The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation
Article
Article Title | The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation |
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ERA Journal ID | 16437 |
Article Category | Article |
Authors | Shahid, Syed Salman (Author), Bikson, Marom (Author), Salman, Humaira (Author), Wen, Peng (Author) and Ahfock, Tony (Author) |
Journal Title | Journal of Neural Engineering |
Journal Citation | 11 (3), pp. 1-19 |
Number of Pages | 19 |
Year | 2014 |
Place of Publication | Bristol, United Kingdom |
ISSN | 1741-2552 |
1741-2560 | |
Digital Object Identifier (DOI) | https://doi.org/10.1088/1741-2560/11/3/036002 |
Web Address (URL) | http://iopscience.iop.org/1741-2552/11/3/036002/ |
Abstract | Objectives: Computational methods are increasingly used to optimize transcranial direct current stimulation (tDCS) dose strategies and yet complexities of existing approaches limit their clinical access. Since predictive modelling indicates the relevance of subject/pathology based data and hence the need for subject specific modelling, the incremental clinical value of increasingly complex modelling methods must be balanced against the computational and clinical time and costs. For example, the incorporation of multiple tissue layers and measured diffusion tensor (DTI) based conductivity estimates increase model precision but at the cost of clinical and computational resources. Costs related to such complexities aggregate when considering individual optimization and the myriad of potential montages. Here, rather than considering if additional details change current-flow prediction, we consider when added complexities influence clinical decisions. Approach: Towards developing quantitative and qualitative metrics of value/cost associated with computational model complexity, we considered field distributions generated by two 4 x 1 high-definition montages (m1 = 4 x 1 HD montage with anode at C3 and m2 = 4 x 1 HD montage with anode at C1) and a single conventional (m3 = C3-Fp2) tDCS electrode montage. We evaluated statistical methods, including residual error (RE) and relative difference measure (RDM), to consider the clinical impact and utility of increased complexities, namely the influence of skull, muscle and brain anisotropic conductivities in a volume conductor model. Significance: Results illustrate the need to rationally balance the role of model complexity, such as anisotropy in detailed current flow analysis versus value in clinical dose design. However, when extending our analysis to include axonal polarization, the results provide presumably clinically meaningful information. Hence the importance of model complexity may be more relevant with cellular level predictions of neuromodulation. |
Keywords | brain stimulation; neuronavigation; tDCS |
ANZSRC Field of Research 2020 | 400308. Medical devices |
320999. Neurosciences not elsewhere classified | |
401706. Numerical modelling and mechanical characterisation | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | School of Mechanical and Electrical Engineering |
City University of New York, United States | |
National University of Sciences and Technology, Pakistan | |
Faculty of Health, Engineering and Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q2728/the-value-and-cost-of-complexity-in-predictive-modelling-role-of-tissue-anisotropic-conductivity-and-fibre-tracts-in-neuromodulation
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