Evidence map›Paper›PMID 42521051›Full record

ArticleNeuroImage2026

Brain signal variability during rest as a neural mechanism underlying cognitive reserve.

Annabell Coors, Yaakov Stern, Christian Habeck

Abstract read
In one paragraph

Article in NeuroImage, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Annabell CoorsCognitive Neuroscience Division, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; Department of Psychiatry, Psychotherapy and Psychosomatic Medicine, University Medical Center Halle, Halle (Saale), Germany; Leibniz Institute for Resilience Research, Mainz, Germany; German Center for Mental Health (DZPG), Site Halle-Jena-Magdeburg, Halle (Saale), Germany.
Yaakov SternCognitive Neuroscience Division, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; The Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA; The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA.
Christian HabeckCognitive Neuroscience Division, Department of Neurology, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA; The Taub Institute for Research in Alzheimer's Disease and the Aging Brain, Columbia University, New York, NY, USA; The Gertrude H. Sergievsky Center, Columbia University, New York, NY, USA. Electronic address: ch629@cumc.columbia.edu.

Funding

Exploring Cognitive Aging Using Reference Ability Neural NetworksR01AG038465 · NIA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI HABECK, CHRISTIAN GEORG, STERN, YAAKOV · 2011 to 2025
$17.0M
NIA NIH HHS R01 AG038465
6 · The paper itself

Abstract

backgroundResting-state brain signal variability has been found to vary with age and cognitive function. Neural flexibility has been suggested as a neural mechanism underlying cognitive reserve (CR), a construct that describes better than expected cognition given brain status. We examined the associations between age, resting-state brain signal variability, and CR.

methodAnalysis was based on resting-state functional neuroimaging data from 470 participants (ages 20-80) from the Reference Ability Neural Network and the CR studies. Brain signal variability was quantified as the log-transformed standard deviation of the time-varying blood-oxygen-level-dependent signal per region. We then derived variability pattern scores related to age, perceptual speed, episodic memory, fluid reasoning, and vocabulary using Scaled Subprofile Modelling principal component analysis. To formally test whether the cognition-related patterns fulfill the requirements for CR, we examined whether each pattern explained additional variance in cognition beyond brain structural measures, age, sex, education. We checked the robustness of our findings using permutation tests. Finally, we determined which brain regions significantly contributed to each pattern.

resultsBrain variability patterns emerged across all four cognitive domains and showed robust associations with CR in the domains of episodic memory, fluid reasoning and vocabulary, explaining between 10.5%-18.3% of variance in cognitive performance. The cognition-related variability patterns inversely mirrored age-related variability patterns in some brain regions.

conclusionsBOLD signal variability plays a role in aging and underlies CR in the domains of episodic memory, fluid reasoning, and vocabulary.

Indexed as

AgingBrainCognitive ReserveRestAdultAgedAged, 80 and overBrain MappingFemaleHumansMagnetic Resonance ImagingMaleMiddle AgedYoung AdultAgingCognitionNoiseResilienceResting-state fMRIScaled subprofile modelling principal component analysis

Identifiers

PMID42521051
PMCPMC13626027

What OpenQuestion holds

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Registered trials

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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.