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
20	Materials and methods — MVPA — Permutation test	In addition to assessing classification accuracy, the performance of the classifiers was also…
21	Materials and methods — MVPA — Validation analysis in an independent data set	We also evaluated the external validity of the SVC and SVR models by implementing the following…
22	Materials and methods — MVPA — Statistical analysis	The demographic and clinical characteristics of individuals with AUD and HCs were computed. Group…
23	Materials and methods — Spectral DCM	The spectral DCM analyses were performed using DCM 12, which was implemented in SPM 12 software…
24	Materials and methods — Spectral DCM	due to the absence of external inputs in the model [22, 26]. The Laplace method with variational…
25	Results — Demographic and clinical characteristics	Table 1 presents the demographic and clinical characteristics of patients with AUD and HCs,…
26	Results — Demographic and clinical characteristics — Classifier evaluation and brain regions that contributed most to the SVC classification in data set 1	The SVC classification results are presented in Fig. 1, indicating that SVC outperformed the other…
27	Results — Demographic and clinical characteristics — Predictor evaluation and brain regions that contributed most to the SVR prediction in data set 1	The SVR prediction results demonstrated that ReHo values can serve as a predictor of addiction…
28	Results — Between-group differences in efficient connectivity in data set 1	The ROIs for DCM analysis were determined based on the results obtained from our MVPA. These ROIs…
29	Results — Between-group differences in efficient connectivity in data set 1	< 0.001), from right dACC to right putamen (t = −3.17, P = 0.002, FDR corrected, P = 0.002) and…
30	Results — Between-group differences in efficient connectivity in data set 1 — Correlation between the mean EC strength and addiction severity in data set 1	Negative correlations (FDR correction, P = 0.05) were observed between addiction severity (AUDIT and…
31	Results — Between-group differences in efficient connectivity in data set 1 — Correlation between the mean EC strength and addiction severity in data set 1	correlation between the mean EC strength and addiction severity (AUDIT and MAST scores) was still…
32	Results — Between-group differences in efficient connectivity in data set 1 — Validation analysis of the SVC classification and SVR prediction in an independent data set	The SVC classification model based on the ReHo images obtained from data set 1 was utilized to…
33	Results — Between-group differences in efficient connectivity in data set 1 — Validation analysis of the SVC classification and SVR prediction in an independent data set	The SVR predictive model derived from data set 1 was utilized to predict the addiction severity of…
34	Results — Between-group differences in efficient connectivity in data set 1 — Between-group differences in efficient connectivity in data set 1 are repeated in data set 2	The differences between groups are illustrated in Fig. 6 and Table 6. The variance between the two…
35	Results — Between-group differences in efficient connectivity in data set 1 — Between-group differences in efficient connectivity in data set 1 are repeated in data set 2	corrected, P = 0.002; data set 1 showed no significance after correction by FDR at P < 0.05).Table…
36	Results — Between-group differences in efficient connectivity in data set 1 — Correlation between the mean EC strength and behavioral measures in data set 2	We observed significant negative correlations (FDR correction, P = 0.05) between addiction severity…
37	Results — Between-group differences in efficient connectivity in data set 1 — Correlation between the mean EC strength and behavioral measures in data set 2	between the mean EC strength and addiction severity (AUDIT and MAST scores) was still evident after…
38	Results — Between-group differences in efficient connectivity in data set 1 — Correlation between the mean EC strength and behavioral measures in data set 2	In data set 2, we observed significant negative correlations between the compulsive scores and mean…
39	Results — EC as a mediator	The mediation analyses revealed that EC strength from the right NACC to left pre-SMA mediated the…

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

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