Residential College | false |
Status | 已發表Published |
Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction | |
Ieong,Hada Fong ha1,4; Gao,Fu2; Yuan,Zhen1,3 | |
2019-12-04 | |
Source Publication | Scientific Reports |
ISSN | 2045-2322 |
Volume | 9Issue:1Pages:18262 |
Abstract | Chronic and recurrent opiate use injuries brain tissue and cause serious pathophysiological changes in hemodynamic and subsequent inflammatory responses. Prefrontal cortex (PFC) has been implicated in drug addiction. However, the mechanism underlying systems-level neuroadaptations in PFC during abstinence has not been fully characterized. The objective of our study was to determine what neural oscillatory activity contributes to the chronic effect of opiate exposure and whether the activity could be coupled to neurovascular information in the PFC. We employed resting-state functional connectivity to explore alterations in 8 patients with heroin dependency who stayed abstinent (>3 months; HD) compared with 11 control subjects. A non-invasive neuroimaging strategy was applied to combine electrophysiological signals through electroencephalography (EEG) with hemodynamic signals through functional near-infrared spectroscopy (fNIRS). The electrophysiological signals indicate neural synchrony and the oscillatory activity, and the hemodynamic signals indicate blood oxygenation in small vessels in the PFC. A supervised machine learning method was used to obtain associations between EEG and fNIRS modalities to improve precision and localization. HD patients demonstrated desynchronized lower alpha rhythms and decreased connectivity in PFC networks. Asymmetric excitability and cerebrovascular injury were also observed. This pilot study suggests that cerebrovascular injury in PFC may result from chronic opiate intake. |
DOI | 10.1038/s41598-019-54316-6 |
URL | View the original |
Indexed By | SCIE ; SSCI |
Language | 英語English |
WOS Research Area | Science & Technology - Other Topics |
WOS Subject | Multidisciplinary Sciences |
WOS ID | WOS:000501435300001 |
Publisher | NATURE PORTFOLIOHEIDELBERGER PLATZ 3, BERLIN 14197, GERMANY |
Scopus ID | 2-s2.0-85075975037 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Biological Imaging and Stem Cell Core Faculty of Health Sciences INSTITUTE OF COLLABORATIVE INNOVATION DEPARTMENT OF PUBLIC HEALTH AND MEDICINAL ADMINISTRATION |
Corresponding Author | Yuan,Zhen |
Affiliation | 1.Bioimging Core,Faculty of Health Sciences,University of Macau,Taipa,Macao 2.Department of Cardiac Surgery,Yale School of Medicine,Yale University,New Haven,United States 3.Centre for Cognitive and Brain Sciences,Institute of Collaborative Innovation,University of Macau,Taipa,Macao 4.Department of Anesthesiology,Yale School of Medicine,Yale University,New Haven,United States |
First Author Affilication | Faculty of Health Sciences |
Corresponding Author Affilication | Faculty of Health Sciences; INSTITUTE OF COLLABORATIVE INNOVATION |
Recommended Citation GB/T 7714 | Ieong,Hada Fong ha,Gao,Fu,Yuan,Zhen. Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction[J]. Scientific Reports, 2019, 9(1), 18262. |
APA | Ieong,Hada Fong ha., Gao,Fu., & Yuan,Zhen (2019). Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction. Scientific Reports, 9(1), 18262. |
MLA | Ieong,Hada Fong ha,et al."Machine learning: assessing neurovascular signals in the prefrontal cortex with non-invasive bimodal electro-optical neuroimaging in opiate addiction".Scientific Reports 9.1(2019):18262. |
Files in This Item: | There are no files associated with this item. |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment