On the right track: Using neuroimaging to improve deep brain stimulation outcomes in psychiatry

The subject of this dissertation is neuroimaging in deep brain stimulation (DBS) treatment of two psychiatric disorders, refractory obsessive-compulsive disorder (OCD) and treatment-resistant depression (TRD). We used advanced imaging techniques to evaluate whether they could be beneficial to treatment outcomes. First, we used tractography to assess how stimulation of certain white matter tracts was related to treatment response. Second, we evaluated advanced diffusion protocols to determine the best way to apply tractography-assisted targeting with the aim of improving stimulation specificity. Finally, we used machine learning on structural MRI data to evaluate whether such a model could aid patient selection. This dissertation thus presents applications of advanced techniques to better understand and optimize DBS in psychiatry.
In both OCD and TRD, which share a common DBS target, distance from the lead to white matter tracts was related to outcome. For OCD, our findings suggest that DBS for OCD may benefit from targeting closer to the supero-lateral medial forebrain bundle (slMFB), whereas the findings for TRD included the anterior thalamic radiation in addition to the slMFB. This highlights the potential of tractographic targeting, although prospective investigation remains necessary to validate its clinical use: preliminary analysis indicates a potential reduction of side-effects.
We found that increasing spatial resolution was more beneficial than increasing angular resolution for discernibility of tracts in the DBS target area, potentially enabling more tract-specific stimulation. Finally, none of the individual-level regression/classification analyses exceeded chance-level performance, suggesting that structural MRI data alone is not suitable for patient selection models.