Biology subjects

Larocco, J.

Publications and source records attributed to Larocco, J..

3 recordsLinked to original sources

bioRxiv2025

A multimodal brain phantom for noninvasive neuromodulation

Noninvasive neuromodulation enables brain stimulation without surgery but requires precise optimization of stimulation parameters to ensure efficacy and safety. Direct testing on human or animal subjects is costly, time intensive, and constrained by ethical and safety considerations. To address these challenges, a low-cost, versatile brain phantom was designed to emulate key biophysical properties across multiple neuromodulation modalities. The phantom was fabricated using ground beef, sodium alginate, and starch, and cast within a custom 3D printed mold. A multimodal test platform was created and validated by integrating established principles from low-intensity focused ultrasound (LIFU), transcranial direct current stimulation (tDCS), and thermal phantom design. Numerical simulations predicted a LIFU peak negative pressure of 0.631 MPa, closely matching the target Pr.3 value, with negligible temperature elevation (<0.01 {degrees}C). Consistent with prior reports, tDCS exposure did not induce lasting alterations in the phantoms physical or electrical properties. The electrical conductivity was 0.11{+/-}0.02 S/m, reflecting water saturation within the phantom matrix; the thermal conductivity averaged 0.557 W/(mK), consistent with reported values for brain tissue analogs. This study primarily evaluated LIFU and tDCS performance; future work should extend characterization to additional modalities such as deep brain stimulation and transcranial magnetic stimulation. Further assessment of the phantoms optical properties would also facilitate photobiomodulation and photoacoustic imaging studies. Overall, this inexpensive, easily fabricated phantom presents a practical and adaptable platform for multimodal neuromodulation research and parameter optimization.

neuroscience↗

bioRxiv2025

Imagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model Integration

Cognitive linguistics posits that language underpins human thought, and this principle has influenced the study and development of large language models (LLMs). In particular, several studies have investigated the metabolic costs of sentence formation using neuroimaging techniques such as positron emission tomography, functional magnetic resonance imaging, electroencephalography (EEG), and imagined speech reconstruction (ISR). In this study, EEG data corresponding to imagined English-language speech phonemes were used for ISR, in combination with an LLM trained on an abridged autobiography. The LLM-generated text responses guided the synthesis of EEG data from relevant phonemes, which were then used to estimate corresponding metabolic activity, and the changes in simulated neurometabolic and electrical parameters were visually represented. Notably, introducing pseudorandom variance significantly (p < 0.001) enhanced the models ability to reflect biological variability. Future directions include expanding the ISR system with lightweight or locally run LLMs, incorporating training data from larger and more diverse populations, and utilizing truly random variability sources. Further optimization for broader hardware compatibility and implementation--such as neural phantoms, emotional context integration, or human-computer interaction platforms--offer promising pathways for advancement. Overall, this work establishes a foundation for the next generation of biologically inspired, modular, and adaptable ISR systems for both research and practical applications.

neuroscience↗

bioRxiv2025

Sustainable Memristors from Shiitake Mycelium for High-Frequency Bioelectronics

Neuromorphic computing, inspired by the structure of the brain, offers advantages in parallel processing, memory storage, and energy efficiency. However, current semiconductor-based neuromorphic chips require rare-earth materials and costly fabrication processes, whereas neural organoids need complex bioreactor maintenance. This study explores shiitake (Lentinula edodes) fungi as a robust, sustainable alternative, exploiting its adaptive electrical signaling, which is akin to neuronal spiking. We demonstrate fungal computing via mycelial networks interfaced with electrodes, showing that fungal memristors can be grown, trained, and preserved through dehydration, retaining functionality at frequencies up to 6 kHz. Notably, shiitake has exhibited radiation resistance, suggesting its viability for aerospace applications. Our findings show that fungal computers can provide scalable, eco-friendly platforms for neuromorphic tasks, bridging bioelectronics and unconventional computing.

bioengineering↗