Using AI and Deep Learning to Predict IQ and Reading Skills in Humans through Analysis of Human Brain White Matter Fascicles
Abstract/Contents
- Abstract
This study uses Artificial Intelligence techniques and white matter data in the human brain, to analyze potential pathways to transhumanism: enhancing human cognitive abilities, and eventually creating a brain-computer interface that could result in brains enhanced by artificial intelligence. For example, using machine learning technologies, we could predict the particular development curves for some brain regions or tracts, and teach particular skills at the right time, when that area’s development is taking place. Moreover, by imaging the brain, we could identify specific brain substrates to stimulate while learning new skills, as performed by some existing researchers and companies,.
Deep Machine Learning models could be used to personalize content for different subjects based on their specific brain psychology, which can be applied in multiple fields, from education to entertainment, marketing, business and sales..
In this research, we have used brain magnetic resonance imaging techniques to identify brain white matter structure (brain tracts in particular) to train deep learning and other shallow artificial intelligence models which predict reading abilities and cognitive skills' scores.
The fractional anisotropy, a measure of how water diffuses within a brain location can be measured along the length of a tract (the FA profile). These profiles correlate with reading proficiency. We use these profiles to analyze the relationship between white matter in the human brain and reading and cognitive abilities. We examine multiple machine learning models that could help predict cognitive skills measures such as IQ and reading proficiency in subjects based on their fractional anisotropy profiles.
Description
| Type of resource | text |
|---|---|
| Date created | June 4, 2020 |
Creators/Contributors
| Author | Roitman, Lucas Martin Agudiez |
|---|---|
| Primary advisor | Wandell, Brian A |
| Advisor | Lerma-Usabiaga, Garikoitz |
Subjects
| Subject | iq |
|---|---|
| Subject | plasticity |
| Subject | connectome |
| Subject | tensor |
| Subject | education |
| Subject | white matter |
| Subject | human brain |
| Subject | biomarker |
| Subject | tractography |
| Subject | oligodendrocytes |
| Genre | Thesis |
Bibliographic information
| Related Publication |
Yeatman, Jason D., et al. "Development of white matter and reading skills." Proceedings of the National Academy of Sciences 109.44 (2012): E3045-E3053.
|
|---|---|
| Location | https://purl.stanford.edu/vd195jw6204 |
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Preferred citation
- Preferred Citation
- Roitman, Lucas Martin Agudiez and Wandell, Brian A and Lerma-Usabiaga, Garikoitz. (2020). Using AI and Deep Learning to Predict IQ and Reading Skills in Humans through Analysis of Human Brain White Matter Fascicles. Stanford Digital Repository. Available at: https://purl.stanford.edu/vd195jw6204
Collection
Master's Theses, Symbolic Systems Program, Stanford University
View other items in this collection in SearchWorksContact information
- Contact
- roitman@alumni.stanford.edu
Also listed in
Loading usage metrics...