Human in-vivo magnetic resonance current density imaging of the brain by 
optimizing head tissue conductivities

Eroglu, HH; Puoni, O; Göksu, C; Gregersen, F; Siebner, HR; Hanson, LG; Thielscher, A

doi:10.1016/j.brs.2021.10.012

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Human in-vivo magnetic resonance current density imaging of the brain by optimizing head tissue conductivities

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Göksu, C
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://www.sciencedirect.com/science/article/pii/S1935861X21002588
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Citation

Eroglu, H., Puoni, O., Göksu, C., Gregersen, F., Siebner, H., Hanson, L., et al. (2021). Human in-vivo magnetic resonance current density imaging of the brain by optimizing head tissue conductivities. Poster presented at 4th International Brain Stimulation Meeting, Charleston, SC, USA.

Cite as: https://hdl.handle.net/21.11116/0000-0009-84AC-0

Abstract

Introduction: Magnetic resonance current density imaging (MRCDI) aims to reconstruct the current flow of transcranial electrical stimulation (TES) in the brain from MR-measurements of the current-induced magnetic field Bz.

Aim: We test the performance of a standard reconstruction algorithm (“projected current density algorithm”, PCD, Jeong et al. 2014) for human brain data. We compare it with current flow simulations using personalized head models.

Methods:1. We generated ground-truth data for the TES current flow and Bz-field using a detailed head model and SimNIBS (www.simnibs.org). We applied the PCD algorithm to the Bz -field and quantified the reconstruction performance by comparison with the ground-truth current flow. We additionally compared the PCD results with simulations using a simple head model (“3c” with scalp, bone and a homogeneous intracranial compartment).

2. We reconstructed the current flow from in-vivo MRCDI data (Göksu et al, 2018) with the PCD algorithm. We also used head models of different complexities (“3c” and “4c”: scalp, skull, CSF & brain) and optimized their conductivities to minimize the root-mean-square difference between the measured and simulated Bz.

Results:

1. For simulated Bz data, the PCD algorithm only coarsely reconstructed the true current flow. Even the simple head model performed better.

2. For measured Bz data, current flows obtained with personalized head models and fitted conductivities explained the measurements better than the current flow reconstructed with the PCD algorithm. This was already the case for the simple head model (3c). The more detailed model (4c) resulted in further statistically significant improvements. However, for all models, the unexplained variance stayed above the noise floor, indicating remaining differences to unknown true current flow.

Conclusions: The PCD algorithm has low accuracy for MRCDI data of the brain. However, MRCDI is useful for evaluations and improvements of current flow simulations with anatomically detailed personalized head models.