Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder.

paper Cited Public

Authors: Song, Hongwen; Yang, Ping; Zhang, Xinyue; Tao, Rui; Zuo, Lin; Liu, Weili; Fu, Jiaxin; Kong, Zhuo; Tang, Rui; Wu, Siyu; Pang, Liangjun; Zhang, Xiaochu
Year: 2024
Journal: Translational psychiatry
PMID: 39294121
DOI: 10.1038/s41398-024-03083-8
PMCID: PMC11411137

Alcohol use disorder (AUD) is a profound psychiatric condition marked by disrupted connectivity among distributed brain regions, indicating impaired functional integration. Previous connectome studies utilizing functional magnetic resonance imaging (fMRI) have predominantly focused on undirected functional connectivity, while the specific alterations in directed effective connectivity (EC) associated with AUD remain unclear. To address this issue, this study utilized multivariate pattern analysis (MVPA) and spectral dynamic causal modeling (DCM). We recruited 32 abstinent men with AUD and 30 healthy controls (HCs) men, and collected their resting-state fMRI data. A regional homogeneity (ReHo)-based MVPA method was employed to classify AUD and HC groups, as well as predict the severity of addiction in AUD individuals. The most informative brain regions identified by the MVPA were further investigated using spectral DCM. Our results indicated that the ReHo-based support vector classification (SVC) exhibits the highest accuracy in distinguishing individuals with AUD from HCs (classification accuracy: 98.57%). Additionally, our results demonstrated that ReHo-based support vector regression (SVR) could be utilized to predict the addiction severity (alcohol use disorders identification test, AUDIT, R = 0.38; Michigan alcoholism screening test, MAST, R = 0.29) of patients with AUD. The most informative brain regions for the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. These findings were validated in an independent data set (35 patients with AUD and 36 HCs, Classification accuracy: 91.67%; AUDIT, R = 0.17; MAST, R = 0.20). The results of spectral DCM analysis indicated that individuals with AUD exhibited decreased EC from the left pre-SMA to the right putamen, from the right dACC to the right putamen, and from the right LOFC to the right NACC compared to HCs. Moreover, the EC strength from the right NACC to left pre-SMA and from the right dACC to right putamen mediated the relationship between addiction severity (MAST scores) and behavioral measures (impulsive and compulsive scores). These findings provide crucial evidence for the underlying mechanism of impaired self-control, risk assessment, and impulsive and compulsive alcohol consumption in individuals with AUD, providing novel causal insights into both diagnosis and treatment.

Fig. 1

Classification results of data set 1.A Six classified methods were trained to classify AUD individuals from HCs using ReHo, ALFF, fALFF, and DC. The overall most accurate classifier was SVC, which was used for subsequent analysis. Mean accuracies of classification are presented in the left graph. Pattern classification of AUD and HCs based on the five resting-state fMRI measures over the whole brain using SVC. ROC curves and the corresponding area under the curve (AUC) are displayed for each resting-state fMRI measure in the right graph. The weight vector represents the relative relevance of each voxel to classify the groups. B The positive weight map for distinguishing HCs from AUD. C The negative weight map for distinguishing HCs from AUD.

Fig. 2

The SVR-based addiction severity prediction results of data set 1.A The MAST scores prediction. B The AUDIT scores prediction; left, the actual addiction severity scores and predicted addiction severity for each patient with AUD; middle, the ReHo values predict the addiction severity scores in patients with AUD; and right, the permutation tests of the prediction model. C The ReHo value maps contribute to the SVR prediction of MAST scores. D The ReHo value maps contribute to the SVR prediction of AUDIT scores.

Fig. 3

Five nodes of the spectral DCM.The model includes the following nodes: right dACC, left pre-SMA, right NACC, right putamen, and right LOFC. The associated time series was used to invert the spectral DCM with a fully connected architecture.

Fig. 4

EC difference between the AUD and HCs in data set 1.A Showing the significantly decreased EC strength from left pre-SMA to left pre-SMA, from left pre-SMA to right putamen, and from right dACC to right putamen; the significantly increased EC strength from right NACC to left pre-SMA. The dotted line indicates weakened connectivity in AUD, and the solid line indicates enhanced connectivity in AUD. B Difference in average EC between AUD and HCs.

Fig. 5

Results of validation analysis of the SVC and SVR models in data set 2.A The classification performance in data set 2 is based on the model obtained from data set 1 in the left graph. The permutation test results are in the right graph. B The prediction for MAST scores in data set 2 is based on the model obtained from data set 1. C The prediction for AUDIT scores in data set 2 is based on the model obtained from data set 1. left, the actual addiction severity scores and predicted addiction severity for each patient with AUD; middle, the ReHo values predict the addiction severity scores in patients with AUD; and right, the permutation tests of the prediction model.

Fig. 6

EC difference between the AUD and HCs in data set 2.A Showing the significant decreased EC strength from left pre-SMA to left pre-SMA, from left pre-SMA to right putamen, from right dACC to right putamen, and from right LOFC to right putamen; the significant increased EC strength from right NACC to left pre-SMA. The dotted line indicates weakened connectivity in AUD, and the solid line indicates enhanced connectivity in AUD. B Difference in average EC between AUD and HCs.

Fig. 7

Correlation between EC strength and behavioral measures.A Significant negative correlation was found between compulsive scores and EC strength from left pre-SMA to right putamen. B Significant negative correlation was found between compulsive scores and EC strength from the right dACC to the right putamen. C Significant positive correlation was found between impulsive scores and EC strength from right NACC to left pre-SMA.

Fig. 8

EC mediated the relationship between behavioral measures and addiction severity.A The EC strength from the right NACC to left pre-SMA fully mediated the association between addiction severity (MAST scores) and impulsive scores. B EC strength from the right dACC to right putamen fully mediated the association between addiction severity (MAST scores) and compulsive scores.

#	Section	Preview
40	Discussion	The present study applied MVPA and spectral DCM analysis methods to investigate the neurobiological…
41	Discussion	the prediction include left pre-SMA, right dACC, right LOFC, right putamen, and right NACC. This…
42	Discussion	Executive control deficit is a crucial characteristic of AUD [41]. Despite being aware of the…
43	Discussion	The dACC is also involved in detecting the presence of cognitive conflict, and error monitoring and…
44	Discussion	AUD individuals are also impaired across several measures of decision-making [60, 61]. In the…
45	Discussion	Moreover, burgeoning evidence suggests that addiction to drugs (e.g., alcohol) is associated with a…
46	Discussion	The most informative regions for the classification and prediction also included the right NACC. As…
47	Discussion	Several researchers have proposed a significant correlation between AUDs and impulse control…
48	Discussion	drinkers or individuals with an AUD, as well as refine diagnostic instruments to increase their…
49	Discussion	Limited research has been conducted to investigate the underlying causal neural mechanisms of…
50	Discussion	ventral tegmental area to the NACC, hippocampus, and medial frontal cortex compared to the control…
51	Discussion	Although our study is the first to identify abnormalities in brain-EC specific to individuals with…
52	Conclusions	For the first time, our results reveal alterations in causal connectivity in individuals with AUD.…

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Atypical effective connectivity from the frontal cortex to striatum in alcohol use disorder.

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