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TMS-based decoding of executed and imagined hand actions
Neurofeedback (NF) is a promising approach for training healthy participants and patients to modulate their motor-related neural activity even in the absence of overt motor output. Motor imagery (MI)-based training, i.e., participants mentally simulate movements, also has beneficial effects on the restoration of impaired motor function. Transcranial magnetic stimulation (TMS) is a non-invasive, low-risk method that is routinely used for psychological or neuroscientific research in human participants. In comparison to electroencephalography, TMS-based NF has great potential to distinguish fine-grained MI tasks such as different hand actions. This is important because daily life activities require complex coordination of hand muscles. A hand function training is critical for individuals with impaired hand function. Our group has developed a new protocol that uses TMS to detect MI-induced motor activity patterns in the primary motor cortex. Here we will use TMS over the primary motor cortex of participants to measure motor evoked potentials (MEPs) in finger muscles during either motor execution or motor imagery of different hand actions, namely holding a bottle, turning a key and opening the hand. Based on the MEPs we provide participants visual feedback. We aim to further develop and validate an online, adaptive classification algorithm that decodes imagined hand actions in healthy volunteers from TMS-evoked MEPs and potentially apply this to stroke survivors.
Our group has recently completed the pilot data acquisition investigating the performance of an adaptive classification algorithm for decoding imaged hand actions during TMS-based NF training. We will continue with data collection and include brain MRI scans to further develop and validate this novel TMS-based NF training protocol.
Keywords: Transcranial magnetic stimulation (TMS), electromyography (EMG), functional magnetic resonance imaging (fMRI), machine learning
**Task Descriptions**
With the already collected dataset and prospective data, the goal of the thesis will be to investigate: (i) the effect of the TMS-based NF training; (ii) the brain changes following the TMS-NF training
**We offer:**
6-month learning experience at the Singapore-ETH Centre. Your stay in Singapore will be supported in Singapore with a monthly allowance. This interesting project offers the unique opportunity to gain experience with advanced/state-of-the-art electrophysiological techniques. Our motivated team with backgrounds in occupational therapy, biomedical engineering and neuroscience is eager to mentor you in neurophysiology, experimental skills, advanced data analysis (e.g., EMG and fMRI data anlysis, machine learning) and statistics. You will become part of a great and motivated, young research team! You will also have opportunities to explore Singapore and get immersed into the Asian culture.
**Your Profile:**
- Matlab and/or Python programming skills and signal processing experience
- Background in biomedical engineering/neuroscience is a plus
- Can work independently but also in a team
- Able to work full time for at least 6 months at the Singapore-ETH Centre in Singapore
- Motivated to conduct experimental testing sessions with human subjects
**Relevant publications:**
- Mihelj, E., Bächinger, M., Kikkert, S., Ruddy, K., & Wenderoth, N. (2021). Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback. Neuroimage, 242, 118463. doi: 10.1016/j.neuroimage.2021.118463
- Ruddy, K., Balsters, J., Mantini, D., Liu, Q., Kassraian-Fard, P., …, Wenderoth, N. (2018). Neural activity related to volitional regulation of cortical excitability. eLife, 7, e40843. doi: 10.7554/eLife.40843.001
- Liang, W. D., Xu, Y., Schmidt, J., Zhang, L. X., & Ruddy, K. L. (2020). Upregulating excitability of corticospinal pathways in stroke patients using TMS neurofeedback: A pilot study. Neuroimage: clinical, 28, 102465. doi: 10.1016/j.nicl.2020.102465
**Task Descriptions**
With the already collected dataset and prospective data, the goal of the thesis will be to investigate: (i) the effect of the TMS-based NF training; (ii) the brain changes following the TMS-NF training
**We offer:**
6-month learning experience at the Singapore-ETH Centre. Your stay in Singapore will be supported in Singapore with a monthly allowance. This interesting project offers the unique opportunity to gain experience with advanced/state-of-the-art electrophysiological techniques. Our motivated team with backgrounds in occupational therapy, biomedical engineering and neuroscience is eager to mentor you in neurophysiology, experimental skills, advanced data analysis (e.g., EMG and fMRI data anlysis, machine learning) and statistics. You will become part of a great and motivated, young research team! You will also have opportunities to explore Singapore and get immersed into the Asian culture.
**Your Profile:**
- Matlab and/or Python programming skills and signal processing experience - Background in biomedical engineering/neuroscience is a plus - Can work independently but also in a team - Able to work full time for at least 6 months at the Singapore-ETH Centre in Singapore - Motivated to conduct experimental testing sessions with human subjects
**Relevant publications:**
- Mihelj, E., Bächinger, M., Kikkert, S., Ruddy, K., & Wenderoth, N. (2021). Mental individuation of imagined finger movements can be achieved using TMS-based neurofeedback. Neuroimage, 242, 118463. doi: 10.1016/j.neuroimage.2021.118463 - Ruddy, K., Balsters, J., Mantini, D., Liu, Q., Kassraian-Fard, P., …, Wenderoth, N. (2018). Neural activity related to volitional regulation of cortical excitability. eLife, 7, e40843. doi: 10.7554/eLife.40843.001 - Liang, W. D., Xu, Y., Schmidt, J., Zhang, L. X., & Ruddy, K. L. (2020). Upregulating excitability of corticospinal pathways in stroke patients using TMS neurofeedback: A pilot study. Neuroimage: clinical, 28, 102465. doi: 10.1016/j.nicl.2020.102465