Biology subjects

Pieramico, G.

Publications and source records attributed to Pieramico, G..

3 recordsLinked to original sources

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↗

bioRxiv2025

NeuroSimo: an open-source software for closed-loop EEG- or EMG-guided TMS

ObjectiveOur goal was to create open-source software for closed-loop EEG-TMS that allows researchers to rapidly prototype and develop novel stimulation paradigms in a high-level programming language. This addresses the limitations of current solutions, which often rely on proprietary hardware and software, limiting their accessibility and customizability, or comprise ad-hoc pipelines tailored to specific use cases. ApproachWe developed NeuroSimo, a software platform that enables arbitrary EEG-TMS stimulation protocols written in Python, leveraging Pythons ecosystem of scientific, neuroimaging, and machine learning libraries. The core software is written in C++ with an embedded Python interpreter and employs the Robot Operating System (ROS 2) for inter-process communication. NeuroSimo runs on real-time-enabled Linux Ubuntu, using LabJack T4 for pulse triggering, and supports two EEG device models (Bittium NeurOne, BrainProducts actiCHamp) and TMS devices that deliver pulses via trigger signals. The software includes a graphical user interface for configuration and performance monitoring, and supports GPU processing for neural network computations. Main resultsIn brain-state-dependent stimulation using the Phastimate algorithm, which targets TMS pulses to the trough of sensorimotor -rhythm, NeuroSimo achieved a median timing error of 0.2 ms (95% CI: 0.2-0.2 ms), a 99th-percentile of 0.6 ms (0.6-0.6 ms), and a maximum of 1.4 ms. SignificanceAs an open-source platform combining Pythons flexibility with real-time closed-loop EEG-TMS, NeuroSimo enables researchers to develop and implement novel therapeutic approaches, marking a significant advance in personalized brain stimulation.

bioengineering↗

bioRxiv2023

Intrinsic motor network connectivity predicts corticospinal excitability

State-dependent non-invasive brain stimulation (NIBS) informed by electroencephalography (EEG) has contributed to the understanding of NIBS inter-subject and inter-session variability. While these approaches focused on local EEG characteristics, it is acknowledged that the brain exhibits an intrinsic long-range dynamic organization in networks. This proof-of-concept study explores whether EEG connectivity of the primary motor cortex (M1) in the pre-stimulation period aligns with the motor network (MN) and how MN state affects responses to transcranial magnetic stimulation (TMS) of M1. One thousand suprathreshold TMS pulses were delivered to left M1 in 8 subjects at rest, with simultaneous EEG. Motor evoked potentials (MEPs) were measured from the right hand. Source-space functional connectivity of left M1 to the whole-brain was assessed using the imaginary part of the Phase Locking Value at the frequency of the sensorimotor {micro}-rhythm in a 1-second window before the pulse. Group-level connectivity revealed functional links between left M1, left supplementary motor area, and right M1. Also, pulses delivered at high MN connectivity states result in a greater MEP amplitude compared to low connectivity states. At single-subject level, this relation is more expressed in subjects that feature an overall high cortico-spinal excitability. In conclusion, this study paves the way for MN connectivity based NIBS. HighlightsO_LIEEG pre-stimulus connectivity of left M1 largely corresponds to the motor network C_LIO_LIStronger motor network (MN) connectivity corresponds to greater MEP amplitudes C_LIO_LILinear regression models based on MN connectivity predicts MEP amplitude C_LI

neuroscience↗