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

Abstract

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.

Global, interhemispheric and intrahemispheric functional connection patterns in male adults with alcohol use disorder

“… 38 The thalamus and caudate both belong to the basal ganglia network, thus implying that the basal ganglia network is also associated with the severity of AUD. 39 Numerous studies have suggested that the thalamus might be an important brain node with an abnormal pattern of functional connection in multiple networks in AUD, including the basal ganglia network, 39 reward network, 40 salience network, 41 executive control network, 41 and default mode network. 39 , 41 Our findings also detected abnormal global, interhemispheric and intrahemispheric FCD of the thalamus, predominantly in the anterior nucleus, which is principally connected to the hippocampus and responsible for amnesia in individuals with AUD.…”

Prediction of brain age in individuals with and at risk for alcohol use disorder using brain morphological features

“…CTL individuals were recruited through advertisements and screened to exclude any personal and/or family history of major medical, psychiatric, or substance-related disorders. A detailed description of this dataset is provided in our previous publications [39,40]. (ii) The FHD dataset had a total of 71 participants (38 males and 33 females), of whom 40 individuals (20 males) were grouped as HiFHD based on their high scores (above the median value of 5) on the measure of family history density (FHD) [41], while the remaining 31 individuals (18 males) were categorized as LoFHD as they had low FHD scores (below the median value of 5) [see Table 2 ].…”

The Collaborative Study on the Genetics of Alcoholism: Overview

“…Similarly, our ability to measure the brain's activity during resting state and during various cognitive tasks with exquisite temporal accuracy, allows us to develop and implement EEG protocols that uniquely address questions regarding the course of AUD. While COGA has maintained its focus on EEG, a subset of COGA participants have been imaged using brain Magnetic Resonance Imaging (MRI) allowing for comparisons between these data 82 . Moreover, genetic differences in COGA participants are now being translated into changes in neuronal function using advanced molecular and cellular tools, potentially leading to novel therapeutic strategies for treating AUD.…”

“…While COGA has maintained its focus on EEG, a subset of COGA participants have been imaged using brain Magnetic Resonance Imaging (MRI) allowing for comparisons between these data. 82 Moreover, genetic differences in COGA participants are now being translated into changes in neuronal function using advanced molecular and cellular tools, potentially leading to novel therapeutic strategies for treating AUD.…”