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Record W4416170525 · doi:10.1007/s10237-026-02080-8

Aging-Related Lateral Ventricular Shape Changes and Corresponding Mechanical Loading Derived from Longitudinal Image Registration

2025· article· en· W4416170525 on OpenAlexfundno aff
Lauren Cunniff, Johannes Weickenmeier

Bibliographic record

VenueBiomechanics and Modeling in Mechanobiology · 2025
Typearticle
Languageen
FieldMedicine
TopicAutomotive and Human Injury Biomechanics
Canadian institutionsnot available
FundersDirectorate for EngineeringNational Institutes of HealthGenentechIXICOH. Lundbeck A/SServierEisaiNorthern California Institute for Research and EducationUniversity of Southern CaliforniaBiogenBioClinicaDivision of Civil, Mechanical and Manufacturing InnovationMeso Scale DiagnosticsU.S. Department of DefenseAlzheimer's Disease Neuroimaging InitiativeNovartis Pharmaceuticals CorporationPfizerEli Lilly and CompanyBristol-Myers SquibbAlzheimer's AssociationCanadian Institutes of Health ResearchNational Science Foundation
KeywordsMagnetic resonance imagingDisplacement (psychology)NeuroimagingCurvatureLateral ventriclesElectromagnetic coilCardiac cycleVolume (thermodynamics)

Abstract

fetched live from OpenAlex

Lateral ventricular enlargement is one of the most prominent features of the aging brain and is clearly visible on structural magnetic resonance imaging. Both longitudinal and cross-sectional imaging studies have shown that ventricular volume progressively increases with age and expands even faster in neurodegenerative diseases such as Alzheimer's disease and related dementias. Strikingly, however, we only have a limited understanding of ventricular shape changes and the corresponding mechanical loads that act on the ventricular wall as we age. Therefore, we propose a framework that uses nonlinear registration to quantify subject-specific brain deformations between two longitudinal scans, maps the resulting warp field onto a ventricular surface template mesh, and quantifies mechanical loading measures including displacement magnitude, curvature change, area stretch, and maximum principal wall strain. From the Alzheimer's Disease Neuroimaging Initiative, we selected a cohort of 50 cognitively normal subjects aged 70-75 years at baseline and with a follow-up scan 4-5 years later. In this group, we observed mostly uniform expansion of the lateral ventricles with an average displacement magnitude of 0.88 ± 0.3 mm across the whole ventricle. At the same time, there are distinct sections of the ventricular wall that experience high mechanical loads with respect to our mechanomarkers. Specifically, maximum mechanical loading consistently localizes along the ventricular edges and atrium while the ventricle's main body exhibits minimal loading. Based on the cohort included in this study, we did not observe sex-based differences with respect to any mechanomarker, noticed that on average 29.2 ± 9.3% of the ventricular wall experience wall area increase, and that on average only 4.4 ± 2.5% of the ventricular wall experience wall shrinking. Interestingly, regions of elevated mechanical loading showed reliable spatial correspondence with periventricular white matter hyperintensity locations in our subjects for whom FLAIR imaging was available (n = 39). Additionally, mechanomarkers showed increased magnitudes with periventricular white matter hyperintensity burden, with curvature change demonstrating the strongest group separation. These findings suggest that ventricular enlargement is associated with localized mechanical stresses that coincide with vulnerable white matter regions. Taken together, we present strong evidence in support of the hypothesis that the mechanical loading associated with age-related ventricular enlargement is intricately linked to periventricular white matter degeneration and corresponding cognitive decline.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.034
GPT teacher head0.293
Teacher spread0.259 · 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 teacher head, not a consensus.

Study designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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