Evidence map›Paper›PMID 39679412›Full record

ArticleBiomedical optics express2024

Cerebral blood flow patterns induced by photoactivation based on laser speckle contrast imaging.

Xuan Zhu, Liang Shi, Pengcheng Li, Jinling Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical optics express, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Xuan ZhuBritton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.
Liang ShiBritton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.ORCID https://orcid.org/0000-0002-4268-4784
Pengcheng LiBritton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.ORCID https://orcid.org/0000-0002-5695-9304
Jinling LuBritton Chance Center for Biomedical Photonics and MoE Key Laboratory for Biomedical Photonics, Advanced Biomedical Imaging Facility, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Neurovascular coupling (NVC) is crucial for maintaining brain function and holds significant implications for diagnosing neurological disorders. However, the neuron type and spatial specificity in NVC remain poorly understood. In this study, we investigated the spatiotemporal characteristics of local cerebral blood flow (CBF) driven by excitatory (VGLUT2) and inhibitory (VGAT) neurons in the mouse sensorimotor cortex. By integrating optogenetics, wavefront modulation technology, and laser speckle contrast imaging (LSCI), we achieved precise, spatially targeted photoactivation of type-specific neurons and real-time CBF monitoring. We observed three distinct CBF response patterns across different locations: unimodal, bimodal, and biphasic. While unimodal and bimodal patterns were observed in different locations for both neuron types, the biphasic pattern was exclusive to inhibitory neurons. Our results reveal the spatiotemporal complexity of NVC across different neuron types and demonstrate our method's ability to analyze this complexity in detail.

Identifiers

PMID39679412
PMCPMC11640580

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.