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Alexandra P. Key

Researcher at Vanderbilt University Medical Center

Publications -  103
Citations -  2189

Alexandra P. Key is an academic researcher from Vanderbilt University Medical Center. The author has contributed to research in topics: Autism & Autism spectrum disorder. The author has an hindex of 22, co-authored 91 publications receiving 1736 citations. Previous affiliations of Alexandra P. Key include University of Louisville & Vanderbilt University.

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Linking brainwaves to the brain: an ERP primer.

TL;DR: Books on the characteristics and possible interpretations of the event-related potential (ERP) peaks commonly identified in research are reviewed to serve as a tutorial for general readers interested in neuropsychological research and as a reference source for researchers using ERP techniques.
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The Dual Nature of Early-Life Experience on Somatosensory Processing in the Human Infant Brain.

TL;DR: It is shown that the degree of prematurity at birth determines the extent to which brain responses to light touch (but not sham) are attenuated at the time of discharge from the hospital, and shed crucial insights into the mechanisms through which common early perinatal experiences may shape the somatosensory scaffolding of later perceptual, cognitive, and social development.
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Improvement in Social Competence Using a Randomized Trial of a Theatre Intervention for Children with Autism Spectrum Disorder.

TL;DR: The results of this pilot clinical trial provide initial support for the efficacy of the theatre-based intervention on social competence in participants with autism spectrum disorder.
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Emotional Reactivity, Regulation and Childhood Stuttering: A Behavioral and Electrophysiological Study.

TL;DR: Results indicated that decreased use of regulatory strategies is related to more stuttering in children who stutter, but no significant differences were found in EEG measurements of emotional reactivity and regulation between CWS and CWNS or between emotion elicitation conditions.
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EEG-Based Affect and Workload Recognition in a Virtual Driving Environment for ASD Intervention

TL;DR: In this paper, a group-level classification model was proposed to recognize affective states and mental workload of individuals with autism spectrum disorder (ASD) during driving skill training using EEG data.