Elizabeth Torres is a Computational Neuroscientist who has developed
theoretical models of sensory-motor integration and motor control since the late 90's. Her models have been translated to clinical applications across
neurodevelopmental and neurodegenerative disorders, as well as applied to
other neurological disorders. She holds a multi-disciplinary tenure-track Assistant Professorship at Rutgers University. Her appointment is shared among the Psychology, Department, the Computer Science Department and the Rutgers Center for Cognitive Science. She also holds and adjunct
faculty position in Neurology (Movement Disorders) at the Medical School of Indiana University in Indianapolis.
Her lab has created new dynamic behavioral biomarkers of Autism, sub-typed Autism severity and identified autistic female traits to help increase early detection reliability. More recently these findings are being used to implement new concepts for earlier intervention and extended to include neural correlates with novel metrics to objectively track change in the developing child. She has published her work in important peer-reviewed journals including the Journal of Neuroscience, the Journal of
Neurophysiology, and Experimental Brain Research and disseminated her findings in the open access platforms of Biomed Central, Plos One and Frontiers. Her edited research topic in Frontiers in Neuroscience has attracted worldwide interest and gained the attention of Nature Reviews Neuroscience this September 2013.
Elizabeth Torres, PhD
The new changes to the DSM-V and the addition of sensory disturbances as a core problem in Autism Spectrum Disorders (ASD) open the possibility of using movement as our ally to improve diagnosis, research and treatments. Movement has been traditionally conceived as a unidirectional stream: as an output stream of information flowing from the Central Nervous System (CNS) to the periphery along efferent channels. However, movement is also a form of sensory input to the CNS that flows in closed loop from the periphery to the brain along afferent channels. The variability inherently present in our movements contains information that our kinesthetic receptors transduce and decode to help guide our actions and help us predict the sensory consequences of our impending decisions. In this way the movement variability present in our behaviors serves as informative sensory feedback and as an amplifier of our internal somatosensation. Movement variability permits the continuous objective quantification of change in natural behaviors as the child grows, develops and is subject to behavioral interventions or to drug treatments.
In this lecture we show new statistical methodology to address the heterogeneity of ASD and to dynamically track changes in all aspects of behavior at different time scales, in real time and longitudinally. More precisely we will show how to identify individually the best sources of sensory guidance for the child, those which sharpen perception, make decisions faster, more accurate and anticipatory, generally scaffolding cognition as they transition into adulthood.
The new changes to the DSM-V and the addition of sensory disturbances as a core problem in Autism Spectrum Disorders (ASD) open the possibility of using movement as our ally to improve diagnosis, research and treatments. Movement has been traditionally conceived as a unidirectional stream: as an output stream of information flowing from the Central Nervous System (CNS) to the periphery along efferent channels. However, movement is also a form of sensory input to the CNS that flows in closed loop from the periphery to the brain along afferent channels. The variability inherently present in our movements contains information that our kinesthetic receptors transduce and decode to help guide our actions and help us predict the sensory consequences of our impending decisions. In this way the movement variability present in our behaviors serves as informative sensory feedback and as an amplifier of our internal somatosensation. Movement variability permits the continuous objective quantification of change in natural behaviors as the child grows, develops and is subject to behavioral interventions or to drug treatments.
In this lecture we show new statistical methodology to address the heterogeneity of ASD and to dynamically track changes in all aspects of behavior at different time scales, in real time and longitudinally. More precisely we will show how to identify individually the best sources of sensory guidance for the child, those which sharpen perception, make decisions faster, more accurate and anticipatory, generally scaffolding cognition as they transition into adulthood.
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