Invasive decoding and stimulation of altered reward computations in depression

点击翻译按钮获取中文摘要

Abstract Depression is a highly prevalent mental health disorder that affects millions of people in the US and causes significant impacts on well-being, rates of disability and health care costs. Despite these substantial impacts and costs, the effectiveness of current therapeutical options for the treatment of depression is limited. In recent years there have been efforts to develop deep-brain stimulation (DBS) strategies guided by results from non-invasive imaging studies. Unfortunately, these have failed to show significant efficacy, likely because of the vast heterogeneity in disease presentation and the lack of sufficient data to understand the neurobiological causes of the disease. Current approaches use unspecific biological (pharmacology) or anatomical (DBS) targets, leading to partial effectiveness and side effects. Patient-specific depictions of the basis of depression would allow tailored treatment designs, but data of sufficient quality is mostly unavailable with standard approaches to the study of neural function. Recent approaches leverage multi-areal invasive electrophysiological recordings in neurosurgical epilepsy patients, which often suffer from co-morbid depression, to collect of high-quality (multi-areal, high signal-to-noise, high temporal resolution) neurophysiological data, and have the potential to allow patient-specific models of disease and targeted neurostimulation. In addition, machine learning methods allow mapping this high-dimensional neural data onto patient’s emotional states centrally affected in depression, and highlight the involvement of single-site and cross-areal activity in limbic regions, including the hippocampus, amygdala and orbitofrontal cortex. However, decoding methods present several challenges of their own. First, they select features associated with self-reported mood - a complex, abstract concept that lacks an objective, quantitative foundation and may be reported differently across patients, making generalization challenging. Second, data-driven methods are not grounded on current models of brain function, and thus lack mechanistic explanations of disease underpinnings. Third, mood is reported in the absence of overt behavior, making it difficult to frame the deficits in the behavioral functions subserved by affected brain areas. Finally, current approaches lack a connection between decoded neural features and stimulation strategies. Here, we propose to address these challenges by combining distributed iEEG recordings, reinforcement learning models of decision-making and machine-learning approaches to study reward processing in relevant brain areas from epilepsy patients with and without comorbid depression. We will examine local activations as well as circuit dynamics (functional connectivity) in orbitofrontal cortex, amygdala and hippocampus during decision- making behavior. We will seek to ground neurophysiological data in reinforcement learning models of decision-making to provide quantitative, reproducible behavioral metrics that impact mood, and to build on long-standing observations regarding damaged reward processing in depression. Finally, we will develop patient-tailored stimulation paradigms for depression constrained by our observations on patient-specific disease presentation. We expect that the combination of invasive recordings with reward modeling will open the door to a theoretically-grounded computational characterization of neurobehavioral deficits in depression, and allow development of novel treatment strategies.