Browsing by Subject "translational"

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    Addressing individual heterogeneity in the neurobehavioral factors related to substance use disorders
    (2024-09) Drossel, Gunner
    Rates of return to use in addiction treatment remain high. We argue that the development of improved treatment options will require advanced understanding of individual heterogeneity in Substance Use Disorders (SUDs). In Chapter one, we hypothesized that considerable individual differences exist in the three functional domains underlying addiction—approach-related behavior, executive function, and negative emotionality. We included N = 593 participants from the enhanced Nathan Kline Institute-Rockland Sample (ages 18–59, 67% female; N = 420 Controls; N = 173 with past SUDs [54% female]). To test our a priori hypothesis that distinct neuro-behavioral subtypes exist within individuals with past SUDs, we conducted a latent profile analysis with all available phenotypic data as input and characterized resting-state brain function for each discovered subtype. In Chapter two, we aimed to validate these subtypes in a sample with a current Cocaine Use Disorder (CocUD). We included N=109 participants from the SUDMEX CONN dataset (ages 22-39, 15% female; N=48 Controls; N=61 with CocUD [30% female]). We conducted a latent profile analysis with all phenotypic data as input and characterized resting-state brain function per subtype. In Chapter three, we investigated translational preclinical results. We summarized the effects of acute or chronic drug (self-) administration on brain function for various classes of drugs and determined consistency with human literature. We performed a systematic literature search and identified 116 studies on in vivo rodent resting-state functional magnetic resonance imaging (n=84) or positron emission tomography (n=41) spanning depressants (n=27), opioids (n=23), stimulants (n=72), and cannabis (n=3). Results overall supported functionally derived subtypes, demonstrating considerable individual heterogeneity in the multi-dimensional impairments in addiction. Additionally, results from reviewing preclinical studies of drug (self-) administration provided evidence of altered resting-state brain function in rodents upon drug administration, implicating the brain’s reward network analogue to human studies. However, alterations were more dynamic than previously known, with dynamic adaptation depending on the length of drug administration. Further incorporation of relevant preclinical findings with neurobehavioral assessment and mechanism-based subtyping of human participants diagnosed with and in treatment for SUDs can aid in contributing to the betterment of interventions and therapies for personalize addiction medicine.
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    Beyond Simple Tests of Value: A neuroeconomic, translational, disease-relevant, and circuit-based approach to resolve the computational complexity of decision making
    (2018-07) Sweis, Brian
    How the brain processes information when making decisions depends on how that information is stored. Distinct neural circuits are capable of storing information in many different ways that are better suited for different situations. The decision-making processes that access those different bits of stored information are not singular and occupy separable neural circuits, each of which can operate in parallel with one another, and each of which can confer different information processing properties based on the neural constraints within which a given computation resides. Such is the framework of recent theories in neuroeconomics, which suggest that decisions are multi-faceted and action-selection processes can arise from fundamentally distinct circuit-specific neural computations. In this thesis, I present a body of work that takes a neuroeconomics approach through a series of experiments that reveal the complexities of multiple, parallel decision-making systems through complex behaviors by moving beyond simple tests of value. In the first half of this thesis, I demonstrate how complex behavioral computations can resolve fundamentally distinct valuation algorithms thought to reside in separable neural circuits. I then translate this approach between human and non-human rodent animal models in order to reveal how multiple, parallel decision-making systems are conserved across species over evolution. In the second half of this thesis, I demonstrate the utility of behavioral economics in disease-relevant and circuit-based studies. If multiple, parallel decision-making processes are thought to be intimately related to the heterogeneous ways in which information can be stored in separable neural circuits, I examine how addiction – a disease which is thought to be a disorder of the neurobiological mechanisms of learning and memory – might alter how stored information is processed in separable decision-making systems uniquely using a mouse model of two different forms of addiction. In doing so, I demonstrate how different forms of addiction give rise to unique, lasting vulnerabilities in fundamentally distinct decision-making computations. These discoveries can aid in resolving neuropsychiatric disease heterogeneity by moving beyond simple tests of value where complex behaviors that are measured can more accurately reflect the neurally distinct computations that underlie those behaviors. Finally, I take a neuromodulation approach and directly alter the strength of synaptic transmission in a circuit-specific manner using optogenetics in mice tested in this neuroeconomic framework. I demonstrate how plasticity alterations in projections between the infralimbic cortex and the nucleus accumbens are capable of giving rise to long-lasting disruptions of self-control related decision processes in a foraging valuation algorithm independent of and separate from a deliberative valuation algorithm measured within the same trial. Furthermore, I developed a novel plasticity measurement tool that is assayed at the neuronal population ensemble level and reveals individual differences in separable decision processes. The second half of the thesis demonstrates a potential biomarker to target as a circuit-computation-specific therapeutic intervention tailored to those types of decision-making dysfunctions. Taken together, I present a body of work in this thesis that demonstrates the utility of moving beyond simple tests of value in order to resolve the computational complexity of decision making.