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David M. Cole

Researcher at University of Zurich

Publications -  23
Citations -  1919

David M. Cole is an academic researcher from University of Zurich. The author has contributed to research in topics: Resting state fMRI & Functional magnetic resonance imaging. The author has an hindex of 12, co-authored 19 publications receiving 1690 citations. Previous affiliations of David M. Cole include Imperial College London & Hammersmith Hospital.

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Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data

TL;DR: It is concluded that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.
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Nicotine replacement in abstinent smokers improves cognitive withdrawal symptoms with modulation of resting brain network dynamics

TL;DR: It is demonstrated that nicotine administration in abstinent smokers modulates dynamic interactions between large-scale cognitive brain networks in the resting state and suggests that altered functional connectivity patterns of these networks reflect their engagement in reward and salience processing during smoking withdrawal.
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Dopamine-Dependent Architecture of Cortico-Subcortical Network Connectivity

TL;DR: Using a combination of functional neuroimaging and pharmacological challenges in healthy volunteers, opposing dopamine agonistic and antagonistic neuromodulatory effects on distributed functional interactions between specific subcortical regions and corresponding neocortical "resting-state" networks are identified.
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Structural substrates for resting network disruption in temporal lobe epilepsy

TL;DR: This multimodal imaging study suggests that in temporal lobe epilepsy, cortical atrophy and microstructural white matter damage impact functional resting connectivity.
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Differential and distributed effects of dopamine neuromodulations on resting-state network connectivity

TL;DR: The observation that dopamine modulates distinct large-scale network connectivity patterns differentially, in both linear and nonlinear fashions, provides support for the objective utility of RSN metrics in classifying the effects and efficacy of psychopharmacological medications.