfer argued further that neuroscientific data and neuroscientific theories should, in principle, be unwelcome in economics

Procedural Learning in Humans

Abstract: Procedural learning is a form of nondeclarative memory, which, in contrast to declarative memory, does not rely on conscious memory for how learning occurred. These distinct forms of memory were discovered as intact ability to learn new skills in the face of dense amnesia following damage to the medial temporal lobe. Procedural learning encompasses a wide range of motor, perceptual, and cognitive skills and is commonly measured as improved task performance. Different types of procedural learning involve varying cortical regions, depending on the task domain, whereas the cerebellum and in particular the striatum play general roles in acquisition and expression of learning.

Chapter 1 – Introduction: A Brief History of Neuroeconomics

Abstract: Publisher Summary This chapter explores historical facts associated with neuroeconomics. The birth of Economics is often traced back to Adam Smith's publication The Wealth of Nations in 1776. With this publication began the classical period of economic theory. Smith described a number of phenomena critical for understanding choice behavior and the aggregation of choices into market activity. These were, in essence, psychological insights. They were relatively ad hoc rules that explained how features of the environment influenced the behavior of a nation of consumers and producers. Despite these impressive accomplishments, “neuroeconomics” is at best a decade old and has yet to demonstrate a critical role in neuroscience, psychology, or economics. Indeed, scholars within neuroeconomics are still debating whether neuroscientific data will provide theory for economists or whether economic theory will provide structure for neuroscience. We hope that both the goals will be accomplished, but the exact form of this contribution is not yet clear. However, there are also skeptical voices. For example, Pareto and Friedman came up with the arguments that economics is only about choices that still lives in the form of fundamentalist critique, whereas Gul and Pesendorfer argued further that neuroscientific data and neuroscientific theories should, in principle, be unwelcome in economics.