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

Author(s):  
Nicholas L. Balderston ◽  
Joanne C. Beer ◽  
Darsol Seok ◽  
Walid Makhoul ◽  
Zhi-De Deng ◽  
...  

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.

2020 ◽  
Author(s):  
Nicholas L Balderston ◽  
Darsol Seok ◽  
Walid Makhoul ◽  
Zhi-De Deng ◽  
Tommaso Girelli ◽  
...  

AbstractBackgroundResting 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 connectivity-based electric-field (e-field) modelling approach to identify optimal single-subject TMS targets using whole-brain rsFC.MethodsWe recruited 91 anxious misery (AM) patients and 25 controls. We measured depression symptoms via standard clinical questionnaires (MADRS/HAMD) and via a data-driven symptom clustering approach (Loss cluster) which used multiple items across 32 clinical measures. We also recorded rsFC in these individuals. We then used a Principal components analysis (PCA) regression to predict symptoms from rsFC and generate a slope vector (M) and intercept (b) for our e-field augmented model. We modeled 17 left dlPFC sites using 24 equally spaced coil orientations. We computed single-subject predicted ΔMADRS/HAMD and Δ Loss scores for each site/orientation combination according to the following equations: ΔMADRS/HAMD = MEX + b, and Δ Loss = MEX + b, where E represents a vectorized summary of the e-field model an X represents the single-subject rsFC matrix.ResultsIn AM patients, our model predicted a significant decrease in depression symptoms (measured by both MADRS/HAMD and Loss cluster) near BA46 for coil orientations perpendicular to the cortical gyrus. In control subjects, no site/orientation combination showed a significant relationship with MADRS/HAMD or Loss symptoms.DiscussionThese results replicate previous work demonstrating the efficacy of left dlPFC stimulation for depression treatment, and predict maximal efficacy near BA46. Importantly, our novel connectivity-based e-field modelling approach predicted a significant decrease in depression symptoms with more focal effects seen for the Loss cluster, and may be an effective way to identify potential TMS treatment responders, as well as to individualize TMS targeting to maximize the therapeutic impact.


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