Ende, G. | bioRxiv Science

bioRxiv2026

Brain Functional Connectivity Signatures of Craving Across Substance Use Disorders: A Transdiagnostic Approach

Craving is a hallmark feature of substance use disorders (SUDs) and a major risk factor for relapse, yet reliable biomarkers that enable individual-level prediction remain scarce. Here, we applied connectome-based predictive modeling (CPM) to resting-state functional magnetic resonance imaging (fMRI) data in a transdiagnostic sample of individuals with cannabis, opioid, or tobacco use disorder (n = 78). Using CPM, we identified a distributed functional brain network that reliably predicted self-reported craving. Computational lesion analyses revealed key contributions from the right medial orbitofrontal cortex, right dorsal posterior cingulate cortex, and left lateral medial frontal gyrus. Importantly, the craving network generalized across two independent datasets. In alcohol-dependent patients (n = 41), the identified craving network, along with its positive and negative subnetworks, predicted distinct cognitive and motivational components of craving. In a second external dataset of smokers (n = 28), the craving network predicted both nicotine craving after abstinence as well as intra-individual changes in craving between sated and craving states. Together, these findings provide evidence for a robust, transdiagnostic craving signature in SUDs. Future work should assess the networks predictive utility for longitudinal outcomes such as relapse risk and treatment response.

neuroscience↗

bioRxiv2024

Dev-Atlas: A reference atlas of functional brain networks for typically developing adolescents

Adolescence is a critical period for neural changes, including maturation of the brains cognitive networks, but also a period of increased vulnerability to psychopathology. It is well accepted that the brain is functionally organized into multiple interacting networks and extensive literature has demonstrated that the spatial and functional organization of these networks shows major age-related changes across the lifespan, but particularly during adolescence. Yet, there is limited option for a reference functional brain atlas derived from typically developing adolescents, which is especially problematic as the reliable and reproducible identification of functional brain networks crucially depends on the use of such reference functional atlases. In this context, we utilized resting-state functional MRI data from a total of 1,391 typically developing youth between the ages of 8 and 17 years to create a new adolescent-specific reference atlas of functional brain networks. We further investigated the impact of age and sex on these networks. Using a multiscale individual component clustering algorithm (MICCA), we identified 24 reliable functional brain networks, classified within six domains: Default-Mode (5 networks), Control (4 networks), Salience (3 networks), Attention (4 networks), Somatomotor (5 networks), and Visual (3 networks). We identified reliable and large effects of age on the spatial topography of these majority of networks, as well as on the functional network connectivity (FNC) between networks. The DMN showed reduced FNC with the other networks with older age. Sex effects were not as widespread. No significant sex-by-age interactions were detected. Overall, we created a novel brain atlas, named Dev-Atlas, focused on a typically developing sample, with the hope that this atlas can be used in future independent developmental network neuroscience studies. Dev-Atlas is freely available to the research community.

neuroscience↗