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Leveraging Neural Networks in Preclinical Alcohol Research

Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based...

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Detalles Bibliográficos
Autores principales: Smith, Lauren C., Kimbrough, Adam
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565429/
https://www.ncbi.nlm.nih.gov/pubmed/32825739
http://dx.doi.org/10.3390/brainsci10090578
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author Smith, Lauren C.
Kimbrough, Adam
author_facet Smith, Lauren C.
Kimbrough, Adam
author_sort Smith, Lauren C.
collection PubMed
description Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural “hubs” involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking.
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spelling pubmed-75654292020-10-26 Leveraging Neural Networks in Preclinical Alcohol Research Smith, Lauren C. Kimbrough, Adam Brain Sci Review Alcohol use disorder is a pervasive healthcare issue with significant socioeconomic consequences. There is a plethora of neural imaging techniques available at the clinical and preclinical level, including magnetic resonance imaging and three-dimensional (3D) tissue imaging techniques. Network-based approaches can be applied to imaging data to create neural networks that model the functional and structural connectivity of the brain. These networks can be used to changes to brain-wide neural signaling caused by brain states associated with alcohol use. Neural networks can be further used to identify key brain regions or neural “hubs” involved in alcohol drinking. Here, we briefly review the current imaging and neurocircuit manipulation methods. Then, we discuss clinical and preclinical studies using network-based approaches related to substance use disorders and alcohol drinking. Finally, we discuss how preclinical 3D imaging in combination with network approaches can be applied alone and in combination with other approaches to better understand alcohol drinking. MDPI 2020-08-21 /pmc/articles/PMC7565429/ /pubmed/32825739 http://dx.doi.org/10.3390/brainsci10090578 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Smith, Lauren C.
Kimbrough, Adam
Leveraging Neural Networks in Preclinical Alcohol Research
title Leveraging Neural Networks in Preclinical Alcohol Research
title_full Leveraging Neural Networks in Preclinical Alcohol Research
title_fullStr Leveraging Neural Networks in Preclinical Alcohol Research
title_full_unstemmed Leveraging Neural Networks in Preclinical Alcohol Research
title_short Leveraging Neural Networks in Preclinical Alcohol Research
title_sort leveraging neural networks in preclinical alcohol research
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7565429/
https://www.ncbi.nlm.nih.gov/pubmed/32825739
http://dx.doi.org/10.3390/brainsci10090578
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