2016 — 2019 |
Saez, Ignacio |
K01Activity Code Description: For support of a scientist, committed to research, in need of both advanced research training and additional experience. |
Electrocorticography of Human Prefrontal Cortex During Value-Based Decision-Making @ University of California Berkeley
Project summary/abstract I am seeking a Mentored Research Scientist Development Award from the National Institute of Mental Health as part of my goal to become an independent investigator studying the neurobiological basis of human decision-making behavior. Candidate The current application proposes to bring together two sets of skills that I acquired in sequential and independent fashion: during my PhD I acquired a strong training in electrophysiology, and my postdoctoral career in the labs of Dr. Read Montague and Dr. Ming Hsu has focused on human cognition and decision-making behavior. The goal of this K01 is to bring these approaches together and acquire further training in advanced electrophysiological analysis methods adequate to the study of human neurosurgical recordings. Environment The career development goals in this proposal are geared towards training in the clinical and advanced methodological aspects of electrocorticographic (ECoG) intracranial recordings in human patients, and to develop the combination of these with decision- making tasks and models. My main mentor, Dr. Robert Knight, will provide guidance and training for the ECoG portion of the training; co-mentor Dr. Ming Hsu will offer additional training in behavioral decision-making tasks and computational models. The mentorship team will oversee progress in the proposed research and guide career development through formal meetings, research oversight, and practical training, including job search mentoring. A network of clinical collaborators will provide clinical training and continued access to ECoG patients, extending into the independent portion of the award: Dr. Edward Chang (UCSF), Dr. Joseph Parvizi (Stanford) and Dr. Jack Lin (UC Irvine). Finally, the proposal will further benefit from support from other UC Berkeley faculty members (Dr. Jose Carmena, Dr. Richard Ivry, Dr.Tom Griffiths and Dr.Don Moore) with interests and expertise close to various aspects of the proposal. Research The research proposal focuses on studying the electrophysiological and oscillatory bases of risky reward-oriented decision-making. Specifically, I will record from patients with extensive prefrontal cortex ECoG coverage (tens to hundreds of electrodes in lateral PFC and orbitofrontal cortex) while they carry out a decision-making task, to test the hypotheses that oscillatory mechanisms reflect local valuation and integrative processes in decision- making. Decision-making is disturbed in numerous psychiatric disorders including addiction and major depressive and psychotic disorders. As such, a deeper understanding of the cortical mechanisms supporting this behavior has the promise to shed new light in a host of disorders relevant to the mission of the NIMH. My background puts me in a unique position to develop this exciting line of research, which I plan to make the core of my independent research career.
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1 |
2021 |
Saez, Ignacio |
R01Activity Code Description: To support a discrete, specified, circumscribed project to be performed by the named investigator(s) in an area representing his or her specific interest and competencies. |
Invasive Decoding and Stimulation of Altered Reward Computations in Depression @ Icahn School of Medicine At Mount Sinai
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.
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0.912 |