Simulated transcranial ultrasound of human language cortex: verifying heard speech needs far less data and signal than decoding it
Abstract
Functional ultrasound could image cortical blood volume through the temporal bone, but the skull leaves a weak signal, and task-evoked cortical imaging through the intact adult skull has not yet been shown. We built a voxel-specific Monte Carlo link-budget simulation of a probe on the left temple and used it to degrade public fMRI of three people who heard 15 hours of stories. On the same anatomical voxels we trained a published state-of-the-art fMRI language decoder (Tang et al., 2023) and two verifiers that only check a candidate text. In clean data the verifiers needed far less training (11 to 12 minutes against 3.2 to 6.6 hours), but all three learned at a similar rate. Noise changed this qualitatively: the verifiers kept gaining evidence while the decoder slowed and, under simulated transcranial physics, stalled, whereas the verifiers still reached their goal under a favorable simulated link budget. This asymmetry suggests that low-SNR modalities such as transcranial ultrasound may support verification well before decoding.
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BibTeX
@misc{shalev2026simulated,
title = {{Simulated transcranial ultrasound of human language cortex: verifying heard speech needs far less data and signal than decoding it}},
author = {Omer Shalev},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.33093},
eprint = {2610.33093},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.33093},
}
RIS
TY - UNPB
TI - Simulated transcranial ultrasound of human language cortex: verifying heard speech needs far less data and signal than decoding it
AU - Omer Shalev
PY - 2026
DA - 2026/10/22
PB - arXiv
DO - 10.48550/arxiv.2610.33093
UR - https://arxiv.org/abs/2610.33093
AB - Functional ultrasound could image cortical blood volume through the temporal bone, but the skull leaves a weak signal, and task-evoked cortical imaging through the intact adult skull has not yet been shown. We built a voxel-specific Monte Carlo link-budget simulation of a probe on the left temple and used it to degrade public fMRI of three people who heard 15 hours of stories. On the same anatomical voxels we trained a published state-of-the-art fMRI language decoder (Tang et al., 2023) and two verifiers that only check a candidate text. In clean data the verifiers needed far less training (11 to 12 minutes against 3.2 to 6.6 hours), but all three learned at a similar rate. Noise changed this qualitatively: the verifiers kept gaining evidence while the decoder slowed and, under simulated transcranial physics, stalled, whereas the verifiers still reached their goal under a favorable simulated link budget. This asymmetry suggests that low-SNR modalities such as transcranial ultrasound may support verification well before decoding.
ER -