Engineering Principles for Scalable Connectomics

Brain tissue sectioning presents a significant challenge in connectomics, particularly when scaling to larger volumes. In the MICrONS 1 mm³ mouse visual cortex dataset, 25.1% of scanned images—representing over a month of imaging work—were discarded due to sectioning defects. Current methods result in material loss during cutting and face limitations in tool wear and process efficiency. This thesis examines tissue sectioning through an engineering lens. Drawing from established machining practices and parallel industries, we propose and evaluate potential improvements to sectioning methods. The work aims to contribute to ongoing efforts in mapping larger connectomes, making it more practical and less error-prone.

Name thesis:degree_name

Bachelor

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Mechanical Engineering

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2025

Author dc:creator

• Garzon Navarro, Monserrate

Advisor dc:contributor.advisor

• Culpepper, Martin L.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/162405

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/162405