Evidence map›Paper›PMID 39616242›Full record

ArticleCommunications biology2024

Reconfiguration of metabolic connectivity in ageing.

Hamish A Deery, Emma X Liang, M Navyaan Siddiqui, Gerard Murray, Katharina Voigt, Robert Di Paolo, Chris Moran, Gary F Egan, Sharna D Jamadar

Abstract read
In one paragraph

Article in Communications biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

  1. Article
  2. Human cerebral cortex organization characterized by functional PET-FDG "Metabolic Connectivity".European journal of nuclear medicine and molecular imaging · 2026
    Article
  3. Article
  4. Article
  5. Article
  6. Brain glucodynamic variability is an essential feature of the metabolism-cognition relationship.Proceedings of the National Academy of Sciences of the United States of America · 2026
    Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. Article
  14. Stable brain PET metabolic networks using a multiple sampling scheme.Network neuroscience (Cambridge, Mass.) · 2025
    Article
  15. 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

9 authors.

Hamish A DeerySchool of Psychological Sciences, Monash University, Melbourne, 3800, Australia.ORCID 0000-0002-1052-4516
Emma X LiangSchool of Psychological Sciences, Monash University, Melbourne, 3800, Australia.
M Navyaan SiddiquiSchool of Psychological Sciences, Monash University, Melbourne, 3800, Australia.
Gerard MurraySchool of Psychological Sciences, Monash University, Melbourne, 3800, Australia.
Katharina VoigtSchool of Psychological Sciences, Monash University, Melbourne, 3800, Australia.
Robert Di PaoloSchool of Psychological Sciences, Monash University, Melbourne, 3800, Australia.
Chris MoranSchool of Public Health and Preventive Medicine, Monash University, 553 St Kilda Rd, Melbourne, VIC, 3004, Australia.
Gary F EganMonash Biomedical Imaging, Monash University, 770 Blackburn Rd, Melbourne, 3800, Australia.
Sharna D JamadarSchool of Psychological Sciences, Monash University, Melbourne, 3800, Australia. sharna.jamadar@monash.edu.ORCID 0000-0001-7222-7181

Funding

Department of Health | National Health and Medical Research Council (NHMRC) APP1174164
6 · The paper itself

Abstract

Information transfer across the brain has a high energetic cost and requires efficient glucose metabolism. Here we use recently developed high temporal resolution functional positron emission tomography (fPET) to create a timecourse of glucose metabolism for individual subjects and assess the relationship between metabolic connectivity and cognitive function in ageing. The metabolic connectomes of 40 younger (mean 27.9 years; range 20-42) and 46 older (mean 75.8; 60-89) adults were characterised by high connectivity in the frontal, temporal, motor, parietal and medial cortices. Older age was associated with lower global integration of metabolic hub regions. In younger adults, a high proportion of glucose was used to support hubs in the frontal regions. Older adults used a higher proportion of a smaller energy budget to support mostly posterior hub regions. This metabolic network topology of older adults was associated with worse cognitive performance. We conclude that ageing is associated with a high glucose cost in hub regions and disrupted information transfer across the metabolic network. Our results highlight the fundamental role that metabolism plays in supporting information transfer in the brain and the unique insights that metabolic connectivity provides into the ageing brain.

Indexed as

AgingBrainGlucosePositron-Emission TomographyAdultAgedAged, 80 and overCognitionConnectomeFemaleHumansMaleMetabolic Networks and PathwaysMiddle AgedYoung AdultGlucose

Identifiers

PMID39616242
PMCPMC11608353

What OpenQuestion holds

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LicenceCC BY-NC-ND
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.