Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6024203 | NeuroImage | 2016 | 9 Pages |
Abstract
Visual sensory substitution devices provide a non-surgical and flexible approach to vision rehabilitation in the blind. These devices convert images taken by a camera into cross-modal sensory signals that are presented as a surrogate for direct visual input. While previous work has demonstrated that the visual cortex of blind subjects is recruited during sensory substitution, the cognitive basis of this activation remains incompletely understood. To test the hypothesis that top-down input provides a significant contribution to this activation, we performed functional MRI scanning in 11 blind (7 acquired and 4 congenital) and 11 sighted subjects under two conditions: passive listening of image-encoded soundscapes before sensory substitution training and active interpretation of the same auditory sensory substitution signals after a 10-minute training session. We found that the modulation of visual cortex activity due to active interpretation was significantly stronger in the blind over sighted subjects. In addition, congenitally blind subjects showed stronger task-induced modulation in the visual cortex than acquired blind subjects. In a parallel experiment, we scanned 18 blind (11 acquired and 7 congenital) and 18 sighted subjects at rest to investigate alterations in functional connectivity due to visual deprivation. The results demonstrated that visual cortex connectivity of the blind shifted away from sensory networks and toward known areas of top-down input. Taken together, our data support the model of the brain, including the visual system, as a highly flexible task-based and not sensory-based machine.
Keywords
FDRfcMRIFWEFWHMBOLDSSDROIGLMFunctional connectivityFunctional MRITop-downFunctional connectivity magnetic resonance imagingfMRIfunctional magnetic resonance imagingfull-width half-maximumPositron emission tomographySensory substitutionfamily-wise errorCSFCerebrospinal fluidGLM, General Linear ModelBrodmann arearegion of interestfalse discovery rateBlindnessblood-oxygen-level dependentPETVision
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Authors
Matthew C. Murphy, Amy C. Nau, Christopher Fisher, Seong-Gi Kim, Joel S. Schuman, Kevin C. Chan,