Edward E. Smith - US grants
Affiliations: | 2004-2012 | Columbia University, New York, NY |
Area:
categorization, working memory, semantic memory, word perceptionWe are testing a new system for linking grants to scientists.
The funding information displayed below comes from the NIH Research Portfolio Online Reporting Tools and the NSF Award Database.The grant data on this page is limited to grants awarded in the United States and is thus partial. It can nonetheless be used to understand how funding patterns influence mentorship networks and vice-versa, which has deep implications on how research is done.
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High-probability grants
According to our matching algorithm, Edward E. Smith is the likely recipient of the following grants.Years | Recipients | Code | Title / Keywords | Matching score |
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2009 — 2010 | Jarskog, Lars Fredrik Smith, Edward E. |
RC1Activity Code Description: NIH Challenge Grants in Health and Science Research |
Using Fmri to Measure Negative Symptoms in Schizophrenia @ Columbia Univ New York Morningside DESCRIPTION (provided by applicant): This application addresses the broad Challenge Area 03: Biomarker Discrimination and Validation, and the specific challenge topic: 03-MG-101, Biomarkers of mental disorders. Schizophrenia, a devastating psychiatric disorder, is chiefly characterized by positive symptoms, such as delusions and hallucinations, and negative symptoms, such as anhedonia (lack of pleasure), avolition (lack of willed-action), and a flattening of affect. While antipsychotic medications reduce positive symptoms, they provided little relief from negative symptoms. Thus patients with severe negative symptoms are likely to suffer continued functional decline, and have a very limited life. We believe that the lack of progress on treating negative symptoms may be due, in part, to the fact that they are typically measured by subjective means (e.g., a clinician's rating, a patient's self-report), and taken at face value. Our goal is to use as markers of negative symptoms the patients'neural responses to various rewards and losses while having their brains scanned by functional Magnetic Resonance Imaging (fMRI). Further, we will determine the consequences of these negative symptoms for basic learning processes. In our first experiment, we will test patients with schizophrenia as well as normal subjects (who are matched to the patients on factors like age and gender) on the same learning task. On each trial of the task, a subject has to decide which of two objects is more likely to lead to a reward (e.g., money), where the probability that each object leads to reward keeps changing slowly. The precise sequence of events on a trial is as follows: (1) the subject first makes a Choice between the objects, (2) next gets Feedback whether his/her choice is correct or wrong, and (3) then gets the actual Outcome--a monetary reward if the choice was Correct, and nothing if the choice was Wrong. Over trials, subjects gradually learn to choose the object that is more likely to lead to reward. To see the connection between this task and negative symptoms, consider the time interval between the Feedback and the Outcome phases. If the feedback is positive the subject should be anticipating reward during this interval, and the neural responses should reflect "anticipatory hedonia". Now consider the time interval between the actual Outcome and the start of the next trial. If money is the Outcome the subject should have a hedonic reaction, and the neural responses should reflect "reactive hedonia". Based on previous research, we know which regions of the brain are responsive to hedonic reactions, one of which is the ventral striatum, a subcortical area. By determining each subject's fMRI activity in these regions during the two time intervals just described, we can determine whether patients with schizophrenia show reduced anticipatory-hedonia, reduced reactive-hedonia, or both. Some behavioral experiments suggest that the main deficit for schizophrenics will be in anticipatory-hedonia, and accordingly we hypothesize that there will be more fMRI activity in the regions of interest in normal subjects than patients when subjects are anticipating a reward, but not necessarily when they are reacting to a reward. The degree of fMRI activity when anticipating a reward may provide a biological marker for the negative symptom of anhedonia;to assess this possibility, we will correlate our fMRI measure with standard clinical measures of anhedonia. We will also determine whether patients with schizophrenia and matched controls differ in their learning processes. When normal controls have their brains imaged while performing this learning task, the ventral striatum is among the main regions activated. Since the same brain region is involved in both learning and hedonia, and since patients show less activity in this region when anticipating a reward, patients may also be impaired in learning. It's not just that both mental activities depend on the same brain region;it's also that learning in this kind of task is known to depend on making predictions about whether reward will occur on that trial, and the prediction process itself seems to be fueled by anticipatory hedonia. Our second experiment is like the first one, except that instead of gaining rewards on some trials now subjects will lose money when their choice is Wrong (their losses will be taken from money given to them at the outset of the experiment). Again, each trial includes three phases: Choice, Feedback, and Outcome, with the latter being either a loss (following the feedback "Wrong"), or nothing (following the feedback "Correct"), and now the subject's goal is to learn to make the choice that will lead to less loss. If the feedback indicates a loss is coming, fMRI activity during the interval between Feedback and Outcome reflects anticipatory-displeasure (or avoidance), while the interval between the Outcome and the end of trial reflects reactive-displeasure. Should we find that, compared to matched controls, patients with schizophrenia again show less fMRI activity during the anticipatory-interval but not during the reactive-interval, we will have biological markers for another negative symptom, affective flattening, or reduced reactivity to emotional states be they positive or negative. PUBLIC HEALTH RELEVANCE: While standard antipsychotic medications reduce the positive symptoms of schizophrenia (e.g., delusions), they provide little benefit for the devastating negative symptoms of this disease (e.g., anhedonia, flattening of affect). The lack of progress in treating negative symptoms may be due to the fact that they are often measured by subjective means (a clinician'rating, a patient's self-report). Our goal is to use brain imaging to provide objective and biological markers of the various negative symptoms. |
0.939 |
2010 | Smith, Edward E. | RC1Activity Code Description: NIH Challenge Grants in Health and Science Research |
Neuroimaging-Based Biomarkers For Two Components of Pain @ Columbia Univ New York Morningside DESCRIPTION (provided by applicant): This application addresses broad Challenge Area (03) Biomarker Discovery and Validation, and Specific Challenge topic 03-DA-101, Biomarkers for Pain. The challenge Pain is a central health problem that affects quality of life and productivity for a large segment of the population. Lost work time due to pain costs America an estimated $65 billion annually. The challenge in this request for applications is to define robust and meaningful biomarkers that can serve as objective, quantitative measures of pain-related processes. Currently, pain assessment is based almost exclusively on patients'self- reports, which are inherently limited by the complex relationship between biological nociceptive (pain-related) processes and patients'verbal or written descriptions of pain. Objective biomarkers would be useful for both understanding pain and for predicting pain when self-reports are unavailable or unreliable. They would accelerate the pace of research on pain and enhance patient care in several ways. First, biomarkers could serve as intermediate outcome measures in clinical trials and treatments, making clinical trials less costly and treatments better matched to patients'individual needs. Second, biomarkers could help develop new interventions for pain that directly target specific brain systems, i.e., with non-invasive brain stimulation, which would open up new possibilities for pain management. Third, research on biomarkers could improve the quality of decision-making about pain in legal contexts. Our approach We outline a proposal for using functional magnetic resonance imaging (fMRI), in conjunction with other methods, to develop and validate biomarkers for two components of reported pain experience in an acute, experimentally induced pain setting. A central part of this endeavor is the use of machine-learning algorithms to develop optimal fMRI-based predictors of pain. Machine-learning based biomarkers are essentially patterns of activity across brain regions with maximal predictive accuracy and discriminative validity for separating physical and non-physical (e.g., social or emotional) pain. These patterns can serve as the basis for tracking multiple components of pain processing without relying on self-report. Rather than simply trying to develop alternative measures that predict self-reports, however, we place a particular emphasis on identifying separable component brain processes that make different contributions to pain reports. By identifying biomarkers for component processes underlying pain, we aim to develop objective, brain-based measures of "intermediate phenotypes" that could be independently targeted for treatment, and so are useful in their own right beyond their ability to provide surrogate measures for pain reports. PUBLIC HEALTH RELEVANCE: The ability to identify the brain components that contribute to pain in a particular individual would allow care providers to select treatments appropriate for the individual. We have specific reason to believe that at least two separable components can be identified. New results from our laboratory suggest that at least two distinct brain networks make separable contributions to reported pain. One network, the "pain-processing network" (PPN), responds to painful peripheral stimulation (e.g., heat on the skin), and includes classic pain-processing regions. A second network, the "emotional appraisal network" (EAN), does not respond to stimulation of the body, but is involved in the cognitive generation of emotion. In our recent fMRI work on pain, both networks appear to make independent contributions to predicting how much pain a person will report in response to a given stimulus. Our goal is to develop biomarkers based on these two putative components. We plan to achieve these broad goals using converging evidence from three complementary approaches, each of which will be pursued in parallel and addressed in one Specific Aim. In Aim 1, we will develop fMRI- based pain biomarkers using machine learning techniques. We will use a set of fMRI data from several acute thermal pain experiments collected in our laboratory over the past 3 years (combined N = 157). These datasets have highly homogenous scanning and experimental protocols and were designed with the goal of fMRI-based pain prediction in mind. Their existence will allow us to accelerate the pace of computational biomarker development. In Aim 2, we will test whether the fMRI-based biomarkers we develop are causally related to pain, using combined fMRI and transcranial magnetic stimulation (TMS). Biomarkers that are causally related to pain are most likely to be useful as targets for drug or other treatments. We will stimulate regions within the PPN and EAN during fMRI scanning, and test whether effects on activity and connectivity in each network predict changes in pain experience. In Aim 3, we will extend our biomarker validation to chronic pain. We will test whether biomarkers developed in Aims 1 and 2 predict patterns of hypersensitivity to sensory stimuli in patients with focal lesions in the PPN and EAN. Together, these approaches provide three complementary ways of developing and validating fMRI-based biomarkers for pain. ) PUBLIC HEALTH RELEVANCE: This application addresses broad Challenge Area (03) Biomarker Discovery and Validation, and Specific Challenge topic 03-DA-101, Biomarkers for Pain. Pain is a central health problem that affects quality of life and productivity for a large segment of the population, but research and clinical care are hampered by the lack of objective, quantitative measures of biological processes that contribute to pain. Pain-processing biomarkers would be useful for both understanding the generation of pain within the brain (and individual differences therein) and for predicting pain when self-reports are unavailable or unreliable. They would accelerate the pace of research on pain and enhance patient care by a) serving as intermediate outcome measures in clinical trials and treatments, making clinical trials less costly and treatments better matched to patients'individual needs;and b) providing a foundation for new treatments that target the brain systems involved directly;and c) providing guidelines for decision-making about pain in legal contexts. |
0.939 |