Computational Pipeline for Mapping Network-level Neurovascular Coupling in Optogenetic Mouse Models

Core Concepts

A deep learning-based computational pipeline enables automated reconstruction and quantification of geometric changes across the cerebral microvascular network in response to optogenetic stimulation of nearby neurons.

Abstract

The content describes the development and application of a deep learning-based computational pipeline for analyzing two-photon fluorescence microscopy (2PFM) data of the cerebral microvascular network in optogenetically-modified mice.

Key highlights:

• The pipeline enables automated segmentation, registration, and graph-based analysis of the microvascular network from 4D 2PFM data.
• It was applied to study neurovascular coupling in Thy1-ChR2-YFP mice, where optogenetic stimulation of pyramidal neurons was used to elicit vascular responses.
• Analysis revealed heterogeneous changes in vessel caliber along individual vessels, with both dilations and constrictions observed.
• Vessels farther from activated neurons tended to constrict, while those closer dilated.
• Vascular responses showed increased coordination (assortativity) with higher stimulation intensities.
• The highest stimulation intensity led to a 4% increase in the efficiency of the capillary network.
• The pipeline enables detailed mapping of neurovascular coupling at the network level, going beyond previous studies focused on individual vessels or tissue-level responses.

Stats

Vascular segments exhibited an average radius change of 24 ± 28% of the resting diameter within individual vessels.
Dilations occurred 16.1±14.3 μm away from the closest neuron, while constrictions occurred 21.9±14.6 μm away.
The assortativity of vascular radius changes increased by 152 ± 65% at 4.3 mW/mm2 blue light stimulation compared to baseline.
The median efficiency of the capillary network increased by 4% during 4.3 mW/mm2 blue light stimulation compared to control.

Quotes

"Neuronal function impairments arise wherever local metabolite supply becomes inadequate, notwithstanding the physiological level of flow across the network as a whole, making mapping of vessel changes across the network of particular importance."
"Vessels farther from activated neurons tended to constrict, while those closer dilated."
"Only the highest photostimulation intensity elicited an increase in the network efficiency."

Key Insights Distilled From

A Deep Learning Pipeline for Mapping in situ Network-level Neurovascular Coupling in Multi-photon Fluorescence Microscopy

by Rozak,M. W.,... at www.biorxiv.org 01-24-2024

https://www.biorxiv.org/content/10.1101/2024.01.24.577045v1

Deeper Inquiries

How could this computational pipeline be extended to study neurovascular coupling in disease models, such as Alzheimer's or stroke

To extend this computational pipeline to study neurovascular coupling in disease models like Alzheimer's or stroke, researchers could incorporate additional imaging modalities and analysis techniques. For Alzheimer's disease, incorporating amyloid and tau imaging alongside the vascular network imaging could provide insights into the interplay between neurodegeneration and vascular dysfunction. In stroke models, integrating perfusion imaging techniques could help correlate changes in blood flow with the observed vascular responses. Furthermore, incorporating genetic or pharmacological manipulations to mimic disease conditions and studying their effects on neurovascular coupling could provide valuable insights into disease mechanisms. Additionally, analyzing the network-level coordination of vascular responses in disease models could help identify specific alterations in blood flow regulation that contribute to disease pathology.

What other factors, beyond neuronal activation, might influence the heterogeneous vascular responses observed within individual vessels

Beyond neuronal activation, several factors could influence the heterogeneous vascular responses observed within individual vessels. One key factor is the presence of contractile cells in the vessel walls, such as smooth muscle cells and pericytes. Changes in the contractility of these cells in response to various stimuli, including neurotransmitters, inflammatory mediators, and metabolic factors, can lead to alterations in vessel caliber. Additionally, factors like local metabolic demand, oxygen levels, and the release of vasoactive substances can also modulate vascular responses. Structural features of the vessel, such as branching patterns, vessel diameter, and vessel wall composition, can also contribute to the heterogeneity in vascular responses. Understanding the complex interplay of these factors is crucial for deciphering the mechanisms underlying neurovascular coupling.

Could the principles of network coordination revealed here provide insights into the fundamental mechanisms governing blood flow regulation in the brain

The principles of network coordination revealed in this study could indeed provide insights into the fundamental mechanisms governing blood flow regulation in the brain. The coordinated responses of vessels within the microvascular network suggest the presence of regulatory mechanisms that ensure efficient blood flow distribution. By studying how changes in vessel caliber are coordinated across the network in response to neuronal activation, researchers can gain a deeper understanding of how blood flow is regulated to meet the metabolic demands of active brain regions. This network-level analysis can help uncover the underlying principles of blood flow regulation, such as the role of vascular tone, vessel connectivity, and feedback mechanisms in maintaining cerebral perfusion. Additionally, studying network coordination in different physiological and pathological conditions could reveal how disruptions in these mechanisms contribute to cerebrovascular diseases and neurodegenerative disorders.