Behavioral recovery takes place even after permanent damage to the entire brain region normally controlling sensorimotor hind limb function in the rat. In our study, 2 weeks after full behavioral recovery from an experimental unilateral permanent brain damage, the topographic representation of the previous paretic hindlimb was investigated by fMRI. The analysis showed that during electrical stimulation of the previously paretic hindlimb, two normally inactive brain regions were now being activated. One region was the non-damaged contralateral sensori-motor cortex and the other region was located lateral to the lesion. These results suggest that behavioral recovery can be explained by functional reorganization and neuromodulation of the brain.

Vigabatrin and gabaculine, both highly specific inhibitors of GABA (gamma-aminobutyric acid) transaminase, cause significant elevation of endogenous GABA levels in brain. The time course of GABA concentration after acute GABA transaminase inhibition was measured quantitatively in the alpha-chloralose-anesthetized rat brain using in vivo selective homonuclear polarization transfer spectroscopy. The blood oxygenation level-dependent (BOLD) effect in functional magnetic resonance imaging (fMRI) has been considered to be coupled tightly to neuronal activation via the metabolic demand of associated glutamate transport. Correlated with the rise in endogenous GABA level after vigabatrin or gabaculine treatment, the intensity of BOLD-weighted fMRI signals in rat somatosensory cortex during forepaw stimulation was found to be reduced significantly. These results are consistent with previous findings that inhibition of GABA transaminase leads to augmented GABA release and potentiation of GABAergic inhibition.

Non-negative matrix factorization (NMF) is a recently popularized technique for learning partsbased, linear representations of non-negative data. The traditional NMF is optimized under the Gaussian noise or Poisson noise assumption, and hence not suitable if the data are grossly corrupted. To improve the robustness of NMF, a novel algorithm named robust nonnegative matrix factorization (RNMF) is proposed in this paper. We assume that some entries of the data matrix may be arbitrarily corrupted, but the corruption is sparse. RNMF decomposes the non-negative data matrix as the summation of one sparse error matrix and the product of two non-negative matrices. An efficient iterative approach is developed to solve the optimization problem of RNMF. We present experimental results on two face databases to verify the effectiveness of the proposed method.

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