Sameer Anil Sheth - US grants

[unreadable] DESCRIPTION (provided by applicant): [unreadable] The objective of this proposal is to investigate the coupling between brain activation and cerebral perfusion. The details behind this neurovascular coupling are yet unclear, but form the basis for several widely used imaging modalities such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and optical imaging of intrinsic signals (OIS). To help clarify the properties of this relationship, this proposal outlines two specific goals: (l) to characterize the spatial and temporal evolution of hemoglobin oxygenation changes within and between vascular and parenchymal compartments; and (2) to determine how well these perfusion signals are coupled to underlying neuronal activity. [unreadable] [unreadable] OIS is well-suited for the proposed studies because if offers high spatial and temporal resolution, as well as the opportunity for simultaneous electrophysiological recording. A technique for extracting hemoglobin (Hb) concentration changes from the OIS images in two spatial dimensions is presented and, in combination with field potential recording, used to study spatial and temporal aspects of neurovascular coupling in rodent somatosensory cortex. The results of these experiments will influence the design and interpretation of perfusion-based brain imaging techniques, especially fMRI. Identifying aspects of the fMRI signal that more closely reflect underlying neuronal activity will improve the technique's ability to localize brain activity. This development would significantly increase its utility for pre-operative surgical planning, for example. Determining whether coupling breaks down in certain instances will also identify possible limitations to these techniques. [unreadable] [unreadable] [unreadable]

? DESCRIPTION (provided by applicant): The human brain has a remarkable ability to flexibly allocate cognitive resources to meet task demands. Cognitive control machinery must maintain clear representation of current context (task demands), outcomes of prior choices, and control options for resolving conflict and adapting upcoming behavioral choices. Deficiencies in any of these operations can contribute to neuropsychiatric dysfunction. Research to date identifies a network of areas, including the dACC, as essential to cognitive control. However, key aspects of cognitive control, including how network components interact, and how they represent and manage conflict, remain controversial. On a technical level, the wide gap between methods used in human and monkey studies raises a host of uncertainties, including the possibility of substantial interspecies differences in cognitive control machinery. We identify and target two major impediments to progress: 1) lack of a robust, yet specific conceptual framework that can integrate disparate empirical findings; and 2) lack of high quality human data spanning the scale from single neuron spikes to population activity. We begin with an integrative conceptual model. The expected value of control (EVC) theory has recently been advanced to explain the role of the dACC in cognitive control. According to this model, the dACC weighs the expected benefit of successfully completing a control-demanding task against the cost required to do so. Based on this calculation, it generates a signal that specifies which (if any) task to perform, and how much control to allocate to the task. Specifically, the EVC model predicts that the dACC should perform the key functions mentioned at the outset: current context monitoring, outcome monitoring, and control signal specification. We test these functions with methods that effectively bridge the gap between single neuron activity in monkeys and noninvasive population measures in humans. We propose a series of experiments using functional imaging and intracranial electrophysiology methods in humans. Subjects will be patients with medically intractable epilepsy scheduled for intracranial electrode implantation for seizure monitoring. Prior to electrode implantation, they will undergo high-resolution fMRI. While implanted, they will perform behavioral tasks designed to test hypotheses regarding the role of the dACC as predicted by EVC theory. We will collect simultaneous single-unit and LFP recordings from dACC and LFPs from lateral PFC. Our broad goal is to validate a comprehensive theory of cognitive control using multi-modality human recordings. We do so by testing 3 Specific Aims aligned with the 3 proposed dACC functions mentioned above. In Specific Aim 1, we clarify the current context monitoring function of the dACC. We test hypotheses that the dACC encodes pure conflict signals, and that these neurons phase-lock to theta rhythms to coordinate dACC communication with other control-related regions. In Specific Aim 2, we define the outcome monitoring function of the dACC. We test hypotheses that human dACC neurons encode reward prediction errors (RPEs) and that these neurons entrain to theta oscillations to generate error- and feedback-related theta signals that can be observed on mid-frontal scalp EEG. Using information transfer analyses, we further hypothesize that RPE information travels from dACC to lateral PFC using theta-range oscillations. In Specific Aim 3, we establish evidence for a control specification function of the dACC. We test hypotheses investigating control signal targeting and intensity specification. Many neuropsychiatric disorders are attributable to the inability to ascribe appropriate value to contextual stimuli, attend to relevant features while ignoring irrelevant ones, and sequentially increase or decrease reward contingencies of actions based on feedback. Examples of disorders of this process include mood/anxiety disorders (OCD, depression, affective aspects of chronic pain), addiction, attention deficit disorders, and psychoses. A clearer appreciation of the neurophysiology of human cognitive control will be essential for the successful understanding and treatment of these behavioral disorders.

ABSTRACT The public health burden of Treatment Resistant Depression (TRD) has prompted clinical trials of deep brain stimulation (DBS) that have, unfortunately, produced inconsistent outcomes. Potential gaps and opportunities include a need: (1) to better understand the neurocircuitry of the disease; (2) for precision DBS devices that can target brain networks in a clinically and physiologically validated manner; and (3) for greater insight into stimulation dose-response relationships. These needs are based on our overarching hypothesis that network- guided neuromodulation is critical for the efficacy of DBS in TRD. This project aims to address the unmet need of TRD patients by identifying brain networks critical for treating depression and to use next generation precision DBS with steering capability to engage these targeted networks and develop a new therapy for TRD. We use the Boston Scientific (BS) Vercise DBS system, which offers a segmented steerable lead with multiple independent current sources that allows true directional steering. Moreover, this system integrates stimulation field modeling (SFM) with MR tractography to predict network engagement. We use an innovative approach of targeting both subgenual cingulate (SGC) and ventral capsule/ventral striatum (VC/VS), which we term corticomesolimbic DBS. These targets are hubs in distinct yet partially overlapping depression networks and emerging basic science literature implicates them in bidirectional modulation of depression circuits. We also apply a paradigm-shifting approach using intracranial stereo-EEG (sEEG) subacutely after DBS implant to evaluate the clinical reliability of steering, SFMs, and tractography and to define and then target the networks mediating symptoms of depression. In Aim 1, in the Epilepsy Monitoring Unit (EMU), we investigate the capability of Vercise to selectively engage distinct brain networks and compare the spatial distribution of evoked network activity and modulation with that predicted by SFM and tractography. In Aim 2, we conduct further studies in the EMU to delineate depression-relevant networks and show behavioral changes with network-targeted stimulation. We use a variety of tasks to probe different symptom domains and novel assessment tools (Computerized Adaptive Testing and Automated Facial Affect Recognition) to enhance classification and model algorithms to optimize stimulation patterns. In Aim 3, we bring the results from Aims 1 and 2 together, to test the therapeutic potential of corticomesolimbic DBS in 12 subjects with TRD, with a focus on safety, feasibility, and preliminary efficacy in a 8-month open label trial with a subsequent randomized, blinded withdrawal of stimulation to assess efficacy. The impact of this proposal includes physiological validation of current ?steering? DBS technology to target specific networks, insights into effects of stimulation parameters on network physiology, an improved understanding of the pathophysiology of depression, and, perhaps most importantly, a novel approach for treating TRD. This research will also pioneer a novel and high-yield test bed for DBS therapy development consistent with BRAIN priorities.

ABSTRACT The public health burden of Treatment Resistant Depression (TRD) has prompted clinical trials of deep brain stimulation (DBS) that have, unfortunately, produced inconsistent outcomes. Potential gaps and opportunities include a need: (1) to better understand the neurocircuitry of the disease; (2) for precision DBS devices that can target brain networks in a clinically and physiologically validated manner; and (3) for greater insight into stimulation dose-response relationships. These needs are based on our overarching hypothesis that network- guided neuromodulation is critical for the efficacy of DBS in TRD. This project aims to address the unmet need of TRD patients by identifying brain networks critical for treating depression and to use next generation precision DBS with steering capability to engage these targeted networks and develop a new therapy for TRD. We use the Boston Scientific (BS) Vercise DBS system, which offers a segmented steerable lead with multiple independent current sources that allows true directional steering. Moreover, this system integrates stimulation field modeling (SFM) with MR tractography to predict network engagement. We use an innovative approach of targeting both subgenual cingulate (SGC) and ventral capsule/ventral striatum (VC/VS), which we term corticomesolimbic DBS. These targets are hubs in distinct yet partially overlapping depression networks and emerging basic science literature implicates them in bidirectional modulation of depression circuits. We also apply a paradigm-shifting approach using intracranial stereo-EEG (sEEG) subacutely after DBS implant to evaluate the clinical reliability of steering, SFMs, and tractography and to define and then target the networks mediating symptoms of depression. In Aim 1, in the Epilepsy Monitoring Unit (EMU), we investigate the capability of Vercise to selectively engage distinct brain networks and compare the spatial distribution of evoked network activity and modulation with that predicted by SFM and tractography. In Aim 2, we conduct further studies in the EMU to delineate depression-relevant networks and show behavioral changes with network-targeted stimulation. We use a variety of tasks to probe different symptom domains and novel assessment tools (Computerized Adaptive Testing and Automated Facial Affect Recognition) to enhance classification and model algorithms to optimize stimulation patterns. In Aim 3, we bring the results from Aims 1 and 2 together, to test the therapeutic potential of corticomesolimbic DBS in 12 subjects with TRD, with a focus on safety, feasibility, and preliminary efficacy in a 8-month open label trial with a subsequent randomized, blinded withdrawal of stimulation to assess efficacy. The impact of this proposal includes physiological validation of current ?steering? DBS technology to target specific networks, insights into effects of stimulation parameters on network physiology, an improved understanding of the pathophysiology of depression, and, perhaps most importantly, a novel approach for treating TRD. This research will also pioneer a novel and high-yield test bed for DBS therapy development consistent with BRAIN priorities.

Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this cognitive flexibility is our ability to perform complex, yet previously un-practiced tasks successfully on the first attempt. We refer to this ability as `ad hoc self-programming': `ad hoc' because these new behavioral repertoires are cobbled together on the fly, based on immediate demand, and then discarded when no longer necessary; `self-programming' because the brain has to configure itself appropriately based on task demands and some combination of prior experience and/or instruction. This type of learning differs importantly from trial-and-error learning, in which responses are sculpted incrementally, based on feedback from previous attempts. In comparison to trial-and-error learning, much less is known about ad hoc self- programmed learning, but it clearly represents a fundamental feature of human intelligence. The overall goal of our research proposal is to understand the neurophysiological and computational basis for ad hoc self-programmed behavior. There have been significant barriers to the study of this topic. Among them are the difficulty of studying these processes in animals who require training (which by definition precludes single-trial self- programming), and the lack of access to opportunities with sufficient spatiotemporal resolution to study neuronal processes in humans. The proposed research seeks to address this gap. We leverage critical advances in neuroscience, neurosurgery, engineering, and computational modelling, including: 1) availability of a large-scale recording platform enabling simultaneous recordings of 100+ neurons from the cortical surface; 2) opportunities to record from dorsolateral prefrontal cortex (dlPFC) in human subjects engaged in a custom-designed behavioral task; 3) developments borrowed from the artificial intelligence community to create advanced neural network models of complex cognitive processes. By applying these innovative methodologies, we focus on addressing our overall goal with three Specific Aims. In Aim 1, we determine what information about the structure of a novel, complex, instructed task is represented in human dlPFC neuronal activity. We also determine how and when this information is encoded, in terms of spiking activity, oscillatory activity, or coherence between the two. In Aim 2, we determine the relationship between these neuronal representations and behavior. We investigate how the robustness and timing of the emergence of required neural representations relates to response accuracy and reaction time. In Aim 3, we develop a computational model of ad hoc self-programmed learning. To do so, we borrow from recent insights in the AI world regarding prefrontal network structure, and also apply our developing understanding of neural representations from the previous Aims. We expect that this innovative approach will revolutionize our understanding of this amazing capacity for immediate, configurable learning that characterizes our everyday lives. In doing so, we will develop new strategies to study mechanisms of rapid, flexible cognitive control in general. A better understanding of human cognitive control and its nuanced capacities will naturally translate into an appreciation of deficiencies in these processes, and how they manifest in the form of neuropsychiatric disorders. This appreciation can then lead to the development of rational, targeted therapies.

Baylor Research Education Program in Neurosurgery Abstract The Baylor Neurosurgery Residency has been in place for over 60 years, and has long been one of the nation?s largest and most well respected neurosurgical training programs. The program has always had a strong academic tradition, and graduates have served as faculty at many leading medical schools, but historically there was a decidedly clinical emphasis. A decade ago, a strategic decision was made to focus on research and research education by capitalizing on the extraordinary scientific resources at Baylor College of Medicine and affiliated institutions in the Texas Medical Center. The residency was retooled to substantially enhance its ability to train the next generation of academic neurosurgeons. In addition to developing a culture that emphasizes evidence-based practice, clinical and basic research, and didactic training in basic neuroscience underlying neurosurgical practice, the program was expanded by a full year in order to provide residents with a deeper and more meaningful academic experience, and the program implemented the Baylor Research Education Program in Neurosurgery funded by an NINDS R25 grant. The Baylor R25 Program empowers an elite subset of trainees in the Baylor Neurosurgery Residency Program to develop into academicians who effectively combine clinical neurosurgery practice with research that advances the field. We select outstanding individual neurosurgery residents who have the background, talent, and motivation to become successful physician-scientists, and then carefully integrate additional specialized research education into their residency training. The R25 program mentors these residents through the entire research process, from project conception to experimental design, data analysis and interpretation, to publication of results, and finally to the development of an effective plan for beginning a career as a physician-scientist, with mentorship extending beyond residency. While the early results of the program reveal clear success in producing neurosurgeon-scientists, the structure and curriculum of the R25 program has been continually revised and enhanced based on ongoing analysis of evaluations from participants and mentors, and an improved iteration of the program has been developed for this renewal application. The hallmark of this program is a research block during the 5th and 6th years of the residency, which has been expanded to 18-months. During this block, the trainee engages in a mentored research project on a near full-time basis. This hands-on approach is the most effective way to prepare young neurosurgeon-scientists for a productive research career by allowing them to conduct research independently, but with enough support to avoid the common pitfalls experienced by young researchers. In addition to carrying out a research project, residents selected for the research education program will be trained in experimental design, scientific writing, oral presentation, and in the responsible conduct of research. Furthermore, they will receive considerable oversight and career counseling from a team of experts mentors with the intent of paving their way to success in obtaining a mentored career development award and becoming a productive physician-scientist.

PROJECT SUMMARY Data sharing is essential to promote equity and maximize the impact of the significant investment in the BRAIN Initiative. Data sharing plans are now required for BRAIN Initiative funding, but there is an urgent need to de- velop specific policies and practice guidelines that address ethical challenges and stakeholder concerns. Data sharing has been the object of study in other fields, such as genomics, but there are distinctive features of the BRAIN Initiative that are likely to present unique challenges and raise new concerns. Our own experience and research with investigators conducting BRAIN Initiative-funded studies of closed loop or adaptive deep brain stimulation suggests that the practice of sharing data is inconsistent and incomplete, despite broad agreement that it is important. Investigators raised ethical challenges with sharing human brain data, including issues re- lated to privacy, consent, interoperability, and competing commercial and professional interests. The objective of this proposal is to engage key stakeholders in a deliberative process to identify challenges and concerns specific to sharing human data from BRAIN Initiative studies and generate empirically informed policy and practice options to facilitate responsible sharing of human data within the BRAIN Initiative. In Aim 1, we will use informational interviews and document analysis to identify BRAIN Initiative-specific data sharing challenges, as well as relevant policy and practice considerations. In Aim 2, we will use semi-structured interviews and sur- veys to evaluate BRAIN Initiative research participants? attitudes, preferences, and concerns about data shar- ing and brain privacy. In Aim 3, we will employ a modified policy Delphi process with diverse stakeholders to prioritize challenges and generate and evaluate policy and practice options that address high-priority chal- lenges. This contribution will be significant because it will provide critical empirical data to inform practice guidelines and future policy development. By engaging diverse stakeholders, this project will help instill trust, avoid inequities, and ensure success of current and future data sharing efforts within the BRAIN Initiative. This project is innovative in its engagement with multiple diverse stakeholder groups and its use of mixed methods incorporated into a modified policy Delphi framework. The work is feasible in our hands as demonstrated by the productivity of this team in prior work, as well as our collective data sharing and neuroethics expertise, and experience with empirical social science methods.

Abstract Humans have a remarkable ability to flexibly interact with the environment. A compelling demonstration of this cognitive flexibility is our ability to respond correctly to novel contextual situations on the first attempt, without prior rehearsal. We refer to this ability as ?ad hoc self-programming?: ?ad hoc? because these new behavioral repertoires are cobbled together on the fly, based on immediate demand, and then discarded when no longer necessary; ?self-programming? because the brain has to configure itself appropriately based on task demands and some combination of prior experience and/or instruction. The overall goal of our research effort is to understand the neurophysiological and computational basis for ad hoc self-programmed behavior. Our previous U01 project (NS 108923) focused on how these programs of action are initially created. Our results thus far have revealed tantalizing notions of how the brain represents these programs and navigates through them. In this proposal, therefore, we focus on the question of how these mental programs are executed. Based on our preliminary findings and critical conceptual work, we propose that the medial temporal lobe (MTL) and ventral prefrontal cortex (vPFC) creates representations of the critical elements of these mental programs, including concepts such as ?rules? and ?locations?, to allow for effective navigation through the algorithm. These data suggest the existence of an ?algorithmic state space? represented in medial temporal and prefrontal regions. This proposal aims to understand the neurophysiological underpinnings of this algorithmic state space in humans. By studying humans, we will profit from our species? powerful capacity for generalization to understand how such state spaces are constructed. We therefore leverage the unique opportunities available in human neuroscience research to record from single cells and population-level signals, as well as to use intracranial stimulation for causal testing, to address this challenging problem. In Aim 1 we study the basic representations of algorithmic state space using a novel behavioral task that requires the immediate formation of unique plans of action. Aim 2 directly compares representations of algorithmic state space to that of physical space by juxtaposing balanced versions of spatial and algorithmic tasks in a virtual reality (VR) environment. Finally, in Aim 3, we test hypotheses regarding interactions between vPFC and MTL using intracranial stimulation.