Co-stimulating the left vmPFC compensates for apathy after levodopa withdrawal in Parkinson's patients with STN DBS.

Introduction: Subthalamic nucleus deep brain stimulation (STN DBS) improves motor symptoms of Parkinson's disease (PD), but its effect on motivation is controversial. Apathy, the lack of motivation, commonly occurs in PD and is often exacerbated after surgery and its concomitant levodopa reduction. Apathy and reward processing are associated with the ventromedial prefrontal cortex (vmPFC), which standard targeting strategies avoid by targeting the dorsolateral STN.

Neural Interoceptive Processing is Modulated by Deep Brain Stimulation to Subcallosal Cingulate Cortex for Treatment Resistant Depression.

Background: Symptoms of depression are associated with impaired interoceptive processing of bodily sensation. The antidepressant effects of subcallosal cingulate deep brain stimulation (SCC DBS) include acute change in bodily sensation, and the SCC target is connected to cortical regions critically involved in interoception. This study tests whether cortical interoceptive processing is modulated by SCC DBS for treatment resistant depression (TRD).

Assessing in-session rumination during CBT for depression: Replication and further evaluation of an observational measure.

This study sought to further evaluate an observational measure of rumination that occurs during psychotherapy (i.e., in-session rumination).

Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments.

Refining Brain Stimulation Therapies: An Active Learning Approach to Personalization.

Brain stimulation holds promise for treating brain disorders, but personalizing therapy remains challenging. Effective treatment requires establishing a functional link between stimulation parameters and brain response, yet traditional methods like random sampling (RS) are inefficient and costly. To overcome this, we developed an active learning (AL) framework that identifies optimal relationships between stimulation parameters and brain response with fewer experiments.