Microstate-specific functional connectivity in Alcohol Use Disorder using resting-state EEG
Poster No:
580
Submission Type:
Late-Breaking Abstract Submission
Authors:
Hanjin Park1
Institutions:
1Ulsan National Institute of Science and Technology, Ulsan, Ulsan
First Author:
Introduction:
Alcohol Use Disorder (AUD) is a chronic condition characterized by neural dysfunction, leading to alterations in brain structure and function. Linked to impaired self-control and impulsive behavior, chronic alcohol use is often referred to as "disconnection syndrome" (Dupuy & Chanraud, 2016). However, previous studies on functional connectivity (FC) in AUD have yielded inconclusive results; some reported hyperconnectivity (Lithari et al., 2012; Park et al., 2017), while others found hypoconnectivity or no significant differences compared to healthy controls (Tcheslavski et al., 2011; Sampedro-Piquero et al., 2024). We hypothesized that this inconsistency may arise from failing to account for the dynamic nature of brain's resting-state. Here, we used resting-state EEG (rsEEG) and microstate analyses to examine FC in AUD while accounting for transient state changes during rest.
Methods:
We used rsEEG data from the AUD group (AG) (N = 26) and the healthy control group (HG) (N = 35), recorded for 5 minutes with eyes-closed (EC) at a sampling rate of 1000 Hz. After excluding the first and last 30 seconds, we analyzed 4 minutes of data from 19 channels (10-20 system) for each individual. Preprocessing steps included band-pass filtering (0.5–51 Hz), re-referencing to the common average, epoching into 1-minute segments, and rejecting bad epochs (>100 μV). Finally, the data were filtered to the alpha frequency band (8–13 Hz) for further analysis. To identify microstates (MS) in rsEEG, we applied a modified k-means clustering algorithm and estimated four distinct states for each group: MS maps A, B, C, and D. By back-fitting MS maps A to D into each subject's rsEEG with a 35 msec temporal smoothing window, we extracted MS sequences and computed the global explained variance (GEV) for each map. To assess functional connectivity (FC) across the microstate sequence, we calculated EEG coherence within a 230 msec time window, focusing on periods where a specific microstate persisted for at least 210 msec. Finally, the FC matrices for each MS map were averaged to derive a representative FC pattern for each state. To quantify the properties of FC networks, we computed graph-theoretical features, including strength, clustering coefficient, closeness, betweenness, and eigenvector centrality. Nodes with higher values in these metrics were identified as hub nodes. In addition, network efficiency was estimated based on small-worldness (SW) for each MS map.
Results:
GEV for MS A to D were 0.07, 0.09, 0.21, and 0.25 in the AG, compared to 0.10, 0.13, 0.14, and 0.25 in the HG. This leads to a higher occurrence of MS C and D in the MS sequences of the AG, whereas the HG exhibited a more balanced MS distribution. The four MS exhibited distinct FC patterns and unique hub nodes. While the overall connectivity structure across the four states remained similar between the two groups, the AG demonstrated fewer hub nodes for each MS map. In addition, network efficiency in MS C was significantly lower in the AG.
Conclusions:
In this study, we characterized transient MS in rsEEG and estimated FC for each MS to examine how chronic alcohol use affects brain's FC. Our data showed a higher occurrence of MS C and D in the AG, whereas the HG exhibited a more balanced MS distribution. Moreover, the network properties of MS C in AG showed distinct network properties compared to HG. Converging evidence from MS studies suggests that MS C in rsEEG is associated with the default-mode network (DMN) (Khanna et al. 2014). Our findings support the notion that alcohol use affects the DMN (Bottino et al, 2025), potentially contributing to disruptions in cognitive functions (Kamarajan et al., 2012; Heuvel & Sporns, 2013).
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 2
Keywords:
Addictions
Electroencephaolography (EEG)
Other - Connectivity, Microstates, resting-state EEG
1|2Indicates the priority used for review
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Provide references using APA citation style.
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Bottino, M., Bocková, N., Poller, N. W., Smolka, M. N., Böhmer, J., Walter, H., & Marxen, M. (2025). Relating Functional Connectivity and Alcohol Use Disorder: A Systematic Review and Derivation of Relevance Maps for Regions and Connections. Human Brain Mapping, 46(2), e70156.
Khanna, A., Pascual-Leone, A., Michel, C. M., & Farzan, F. (2015). Microstates in resting-state EEG: current status and future directions. Neuroscience & Biobehavioral Reviews, 49, 105-113.
Kamarajan, C., Rangaswamy, M., Manz, N., Chorlian, D. B., Pandey, A. K., Roopesh, B. N., & Porjesz, B. (2012). Topography, power, and current source density of theta oscillations during reward processing as markers for alcohol dependence. Human Brain Mapping, 33(5), 1019-1039.
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