Relapse in alcohol dependence is characterized by disrupted modular brain network organization

Patients suffering from alcohol dependence (AD) experience high relapse rates. Prior studies investigating the organization of resting-state functional connectivity networks in AD using graph theory typically focused on alterations of the whole network (macroscale) or on aberrations of single brain regions (microscale). However, little is known about the complex dynamics and interactions among different brain regions and neural systems, i.e. the network organization at mesoscale. To investigate mesoscale network alterations, we applied a data-driven community detection algorithm to identify the modular structure of functional brain networks and assess its association with relapse over a 12-month follow-up period in alcohol-dependent patients (relapsers, REL, n = 59; abstainers, ABS, n = 28) and age- and sex-matched controls (CON, n = 83). Our results reveal differences in the modular organization in REL, marked by a fragmentation and reorganization of major functional modules. Across individuals, functional modules of REL exhibited higher modular variability, particularly in brain regions associated with behavioral and emotional regulatory processes. Conversely, prefrontal reward-related brain regions were more central for inter-module communication in REL, emerging as functional brain hubs. Furthermore, higher overall modular variability significantly predicted time to relapse during follow-up. Collectively, our results shed light on potential neural substrates of relapse risk in alcohol dependence, which may foster the development of targeted interventions to promote sustained abstinence.