Introduction Obsessive‐compulsive disorder (OCD) is among the most disabling chronic psychiatric disorders and has a significant negative impact on multiple domains of quality of life. Deep brain stimulation (DBS) is a treatment option for severe therapy‐resistant OCD. Objective To provide a detailed clinical description and treatment outcome analysis in a cohort of eight refractory OCD patients receiving ventral capsule/ventral striatum (VC/VS) stimulation with the intention to validate discriminating fiber bundles previously associated with clinical response. Materials and Methods The primary outcome measure (the Yale‐Brown Obsessive Compulsive Scale [Y‐BOCS]) and secondary outcomes depressive symptoms, anxiety, and quality of life were retrospectively analyzed. DBS leads were warped into standard stereotactic space. A normative connectome was used to identify the neural network associated with clinical outcome. Results With a median stimulation duration of 26 months, patients exhibited a mean Y‐BOCS reduction of 10.5 resulting in a response rate of 63%. Modulation of a fiber bundle traversing the anterior limb of the internal capsule (ALIC) was associated with Y‐BOCS reduction. This fiber bundle connected the frontal regions to the subthalamic nucleus (STN) and was functionally identified as the hyperdirect pathway of the basal ganglia circuitry. Conclusion Our findings show that in VC/VS stimulation, the neural network associated with clinical outcome shows overlap with that of previously described for other targets namely the anterior limb of the internal capsula, the nucleus accumbens, or the STN, which supports the evolvement from the concept of an optimal gray matter target to conceiving the target as part of a symptom modulating network.

A BS TRACT: Background: Anxiety disorders are among the most prevalent and disabling neuropsychiatric syndromes in patients with Parkinsonʼs disease (PD), but no randomized controlled treatment trials of anxiety have been published to date. Objective: The aim of this study was to assess the effectiveness of cognitive behavioral therapy (CBT) in the treatment of anxiety in patients with PD. Methods: Forty-eight patients with PD with anxiety were randomized 1:1 between CBT and clinical monitoring only (CMO). The CBT program was developed to specifically address anxiety symptoms in PD and consisted of 10 weekly sessions. Assessments were conducted by blinded assessors at baseline, at the end of the intervention, after 3 months, and after 6 months (CBT group only). Main outcome measures were the Hamilton Anxiety Rating Scale (HARS) and the Parkinson Anxiety Scale (PAS). Results: Both the CBT and CMO groups showed clinically relevant improvement. Although there was no between-group difference in outcome on the Hamilton Anxiety Rating Scale (6.7-point reduction in the CBT group versus 3.9-point reduction in the CMO group; P = 0.15), there was both a statistically significant and a clinically relevant between-group difference on the total PAS in favor of CBT (9.9-point reduction in the CBT group versus 5.2-point reduction in the CMO group; P = 0.012), which was due to improvement on the PAS subscales for episodic (situational) anxiety and avoidance behavior. This greater improvement was maintained at 3-and 6-month follow-ups. Conclusion: CBT is an effective treatment for anxiety in patients with PD and reduces situational and social anxiety, as well as avoidance behavior.

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Introduction Despite careful patient selection for subthalamic nucleus deep brain stimulation (STN DBS), some Parkinson’s disease patients show limited improvement of motor disability. Innovative predictive analysing methods hold potential to develop a tool for clinicians that reliably predicts individual postoperative motor response, by only regarding clinical preoperative variables. The main aim of preoperative prediction would be to improve preoperative patient counselling, expectation management, and postoperative patient satisfaction. Methods We developed a machine learning logistic regression prediction model which generates probabilities for experiencing weak motor response one year after surgery. The model analyses preoperative variables and is trained on 89 patients using a five-fold cross-validation. Imaging and neurophysiology data are left out intentionally to ensure usability in the preoperative clinical practice. Weak responders (n = 30) were defined as patients who fail to show clinically relevant improvement on Unified Parkinson Disease Rating Scale II, III or IV. Results The model predicts weak responders with an average area under the curve of the receiver operating characteristic of 0.79 (standard deviation: 0.08), a true positive rate of 0.80 and a false positive rate of 0.24, and a diagnostic accuracy of 78%. The reported influences of individual preoperative variables are useful for clinical interpretation of the model, but cannot been interpreted separately regardless of the other variables in the model. Conclusion The model’s diagnostic accuracy confirms the utility of machine learning based motor response prediction based on clinical preoperative variables. After reproduction and validation in a larger and prospective cohort, this prediction model holds potential to support clinicians during preoperative patient counseling.