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

Abstract

Resting 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.

Stimulation Effects Mapping for Optimizing Coil Placement for Transcranial Magnetic Stimulation

“…We have applied the proposed SEM framework to construct an optimal coil placement and demonstrated its repeatability across three independent datasets, employing approximately 5 million simulations. This is consistent with previous studies that have utilized an optimization strategy based on simulated E-fields, yielding consistent results [12, 16, 20] . Constructing an optimal coil placement for brain targets is desirable due to the computational and labor-intensive nature of the simulation process.…”

Targeting the pathological network: Feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders

“…An interesting line of research focuses on identifying networks associated with treatment-induced side effects ( Horn and Fox, 2020 ), and the results might be integrated into the proposed model as a “to-avoid” network in planning treatment. Apart from searching nodes of the pathological network, Balderston used a data-driven approach to link rsFC and symptoms of depression ( Balderston et al, 2021 ), demonstrating the feasibility of edge-based targeting in TMS treatment. Such an edge-based pathological network will be considered in our model in the future.…”

“…In each cohort, we defined a cranial search space covering traditional TMS sites for the two diseases. For MDD the search space had 125 positions × 12 orientations and covered a broad area of left DLPFC ( Lefaucheur et al, 2014 ; Xiao et al, 2018 ; Cash et al, 2020 ; Balderston et al, 2021 ). For schizophrenia with AVH, the search space had 122 positions × 12 orientations and covered a broad area including left superior temporal gyrus (STG) and left temporoparietal junction (TPJ), which have been adopted in TMS treatments for schizophrenia with AVH ( Hoffman et al, 2003 , 2013 ; Klirova et al, 2013 ; Lefaucheur et al, 2014 ; Paillère-Martinot et al, 2017 ; Xiao et al, 2018 ).…”

Targeting the pathological network: feasibility of network-based optimization of transcranial magnetic stimulation coil placement for treatment of psychiatric disorders

“…In each cohort, we defined a cranial search space covering traditional TMS sites for the two diseases. For MDD the search space had 125 positions × 12 orientations and covered a broad area of left DLPFC (Lefaucheur et al, 2014; Xiao et al, 2018; Cash et al, 2020; Balderston et al, 2021). For AVH, the search space had 122 positions × 12 orientations and covered a broad area including left STG and left TPJ, which have been adopted in TMS treatments for AVH (Hoffman et al, 2003, 2013; Klirova et al, 2013; Lefaucheur et al, 2014; Paillère-Martinot et al, 2017; Xiao et al, 2018).…”