A probabilistic transcranial magnetic stimulation localization method. (3rd September 2021)
- Record Type:
- Journal Article
- Title:
- A probabilistic transcranial magnetic stimulation localization method. (3rd September 2021)
- Main Title:
- A probabilistic transcranial magnetic stimulation localization method
- Authors:
- Kataja, Juhani
Soldati, Marco
Matilainen, Noora
Laakso, Ilkka - Abstract:
- Abstract: Objective. Transcranial magnetic stimulation (TMS) can be used to safely and noninvasively activate brain tissue. However, the characteristic parameters of the neuronal activation have been largely unclear. In this work, we propose a novel neuronal activation model and develop a method to infer its parameters from measured motor evoked potential signals. Approach. The connection between neuronal activation due to an induced electric field and a measured motor threshold is modeled. The posterior distribution of the model parameters are inferred from measurement data using Bayes' formula. The measurements are the active motor thresholds obtained with multiple stimulating coil locations, and the parameters of the model are the location, preferred direction of activation, and threshold electric field value of the activation site. The posterior distribution is sampled using a Markov chain Monte Carlo method. We quantify the plausibility of the model by calculating the marginal likelihood of the measured thresholds. The method is validated with synthetic data and applied to motor threshold measurements from the first dorsal interosseus muscle in five healthy participants. Main results. The method produces a probability distribution for the activation location, from which a minimal volume where the activation occurs with 95% probability can be derived. For eight or nine stimulating coil locations, the smallest such a volume obtained was approximately 100 mm 3 . The 95%Abstract: Objective. Transcranial magnetic stimulation (TMS) can be used to safely and noninvasively activate brain tissue. However, the characteristic parameters of the neuronal activation have been largely unclear. In this work, we propose a novel neuronal activation model and develop a method to infer its parameters from measured motor evoked potential signals. Approach. The connection between neuronal activation due to an induced electric field and a measured motor threshold is modeled. The posterior distribution of the model parameters are inferred from measurement data using Bayes' formula. The measurements are the active motor thresholds obtained with multiple stimulating coil locations, and the parameters of the model are the location, preferred direction of activation, and threshold electric field value of the activation site. The posterior distribution is sampled using a Markov chain Monte Carlo method. We quantify the plausibility of the model by calculating the marginal likelihood of the measured thresholds. The method is validated with synthetic data and applied to motor threshold measurements from the first dorsal interosseus muscle in five healthy participants. Main results. The method produces a probability distribution for the activation location, from which a minimal volume where the activation occurs with 95% probability can be derived. For eight or nine stimulating coil locations, the smallest such a volume obtained was approximately 100 mm 3 . The 95% probability volume intersected the pre-central gyral crown and the anterior wall of the central sulcus, and the preferred direction was perpendicular to the central sulcus, both findings being consistent with the literature. Furthermore, it was not possible to rule out if the activation occurred either in the white or grey matter. In one participant, two distinct activations sites were found while others exhibited a unique site. Significance. The method is both generic and robust, and it lays a foundation for a framework that enables accurate analysis and characterization of TMS activation mechanisms. … (more)
- Is Part Of:
- Journal of neural engineering. Volume 18:Number 4(2021)
- Journal:
- Journal of neural engineering
- Issue:
- Volume 18:Number 4(2021)
- Issue Display:
- Volume 18, Issue 4 (2021)
- Year:
- 2021
- Volume:
- 18
- Issue:
- 4
- Issue Sort Value:
- 2021-0018-0004-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09-03
- Subjects:
- transcranial magnetic stimulation -- motor evoked potential -- motor threshold -- cortical mapping -- Bayes formula -- probabilistic inference -- motor cortex
Neurosciences -- Periodicals
Biomedical engineering -- Periodicals
612.8 - Journal URLs:
- http://iopscience.iop.org/1741-2552/ ↗
http://ioppublishing.org/ ↗ - DOI:
- 10.1088/1741-2552/ac1f2b ↗
- Languages:
- English
- ISSNs:
- 1741-2560
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 18516.xml