Brain fiber tract plasticity in experimental spinal cord injury: diffusion tensor imaging.

Diffusion tensor imaging (DTI) and immunohistochemistry were performed in spinal cord injured rats to understand the basis for activation of multiple regions in the brain observed in functional magnetic resonance imaging (fMRI) studies. The measured fractional anisotropy (FA), a scalar measure of diffusion anisotropy, along the region encompassing corticospinal tracts (CST) indicates significant differences between control and injured groups in the 3 to 4 mm area posterior to bregma that correspond to internal capsule and cerebral peduncle. Additionally, DTI-based tractography in injured animals showed increased number of fibers that extend towards the cortex terminating in the regions that were activated in fMRI. Both the internal capsule and cerebral peduncle demonstrated an increase in GFAP-immunoreactivity compared to control animals. GAP-43 expression also indicates plasticity in the internal capsule. These studies suggest that the previously observed multiple regions of activation in spinal cord injury are, at least in part, due to the formation of new fibers.

Learning reward timing in cortex through reward dependent expression of synaptic plasticity.

The ability to represent time is an essential component of cognition but its neural basis is unknown. Although extensively studied both behaviorally and electrophysiologically, a general theoretical framework describing the elementary neural mechanisms used by the brain to learn temporal representations is lacking. It is commonly believed that the underlying cellular mechanisms reside in high order cortical regions but recent studies show sustained neural activity in primary sensory cortices that can represent the timing of expected reward. Here, we show that local cortical networks can learn temporal representations through a simple framework predicated on reward dependent expression of synaptic plasticity. We assert that temporal representations are stored in the lateral synaptic connections between neurons and demonstrate that reward-modulated plasticity is sufficient to learn these representations. We implement our model numerically to explain reward-time learning in the primary visual cortex (V1), demonstrate experimental support, and suggest additional experimentally verifiable predictions.