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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 PublicationScientific Reports
ISSN2045-2322
Volume9Issue: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.

DOI10.1038/s41598-019-54316-6
URLView the original
Indexed BySCIE ; SSCI
Language英語English
WOS Research AreaScience & Technology - Other Topics
WOS SubjectMultidisciplinary Sciences
WOS IDWOS:000501435300001
PublisherNATURE PORTFOLIOHEIDELBERGER PLATZ 3, BERLIN 14197, GERMANY
Scopus ID2-s2.0-85075975037
Fulltext Access
Citation statistics
Document TypeJournal article
CollectionBiological Imaging and Stem Cell Core
Faculty of Health Sciences
INSTITUTE OF COLLABORATIVE INNOVATION
DEPARTMENT OF PUBLIC HEALTH AND MEDICINAL ADMINISTRATION
Corresponding AuthorYuan,Zhen
Affiliation1.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 AffilicationFaculty of Health Sciences
Corresponding Author AffilicationFaculty 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.
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