Neural State Interventions
Stroke, a major cause of long-term disability, frequently leads to motor impairments that necessitate significant rehabilitation. Traditional methods have their drawbacks, sparking interest in Brain-Computer Interfaces (BCIs) and extended reality (XR) as innovative tools for motor recovery. One significant challenge in BCI-based interventions is the “BCI inefficiency”, where some individuals struggle to gain effective control. In my current study I investigate whether L-Dopa, a dopamine precursor, will enhance BCI learning and accuracy by improving the neural signals associated with motor control.
Investigation of a new stroke therapy
The neurotech stroke clinical trial (ClinicalTrials.gov: NCT06116942) explores whether an innovative technology-based approach can help individuals who have had a stroke and can no longer move their hands with ease. Our approach consists of a combination of two technologies: repetitive Transcranial Magnetic Stimulation (rTMS) and a Brain-Computer Interface (BCI). The former entails the application of magnetic fields over the head to increase the excitability of motor cortices, whereas the latter consists of a subject-tailored neurofeedback training to bias the motor system towards adaptive plasticity.
Nikolai Kapralov
In M/EEG analyses, it is often convenient to extract a time series of activity from the brain regions of interest (ROI). However, multiple pipelines can be used for extraction of ROI activity, and there is no consensus on the gold standard among existing pipelines. My Ph.D. project addresses this problem via several complementary approaches. First, we performed a multiverse analysis to compare different pipelines using the same dataset and illustrate how the choice of the pipeline affects the estimated values of SNR and connectivity (Kapralov et al., 2024, J Neural Eng). Second, we used cross-talk function (CTF) to quantify the amount of remaining field spread and observed that its effect on the estimation of activity and connectivity is non-uniform across the cortex (Kapralov et al., in prep.). Currently, we investigate how CTF can be used to optimize the extraction of ROI activity for template and individual head models. Overall, our results illustrate the challenges associated with extraction of ROI activity and provide solutions that can be fine-tuned to the needs of performed analysis.
Transcranial magnetic stimulation (TMS) is widely used for neuromodulation in both research and clinical settings. We are investigating the immediate and long-lasting effects of a single modulatory TMS session in healthy volunteers. We are looking into structural and functional plasticity using magnetic resonance imaging (MRI), including resting-state functional MRI, voxel-based morphometry, and diffusion-weighted imaging. Our goal is to better understand the potential underlying plasticity mechanisms that may ultimately inform clinical applications of enhancing brain plasticity with TMS.