<p>The brain’s dopaminergic system is critical to adaptive behaviors, and is centrally implicated in various pathologies. For decades, research has aimed at better characterizing what drives the mesolimbic dopamine system and the resulting influence on brain physiology and behavior in both humans and animals. To date, the dominant modes of research have relied on extrinsic approaches: pharmacological manipulations, direct brain stimulation, or delivering behavioral incentives in laboratory tasks. A critical open question concerns whether individuals can modulate activation within this system volitionally. That is, can individuals use self-generated thoughts and imagery to invigorate this system on their own? This process can be referred to as “cognitive neurostimulation” -- a precise and non-invasive stimulation of neural systems via cognitive and behavioral strategies. And if not, can they be taught to do so? Recent technological advances make it feasible to present human participants with information about ongoing neural activations in a fast and spatially precise manner. Such feedback signals might enable individuals to eventually learn to control neural systems via fine-tuning of behavioral strategies. The studies described herein investigate whether individuals can learn to volitionally invigorate activation within the mesolimbic reward network. We demonstrate that under the right training context, individuals can successfully learn to generate cognitive states that elicit and sustain activation in the ventral tegmental area (VTA), the source of dopamine production within the mesolimbic network. Although participants were explicitly trained to increase VTA activation, multiple mesolimbic regions exhibited increased connectivity during and after training. Together, these findings suggest new frameworks for aligning psychological and biological perspectives, and for understanding and harnessing the power of neuromodulatory systems.</p> / Dissertation
| Identifer | oai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/10470 |
| Date | January 2015 |
| Creators | MacInnes, Jeff |
| Contributors | Adcock, Rachel A |
| Source Sets | Duke University |
| Detected Language | English |
| Type | Dissertation |
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