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Michael D. Greicius

Researcher at Stanford University

Publications -  143
Citations -  41591

Michael D. Greicius is an academic researcher from Stanford University. The author has contributed to research in topics: Default mode network & Medicine. The author has an hindex of 52, co-authored 122 publications receiving 36176 citations. Previous affiliations of Michael D. Greicius include Northwestern University.

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Dissociable Intrinsic Connectivity Networks for Salience Processing and Executive Control

TL;DR: Two distinct networks typically coactivated during functional MRI tasks are identified, anchored by dorsal anterior cingulate and orbital frontoinsular cortices with robust connectivity to subcortical and limbic structures, and an “executive-control network” that links dorsolateral frontal and parietal neocortices.
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Functional connectivity in the resting brain: A network analysis of the default mode hypothesis

TL;DR: This study constitutes, to the knowledge, the first resting-state connectivity analysis of the default mode and provides the most compelling evidence to date for the existence of a cohesive default mode network.
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Default-mode network activity distinguishes Alzheimer's disease from healthy aging: Evidence from functional MRI

TL;DR: A goodness-of-fit analysis applied at the individual subject level suggests that activity in the default-mode network may ultimately prove a sensitive and specific biomarker for incipient AD.
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Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network

TL;DR: The results demonstrate that resting-state functional connectivity reflects structural connectivity and that combining modalities can enrich the understanding of these canonical brain networks.
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Resting-State Functional Connectivity in Major Depression: Abnormally Increased Contributions from Subgenual Cingulate Cortex and Thalamus

TL;DR: The findings provide cross-modality confirmation of PET studies demonstrating increased thalamic and subgenual cingulate activity in major depression and suggest that a quantitative, resting-state fMRI measure could be used to guide therapy in individual subjects.