Cargando…

Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures

Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can diff...

Descripción completa

Detalles Bibliográficos
Autores principales: Kamarajan, Chella, Ardekani, Babak A., Pandey, Ashwini K., Kinreich, Sivan, Pandey, Gayathri, Chorlian, David B., Meyers, Jacquelyn L., Zhang, Jian, Bermudez, Elaine, Kuang, Weipeng, Stimus, Arthur T., Porjesz, Bernice
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137599/
https://www.ncbi.nlm.nih.gov/pubmed/35621425
http://dx.doi.org/10.3390/bs12050128
_version_ 1784714417087184896
author Kamarajan, Chella
Ardekani, Babak A.
Pandey, Ashwini K.
Kinreich, Sivan
Pandey, Gayathri
Chorlian, David B.
Meyers, Jacquelyn L.
Zhang, Jian
Bermudez, Elaine
Kuang, Weipeng
Stimus, Arthur T.
Porjesz, Bernice
author_facet Kamarajan, Chella
Ardekani, Babak A.
Pandey, Ashwini K.
Kinreich, Sivan
Pandey, Gayathri
Chorlian, David B.
Meyers, Jacquelyn L.
Zhang, Jian
Bermudez, Elaine
Kuang, Weipeng
Stimus, Arthur T.
Porjesz, Bernice
author_sort Kamarajan, Chella
collection PubMed
description Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls.
format Online
Article
Text
id pubmed-9137599
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-91375992022-05-28 Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures Kamarajan, Chella Ardekani, Babak A. Pandey, Ashwini K. Kinreich, Sivan Pandey, Gayathri Chorlian, David B. Meyers, Jacquelyn L. Zhang, Jian Bermudez, Elaine Kuang, Weipeng Stimus, Arthur T. Porjesz, Bernice Behav Sci (Basel) Article Individuals with alcohol use disorder (AUD) may manifest an array of neural and behavioral abnormalities, including altered brain networks, impaired neurocognitive functioning, and heightened impulsivity. Using multidomain measures, the current study aimed to identify specific features that can differentiate individuals with AUD from healthy controls (CTL), utilizing a random forests (RF) classification model. Features included fMRI-based resting-state functional connectivity (rsFC) across the reward network, neuropsychological task performance, and behavioral impulsivity scores, collected from thirty abstinent adult males with prior history of AUD and thirty CTL individuals without a history of AUD. It was found that the RF model achieved a classification accuracy of 86.67% (AUC = 93%) and identified key features of FC and impulsivity that significantly contributed to classifying AUD from CTL individuals. Impulsivity scores were the topmost predictors, followed by twelve rsFC features involving seventeen key reward regions in the brain, such as the ventral tegmental area, nucleus accumbens, anterior insula, anterior cingulate cortex, and other cortical and subcortical structures. Individuals with AUD manifested significant differences in impulsivity and alterations in functional connectivity relative to controls. Specifically, AUD showed heightened impulsivity and hypoconnectivity in nine connections across 13 regions and hyperconnectivity in three connections involving six regions. Relative to controls, visuo-spatial short-term working memory was also found to be impaired in AUD. In conclusion, specific multidomain features of brain connectivity, impulsivity, and neuropsychological performance can be used in a machine learning framework to effectively classify AUD individuals from healthy controls. MDPI 2022-04-28 /pmc/articles/PMC9137599/ /pubmed/35621425 http://dx.doi.org/10.3390/bs12050128 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kamarajan, Chella
Ardekani, Babak A.
Pandey, Ashwini K.
Kinreich, Sivan
Pandey, Gayathri
Chorlian, David B.
Meyers, Jacquelyn L.
Zhang, Jian
Bermudez, Elaine
Kuang, Weipeng
Stimus, Arthur T.
Porjesz, Bernice
Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures
title Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures
title_full Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures
title_fullStr Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures
title_full_unstemmed Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures
title_short Differentiating Individuals with and without Alcohol Use Disorder Using Resting-State fMRI Functional Connectivity of Reward Network, Neuropsychological Performance, and Impulsivity Measures
title_sort differentiating individuals with and without alcohol use disorder using resting-state fmri functional connectivity of reward network, neuropsychological performance, and impulsivity measures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9137599/
https://www.ncbi.nlm.nih.gov/pubmed/35621425
http://dx.doi.org/10.3390/bs12050128
work_keys_str_mv AT kamarajanchella differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT ardekanibabaka differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT pandeyashwinik differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT kinreichsivan differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT pandeygayathri differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT chorliandavidb differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT meyersjacquelynl differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT zhangjian differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT bermudezelaine differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT kuangweipeng differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT stimusarthurt differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures
AT porjeszbernice differentiatingindividualswithandwithoutalcoholusedisorderusingrestingstatefmrifunctionalconnectivityofrewardnetworkneuropsychologicalperformanceandimpulsivitymeasures