ACADEMIC AND RESEARCH PEER-REVIEWED MEDICAL JOURNALISSN 1727-2378 (Print)         ISSN 2713-2994 (Online)
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Abnormalities in Microstructural Brain Connectivity in Patients with Opioid or Alcohol Dependence

DOI:10.31550/1727-2378-2020-19-4-35-42
Bibliography link: Tarumov D.A., Trufanov A.G., Zheleznyak I.S., Shamrey V.K., Malakhovsky V.N. Abnormalities in Microstructural Brain Connectivity in Patients with Opioid or Alcohol Dependence. Doctor.Ru. 2020; 19(4): 35–42. (in Russian) DOI: 10.31550/1727-2378-2020-19-4-35-42
13 July 13:13

Objective of the Study: To identify microstructural brain abnormalities in patients with opioid or alcohol dependence using magnetic resonance imaging (MRI) tractography.

Study Design: This was a comparative, cohort study.

Materials and Methods: Two hundred and forty-six people underwent MRI tractography, including 76 patients with alcohol dependence and 170 patients with opioid dependence. The control group consisted of 150 healthy people without any symptoms of dependence.

Study Results: MRI tractography revealed disruption of the cortico-subcortical connections, a characteristic sign of abnormal brain connectivity. In all patients with drug dependence, a number of neural network parameters (density, clustering coefficient, transitivity, and local efficiency) were significantly reduced compared with normal, regardless of the length of disease remission. In contrast, patients with alcohol dependence showed higher values for these network parameters.

Conclusion: Signs of white matter connectivity disruption identified by MRI tractography may be viewed as a predictor of alcohol or drug dependence. In the future these findings may be used to predict risk for addictive disorders.

Contributions: Tarumov, D.A. and Trufanov, A.G. — collected material; conducted the study; processed neurofunctional data; contributed to the general interpretation and analysis of results; writing the paper; Zheleznyak, I.S., Shamrey, V.K. and Malakhovsky, V.N. — contributed to the general interpretation and analysis of results; writing the paper.

Conflict of interest: The authors declare that they do not have any conflict of interests.

D.A. Tarumov (Corresponding author) — S.M. Kirov Military Medical Academy (a Federal Government-funded Military Educational Institution of Higher Education), Russian Federation Ministry of Defense; 6 Academician Lebedev St., St. Petersburg, Russian Federation 194044. eLIBRARY.RU SPIN: 7608-5045. ORCID: https://orcid.org/0000-0002-9874-5523. E-mail: Tarumov@live.ru

A.G. Trufanov — S.M. Kirov Military Medical Academy (a Federal Government-funded Military Educational Institution of Higher Education), Russian Federation Ministry of Defense; 6 Academician Lebedev St., St. Petersburg, Russian Federation 194044. eLIBRARY.RU SPIN: 7335-6463. E-mail: TrufanovArt@gmail.com

I.S. Zheleznyak — S.M. Kirov Military Medical Academy (a Federal Government-funded Military Educational Institution of Higher Education), Russian Federation Ministry of Defense; 6 Academician Lebedev St., St. Petersburg, Russian Federation 194044. eLIBRARY.RU SPIN: 1450-5053. E-mail: igzh@bk.ru

V.K. Shamrey — S.M. Kirov Military Medical Academy (a Federal Government-funded Military Educational Institution of Higher Education), Russian Federation Ministry of Defense; 6 Academician Lebedev St., St. Petersburg, Russian Federation 194044. ORCID: https://orcid.org/0000-0002-1165-6465. E-mail: ShamreyV.K@yandex.ru

V.N. Malakhovsky — S.M. Kirov Military Medical Academy (a Federal Government-funded Military Educational Institution of Higher Education), Russian Federation Ministry of Defense; 6 Academician Lebedev St., St. Petersburg, Russian Federation 194044. E-mail: MalakhovskyVova@gmail.com

Доктор.ру

Fig. 1. Overall structure of brain connectivity matrices generated by the AAL atlas for patients with drug dependence (А) and healthy subjects (Б) (p < 0.05)

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Fig. 2. Brain connectivity in patients with drug dependence (А) and healthy subjects (Б) as evaluated by analysis of the graphs (p < 0.05). 

Note: Spheres represent cortical and subcortical structures, and lines represent brain connections. There is a significant reduction in the number of connections in drug-dependent patients

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Table
Characteristics of the artificial network in patients with dependence, conv. units

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Fig. 3. Differences between connectivity matrices in patients with alcohol dependence (А), drug dependence (Б), and subjects from the control group (В) (p < 0.05)

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Fig. 4. Matrices of microstructural connectivity. An abnormal conglomeration of subcortical connections in patients with alcohol dependence (А) and normal findings (Б) (tractography data) (p < 0.05)

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Fig. 5. Modeling of connectivity between amygdala and hippocampus in patients with alcohol dependence using DSI Studio’s fiber-tracking function (p < 0.05)

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Fig 6. A plot of tract length in the amygdala and hippocampus complex versus global fractional anisotropy (p < 0.05)

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Fig 7. Disruption of cortical and subcortical brain microstructures in patients with drug dependence (p < 0.05)

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Fig 8. Brain connectivity maps for patients with drug dependence (А) and healthy subjects (Б), computed from tractography data (group analysis) (p < 0.05)

Note: There is a total loss of microstructural connections between the medial frontal cortex and subcortical structures in drug-dependent patients

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Fig 9. Plots of tract length in the cingulate gyrus versus global fractional anisotropy in patients with drug dependence (А), alcohol dependence (Б), and healthy subjects (В), (p < 0.05 for comparison with normal in both cases)

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Fig 10. Reduced global fractional anisotropy (GFA) in the cingulate gyrus and cerebellum in drug-dependent patients (А) and increased GFA in patients with alcohol dependence (Б) (p < 0.05)

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Received: 06.02.2020
Accepted: 03.03.2020

13 July 13:13
LITERATURE
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