Proximity to an SGC-DLPFC Individualized Functional Target and outcomes in large rTMS clinical trials for Treatment-Resistant Depression

Abstract

Background: Targeting methods for repetitive transcranial magnetic stimulation (rTMS) in patients with depression now include the use of individual functional scans to target specific functional connectivity (FC) patterns obtained from functional magnetic resonance imaging (fMRI). Potential biomarkers of rTMS response include target FC with the subgenual anterior cingulate cortex (SGC) or the causal depression circuit (CDC), each of which may be candidates for individualized functional targets (iFTs). We assessed the relationship of these two approaches to clinical outcomes in two large rTMS clinical trials.

Methods: 501 subjects with moderate to severe depression underwent 4-6 weeks of daily rTMS to the left dorsolateral prefrontal cortex (DLPFC), targeted using neuronavigation to a common group-based functional target. Resting-state scans acquired at baseline were used to retrospectively compute iFTs using either SGC-DLPFC or CDC-DLPFC FC. The Euclidean distance from the group-based target used in the trial to the centre of gravity of each iFT was computed and correlated with outcomes.

Results: Most subjects' iFTs were within 2cm of their group-based target. Proximity to either the SGC- or CDC-iFT was not associated with better outcomes. Sensitivity analyses accounting for treatment target FC, methodology, data quality, or treatment parameters did not change the results.

Conclusions: Proximity to SGC- or CDC-derived iFTs was not associated with better outcomes in patients who received neuronavigated rTMS to a group-based target. Prospective randomized clinical trials comparing neuronavigated group-based target to neuronavigated iFTs are needed.

Figure 1.

Methods for computing the location of the iFT for each subject, as demonstrated in a representative subject. A) SGC-DLPFC functional connectivity was computed by taking the SGC seedmap timeseries and correlating it with each timeseries in the DLPFC mask. Next, the 0.5% most anti-correlated voxels (below 0.5%) were identified, and the largest cluster of subthreshold voxels was selected as the optimal cluster. B) CDC-DLPFC functional connectivity was computed by taking the CDC seedmap timeseries and correlating it with each timeseries in the DLPFC mask. Next, the 0.5% most correlated voxels (above 99.5%) were identified, and the largest cluster of supra-threshold voxels was selected as the optimal cluster. In both scenarios, iFT was computed by taking the centre of gravity of the largest cluster.

Figure 2.

Topographical distribution of optimal targets and their proximity to the actual treatment target. For the SGC-iFT approach, (A) shows the distribution of individualized targets within the DLPFC (mapped over mean FC with the SGC seedmap timeseries) and (B) plots the relationship between iFT proximity and treatment response. Similarly, for the CDC-iFT approach, (C) shows the topographical distribution of individualized targets (mapped over the mean FC with the CDC timeseries) while (D) plots the relationship between iFT proximity and treatment response. For (A) and (B), grey circles represent responders, black circles represent non-responders, and the treatment target is denoted by the larger white circle. ρ (rho), p (p value)

Figure 3.

Comparison between the iFT derivation methods. Mean whole-brain FC maps were computed for both the SGC-iFT (A) and CDC-iFT approaches (B), which show a strong inverse spatial correlation (M=-0.84, SD = 0.28). (C) The distribution of distances derived from each iFT approach are shown as stacked histograms. (D) Distribution of the distance between each subject’s SGC-iFT and CDC-iFT. Vertical dashed line represents the median (equal to 7.94).