How the brain monitors ongoing behavior for performance errors is a central question of cognitive neuroscience. Diminished awareness of performance errors limits the extent to which humans engage in corrective behavior and has been linked to loss of insight in a number of psychiatric syndromes (e.g., attention deficit hyperactivity disorder, drug addiction). These conditions share alterations in monoamine signaling that may influence the neural mechanisms underlying error processing, but our understanding of the neurochemical drivers of these processes is limited. We conducted a randomized, double-blind, placebo-controlled, cross-over design of the influence of methylphenidate, atomoxetine, and citalopram on error awareness in 27 healthy participants. The error awareness task, a go/no-go response inhibition paradigm, was administered to assess the influence of monoaminergic agents on performance errors during fMRI data acquisition. A single dose of methylphenidate, but not atomoxetine or citalopram, significantly improved the ability of healthy volunteers to consciously detect performance errors. Furthermore, this behavioral effect was associated with a strengthening of activation differences in the dorsal anterior cingulate cortex and inferior parietal lobe during the methylphenidate condition for errors made with versus without awareness. Our results have implications for the understanding of the neurochemical underpinnings of performance monitoring and for the pharmacological treatment of a range of disparate clinical conditions that are marked by poor awareness of errors.
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http://dx.doi.org/10.1523/JNEUROSCI.4052-11.2012 | DOI Listing |
Surg Innov
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Morristown Medical Center, Department of Surgery, Morristown, NJ, USA.
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Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, United Arab Emirates.
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Centre for Biotechnology, Kalasalingam Academy of Research and Education, Krishnankoil, India.
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Intelligent Transportation Thrust, Systems Hub, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511455, China.
Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO, PM, and PM, and extracted features from ∼382,000 SVIs at multiple angles (0°, 90°, 180°, 270°) and buffer radii (100-500 m).
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