Synchrony between sensory and cognitive networks is associated with subclinical variation in autistic traits
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2015-03-23
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© 2015 Young, Smith, Coutlee and Huettel.Individuals with autistic spectrum disorders exhibit distinct personality traits linked to attentional, social, and affective functions, and those traits are expressed with varying levels of severity in the neurotypical and subclinical population. Variation in autistic traits has been linked to reduced functional and structural connectivity (i.e., underconnectivity, or reduced synchrony) with neural networks modulated by attentional, social, and affective functions. Yet, it remains unclear whether reduced synchrony between these neural networks contributes to autistic traits. To investigate this issue, we used functional magnetic resonance imaging to record brain activation while neurotypical participants who varied in their subclinical scores on the Autism-Spectrum Quotient (AQ) viewed alternating blocks of social and nonsocial stimuli (i.e., images of faces and of landscape scenes). We used independent component analysis (ICA) combined with a spatiotemporal regression to quantify synchrony between neural networks. Our results indicated that decreased synchrony between the executive control network (ECN) and a face-scene network (FSN) predicted higher scores on the AQ. This relationship was not explained by individual differences in head motion, preferences for faces, or personality variables related to social cognition. Our findings build on clinical reports by demonstrating that reduced synchrony between distinct neural networks contributes to a range of subclinical autistic traits.
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Young, JS, DV Smith, CG Coutlee and SA Huettel (2015). Synchrony between sensory and cognitive networks is associated with subclinical variation in autistic traits. Frontiers in Human Neuroscience, 9(MAR). 10.3389/fnhum.2015.00146 Retrieved from https://hdl.handle.net/10161/10251.
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Scott Huettel
Research in my laboratory investigates the brain mechanisms underlying economic and social decision making; collectively, this research falls into the field of “decision neuroscience” or "neuroeconomics". My laboratory uses fMRI to probe brain function, behavioral assays to characterize individual differences, and other physiological methods (e.g., eye tracking, pharmacological manipulation, genetics) to link brain and behavior. Concurrent with research on basic processes, my laboratory has also investigated the application of new analysis methods for fMRI data, including functional connectivity analyses, pattern classification analyses, and combinatoric multivariate approaches. We have also been applying computational methods to problems in behavioral economics and consumer decision making.
I have also been very active in outreach, mentorship, and educational activities; as examples, I am lead author on the textbook Functional Magnetic Resonance Imaging (Sinauer Associates; 3rd edition in 2014), I teach Fundamentals of Decision Science, Decision Neuroscience and Neuroethics, and many of my postdoctoral and graduate trainees now lead research laboratories of their own.
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