R
Raymond J. Dolan
Researcher at University College London
Publications - 940
Citations - 150202
Raymond J. Dolan is an academic researcher from University College London. The author has contributed to research in topics: Prefrontal cortex & Functional magnetic resonance imaging. The author has an hindex of 196, co-authored 919 publications receiving 138540 citations. Previous affiliations of Raymond J. Dolan include VU University Amsterdam & McGovern Institute for Brain Research.
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Cohort Profile: The NSPN 2400 Cohort: a developmental sample supporting the Wellcome Trust NeuroScience in Psychiatry Network.
Beatrix Kiddle,Becky Inkster,Gita Prabhu,Michael Moutoussis,Kirstie Whitaker,Edward T. Bullmore,Raymond J. Dolan,Peter Fonagy,Ian M. Goodyer,Ian M. Goodyer,Peter B. Jones,Peter B. Jones +11 more
TL;DR: The NSPN 2400 Cohort is a developmental sample supporting the Wellcome Trust NeuroScience in Psychiatry Network and aims to provide a “robust sample” for the design of future cohorts.
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Ect and cerebral atrophy - a computed tomographic study
TL;DR: The case‐notes of 41 elderly depressives who underwent computed tomography were examined and the ECT history of each patient was assessed, and a significant relationship was demonstrated between frontal lobe atrophy and ECT.
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Dissociating intentional learning from relative novelty responses in the medial temporal lobe.
TL;DR: It is demonstrated that intentional encoding/retrieval of stimulus-category associations and automatic novelty/familiarity assessment of stimuli are processed in anatomically dissociable neuronal ensembles within the MTL memory system.
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Quantitative Computed Tomography in Elderly Depressed Patients
TL;DR: In the depressed patients ventricular dilatation, which had already been shown to predict increased mortality, was also associated with lower HU levels, and the significance of these and other findings is discussed.
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Imaging Informational Conflict: A Functional Magnetic Resonance Imaging Study of Numerical Stroop
TL;DR: It is shown in 18 healthy adults that numerical magnitudes of numbers call for higher processing requirements than physical sizes, and the enhanced activation elicited by numerical magnitude is not modulated by task relevance, indicating autonomous processing.