One major challenge in developmental neuroscience research is the sheer number of choices researchers face when addressing even a single research question. Even once data collection is complete, the journey from raw data to interpretation of findings may depend on numerous decisions. To address this issue, this dissertation explores “multiverse” analysis techniques for following many analytical paths at once in the same dataset.
In chapter 1, multiverses are used to examine which analyses of age-related change in amygdala-medial prefrontal cortex circuitry are robust versus sensitive to researcher decisions. Chapter 2 uses multiverse analysis to identify optimal solutions for mitigating breathing-induced artifacts in resting-state functional magnetic resonance imaging data. Chapter 3 uses a variety of model specifications to characterize simultaneous reward learning strategies in youth contingent on both visual task cues and spatial-motor information.
Despite varied approaches and goals, each of the three studies highlight the benefits of conducting multiple parallel analyses for both addressing questions in developmental neuroscience and deepening understanding of the methods used to address them.
| Identifer | oai:union.ndltd.org:columbia.edu/oai:academiccommons.columbia.edu:10.7916/s8qh-nt87 |
| Date | January 2022 |
| Creators | Bloom, Paul Alexander |
| Source Sets | Columbia University |
| Language | English |
| Detected Language | English |
| Type | Theses |
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