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Functional Imaging and Robotics for Sensorimotor Transformation

This proposed research addresses the question of how the brain transforms somatosensory information from the hand into motor commands to control the hand during tasks such as object manipulation and dexterous finger movements. Und... ver más

02/04/2016

ETH Zürich

277K€

Presupuesto del proyecto: 277K€

Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo FP7 notifico la concesión del proyecto el día 2016-04-02 No tenemos la información de la convocatoria

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1 Participantes

ETH Zürich

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Líder del proyecto

EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 277K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto This proposed research addresses the question of how the brain transforms somatosensory information from the hand into motor commands to control the hand during tasks such as object manipulation and dexterous finger movements. Understanding the sensorimotor transformations performed by the brain to control upper limb movement is fundamental for a variety of fields, including motor development, athletic and artistic performance, sensorimotor rehabilitation, assistive technology and humanoid robotics. This project will investigate and model the computational processes underlying the sensorimotor transformations taking place in the brain during dexterous movements of the hand in neurologically normal individuals and individuals with impaired hand function following a stroke. This will be achieved by creating a set of novel haptic tasks implemented on state-of-the-art high fidelity haptic devices and quantifying the changes in performance and brain activity that occur as the tasks are learned. The changes in functional connectivity within brain networks as the tasks are learned will be used to determine how sensorimotor transformations are carried out in the brain. The performance measures and the way in which haptic features such as texture, compliance and shape are related to changes in motion and force during learning of the haptic tasks will be used to develop a computational model of how somatosensory information is transformed into commands to muscles that control the hand. Differences in changes in functional connectivity in brain networks between neurologically normal individual and post-stroke subjects during learning of haptic tasks will be used to advance our understanding of changes to the sensorimotor network of the brain following stroke. The computational model will be used to investigate the mechanisms responsible for differences in performance.

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