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  • the Korean Society for Brain and Neural Sciences

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Original Article

Exp Neurobiol 2023; 32(5): 362-369

Published online October 31, 2023

https://doi.org/10.5607/en23026

© The Korean Society for Brain and Neural Sciences

Graph Theoretical Analysis of Brain Structural Connectivity in Patients with Alcohol Dependence

Hyunjung Lee1, Joon Hyung Jung1,3, Seungwon Chung1,2, Gawon Ju1,2, Siekyeong Kim1,2, Jung-Woo Son1,2, Chul-Jin Shin1,2, Sang Ick Lee1,2 and Jeonghwan Lee1,2*

1Department of Psychiatry, Chungbuk National University Hospital, Cheongju 28644,
2Department of Psychiatry, College of Medicine, Chungbuk National University, Cheongju 28644,
3Department of Psychiatry, College of Medicine, Seoul National University, Seoul 03080, Korea

Correspondence to: *To whom correspondence should be addressed.
TEL: 82-43-269-6051, FAX: 82-43-267-7951
e-mail: jeonghwan@cbnuh.or.kr

Received: August 3, 2023; Revised: October 19, 2023; Accepted: October 26, 2023

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

This study aimed to compare brain structural connectivity using graph theory between patients with alcohol dependence and social drinkers. The participants were divided into two groups; the alcohol group (N=23) consisting of patients who had been hospitalized and had abstained from alcohol for at least three months and the control group (N=22) recruited through advertisements and were social drinkers. All participants were evaluated using 3T magnetic resonance imaging. A total of 1000 repeated whole-brain tractographies with random parameters were performed using DSI Studio. Four hundred functionally defined cortical regions of interest (ROIs) were parcellated using FreeSurfer based on the Schaefer Atlas. The ROIs were overlaid on the tractography results to generate 1000 structural connectivity matrices per person, and 1000 matrices were averaged into a single matrix per subject. Graph analysis was performed through igraph R package. Graph measures were compared between the two groups using analysis of covariance, considering the effects of age and smoking pack years. The alcohol group showed lower local efficiency than the control group in the whole-brain (F=5.824, p=0.020), somato-motor (F=5.963, p=0.019), and default mode networks (F=4.422, p=0.042). The alcohol group showed a lower global efficiency (F=5.736, p=0.021) in the control network. The transitivity of the alcohol group in the dorsal attention network was higher than that of the control (F=4.257, p=0.046). Our results imply that structural stability of the whole-brain network is affected in patients with alcohol dependence, which can lead to ineffective information processing in cases of local node failure.


Keywords: Alcohol, Brain, Diffusion tensor imaging, Tractography, Connectome

The brain is a delicate and complex organ that requires careful balancing of neurotransmitters. Chronic alcohol ingestion interferes with this balance, making it difficult for the brain to perform its role and affects physical and mental health. Alcohol acts on the central nervous system, impacts communication, information processing pathways [1], and the brain structure [2].

Alcohol causes structural and functional damage to the brain [3]. Several studies have directly evaluated the structure and function of the brain. Brain connectivity analyses based on brain images can be classified as either structural or functional. Structural connectivity can be analyzed using diffusion tensor Image (DTI) from magnetic resonance imaging (MRI). The DTI can be used to obtain the physical wiring between the cortical regions through the nerve bundle. This is based on the ability of water molecules to diffuse more readily along the major axis of the fiber bundle than perpendicular to it, enabling the estimation of fiber direction. Brain functional connectivity is estimated using the temporal coherence of fluctuations in blood oxygen level-dependent signals across brain regions. Functional connectivity measures are commonly derived from resting-state functional magnetic resonance imaging (fMRI) data [4].

A study on the association between alcohol consumption and the functional connectivity of resting networks measured by brain waves in healthy social drinkers suggests that alcohol increases inhibitory neurotransmitters and strengthens the functional connectivity between regions of the brain [5]. However, this was the result of a study on acute alcohol intake that can be considered a physiological effect of moderate alcohol consumption. This contrasts with the decrease in inhibitory neurotransmitters and poor functional connectivity of the brain observed in chronic drinkers [6, 7]. Heavy drinkers have lower functional connectivity in the visual and frontal cortices, default mode network, and thalamus, and the cerebellum showed higher functional connections compared with the control group according to resting-state functional MRI study [8]. This suggests that excessive alcohol consumption contributes to decreased cognitive ability and increased cerebellar connectivity, resulting in a compensatory effect on behavioral performance. Another study using fMRI confirmed that the structural and functional connectivity of the default mode network was related to the severity of alcohol use disorder in participants with alcohol dependence [9]. Various studies have emphasize the importance of understanding the network-level structural changes in the brain related to alcohol consumption [10-12].

Cognitive functions such as language, emotion, and attention employ various brain regions (not adjacent to each other) that interact with each other by organizing a network. The brain network is a structure consisting of nodes and edges, wherein each region is organically connected. Graph theory can explain brain networks. Here, a node refers to the region of interest in the brain. Various cognitive processes occur through information exchange and integration between the nodes within a brain network. Two main methods are used to evaluate the structural connectivity of brain networks. The first method measures the overall structural stability of the network, that is, the cohesion of the network. This can be evaluated using the number of connections between regions (node degree), transitivity, and efficiency. Using these measures, the stability of whole or partial brain connections can be quantified. The second method measures the degree of the center position and the degree of concentration in the center. That is, it quantifies the importance of each node while considering its network structure. This can be quantified as the centrality [13, 14].

This study aimed to test the hypothesis that the graph measures from whole-brain connectometry is disrupted in patients with alcohol dependence. Physical wiring between the brain cortical regions was estimated using a tractography approach, and the properties of structural network connectivity were analyzed using graph theory.

Participants

Participants were divided into two groups. The alcohol group comprised male patients admitted to alcoholic hospitals, all of whom were diagnosed with alcohol dependence. All participants in the alcohol group abstained from alcohol for at least 3 months and had completed detoxification and withdrawal pharmacotherapy. The control group was recruited through community advertisements. All participants in the control group were social drinkers, had never been diagnosed with alcohol dependence before, and did not meet the alcohol dependence diagnostic criteria at the time of the study. Participants were given a diagnostic and statistical manual for mental disorders and a structured clinical interview based on the 4th edition. Participants with major mental disorders, neurological diseases, severe systemic diseases, or other substance use disorders were excluded. Thus, 23 participants in the alcohol group and 22 participants in the control group were included in the study.

All participants underwent the Korean version of the Alcohol Use Disorder Identification Test (AUDIT-K) and the Brief version of Michigan Alcoholism Screening Test (Brief-MAST) [15]. The AUDIT-K can be used to identify dangerous drinking and alcohol use disorders by evaluating individuals’ alcohol consumption, frequency, dependence symptoms, and problem behavior [13]. The MAST is an alcoholism screening test that is widely used worldwide owing to its high validity and reliability [14]. Twenty-five questions related to alcohol dependence were answered with “yes-no,” which can be a sensitive indicator for alcohol addiction since it deals with behavior rather than the amount of alcohol consumed or hypothetical psychological factors. Brief-MAST is an abbreviated version of MAST consisting of ten questions and is highly correlated with alcohol intake [15]. Brief MAST was effective in distinguishing alcohol dependence as well as MAST [16]. Quantitative information on personal alcohol consumption patterns was obtained through structured interviews. This study investigated the total amount of lifetime alcohol intake by translating lifetime drinking history (LDH), which retrospectively records changes in drinking patterns such as alcohol consumption, maximum amount of drinking once, and frequency from the year of steady drinking to participation in the study. One standard drink is approximately equal to 13.6 grams of pure alcohol [17]. All participants provided signed informed content. This study was reviewed and approved by the Ethics Committee of Chungbuk National University.

Image acquisition

All participants were scanned at the Ochang Campus of the Korea Basic Science Institute in Cheongju, Republic of Korea using a 3 Tesla Philips Achieva (Philips Medical Systems, Netherlands) MRI scanner. The scan protocol included high-resolution T1 weighted magnetization-prepared rapid gradient echo (MPRAGE) imaging and diffusion tensor imaging (DTI). The parameters for T1-MPRAGE imaging were obtained using the following protocols: repetition time=6.8 ms, echo time=3.1 ms, field of view=256 mm, flip angle=9°, voxel size=1.0×1.0×1.2 mm, and 170 slices without gaps. The DTI were acquired using the following parameters: voxel size=2.0×2.0×3.0 mm, TE=70 ms, TR6332 ms; number of volumes=34, bandwidth=2725.2 Hz/pixel. Images were acquired with 34 different diffusion directions with a b-value of 1000 s/mm2 including one b-value=0 s/mm2.

Image analysis

Cortical parcellation

T1-MPRAGE imaging was analyzed using FreeSurfer software (version 7.1.1). The preprocessing steps were described in previous studies [18-20]. We parcellated an individual subject’s brain into 400 regions using the Schaefer Atlas to build functionally defined cortical regions of interest (ROI), which delineates the brain cortex based on intrinsic functional connectivity [21]. All ROIs belonged to one of the seven putative networks (visual, somato-motor, dorsal attention, salience ventral attention, limbic, control, and default mode networks) presented by Yeo et al. [22].

Tractography and structural connectivity matrix

Preprocessing and whole-brain fiber-tracking were performed using DSI Studio. The DTIs were reconstructed with generalized q-sampling imaging (GQI) applying a diffusion sampling length ratio of 1.5, which was optimized through careful visual inspection of the regions of existing crossing fibers [23]. Whole-brain tractography was performed 1000 times using a deterministic fiber-tracking algorithm for each subject with random parameters. Following previous studies, angular thresholds between 45° and 80° and fractional anisotropy (FA) between 0.01~0.10 were randomly selected 1000 times to minimize the effect of high tractography variability [24, 25]. The fiber count (250,000 streamlines), fiber length (10~800 mm), and step size (1 mm) were set to constant values based on a previous study [26]. Four hundred cortical ROIs were loaded onto each whole-brain tractography image. In turn, 1000 connectivity matrices consisting of 400 cortical ROIs as nodes and fiber counts as edges were computed. These matrices were averaged into a single adjacency matrix per subject. We obtained an average connectivity matrix consisting of edges with an FA value, and connections with an average FA of 0.001 or more to rule out false-positive tracking results. A flow chart of image analysis is shown in Fig. 1.

Graph measure

The adjacency matrix generated from the above process consists of nodes representing anatomical regions and edges representing the weighted structural connections of two given nodes. Graph measures such as transitivity, local and global efficiency, and eigenvector centrality were computed from the adjacency matrix. Transitivity measures the density of the adjacent interconnected nodes that form closely connected groups [27]. Efficiency is inversely related to the path length, making it easier to estimate the topological distance in the network [28]. The importance of the nodes was estimated using eigenvector centrality (which considers a weight). It was measured for each node and averaged within an individual network [29]. Graph measures were calculated using iGraph package in the CRAN R static package version 4.3.0.

Statistical analysis

Statistical analyses were performed using CRAN R static package. Demographic variables, AUDIT-K, Brief-MAST, and LDH were compared between the two groups using Student’s t-test. Graph measures were compared via an analysis of covariance with age and pack years of smoking as covariates. A bivariate correlation analysis was conducted on LDH and graph measures within the alcohol group to assess whether the amount of alcohol consumed, and duration of drinking affected structural connectivity.

Demographic characteristics

A comparison of the demographic characteristics between the alcohol and control groups is presented in Table 1. All the participants were male, and there was no difference between the groups in terms of age, years of education, and smoking amount. AUDIT-K (t=6.548, p<0.001), Brief-MAST (t=10.916, p<0.001), and LDH (t=3.040, p=0.004) levels were significantly higher in the alcohol group than in the control group.

Graph measure

As shown in Fig. 2, graph visualization showed that nodes of the alcohol group were sparsely distributed and less densely connected compared with the control group. Table 2 show the statistical difference of graph measure of the brain structural connectivity. Local efficiency was lower in the whole-brain network (F=5.824, p=0.020), the somato-motor network (F=5.963, p=0.019), and the default mode network (F=4.422, p=0.042) in the alcohol group among several indicators of structural connectivity. Global efficiency of the control network was lower in the alcohol group (F=5.736, p=0.021). The transitivity of the alcohol group in the dorsal attention network was higher than that of the control group (F=4.257, p=0.046).

To confirm whether amount of lifetime alcohol consumption affected structural connectivity, bivariate correlation analysis between LDH levels and graph measures was conducted in the alcohol group. There was no significant associations between LDH levels and graph measures.

The structural connectivity of the brain that was estimated using graph theory was compared between the alcohol and healthy control groups. The node efficiency of the whole-brain network was lower in the alcohol group compared with control group. Node efficiency refers to the sum of the reciprocals of the shortest path length included in each node. In other words, larger the value of node efficiency, the more efficient the network. Specifically, global efficiency is defined as the average of the inverse distances between all pairs of nodes, and the average node efficiency that considers only neighboring nodes in an actual cluster is called local efficiency. If the relatively close regions were well clustered, the local efficiency would be high. In a study comparing the brain networks of male patients with alcohol dependence and healthy controls using resting-state fMRI [30], global efficiency was further reduced in the alcohol dependence group. In the alcohol dependence group, the local efficiency of the left orbitofrontal cortex (OFC) was higher than in the control group, and the local efficiencies of the right OFC, right fusiform gyrus (FFG), right temporal pole, right inferior occipital gyrus, and left insula were lower. This finding suggests that motivation and facial-voice integration related networks are associated with alcohol-related anomalies. Our study showed significant differences in the local efficiency of the whole-brain, somato-motor, and default mode networks. The local efficiency in the alcohol group was lower than that in the control group. This implies that each node in the alcohol group network is clustered relatively inefficiently with the surrounding nodes, forming a relatively unstable network structure that is vulnerable to local node failure. In the control network, the global efficiency of the alcohol group was lower than that of the control group, suggesting that information movement does not occur efficiently in the control network of the alcohol group.

The somato-motor network is also known as the sensorimotor network. In this study, the local efficiency of the somato-motor network in the alcohol group was lower than that in the control group. This suggested that an efficient structure was not organized. The somato-motor network processes body sensations and sends signals to the motor cortex to execute the appropriate motor responses. A previous study found a decrease in resting functional connectivity between the right lateral occipital cortex and somato-motor network in individuals with impulsivity [31]. Lowered local efficiency of the somato-motor network in the alcohol group can be structural neural correlate of increased impulsivity, as it does not work properly to recognize and integrate perceptual information to control behavior.

The default mode network plays an important role in processes of autobiographical information about oneself and self-directed mental activities. A previous study revealed that acute alcohol exposure affects the resting functional connectivity within the default mode network [32]. This may be related to the damage to memory encoding and self-reference processes commonly observed during alcoholism. Another study investigating the functional connectivity of the default mode network in alcoholics using resting-state fMRI found that the brain regions of the default mode network were less efficient in the alcohol group [33]. In our study, the local efficiency of the default mode network was impaired in the alcohol group.

The control network is also called the frontoparietal network. The interconnections within the control network are the basis of the main cognitive function execution and are reduced in patients with chronic alcoholism [34]. Similarly, our study found that the global efficiency of the control network in the alcohol group was lower than that in the control group. Low global efficiency means that the connection path between distant areas is relatively long; therefore, information movement cannot occur efficiently. This can contribute to inhibition of cognitive control, emotional regulation, problem-solving, working memory-related tasks, and decision-making in the patients with alcohol dependence from being efficiently performed.

Transitivity indicates overall probability of the existence of closely connected modules by interconnecting adjacent network nodes. In this study, the alcohol group had higher transitivity than the control group in the dorsal attention network. The dorsal attention network is associated with the attentional concentration. It is responsible for specific cognitive functions, such as selective attention, continuous attention, and maintenance of the type of task and solution strategy. In previous study, resting functional connectivity was significantly reduced in some regions of interest in the cingulo-opercular network and dorsal attention network in the alcohol group [35]. Several studies suggest that brain structural network alteration compensate the neurodegenerative disease and its functional impairment [36, 37]. It can be assumed that it compensates for the decrease in functional connectivity by increasing transitivity in the alcohol group after comparing the results of this study with those of a previous study. The dorsal attention network centrality which indicates the importance of each node showed a higher value in the alcohol group compared with the control group, although there was no significant difference.

The main limitations of this study were as follows. A neurocognitive function test was not performed; therefore, the relationship between structural changes in the brain and actual cognitive function was not examined. It is necessary to consider ways to correct this since there may be differences in individual recovery ability, such as the results of previous studies showing that people with brain-delivered neurotic factor (BDNF) homozygotes have a better degree of structural recovery after drinking [38]. The lack of female participants limits generalizability of our study results. It will be necessary to recruit male and female participants with neurocognitive tests to compare the results. Lastly, there was an absence of multiple comparison corrections. Our study involved comparisons across seven specific networks while previous studies did not apply these corrections when comparing global network parameters [39, 40]. Research on DTI-based graph theoretical analysis in alcohol dependence is scarce. Therefore, our findings, despite their limitations, could serve as a foundation for further studies.

In conclusion, this study compared graph-theoretic brain structural connectivity between patients with alcohol dependence and healthy controls. The local efficiencies of the whole-brain, somato-motor, and default mode networks were lower in the alcohol group than in the control group. This suggests that the vulnerability of brain cortical wiring in the alcohol group was greater than that in the control group. In contrast, the transitivity of the dorsal attention network was greater in the alcohol group than that in the control group. It can be assumed that this compensates for the functional impairment. The strength of our study is that it evaluated the structural connectivity of novel brain methods (including 1000 iterations of tractography) which can reduce false-positive tracking results. Furthermore, the significances of the group differences were in the analysis of covariance, which corrected for the effects of age and smoking amount. Future studies are needed to identify the mechanisms of alterations in brain structural connectivity, their association with cognitive damage, and the recurrence of alcohol dependence.

Fig. 1. Flowchart of brain structural network construction based on whole-brain white matter tractography. (A) Diffusion tenser image. (B) Whole-brain tractography performed 1000 times using random parameters. (C) High-resolution T1-MPRAGE. (D) Cortical parcellation of functionally defined 400 cortical regions of interest (ROI) using the Schaefer Atlas. (E) Brain structural network consisting of 400 cortical ROIs (nodes) and fiber counts (edges). (F) Adjacency matrix generated from the structural network of the brain. Vis, visual network; SomMot, somato-motor network; DorsAttn, dorsal attention network; SalVentAttn, salience ventral attention network; Limbic, limbic network; Cont, control network; Default, default mode network; LH, left hemisphere; RH, right hemisphere.
Fig. 2. Visualiation of the whole-brain network. The whole-brain network generated from averaged adjacency matrix of each group. Radius of nodes was set to rescaled node degree. Transparency and width of the edge were set to rescaled fiber counts. Nodes of the alcohol group are sparsely distributed and less densely connected compared with the control group. Vis, visual network; SomMot, somato-motor network; DorsAttn, dorsal attention network; SalVentAttn, salience ventral attention network; Limbic, limbic network; Cont, control network; Default, default mode network.
Table. 1.

Comparison of demographic variables and scales (mean±SD)

Alcohol (n=23)Control (n=22)Tp
Age50.2±7.450.6±6.1-0.1850.854
Education (years)13.7±2.813.8±3.1-0.1470.891
Smoking (pack/years)25.1±9.920.3±25.20.8460.402
AUDIT-K28.5±10.611.0±6.96.548<0.001
Brief-MAST20.8±7.52.0±3.010.916<0.001
LDH (drinks)74955.9±85566.818295.9±17893.53.0400.004
Sobriety period (months)12.8±17---

AUDIT-K, Korean version of the Alcohol Use Disorder Identification Test; Brief-MAST, Brief Michigan Alcoholism Screening Test; LDH, lifetime drinking history.


Table. 2.

Comparison of graph measures (mean±SD)

NetworkGraph measureAlcohol (n=23)Control (n=22)FESp
Whole-brain networkTransitivity0.517±0.0340.519±0.0220.0560.0010.814
Centrality0.758±0.0400.758±0.0300.0000.0000.987
Local efficiency0.170±0.0050.173±0.0055.8240.1250.020
Global efficiency0.137±0.0090.141±0.0082.040.0480.161
Visual networkTransitivity0.701±0.0500.704±0.040.0930.0010.761
Centrality0.426±0.0600.436±0.0530.2580.0080.615
Local efficiency0.301±0.0180.309±0.0191.9420.0440.171
Global efficiency0.280±0.0160.285±0.0190.8290.0170.368
Somato-motor networkTransitivity0.655±0.0240.665±0.0232.6880.0570.109
Centrality0.697±0.0770.690±0.0610.0410.0020.840
Local efficiency0.201±0.0200.215±0.0195.9630.1280.019
Global efficiency0.138±0.0230.152±0.0233.7660.0840.059
Dorsal attention networkTransitivity0.580±0.03500.556±0.0434.2570.0970.046
Centrality0.771±0.0330.751±0.0403.1720.0810.082
Local efficiency0.132±0.0310.139±0.0250.8920.0150.351
Global efficiency0.067±0.0240.075±0.0211.7040.0380.199
Salience ventral attention networkTransitivity0.694±0.0540.720±0.0631.6750.0540.203
Centrality0.642±0.0680.639±0.0660.0280.0000.868
Local efficiency0.188±0.0300.174±0.0282.1580.0580.149
Global efficiency0.127±0.0330.123±0.0340.1150.0050.737
Limbic networkTransitivity0.790±0.0640.766±0.0561.5880.0380.215
Centrality0.309±0.0890.325±0.0850.2900.0090.593
Local efficiency0.268±0.0390.272±0.0340.2790.0030.600
Global efficiency0.260±0.0430.263±0.0360.1400.0010.710
Control networkTransitivity0.265±0.0390.275±0.0380.5280.0160.471
Centrality0.355±0.0820.351±0.1010.0620.0010.805
Local efficiency0.150±0.0230.163±0.0243.2040.0740.081
Global efficiency0.087±0.0200.103±0.0225.7360.1320.021
Default mode networkTransitivity0.614±0.0360.633±0.0322.9320.0720.094
Centrality0.355±0.0820.351±0.1010.0620.0010.805
Local efficiency0.263±0.0160.273±0.0164.4220.0940.042
Global efficiency0.207±0.0220.215±0.0211.2520.0290.270

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