Univariate and Multivariate fMRI Investigations of Delay Discounting and Episodic Future Thinking in Alcohol Use Disorder
| dc.contributor.author | Deshpande, Harshawardhan Umakant | en |
| dc.contributor.committeechair | LaConte, Stephen M. | en |
| dc.contributor.committeemember | Poelzing, Steven | en |
| dc.contributor.committeemember | VandeVord, Pamela J. | en |
| dc.contributor.committeemember | Casas, Brooks | en |
| dc.contributor.committeemember | Bickel, Warren K. | en |
| dc.contributor.department | Department of Biomedical Engineering and Mechanics | en |
| dc.date.accessioned | 2020-12-20T07:00:28Z | en |
| dc.date.available | 2020-12-20T07:00:28Z | en |
| dc.date.issued | 2019-06-28 | en |
| dc.description.abstract | Alcohol use disorder (AUD) remains a major public health concern globally with substantially increased mortality and a significant economic burden. The low rates of treatment and the high rates of relapse mean that excessive alcohol consumption detrimentally affects many aspects of the user's life and the lives of those around them. One reason for the low efficacy of treatments for AUD could be an unclear understanding of the neural correlates of the disease. As such, the studies in this dissertation aim at elucidating the neural mechanisms undergirding AUD, which could lead to more efficacious treatment and rehabilitation strategies. The propensity for impulsive decision making (choosing smaller, sooner rewards over larger, later ones) also known as delay discounting (DD), is an established risk-factor for a variety of substance abuse disorders, including AUD. Brain mapping of DD routinely uses modalities such as blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI). However, the extent to which these brain activation maps reflect the characteristics of impulsive behavior has not been directly studied. To examine this, we used multi-voxel pattern analysis (MVPA) methods such as multivariate classification using Support Vector Machine (SVM) algorithms and trained accurate classifiers of high vs. low impulsivity with individual fMRI brain maps. Our results demonstrate that brain regions in the prefrontal cortex encode neuroeconomic decision making characterizing DD behavior and help classify individuals with low impulsivity from individuals with high impulsivity. Individuals suffering from addictive afflictions such as AUD are often unable to plan for the future and are trapped in a narrow temporal window, resulting in short-term, impulsive decision making. Episodic future thinking (EFT) or the ability to project oneself into the future and pre-experience an event, is a rapidly growing area of addiction research and individuals suffering from addictive disorders are often poor at it. However, it has been shown across healthy individuals and disease populations (addiction, obesity) that practicing EFT reduces impulsive decision making. We provided real-time fMRI neurofeedback to alcohol users while they performed EFT inside the MR scanner to aid them in successfully modulating their thoughts between the present and the future. After the scanning session, participants made more restrained choices when performing a behavioral task outside the scanner, demonstrating an improvement in impulsivity. These two neuroimaging studies interrogate the brain mechanisms of delay discounting and episodic future thinking in alcohol use disorder. Successful classification of impulsive behavior as demonstrated in the first study could lead to accurate prediction of treatment outcomes in AUD. The second study suggests that rtfMRI provides direct access to brain mechanisms regulating EFT and highlights its potential as an intervention for impulsivity in the context of AUD. The work in this dissertation thus investigates important cognitive process for the treatment of alcohol use disorder that could pave the way for novel therapeutic interventions not only for AUD, but also for a wide spectrum of other addictive disorders. | en |
| dc.description.abstractgeneral | Alcohol use disorder (AUD) remains a major public health concern globally with substantially increased mortality and a significant economic burden. The low rates of treatment and the high rates of relapse mean that excessive alcohol consumption detrimentally affects many aspects of the user’s life and the lives of those around them. One reason for the low efficacy of treatments for AUD could be an unclear understanding of the brain regions affected by it. As such, the studies in this dissertation aim at elucidating the neural mechanisms undergirding AUD, which could lead to more efficacious treatment and rehabilitation strategies. The propensity for impulsive decision making (choosing smaller, sooner rewards over larger, later ones) also known as delay discounting (DD), is an established risk-factor for a variety of substance abuse disorders, including AUD. Brain mapping of DD routinely uses modalities such as blood-oxygenation-level-dependent functional magnetic resonance imaging (BOLD fMRI). However, the extent to which these brain activation maps reflect the characteristics of impulsive behavior has not been directly studied. To examine this, we searched for highly reproducible spatial patterns of brain activation that differ across experimental conditions (multi-voxel pattern analysis) and trained accurate classifiers of high vs. low impulsivity with individual fMRI brain maps. Our results demonstrate that brain regions in the prefrontal cortex encode neuroeconomic decision making and help classify individuals with low impulsivity from individuals with high impulsivity. Individuals suffering from addictive afflictions such as AUD are often unable to plan for the future and are trapped in a narrow temporal window, resulting in short-term, impulsive decision making. Episodic future thinking (EFT) or the ability to project oneself into the future and pre-experience an event, is a rapidly growing area of addiction research. However, it has been shown across healthy individuals and disease populations (addiction, obesity) that practicing EFT reduces impulsive decision making. We provided v real-time fMRI neurofeedback to alcohol users while they performed EFT inside the MR scanner to aid them in successfully modulating their thoughts between the present and the future. After the scanning session, participants made more restrained choices when performing a behavioral task outside the scanner, demonstrating an improvement in impulsivity. These two neuroimaging studies interrogate the brain mechanisms of delay discounting and episodic future thinking in alcohol use disorder. Successful classification of impulsive behavior as demonstrated in the first study could lead to accurate prediction of treatment outcomes in AUD. The second study suggests that rtfMRI provides direct access to brain mechanisms regulating EFT and highlights its potential as an intervention for impulsivity in the context of AUD. The work in this dissertation thus investigates important cognitive process for the treatment of alcohol use disorder that could pave the way for novel therapeutic interventions not only for AUD, but also for a wide spectrum of other addictive disorders. | en |
| dc.description.degree | Doctor of Philosophy | en |
| dc.format.medium | ETD | en |
| dc.format.mimetype | application/pdf | en |
| dc.identifier.other | vt_gsexam:21235 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/101551 | en |
| dc.language.iso | en | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | fMRI | en |
| dc.subject | addiction | en |
| dc.subject | alcohol use disorder | en |
| dc.subject | neurofeedback | en |
| dc.subject | support vector machine | en |
| dc.subject | Machine learning | en |
| dc.subject | delay discounting | en |
| dc.title | Univariate and Multivariate fMRI Investigations of Delay Discounting and Episodic Future Thinking in Alcohol Use Disorder | en |
| dc.type | Dissertation | en |
| dc.type.dcmitype | Text | en |
| thesis.degree.discipline | Biomedical Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Doctor of Philosophy | en |
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