Evidence map›Paper›PMID 42133512›Full record

ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2026

Mobility Function and Aperiodic Electrocortical Activity in Younger and Older Adults.

Charlotte R DeVol, Chang Liu, Jacob Salminen, Erika M Pliner, Arkaprava Roy, Chris J Hass, David J Clark, Todd M Manini, Rachael D Seidler, Daniel P Ferris

Abstract read
In one paragraph

Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 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

5 · Who and what money

Authors and funding

10 authors.

Charlotte R DeVol
Chang Liu
Jacob Salminen
Erika M Pliner
Arkaprava Roy
Chris J Hass
David J Clark
Todd M Manini
Rachael D Seidler
Daniel P Ferris

Funding

Multimodal imaging of brain activity to investigate walking and mobility decline in older adultsU01AG061389 · NIA · UNIVERSITY OF FLORIDA · PI CLARK, DAVID J, MANINI, TODD · 2018 to 2023
$5.8M
Translational Research Training on Aging and Mobility (TRAM)T32AG062728 · NIA · UNIVERSITY OF FLORIDA · PI Todd Manini · 2020 to 2026
$2.0M
Efficacy of balance training with intermittent sensory perturbationsF32AG072808 · NIA · UNIVERSITY OF FLORIDA · PI PLINER, ERIKA M · 2021 to 2023
$145k
NIA NIH HHS F32 AG072808NIA NIH HHS T32 AG062728NIA NIH HHS U01 AG061389
6 · The paper itself

Abstract

Mobility declines with age to the extent that walking speed is often considered a vital sign. Identifying electrocortical changes behind this decline would aid with early identification and intervention. Electroencephalography (EEG) metrics may provide insight into neural factors contributing to mobility decline with aging. Recent research has shown a differentiation in aperiodic EEG across age groups, cognitive abilities, and populations with neurological injury. Aperiodic EEG is defined as the broadband, or non-oscillatory, component of the EEG power spectrum described by an exponent and offset. However, it is unknown if aperiodic EEG differs between mobility tasks or brain regions. The purpose of this study was to 1) compare aperiodic EEG in healthy older and younger adults at rest and while walking and 2) determine if oscillatory and aperiodic EEG in sensorimotor brain regions are predictors of a slower walking speed, regardless of age and other demographic factors. We analyzed EEG collected while participants were sitting at rest and walking on a treadmill in 31 younger adults (age: $24~\pm ~4$ , mean ± s.d.) and 59 older adults (age: $74~\pm ~6$ ), with no known cognitive decline or neuromuscular impairment. We found that older adults had lower aperiodic exponent and offset at both rest and during walking, but only a subset of brain regions showed age group differences. Using machine learning methods, we found that right sensorimotor alpha power, left sensorimotor aperiodic offset, and left sensorimotor beta power had the largest effect on individualized walking speed, after the demographics of age, waist circumference, and sex. These results suggest age differences in aperiodic EEG are regionally specific, and that aperiodic and oscillatory EEG describe differences in individualized walking speed that demographics alone cannot.

Indexed as

AgingElectroencephalographyWalkingWalking SpeedAdultAgedAged, 80 and overFemaleHumansMaleMiddle AgedSensorimotor CortexYoung Adult

Identifiers

PMID42133512
PMCPMC13398746

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.