Reversal of parkinsonian symptoms in primates by antagonism of excitatory amino acid transmission: potential mechanisms of action.

Parkinsonism is characterised by overactive glutamatergic transmission in the cortico-striatal and subthalamo-medial pallidal pathways. Local blockade of glutamatergic transmission in these pathways can alleviate parkinsonian symptoms. The effectiveness of the treatment, however, is often limited by the simultaneous appearance of unwanted side-effects. These side-effects, including ataxia and dissociative anaesthesia, are particularly problematic when N-methyl-D-aspartate (NMDA) antagonists are used. In an attempt to overcome these problems we have attempted to manipulate excitatory amino acid (EAA)-mediated neurotransmission indirectly by targeting the NMDA receptor associated modulatory sites. We review evidence which demonstrates that antagonists for both the NMDA associated glycine and polyamine sites can reverse parkinsonian symptoms when injected intra-cerebrally in both MPTP-treated and bilateral 6-OHDA lesioned marmosets without eliciting unwanted side-effects. We further review preliminary data which suggest that ifenprodil, a polyamine site antagonist, has striking anti-parkinsonian actions in the marmoset. Potential mechanisms of action underlying these effects are discussed in terms of NMDA receptor subtypes and the neuroanatomical locus of action. The anti-parkinsonian efficacy of intra-striatally administered EAA antagonists leads us to question the view of dopamine acting in the striatum as a simple neuromodulator

A supervised clustering approach for fMRI-based inference of brain states

We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task

Beyond brain reading: randomized sparsity and clustering to simultaneously predict and identify

International audienceThe prediction of behavioral covariates from functional MRI (fMRI) is known as brain reading. From a statistical standpoint, this challenge is a supervised learning task. The ability to predict cognitive states from new data gives a model selection criterion: prediction accu- racy. While a good prediction score implies that some of the voxels used by the classifier are relevant, one cannot state that these voxels form the brain regions involved in the cognitive task. The best predictive model may have selected by chance non-informative regions, and neglected rele- vant regions that provide duplicate information. In this contribution, we address the support identification problem. The proposed approach relies on randomization techniques which have been proved to be consistent for support recovery. To account for the spatial correlations between voxels, our approach makes use of a spatially constrained hierarchical clustering algorithm. Results are provided on simulations and a visual experiment

RPGR protein complex regulates proteasome activity and mediates store-operated calcium entry

Ciliopathies are a group of genetically heterogeneous disorders, characterized by defects in cilia genesis or maintenance. Mutations in the RPGR gene and its interacting partners, RPGRIP1 and RPGRIP1L, cause ciliopathies, but the function of their proteins remains unclear. Here we show that knockdown (KD) of RPGR, RPGRIP1 or RPGRIP1L in hTERT-RPE1 cells results in abnormal actin cytoskeleton organization. The actin cytoskeleton rearrangement is regulated by the small GTPase RhoA via the planar cell polarity (PCP) pathway. RhoA activity was upregulated in the absence of RPGR, RPGRIP1 or RPGRIP1L proteins. In RPGR, RPGRIP1 or RPGRIP1L KD cells, we observed increased levels of DVl2 and DVl3 proteins, the core components of the PCP pathway, due to impaired proteasomal activity. RPGR, RPGRIP1 or RPGRIP1L KD cells treated with thapsigargin (TG), an inhibitor of sarcoendoplasmic reticulum Ca2+ - ATPases, showed impaired store-operated Ca2+ entry (SOCE), which is mediated by STIM1 and Orai1 proteins. STIM1 was not localized to the ER-PM junction upon ER store depletion in RPGR, RPGRIP1 or RPGRIP1L KD cells. Our results demonstrate that the RPGR protein complex is required for regulating proteasomal activity and for modulating SOCE, which may contribute to the ciliopathy phenotype