Biology subjects

Soto, A. M.

Publications and source records attributed to Soto, A. M..

4 recordsLinked to original sources

bioRxiv2026

Adaptive Bayesian localization of motor representation areas

Transcranial magnetic stimulation (TMS) enables non-invasive localization of cortical motor representations, with important clinical applications in presurgical planning. Existing methods either disregard spatial information about the TMS-induced electric field (E-field) or use acquisition schemes that do not leverage previously elicited motor responses to guide subsequent stimulation. We present an adaptive Bayesian localization method that combines real-time E-field optimization with per-trial probabilistic inference. The cortical origin of the motor responses is represented as a spatial probability distribution that is updated after each stimulus. Subsequent stimuli are then optimized to maximize the expected localization improvement given previous responses. We validated the method experimentally, using multi-locus TMS for adaptive localization and single-coil TMS as a non-adaptive reference with randomized coil placements. Across eight subjects, the adaptive protocol at least halved the number of stimuli required for stable localization compared to the randomized protocol, converging in 60 stimuli on average, with 95% highest-density regions often below 10 mm2 by 150 stimuli.

neuroscience↗

bioRxiv2025

Fast and standardized motor-hotspot determination with automated TMS mapping

Determining the optimal stimulation target for motor responses (motor hotspot) and the required intensity for reliably eliciting said responses (motor threshold) are common procedures in transcranial magnetic stimulation (TMS) research and treatments. However, the procedures for determining them are user-dependent, slow, and lack standardization, leading to long stimulation sessions with potentially inadequate outcomes. Partially automated algorithms for determining the motor threshold have been developed, but the motor hotspot is still largely mapped by hand. Automating the hotspot mapping will accelerate the process and improve standardization and accuracy. We developed a fully automated algorithm for finding the motor hotspot with multi-locus TMS and Bayesian optimization. Tested online in five healthy participants, the algorithm located motor hotspots with (mean {+/-} 95% CI) 2.1 {+/-} 0.7 mm and 6 {+/-} 2{degrees} difference from the global best target with only (mean) 47 stimuli. This is a significant improvement from previous motor-mapping algorithms, which do not optimize for stimulation location and orientation simultaneously. This accurate, fast, and user-independent procedure paves the way for faster experimental processes and more streamlined clinical applications.

neuroscience↗

bioRxiv2024

Intensifying marine heatwaves and limited protection threaten global kelp forests

Kelp forests are one of the earths most productive ecosystems and are at the greatest risk from climate change, yet little is known regarding their future threats and current conservation status. By combining a global remote sensing dataset of floating kelp forests with climate data and projections, we find that exposure to projected marine heatwaves will increase [~]8 times compared to contemporary (2001-2020) exposure for intermediate climate scenarios. While exposure will intensify for all forests, climate refugia emerge for some southern hemisphere kelp forests, which have lower exposure to contemporary and projected marine heatwaves. Under these escalating threats, less than 3% of global kelp forests are currently within highly restrictive marine protected areas, the most effective conservation measure for providing climate resilience. Our findings emphasize the urgent need to increase the global protection of kelp forests and set bolder climate adaptation goals.

ecology↗

bioRxiv2019

A combined morphometric and statistical approach to assess non-monotonicity in the developing mammary gland of rats in the CLARITY-BPA study

BackgroundCLARITY-BPA is a rare collaboration of guideline-compliant (core) studies and academic hypothesis-based studies to assess the effects of bisphenol A (BPA).\n\nObjectives1) determine BPAs effects on the developing rat mammary gland using new quantitative and established semi-quantitative methods in two labs, 2) develop a software tool for semi-automatic evaluation of quantifiable aspects of the mammary ductal tree, and 3) compare those methods.\n\nMethodsSprague Dawley rats were exposed to BPA, vehicle, or positive control (ethinyl estradiol, EE2) by oral gavage beginning on gestational day 6 and continuing with direct dosing of the pups after birth. There were two studies; sub-chronic and chronic. The latter used two exposure regimes, one stopping at PND21 the other continuing until tissue harvest. Glands were harvested at multiple time points; whole mounts and histological specimens were analyzed blinded to treatment.\n\nResultsThe subchronic studys semiquantitative analysis revealed no significant differences between control and BPA dose groups at PND21; whereas at PND90 there were significant differences between control and the lowest BPA dose and between control and the lowest EE2 dose in animals in estrus. Quantitative, automatized analysis of the chronic PND21 specimens displayed non-monotonic BPA effects with a breaking point between the 25 and 250g/kg/day doses. This breaking point was confirmed by a global statistical analysis of chronic study animals at PND90 and 6 months analyzed by the quantitative method. The BPA response was different from the EE2 effect for many features.\n\nConclusionsBoth the semiquantitative and the quantitative methods revealed non-monotonic effects of BPA. The quantitative unsupervised analysis used 91 measurements and produced the most striking non-monotonic dose-response curves. At all-time points, lower doses resulted in larger effects, consistent with the core study which revealed a significant increase of mammary adenocarcinoma incidence in the stop-dose animals at the lowest BPA dose tested.

developmental biology↗