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Brain physiological pulsations are linked to sleep architecture and cognitive performance in older adults

2025· article· en· W4409195381 on OpenAlexaff
Rodolphe Nenert, Christina Mueller, Corina Catiul, Jennifer Pilkington, Pierre LeVan, Ayushe A. Sharma, Jerzy P. Szaflarski, Amy W. Amara

Bibliographic record

VenueNeuroImage · 2025
Typearticle
Languageen
FieldNeuroscience
TopicCerebrospinal fluid and hydrocephalus
Canadian institutionsAlberta Children's Hospital
FundersNational Institute of Neurological Disorders and StrokeEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of Alabama at BirminghamEvelyn F. McKnight Brain Research FoundationState of AlabamaMichael J. Fox Foundation for Parkinson's Research
KeywordsSleep architectureSleep (system call)CognitionEffects of sleep deprivation on cognitive performanceNeurosciencePsychologyCognitive architectureCognitive psychologyAudiologyPhysical medicine and rehabilitationMedicineElectroencephalographyComputer sciencePolysomnography

Abstract

fetched live from OpenAlex

BACKGROUND: The glymphatic system facilitates efficient waste clearance in the brain through the movement of cerebrospinal fluid (CSF) along perivascular spaces. Animal studies have demonstrated that glymphatic efficiency declines with age, but evidence for such decline in humans is limited. We hypothesized that reduced glymphatic efficiency in older adults may be related to age-related worsening of sleep quality, potentially contributing to cognitive impairment. METHODS: 20 participants aged ≥60 years provided multi-dimensional cognitive measures, overnight polysomnography, and Magnetic Resonance Encephalography (MREG) performed the morning following the PSG. MREG is a single-shot, three-dimensional (3D) sequence employing a spherical stack-of-spirals trajectory that undersamples 3D k-space, enabling whole-brain data acquisition every 100 milliseconds to non-invasively and dynamically assess brain physiological pulsations. Spectral power and optical flow analyses quantified physiological pulsations within cardiovascular (CvB; 0.52-1.6 Hz), respiratory (RFB; 0.11-0.44 Hz), and low-frequency (LFB; 0.008-0.1 Hz) bands. These measures were correlated with cognitive test scores and sleep parameters assessed by overnight polysomnography. RESULTS: Significant associations emerged between physiological pulsations, sleep, and cognitive measures. Cardiovascular pulsation strength correlated with non-rapid eye movement (NREM) stage 3 (N3) sleep percentage (peak voxel in right frontal pole; r = 0.72, p < 0.001) and language domain performance (left calcarine gyrus; r = 0.56, p = 0.01). Respiratory pulsations correlated strongly with sleep onset latency (right inferior temporal gyrus; r = 0.75, p < 0.001). Additionally, low-frequency pulsations were associated with sleep onset latency (right precentral gyrus; r = 0.67, p = 0.002). These findings suggest that glymphatic efficiency, as reflected by brain pulsations, is closely linked to sleep quality and cognitive performance in older adults, particularly involving cortical and subcortical structures relevant to cognitive and sleep regulatory functions. CONCLUSION: This study uniquely demonstrates that brain physiological pulsations measured non-invasively with MREG are significantly associated with sleep architecture and cognitive performance in older adults. These findings underscore the potential of MREG to assess glymphatic function and provide important insights into the mechanisms linking sleep disturbances, cognitive decline, and aging. The identified correlations between pulsations and specific brain regions highlight potential pathways through which impaired glymphatic function could contribute to cognitive decline in older adults, suggesting promising avenues for future clinical and research applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.017
GPT teacher head0.273
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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