A data-driven ontology of brain function : engineered, interrogated, and clinically applied
Abstract/Contents
- Abstract
- In modern healthcare, there is no accepted system for diagnosing disorders of mental function based on the altered brain systems that cause them. In neuroscience, there is no consensus on what a brain system is. This thesis takes a data-driven approach to mapping domains of brain function across the human neuroimaging literature. Neural circuits and associated mental functions were mapped from the brain coordinate data and full texts of nearly 20,000 studies. The resulting domains characterize several novel brain circuits that are absent from the conceptually dominant expert-determined frameworks in the field. Having engineered a framework for brain function, the next aim was to interrogate whether its domains adhere to basic organizational principles. Are the domains reproducible, meaning that their circuits and functions predict one another in held-out articles? Are they modular, partitioning the literature into homogeneous and separable subsets? Are they generalizable, serving as prototypes of circuits and functions observed in single studies? It is demonstrated here how competing knowledge frameworks can be evaluated against a common set of standards. The data-driven framework is consistently seen to offer leading performance. The second aim of the thesis was to apply the data-driven framework to predict clinical outcomes. The notes from over 20 million healthcare visits were rated by semantic similarity to domains, and these dimensional phenotypes were found to outperform binary diagnoses in predicting future psychotropic prescriptions, hospital visits, and all-cause mortality. Though the data-driven framework was not designed to have clinical relevance, its prognostic performance is on par with frameworks crafted specifically for psychiatric applications. The third and final aim of the thesis was to systematically quantify discrepancies between article texts and brain coordinate data. Computational approaches to ontology engineering are expected to mitigate certain forms of bias, while others may be inherent to the neuroimaging literature. The findings point to the influence of the publication incentive structure on how neuroimaging data are interpreted by neuroscientists. The collective results illustrate the scientific, clinical, meta-scientific interest in engineering a data-driven framework for brain function. They suggest a potential for biological knowledge frameworks to go beyond guiding diagnosis and to generate the prognoses that have long eluded the standard of care for disorders of brain function.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Beam, Elizabeth Helen |
|---|---|
| Degree supervisor | Etkin, Amit, 1976- |
| Thesis advisor | Etkin, Amit, 1976- |
| Thesis advisor | McFarland, Daniel A |
| Thesis advisor | Poldrack, Russell A |
| Thesis advisor | Potts, Christopher, 1977- |
| Degree committee member | McFarland, Daniel A |
| Degree committee member | Poldrack, Russell A |
| Degree committee member | Potts, Christopher, 1977- |
| Associated with | Stanford University, Neurosciences Program |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Elizabeth Helen Beam. |
|---|---|
| Note | Submitted to the Neurosciences Program. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Elizabeth Helen Beam
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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