Understanding intracranial aneurysm sounds via high-fidelity fluid-structure-interaction modelling
Bibliographic record
Abstract
BACKGROUND: Since the 1960s, the origins of intracranial aneurysm bruits and musical murmurs have been debated, with proposed mechanisms ranging from self-excitation (i.e., resonance) by stable pulsatile flow, to vibration caused by unstable (laminar vortex shedding or turbulent) flow. This knowledge gap has impeded the use of intracranial sounds a marker of aneurysm remodelling or rupture risk. New computational techniques now allow us to model these phenomena. METHODS: We performed high-fidelity fluid-structure interaction simulations capable of understanding the magnitude and mechanisms of such flow-induced vibrations, under pulsatile flow conditions. Six cases from a previous cohort were used. RESULTS: In five cases, underlying flow instabilities present as broad-band, random vibrations, consistent with previously-described bruits, while the sac also exhibits resonance, rocking back and forth in different planes of motion, consistent with previously described musical murmurs. Both types of vibration have amplitudes in the range of 0.1 to 1 μm. The murmurs extend into diastole, after the underlying flow instability has dissipated, and do not exhibit the characteristic repeating frequency harmonics of previously hypothesized vortex-shedding mechanisms. The remaining case with stable pulsatile flow does not vibrate. Spectrograms of the simulated vibrations are consistent with previously reported microphone and Doppler ultrasound recordings. CONCLUSIONS: Our results provide a plausible explanation for distinct intracranial aneurysm sounds and characterize the mechanical environment of a vibrating aneurysm wall. Future work should aim to quantify the deleterious effects of these overlooked stimuli on the vascular wall, to determine which changes to the wall makeup are associated with vibration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".