Assessment of hyperacute cerebral ischemia using laser speckle contrast imaging.

Brain connectivity Cerebral blood flow Functional connectivity Ischemia

Journal

Brain connectivity
ISSN: 2158-0022
Titre abrégé: Brain Connect
Pays: United States
ID NLM: 101550313

Informations de publication

Date de publication:
18 Sep 2024
Historique:
medline: 18 9 2024
pubmed: 18 9 2024
entrez: 18 9 2024
Statut: aheadofprint

Résumé

Accurate diagnosis of cerebral ischemia severity is crucial for clinical decision-making. Laser speckle contrast imaging based cerebral blood flow imaging can help assess the severity of cerebral ischemia by monitoring changes in blood flow. In this study, we simulated hyperacute ischemia in rats, isolating arterial and venous flow-related signals from cortical vasculature. Pearson correlation was used to examine the correlation between damaged vessels. Granger causality analysis was utilized to investigate causality correlation in ischemic vessels. Resting state analysis revealed a negative Pearson correlation between regional arteries and veins. Following cerebral ischemia induction, a positive artery-vein correlation emerged, which vanished after blood flow reperfusion. Granger causality analysis demonstrating enhanced causality coefficients for middle artery-vein pairs during occlusion, with a stronger left-right arterial effect than that of right-left, which persisted after reperfusion. These processing approaches amplify the understanding of cerebral ischemic images, promising potential future diagnostic advancements.

Identifiants

pubmed: 39291777
doi: 10.1089/brain.2024.0026
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Bochao Niu (B)

UESTC, Chengdu, China; niubochao011716@163.com.

Guan Sihai (G)

University of Electronic Science and Technology of China, School of Life Science and Technology, 2006 XIYUAN AVENUE, WEST HI-TECH DISTRICT,CHENGDU, Chengdu, China, 610054; gcihey@sina.cn.

Hongyan Gong (H)

University of Electronic Science and Technology of China, School of Life Science and Technology, Chengdu, China; gonghongyan@uestc.std.edu.cn.

Peng Hu (P)

University of Electronic Science and Technology of China, School of Life Science and Technology, Chengdu, China; penghu01@outlook.com.

Pushti Shah (P)

New Jersey Institute of Technology, Department of Biomedical Engineering, Newark, New Jersey, United States; ps234@njit.edu.

Xiqin Liu (X)

University of Electronic Science and Technology of China, School of Life Science and Technology, Chengdu, China; xqliu63@gmail.com.

Yang Xia (Y)

University of Electronic Science and Technology of China, School of Life Science and Technology, Chengdu, China; xiayang@uestc.edu.cn.

Dezhong Yao (D)

University of Electronic Science and Technology of China, school of life science and technology, North Jianshe road #4, Chengdu, China, 610054; dyao@uestc.edu.cn.

Benjamin Klugah-Brown (B)

University of Electronic Science and Technology of China, Qingshuihe Campus:No.2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, China, 611731; bklugah@gmail.com.

Bharat B Biswal (BB)

New Jersey Institute of Technology, Department of Biomedical Engineering, 607 Fenster Hall, University Heights, Newark, New Jersey, United States, 07102; bbiswal@gmail.com.

Classifications MeSH