Evidence map›Paper›PMID 28112460›Full record

ArticleHuman brain mapping2017

From hippocampus to whole-brain: The role of integrative processing in episodic memory retrieval.

Benjamin R Geib, Matthew L Stanley, Nancy A Dennis, Marty G Woldorff, Roberto Cabeza

Open access · greenAbstract read
In one paragraph

Article in Human brain mapping, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

34 citing papers in PubMed, 1 synthesis or guideline pooled it, 83 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Longitudinal hippocampal axis in large-scale cortical systems underlying development and episodic memory.Proceedings of the National Academy of Sciences of the United States of America · 2024
    Article
  4. Article
  5. Exploring memory-related network via dorsal hippocampus suppression.Network neuroscience (Cambridge, Mass.) · 2024
    Article
  6. Article
  7. Multiple traces and altered signal-to-noise in systems consolidation: Evidence from the 7T fMRI Natural Scenes Dataset.Proceedings of the National Academy of Sciences of the United States of America · 2022
    Article
  8. Review
  9. Article
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  17. Article
  18. Network topology of symbolic and nonsymbolic number comparison.Network neuroscience (Cambridge, Mass.) · 2020
    Article
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  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Benjamin R GeibDepartment of Psychology and Neuroscience, Duke University, Durham, North Carolina.
Matthew L StanleyDepartment of Psychology and Neuroscience, Duke University, Durham, North Carolina.ORCID 0000-0002-3628-6253
Nancy A DennisDepartment of Psychology, Pennsylvania State University, University Park, Pennsylvania.
Marty G WoldorffDepartment of Psychology and Neuroscience, Duke University, Durham, North Carolina.
Roberto CabezaDepartment of Psychology and Neuroscience, Duke University, Durham, North Carolina.
Duke University · USPennsylvania State University · US

Funding

Effects of Aging on Visual Memory: Neuroimaging StudiesR01AG019731 · NIA · DUKE UNIVERSITY · PI CABEZA, ROBERTO · 2002 to 2017
$4.6M
NIA NIH HHS R01 AG019731
6 · The paper itself

Abstract

Multivariate functional connectivity analyses of neuroimaging data have revealed the importance of complex, distributed interactions between disparate yet interdependent brain regions. Recent work has shown that topological properties of functional brain networks are associated with individual and group differences in cognitive performance, including in episodic memory. After constructing functional whole-brain networks derived from an event-related fMRI study of memory retrieval, we examined differences in functional brain network architecture between forgotten and remembered words. This study yielded three main findings. First, graph theory analyses showed that successfully remembering compared to forgetting was associated with significant changes in the connectivity profile of the left hippocampus and a corresponding increase in efficient communication with the rest of the brain. Second, bivariate functional connectivity analyses indicated stronger interactions between the left hippocampus and a retrieval assembly for remembered versus forgotten items. This assembly included the left precuneus, left caudate, bilateral supramarginal gyrus, and the bilateral dorsolateral superior frontal gyrus. Integrative properties of the retrieval assembly were greater for remembered than forgotten items. Third, whole-brain modularity analyses revealed that successful memory retrieval was marginally significantly associated with a less segregated modular architecture in the network. The magnitude of the decreases in modularity between remembered and forgotten conditions was related to memory performance. These findings indicate that increases in integrative properties at the nodal, retrieval assembly, and whole-brain topological levels facilitate memory retrieval, while also underscoring the potential of multivariate brain connectivity approaches for providing valuable new insights into the neural bases of memory processes. Hum Brain Mapp 38:2242-2259, 2017. © 2017 Wiley Periodicals, Inc.

Indexed as

Memory, EpisodicAdolescentAdultAfferent PathwaysBrainBrain MappingFemaleHippocampusHumansImage Processing, Computer-AssistedMagnetic Resonance ImagingMaleMental RecallOxygenPsycholinguisticsVerbal LearningOxygenfunctional magnetic resonance imaginggraph theorymedial temporal lobesmodularityrecall

Identifiers

PMID28112460
PMCPMC5460662
OpenAlexW2582351269

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.