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The neural underpinnings of working memory (WM) have been of continuous scientific interest for decades. As the understanding of WM progresses and new theories, such as the distributed view of WM, develop, the need to advance the methods used to study WM also arises. This perspective discusses how building from the state-of-the-art in the field of transcranial magnetic stimulation (TMS), and utilising cortico-cortical TMS, may pave the way for testing some of the predictions proposed by the distributed WM view. Further, after briefly discussing current barriers that need to be overcome for implementing cortico-cortical TMS for WM research, examples of how cortico-cortical TMS may be employed in the context of WM research are provided, guided by the ongoing debate on the sensory recruitment framework.
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Uncertainty quantification of TMS simulations considering MRI segmentation errors
Abstract Objective. Transcranial magnetic stimulation (TMS) is a non-invasive brain stimulation method that is used to study brain function and conduct neuropsychiatric therapy. Computational methods that are commonly used for electric field (E-field) dosimetry of TMS are limited in accuracy and precision because of possible geometric errors introduced in the generation of head models by segmenting medical images into tissue types. This paper studies E-field prediction fidelity as a function of segmentation accuracy. Approach. The errors in the segmentation of medical images into tissue types are modeled as geometric uncertainty in the shape of the boundary between tissue types. For each tissue boundary realization, we then use an in-house boundary element method to perform a forward propagation analysis and quantify the impact of tissue boundary uncertainties on the induced cortical E-field. Main results. Our results indicate that predictions of E-field induced in the brain are negligibly sensitive to segmentation errors in scalp, skull and white matter (WM), compartments. In contrast, E-field predictions are highly sensitive to possible cerebrospinal fluid (CSF) segmentation errors. Specifically, the segmentation errors on the CSF and gray matter interface lead to higher E-field uncertainties in the gyral crowns, and the segmentation errors on CSF and WM interface lead to higher uncertainties in the sulci. Furthermore, the uncertainty of the average cortical E-fields over a region exhibits lower uncertainty relative to point-wise estimates. Significance. The accuracy of current cortical E-field simulations is limited by the accuracy of CSF segmentation accuracy. Other quantities of interest like the average of the E-field over a cortical region could provide a dose quantity that is robust to possible segmentation errors.
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- PAR ID:
- 10350677
- Date Published:
- Journal Name:
- Journal of Neural Engineering
- Volume:
- 19
- Issue:
- 2
- ISSN:
- 1741-2560
- Page Range / eLocation ID:
- 026022
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1088/1741-2552/ac5586
