Development of a Causal Connectome in Major Depressive Disorder Using Machine Learning
Abstract/Contents
- Abstract
- Major depressive disorder (MDD) is the leading cause of disability worldwide, affecting more than 300 million people [1, 2]. While the first and primary approach to treating MDD is with antidepressant medications, transcranial magnetic stimulation (TMS) is an emerging non-invasive tool for stimulating brain networks and has proven to be effective for treatment of MDD [3, 4]. Currently, TMS for MDD targets the depression pathophysiology at the left DLPFC [12]. However, relatively low response rates have been observed when TMS is administered in the DLPFC, with other brain regions such as the medial prefrontal cortex (MPFC) or the anterior insula showing promise [13-15]. Therefore, we analyze functional connectivity networks to 1) better understand the pathophysiology of MDD and 2) elucidate potential novel treatment targets. We record TMS-evoked potentials (TEP) from 6 regions of interests (ROIs) that broadly covered the brain for 38 medication-free healthy patients and 27 patients with MDD after single pulses of TMS in 26 stimulation sites across the brain. We hypothesized that the local (left prefrontal) early TEP (20-80ms) after stimulation is more effective at differentiating between patients with MDD and healthy controls based on previous studies [30, 31]. However, we found that the early TEPs (20-80ms) were not able to differentiate between the MDD subgroup and the healthy patient subgroup. Furthermore, our results demonstrated that brain responses from TMS were not localized in the DLPFC, but at various regions of the brain such as the central and occipital regions. There was spatial variance identified in these connectivity networks between stimulation sites and ROIs widespread across the brain. Together, this work reveals that there is greater spatial and temporal diversity in connectivity networks of MDD.
Description
| Type of resource | text |
|---|---|
| Date modified | July 17, 2023 |
| Publication date | July 17, 2023; May 2023 |
Creators/Contributors
| Author | Kim, Naryeong |
|---|---|
| Thesis advisor | Keller, Corey |
| Thesis advisor | Deisseroth, Karl |
| Thesis advisor | Lee, Jin Hyung |
| Department | Bioengineering |
Subjects
| Subject | Magnetic brain stimulation |
|---|---|
| Genre | Text |
| Genre | Thesis |
Bibliographic information
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- 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 4.0 International license (CC BY-NC).
Preferred citation
- Preferred citation
- Kim, N. (2023). Development of a Causal Connectome in Major Depressive Disorder Using Machine Learning . Stanford Digital Repository. Available at https://purl.stanford.edu/mh136tx4998. https://doi.org/10.25740/mh136tx4998.
Collection
Undergraduate Theses, School of Engineering
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- Contact
- naryeong@stanford.edu
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