Anisotropic conductivity modeling for tDCS in Parkinson’s disease using multidimensional diffusion MRI
5 affiliations
- 1Department of Biomedical Engineering, The City College of New York, City University of New York, New York, NY, United States
- 2Department of Biomedical Engineering and Health Systems, KTH Royal Institute of Technology, Stockholm, Sweden
- 3Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden
- 4Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden
- 5Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
Abstract
Introduction: Predictions of the electric field (E-field) induced by transcranial direct current stimulation (tDCS) depend on how brain conductivity is modeled. White matter anisotropy is conventionally estimated from single-shell diffusion tensor imaging (DTI). Multidimensional diffusion MRI (MD-dMRI), specifically q-space trajectory imaging (QTI), instead gives a mean tensor expected to carry less kurtosis bias. Our primary question was whether replacing the conventional single-shell tensor with this mean tensor would change the predicted E-field.
Methods: We built, to our knowledge, the first MD-dMRI tDCS conductivity model and compared it against DTI and isotropic models in 29 participants (12 with Parkinson’s disease and 17 controls) across four montages, with the same mesh, electrodes, and solver.
Results: The two anisotropic models differed mainly in tensor orientation (18–25 degrees across the white matter), with E-field magnitudes differing by 6.6% or less. E-fields were not significantly different between patients and controls in any region or montage (which was an exploratory, underpowered comparison). Whole-brain E-field correlated with MR elastography stiffness (partial r = 0.58) but attenuated to non-significance once cerebrospinal fluid (CSF) morphology was accounted for (r = 0.06 to 0.09).
Discussion: With no ground-truth E-field or conductivity available, the study establishes the feasibility of the MD-dMRI model and characterizes E-field sensitivity rather than improved dosimetry accuracy. The choice of diffusion tensor is second order for the predicted E-field, which is primarily influenced by individual anatomy. For Parkinson’s disease, modeling efforts should focus on CSF- and atrophy-aware head models and dose normalization, rather than a more complex diffusion tensor.
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BibTeX
@article{jurado2026anisotropic,
title = {{Anisotropic conductivity modeling for tDCS in Parkinson’s disease using multidimensional diffusion MRI}},
author = {Santiago Osorio Jurado and Mikael Skorpil and Per Svenningsson and Rodrigo Moreno and Christoffer Olsson},
journal = {Frontiers in Human Neuroscience},
year = {2026},
month = oct,
doi = {10.3389/fnhum.2026.1972676},
url = {https://doi.org/10.3389/fnhum.2026.1972676},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Anisotropic conductivity modeling for tDCS in Parkinson’s disease using multidimensional diffusion MRI
AU - Santiago Osorio Jurado
AU - Mikael Skorpil
AU - Per Svenningsson
AU - Rodrigo Moreno
AU - Christoffer Olsson
PY - 2026
DA - 2026/10/21
JO - Frontiers in Human Neuroscience
DO - 10.3389/fnhum.2026.1972676
UR - https://doi.org/10.3389/fnhum.2026.1972676
AB - Introduction: Predictions of the electric field (E-field) induced by transcranial direct current stimulation (tDCS) depend on how brain conductivity is modeled. White matter anisotropy is conventionally estimated from single-shell diffusion tensor imaging (DTI). Multidimensional diffusion MRI (MD-dMRI), specifically q-space trajectory imaging (QTI), instead gives a mean tensor expected to carry less kurtosis bias. Our primary question was whether replacing the conventional single-shell tensor with this mean tensor would change the predicted E-field. Methods: We built, to our knowledge, the first MD-dMRI tDCS conductivity model and compared it against DTI and isotropic models in 29 participants (12 with Parkinson’s disease and 17 controls) across four montages, with the same mesh, electrodes, and solver. Results: The two anisotropic models differed mainly in tensor orientation (18–25 degrees across the white matter), with E-field magnitudes differing by 6.6% or less. E-fields were not significantly different between patients and controls in any region or montage (which was an exploratory, underpowered comparison). Whole-brain E-field correlated with MR elastography stiffness (partial r = 0.58) but attenuated to non-significance once cerebrospinal fluid (CSF) morphology was accounted for (r = 0.06 to 0.09). Discussion: With no ground-truth E-field or conductivity available, the study establishes the feasibility of the MD-dMRI model and characterizes E-field sensitivity rather than improved dosimetry accuracy. The choice of diffusion tensor is second order for the predicted E-field, which is primarily influenced by individual anatomy. For Parkinson’s disease, modeling efforts should focus on CSF- and atrophy-aware head models and dose normalization, rather than a more complex diffusion tensor.
SN - 1662-5161
ER -