アイテム詳細

要旨

The perception of self-motion is a product of the integration of information from both visual and nonvisual cues, to which the vestibular system is a central contributor. It is well documented that self-motion dysfunction leads to impaired movement and balance, dizziness and falls, and yet our knowledge of the neuronal processing of self-motion signals remains relatively sparse. Here we present two studies extending an emerging line of research trying to obtain electroencephalographic (EEG) recordings while participants engage in real-world tasks. The first study investigated the feasibility of acquiring high-density event-related brain potential (ERP) recordings during treadmill walking. Participants performed a visual response inhibition task - designed to evoke a P3 component for correct response inhibitions and an error-related negativity (ERN) for incorrect commission errors - while speed of walking was experimentally manipulated. Robust P3 and ERN components were obtained under all experimental conditions - while participants were stationary, walking at moderate speed (2.4 km/hour), or walking rapidly (5km/hour). Signal-to-noise ratios were remarkably similar across conditions, pointing to the feasibility of high-fidelity ERP recordings under relatively vigorous activity regimens. In the second study, high-density electroencephalographic recordings were deployed to investigate the neural processes associated with vestibular detection of changes in heading. Participants were translated linearly 7.8 cm on a motion platform using a one second motion profile, at a 45 angle leftward or rightward of straight ahead. These headings were presented with a stimulus probability of 80-20 . Participants responded when they detected the infrequent direction change via button-press. Statistical parametric mapping showed that ERP to standard and target movements differed significantly from 490 to 950 ms post-stimulus. Topographic analysis showed that this difference had a typical P3 topography. These studies provide highly promising methods for gaining insight into the neurophysiological correlates of self-motion in more naturalistic environmental settings.