Longitudinal tracking of neural vascular recovery post microinfarct using multimodal neural platform

Ischemic stroke is a leading cause of morbidity and mortality worldwide, with hundreds of thousands of cases occurring annually. The disease is caused by the obstruction or reduction of blood flow to part of the brain, typically due to the buildup of cholesterol-containing fatty deposits called plaques in an artery or one of its branches. Microinfarcts are a milder form of ischemic stroke with tiny area of tissue damage resulting from blockage of small cerebral blood vessels such as arterioles and capillaries. In this study, we induced mini-scale photo-thrombotic strokes in aged mice to investigate how neural activities respond to such small-scale occlusion. We used ultra-flexible nanoelectrode thread probes, two-photon imaging, and speckle imaging to track neural activities, microvascular structure, and regional cerebral blood flow longitudinally post-stroke. Our findings reveal several important insights about this transient local damage in an aged mouse model. Firstly, we observed that neural activity near the infarct site recovers to baseline levels at the same pace as the capillary bed after mini-scale stroke induction. Secondly, our cell-type-specific analysis of single neurons revealed that the excitability of fast-spiking narrow interneurons is dampened the most among all cell types during this minor ischemic induction and recovery process. Thirdly, we found that neuronal damage is depth-related, with shallower layers being more severely affected than deeper layers. Lastly, our results suggest that spike phase locking at the low gamma band, which is dominant in the shallow cortical layer, is weakly but long-lastingly compromised, indicating an interruption in large-scale neuronal assembly communication. Overall, these findings shed light on the neurovascular impact of a photo-thrombosis microinfarct, including its effects on capillary structure, the response of individual neurons, and the functioning of large-scale neural networks.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Masters

Discipline thesis:degree_discipline

Engineering

Grantor

Rice University

Year dc:date.issued

2023

Author dc:creator

• Jin, Yifu

Advisor dc:contributor.advisor

• Luan, Lan

• Microinfarct Electrophysiology

Statement dc:rights

• Copyright is held by the author, unless otherwise indicated. Permission to reuse, publish, or reproduce the work beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.

Language dc:language.iso

eng

Handle dc:identifier.uri

https://hdl.handle.net/1911/115340

OAI identifier oai:identifier

oai:repository.rice.edu:1911/115340