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Ann M. Graybiel

Researcher at McGovern Institute for Brain Research

Publications -  360
Citations -  53036

Ann M. Graybiel is an academic researcher from McGovern Institute for Brain Research. The author has contributed to research in topics: Striatum & Basal ganglia. The author has an hindex of 121, co-authored 350 publications receiving 49771 citations. Previous affiliations of Ann M. Graybiel include Case Western Reserve University & Tufts University.

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Dysregulation of CalDAG-GEFI and CalDAG-GEFII predicts the severity of motor side-effects induced by anti-parkinsonian therapy.

TL;DR: CalDAG-GEFs control ERK cascades, which are implicated in l-DOPA-induced dyskinesias, and have differential compartmental expression patterns in the striatum, and it is suggested that they may be key molecules involved in the expression of the dysKinesias.
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Striosomes and extrastriosomal matrix contain different amounts of immunoreactive choline acetyltransferase in the human striatum

TL;DR: The cholinergic neuropil was not uniformly distributed in the striatum, and especially in the caudate nucleus ChAT-poor zones corresponding to acetylcholinesterase (AChE)-poor striosomes were identified.
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Differential Metabolic Activity in the Striosome and Matrix Compartments of the Rat Striatum during Natural Behaviors

TL;DR: Findings show that during relatively neutral behavioral conditions the balance of activity between the two compartments favors the matrix and suggest that this balance is present in the striatum as part of normal behavior and processing of afferent activity.
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Intrastriatal grafts derived from fetal striatal primordia: II. Reconstitution of cholinergic and dopaminergic systems.

TL;DR: Reconstitution of striatal cholinergic and dopaminergic systems was studied in intrastriatal grafts derived from embryonic day 15 rat striatal primordia and implanted into adult host rats in which unilateral ibotenic acid lesions had previously been made in the striatum.

Shifting responsibly: the importance of striatal modularity to reinforcement learning in uncertain environments

TL;DR: In this paper, a simple modular reinforcement learning (RL) model is proposed for the striatum and the direct and indirect pathways of the basal ganglia, which is based on simple assumptions that while the direct pathway may promote actions based on striatal action values, the indirect pathway may act as a gating network that facilitates or suppresses behavioral modules on the basis of striatal responsibility signals.