Evidence map›Paper›PMID 33534925›Full record

ArticleHuman brain mapping2021

Age differences in functional network reconfiguration with working memory training.

Alexandru D Iordan, Kyle D Moored, Benjamin Katz, Katherine A Cooke, Martin Buschkuehl, Susanne M Jaeggi, Thad A Polk, Scott J Peltier, John Jonides, Patricia A Reuter-Lorenz

Abstract read
In one paragraph

Article in Human brain mapping, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. 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

10 authors.

Alexandru D IordanDepartment of Psychology, University of Michigan, Ann Arbor, Michigan, USA.ORCID 0000-0002-1391-785X
Kyle D MooredDepartment of Mental Health, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Benjamin KatzDepartment of Human Development and Family Science, Virginia Tech, Blacksburg, Virginia, USA.
Katherine A CookeDepartment of Psychology, University of Michigan, Ann Arbor, Michigan, USA.
Martin BuschkuehlMIND Research Institute, Irvine, California, USA.
Susanne M JaeggiSchool of Education, University of California-Irvine, Irvine, California, USA.
Thad A PolkDepartment of Psychology, University of Michigan, Ann Arbor, Michigan, USA.
Scott J PeltierFunctional MRI Laboratory, University of Michigan, Ann Arbor, Michigan, USA.
John JonidesDepartment of Psychology, University of Michigan, Ann Arbor, Michigan, USA.
Patricia A Reuter-LorenzDepartment of Psychology, University of Michigan, Ann Arbor, Michigan, USA.

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
EPIDEMIOLOGY AND BIOSTATISTICS OF AGINGT32AG000247 · NIA · JOHNS HOPKINS UNIVERSITY · PI Karen J. Bandeen-Roche · 1996 to 2026
$13.2M
Institutional Career Development CoreKL2TR002241 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ELLINGROD, VICKI L · 2017 to 2021
$6.9M
MRI Scanner for Functional Brain ImagingS10OD012240 · OD · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI NOLL, DOUGLAS C · 2012 to 2012
$2.0M
Working Memory Training, Neural Correlates, and AgingR21AG045460 · NIA · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI REUTER-LORENZ, PATRICIA A. · 2014 to 2015
$420k
NCATS NIH HHS KL2 TR002241NCATS NIH HHS UL1 TR002240NIA NIH HHS R21 AG045460NIA NIH HHS T32 AG000247NIH HHS S10 OD012240NIH Office of the Director 1S10OD012240-01A1
6 · The paper itself

Abstract

Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.

Indexed as

ConnectomePractice, PsychologicalAdolescentAdultAgedAge FactorsAgingDefault Mode NetworkFemaleHumansMagnetic Resonance ImagingMaleMemory, Short-TermMiddle AgedNerve NetYoung Adultcingulo-opercular networkglobal efficiencygraph theoryintrinsic activityparticipation coefficientSternberg tasktask-related connectivity

Identifiers

PMID33534925
PMCPMC7978135

What OpenQuestion holds

Textmetadata
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.