Biology subjects

Labib, S.

Publications and source records attributed to Labib, S..

3 recordsLinked to original sources

bioRxiv2026

Machine Learning Assisted Optimization Framework for Designing Transcranial Focused Ultrasound Phased Array Transducer for Deep Brain Neuromodulation in Mice

Transcranial focused ultrasound is an emerging noninvasive neuromodulation technique offering high spatial precision and deep penetration. However, in deep brain neuromodulation in mice, the skull base attenuates the signal, distorting the focal region and creating off-target peaks. This study presents a machine-learning-driven simulation framework to optimize a bowl-shaped phased-array transducer design for hypothalamic targeting and compares its performance with that of time-reversal phase conjugation and a single-element baseline. A computed tomography-based mouse head model was used for full-wave acoustic simulations with a fixed bowl geometry (10 mm aperture, 6 mm radius of curvature). Designs were evaluated across various parameters, including operating frequency (0.2-1.5 MHz), active element count (16, 32, 64, 128), and element diameter (300-550 m). The evaluation employed four metrics: the presence of a -3 dB focal region within the hypothalamic area, axial focal length defined by the -3 dB full-width at half maximum, focal fragmentation measured by the -3 dB blob count, and targeting displacement. Random Forest surrogate models were trained in simulation outputs and paired with the Non-dominated Sorting Genetic Algorithm II to reduce computational costs during multi-objective optimization. The forward-excitation-optimized phased-array design (0.73 MHz, 128 elements, 381 m element diameter) achieved a focal region at the hypothalamic target with a full width at half maximum of 0.67 mm, a blob count of 1, and a targeting displacement of 0.38 mm when placed 1 mm below the nominal position. Time-reversal phase conjugation further improved confinement and targeting (full width at half maximum: 0.59 mm; displacement: 0.37 mm). Limitations include reliance on a single mouse anatomy, and incorporating additional CT-derived anatomies should enhance generalizability across strains, ages, and sexes. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/727023v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@62de15org.highwire.dtl.DTLVardef@e26e57org.highwire.dtl.DTLVardef@1ba4893org.highwire.dtl.DTLVardef@f2c77a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIA CT-based acoustic simulation and machine-learning framework was developed to optimize bowl-shaped phased-array transducers for mouse hypothalamic tFUS neuromodulation. C_LIO_LIRandom Forest surrogate models coupled with NSGA-II efficiently identified optimized array designs across frequency, element count, and element diameter. C_LIO_LIThe optimized phased-array design produced a compact hypothalamic focus with submillimeter targeting displacement, with further confinement achieved using time-reversal phase conjugation. C_LI

bioengineering↗

bioRxiv2025

Machine learning-based optimization of a single-element transcranial focused ultrasound transducer for deep brain neuromodulation in mice

Transcranial focused ultrasound is an emerging noninvasive neuromodulation technique that offers high spatial precision and the potential for deep brain penetration. However, due to skull-induced attenuation and acoustic aberrations, precisely stimulating deep brain regions in mice remains challenging. To address this challenge, this study introduces a machine-learning-based computational framework to optimize single-element transducer designs for accurate deep-brain targeting in a mouse model. This framework includes a surrogate model consisting of a Random Forest regressor and classifier, trained on acoustic simulation results to predict performance from design parameters. A total of 72 transducer designs were simulated across coronal and sagittal planes, systematically varying frequency (1-6 MHz), radius of curvature (5-7 mm), and f-number (0.58-1.0). Each design was evaluated using five performance metrics: focal length, focal shape, maximum pressure at the focal region, pressure maximum location, and sidelobe suppression. The surrogate models were then combined with the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to perform multi-objective optimization and identify high-performing transducer designs. The optimized design produced a compact, symmetric focal region and accurate energy delivery to deep targets, with minimal off-target exposure, even in complex skull anatomy. Results show that lower f-numbers, moderate radius of curvature, and higher frequencies facilitate precise deep brain targeting. Overall, this data-driven approach enables practical design of single-element transducers for deep-brain neuromodulation in mice and provides a framework for designing transcranial transducers for other brain targets, potentially accelerating the clinical translation of focused ultrasound technologies.

bioengineering↗

bioRxiv2022

Cartilage-penetrating hyaluronic acid hydrogel preserves tissue content and reduces chondrocyte catabolism

Articular cartilage injuries have a limited healing capacity and, due to inflammatory and catabolic activities, often experience progressive degeneration towards osteoarthritis. Current repair techniques generally provide short-term symptomatic relief; however, the regeneration of hyaline cartilage remains elusive, leaving both the repair tissue and surrounding healthy tissue susceptible to long-term wear. Therefore, methods to preserve cartilage following injury, especially from matrix loss and catabolism, are needed to delay, or even prevent, the deteriorative process. The goal of this study was to develop and evaluate a cartiage-penetrating hyaluronic-acid (HA) hydrogel to improve damaged cartilage biomechanics and prevent tissue degeneration. At time zero, the HA-based hydrogel provided a 46.5% increase in compressive modulus and a decrease in permeability after simulated degeneration of explants (collagenase application). Next, in a degenerative culture model (interleukin-1 {beta} [IL-1{beta}] for 2 weeks), hydrogel application prior to or midway through the culture mitigated detrimental changes to compressive modulus and permeability observed in non-treated explants. Furthermore, localized loss of proteoglycan was observed in degenerative culture conditions alone (non-treated), but hydrogel administration significantly improved the retention of matrix elements. Finally, NITEGE staining and gene expression analysis showed the ability of the HA gel to decrease chondrocyte catabolic activity. These results highlight the importance of reinforcing damaged cartilage with a biomaterial system to both preserve tissue content and reduce catabolism associated with injury and inflammation.

bioengineering↗