scholarly journals Four electric field modeling methods of Dosing Prefrontal Transcranial Magnetic Stimulation (TMS): Introducing APEX MT dosimetry

2021 ◽  
Author(s):  
Kevin A. Caulfield ◽  
Xingbao Li ◽  
Mark S. George
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Field ◽  
Magnetic Stimulation ◽  
Modeling Methods ◽  
Field Modeling

scholarly journals Electric Field Modeling for Transcranial Magnetic Stimulation and Electroconvulsive Therapy

2019 ◽  
pp. 75-84 ◽  
Author(s):  
Zhi-De Deng ◽  
Conor Liston ◽  
Faith M. Gunning ◽  
Marc J. Dubin ◽  
Egill Axfjörð Fridgeirsson ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Field ◽  
Electroconvulsive Therapy ◽  
Magnetic Stimulation ◽  
Field Modeling

scholarly journals Proof of concept study to develop a novel connectivity-based electric-field modelling approach for individualized targeting of transcranial magnetic stimulation treatment

2021 ◽  
Author(s):  
Nicholas L. Balderston ◽  
Joanne C. Beer ◽  
Darsol Seok ◽  
Walid Makhoul ◽  
Zhi-De Deng ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Field ◽  
Magnetic Stimulation ◽  
Depression Symptoms ◽  
Single Subject ◽  
Proof Of Concept ◽  
Concept Study ◽  
Field Modelling ◽  
Left Dlpfc ◽  
Modelling Approach

AbstractResting state functional connectivity (rsFC) offers promise for individualizing stimulation targets for transcranial magnetic stimulation (TMS) treatments. However, current targeting approaches do not account for non-focal TMS effects or large-scale connectivity patterns. To overcome these limitations, we propose a novel targeting optimization approach that combines whole-brain rsFC and electric-field (e-field) modelling to identify single-subject, symptom-specific TMS targets. In this proof of concept study, we recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms (MADRS/HAMD) and recorded rsFC. We used a PCA regression to predict symptoms from rsFC and estimate the parameter vector, for input into our e-field augmented model. We modeled 17 left dlPFC and 7 M1 sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD scores for each site/orientation using the e-field augmented model, which comprises a linear combination of the following elementwise products (1) the estimated connectivity/symptom coefficients, (2) a vectorized e-field model for site/orientation, (3) rsFC matrix, scaled by a proportionality constant. In AM patients, our connectivity-based model predicted a significant decrease depression for sites near BA9, but not M1 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant predicted change. These results corroborate previous work suggesting the efficacy of left dlPFC stimulation for depression treatment, and predict better outcomes with individualized targeting. They also suggest that our novel connectivity-based e-field modelling approach may effectively identify potential TMS treatment responders and individualize TMS targeting to maximize the therapeutic impact.

scholarly journals A generalized workflow for conducting electric field–optimized, fMRI-guided, transcranial magnetic stimulation

2020 ◽  
Vol 15 (11) ◽  
pp. 3595-3614 ◽  
Author(s):  
Nicholas L. Balderston ◽  
Camille Roberts ◽  
Emily M. Beydler ◽  
Zhi-De Deng ◽  
Thomas Radman ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Field ◽  
Magnetic Stimulation

Electric field estimation in deep transcranial magnetic stimulation

2015 ◽  
Vol 8 (2) ◽  
pp. 327 ◽  
Author(s):  
V. Guadagnin ◽  
M. Parazzini ◽  
S. Fiocchi ◽  
I. Liorni ◽  
Y. Roth ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Electric Field ◽  
Magnetic Stimulation ◽  
Field Estimation
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