Comprehensive Summary
Transcranial-focused ultrasound stimulation (tFUS) has risen as an approach to non-invasive modulation of neural circuits, offering a technique of high spatial precision and penetration depth. This study aimed to develop a method to assess the neuroplastic modifications of tFUS stimulation. To interpret electroencephalogram (EEG) recordings and tFUS responses, an integrated longitudinal evaluation protocol (ILEP) model was used. EEG signals were used to quantify evoked potentials, and they were processed for time-frequency decomposition, event-related potential analysis, and machine learning classifiers to differentiate between short-term and long-term neuroplastic effects. Statistical modeling, neural network-based pattern recognition, and deep learning models were also utilized to analyze neuromodulation over time. It was found that the ILEP model is effective in using EEG biomarkers to distinguish between short-term and long-term brain activity modifications. These EEG modifications are consistent with neuroadaptive processes, and the model was able to accurately detect neural adaptive plasticity. With training, there is potential for the model to be integrated into a closed-loop neuromodulation system.
Outcomes and Implications
The integrated longitudinal evaluation protocol (ILEP) model introduces an accurate and reliable method for quantifying short and long-term neuroplasticity and adaptation. This is important for effective and safe treatments of neurological and mental disorders, as it is a non-invasive stimulation technique. This means that there is the capacity to induce neuroplasticity and modify cortical excitability outside of surgical intervention. Before clinical implementation, this method requires further investigation into its safety and long-term therapeutic effects, including training and testing on a diverse patient population. There is also the opportunity to tune the model to certain neuropsychiatric, neurodegenerative, or cognitive impairment disorders.