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Neuroeconomic Markers of Opioid Use ...

Lopez-Guzman, Silvia.

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Neuroeconomic Markers of Opioid Use Disorder Outcomes: A Computational Psychiatry Approach.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Neuroeconomic Markers of Opioid Use Disorder Outcomes: A Computational Psychiatry Approach./
作者:	Lopez-Guzman, Silvia.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	196 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
Contained By:	Dissertation Abstracts International79-08B(E).
標題:	Neurosciences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10682960
ISBN:	9780355773743

Neuroeconomic Markers of Opioid Use Disorder Outcomes: A Computational Psychiatry Approach.
Lopez-Guzman, Silvia.

Neuroeconomic Markers of Opioid Use Disorder Outcomes: A Computational Psychiatry Approach. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 196 p.

Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.

Thesis (Ph.D.)--New York University, 2018.

The exponential increase in opioid addiction cases in the country has resulted in hundreds of thousands of individuals seeking treatment services. Medication Assisted Treatment (MAT)---a combination of an opioid substitute and psychosocial intervention---is the gold standard for opioid use disorder. It improves clinical outcomes, reducing the risk of overdose and of infectious comorbidities. However, two major issues challenge its effectiveness. First, access and availability to MAT facilities is limited, and second, MAT is hindered by very high rates of relapse and dropout. Patients often dangerously combine their opioid maintenance medication with illicit opioids like heroin or fentanyl, risking an overdose.

ISBN: 9780355773743Subjects--Topical Terms:

588700
Neurosciences.

Neuroeconomic Markers of Opioid Use Disorder Outcomes: A Computational Psychiatry Approach.
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300 $a 196 p.
500 $a Source: Dissertation Abstracts International, Volume: 79-08(E), Section: B.
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520 $a The exponential increase in opioid addiction cases in the country has resulted in hundreds of thousands of individuals seeking treatment services. Medication Assisted Treatment (MAT)---a combination of an opioid substitute and psychosocial intervention---is the gold standard for opioid use disorder. It improves clinical outcomes, reducing the risk of overdose and of infectious comorbidities. However, two major issues challenge its effectiveness. First, access and availability to MAT facilities is limited, and second, MAT is hindered by very high rates of relapse and dropout. Patients often dangerously combine their opioid maintenance medication with illicit opioids like heroin or fentanyl, risking an overdose.
520 $a To tackle these problems, we need to understand the mechanisms behind relapse and develop tools that can aid in the personalized prediction and prevention of these events. Neuroscience has made great progress over the past 40 years in the understanding of the circuitry and neuromodulatory mechanisms involved in addiction and relapse. However, these insights have not yet translated into the clinical tools we need. One way to bridge addiction neurobiology and clinical reality is to utilize computational models of behavior that 1) serve to objectively parametrize the decision processes that could be leading to drug taking during treatment, and 2) have been shown to engage the very neural substrates that are relevant to relapse and to the vicious cycle of addiction.
520 $a In this dissertation, my collaborators and I propose to deploy this computational psychiatry approach to focus on two decision-making dimensions that are directly relevant to the problem of engaging in drug taking while seeking abstinence: impulsive (impatient) decision-making, and risky decision-making. Several previous studies have laid the groundwork for this, suggesting these processes are relevant to opioid use disorder, may reflect both stable personality traits and context-dependent effects, and are potentially affected by treatment.
520 $a We followed a cohort of patients with opioid use disorder in a MAT clinic for the first seven months of their treatment, and a cohort of matched controls. Along this follow-up period, we assessed repeatedly their intertemporal choice and risk-uncertainty preferences, and derived 3 individualized computational parameters from validated neuroeconomic models---discount rate, risk tolerance, and ambiguity tolerance---as putative measures of impulsive choice, attitudes to known risks, and attitudes to unknown risks. Simultaneously, we assessed craving, withdrawal, and anxiety levels in our patient cohort, establishing the relationship between the behavioral decision-making parameters and the symptomatology. Most importantly, we evaluated the parameters' contribution to individual clinical outcomes, such as concomitant illicit drug use and full relapse.
520 $a Our results indicate that standard models of intertemporal choice result in systematically biased discount rates, due to an often-ignored distortion effect caused by risk preferences. This can lead to wrong interpretations about differences in impulsivity across groups of individuals and conditions. Our new proposed modeling approach allowed us to disassociate these parameters so that they reflect separable decision-making processes.
520 $a Using this methodology, we found that relative increases in impulsive decision-making and in tolerance of unknown risks were proximally predictive of concomitant opioid use. We also found a correlation between fluctuations in craving and fluctuations in discounting. Having established this behaviorally, we explored the connection between illicit opioid use in these patients and the neural mechanisms behind impulsive decision-making. We identified a link between craving and the neural correlates of intertemporal choice that could point to the computational mechanism behind craving and relapse. Taken together, our results show a distinguishable role for these specific value computations in relating to treatment outcomes for opioid use disorder. We found that they have good clinical validity, and may be useful in the design of computational predictive tools for relapse.
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