Modelling the mind Neuromodulation in psychiatric disorders

This thesis aimed to improve our understanding of mechanisms for treatment-resistant psychiatric disorders, which are a major challenge in developed countries. Advanced modeling techniques were used to study brain activity in patients with treatment-resistant Major Depressive Disorder (MDD) and Obsessive-Compulsive Disorder (OCD).
We investigated the effects of deep brain stimulation (DBS) in OCD patients, focusing on mood and anxiety. Results indicated that DBS reduced connectivity between specific brain regions and altered the influence of others, shedding light on the neural processes behind mood and anxiety regulation.
In the case of DBS for treatment-resistant MDD, findings revealed altered connectivity in key brain regions involved in emotion and reward processing, indicating that DBS induces complex modifications in the limbic network.
We explored the relationship between modelled electric field strength and electroconvulsive therapy (ECT) efficacy. Stronger electric fields in certain brain regions during ECT were associated with less favorable treatment outcomes. This suggests the potential for personalized ECT protocols based on individualized electric field distributions.
Additionally, we investigated intracranial signals recorded from DBS electrodes to predict symptoms in OCD patients. Machine learning models achieved moderate success, but the effectiveness varied between patients and recording sites. This implies that closed-loop stimulation may be a viable approach for OCD treatment, though intracranial biomarkers appear to be specific to individual patients rather than the disorder as a whole.
In summary, this thesis contributes valuable insights into neurostimulation for treatment-resistant psychiatric disorders, offering potential avenues for more effective and personalized treatment strategies to improve patient outcomes.