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Trevor W. Robbins

Researcher at University of Cambridge

Publications -  1184
Citations -  177352

Trevor W. Robbins is an academic researcher from University of Cambridge. The author has contributed to research in topics: Prefrontal cortex & Cognition. The author has an hindex of 231, co-authored 1137 publications receiving 164437 citations. Previous affiliations of Trevor W. Robbins include Centre national de la recherche scientifique & Massachusetts Institute of Technology.

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D2 receptors and cognitive flexibility in marmosets: tri-phasic dose-response effects of intra-striatal quinpirole on serial reversal performance.

TL;DR: Data from this study support previous human and monkey neuroimaging studies by providing causal evidence of a U-shaped function describing how dopamine modulates cognitive flexibility in the primate striatum.
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Excitotoxic lesions of the mediodorsal thalamic nucleus attenuate intravenous cocaine self-administration

TL;DR: Results indicate an enhanced sensitivity to the reinforcing effects of response-contingent cocaine in rats with excitotoxic lesions of the MD.
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Reduced Glutamate Turnover in the Putamen Is Linked With Automatic Habits in Human Cocaine Addiction.

TL;DR: Patients with CUD exhibit enhanced habitual behavior, as assessed both by questionnaire and by a laboratory paradigm of contingency degradation, which is related to a reduced glutamate turnover in the putamen, suggesting a dysregulation of habits caused by chronic cocaine use.
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Acute naltrexone does not remediate fronto-striatal disturbances in alcoholic and alcoholic polysubstance-dependent populations during a monetary incentive delay task.

TL;DR: It is confirmed that both substance‐dependent groups exhibit substantial neural deficits during an MID task, despite being in long‐term abstinence, and naltrexone treatment was unable to remediate disturbances within fronto‐striatal regions during reward anticipation and ‘missed’ rewards in either substance‐ dependent group.
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Diagnostic Classification for Human Autism and Obsessive-Compulsive Disorder Based on Machine Learning From a Primate Genetic Model.

TL;DR: The identified core regions may serve as a basis for building markers for ASD and OCD diagnoses, as well as measures of symptom severity, and may inform future development of machine-learning models for psychiatric disorders.