Daniel N. Barry
University College London
15 Papers
10 Citations
Daniel N. Barry is an academic researcher from University College London. The author has contributed to research in topics: Recall & Autobiographical memory. The author has an hindex of 6, co-authored 15 publications. Previous affiliations of Daniel N. Barry include UCL Institute of Neurology & Wellcome Trust Centre for Neuroimaging.
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Papers
Remote Memory and the Hippocampus: A Constructive Critique.
TL;DR: It is proposed that the hippocampus reconstructs remote memories in the absence of the original trace by assembling consolidated neocortical elements into spatially coherent scenes that form the basis of unfolding memory events.
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The Neural Dynamics of Novel Scene Imagery.
TL;DR: It is concluded that episodic memory and scene imagination share fundamental neural dynamics and the process of constructing vivid, spatially coherent, contextually appropriate scene imagery is strongly modulated by vmPFC.
90
Segmenting subregions of the human hippocampus on structural magnetic resonance image scans: An illustrated tutorial.
Marshall A. Dalton,Peter Zeidman,Daniel N. Barry,Elaine Williams,Eleanor A. Maguire +4 more
- 06 Apr 2017
TL;DR: This in-depth protocol incorporates the most recent cyto- and chemo-architectural evidence and includes a series of comprehensive figures which compare slices of histologically stained tissue with equivalent 3T images.
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vmPFC drives hippocampal processing during autobiographical memory recall regardless of remoteness
TL;DR: ThevmPFC drove hippocampal activity during recall initiation and also as AMs unfolded over subsequent seconds, and this effect was evident regardless of AM age, re-cast the positions of the hippocampus and the vmPFC in the AM retrieval hierarchy.
58
Testing covariance models for MEG source reconstruction of hippocampal activity.
George C. O'Neill,Daniel N. Barry,Tim M. Tierney,Stephanie Mellor,Eleanor A. Maguire,Gareth R. Barnes +5 more
TL;DR: In this paper, the Empirical Bayesian Beamfomer (EBB) was revised to accommodate specific a-priori correlated source models and showed that incorporating a correlated hippocampal source model significantly improved model evidence.