Search arXivSearch

arXiv · 2601.02143

Responses of the Neurobiological Craving Signature to smoking versus alternative social rewards predict craving and monthly smoking in adolescents

Abstract

Smoking remains the leading cause of preventable mortality worldwide. Adolescents are particularly vulnerable to the development of tobacco addiction due to ongoing brain maturation and susceptibility to social influences, such as exposure to environmental tobacco smoke (ETS). Craving -the strong desire to use drugs -already emerges with non-daily tobacco use and predicts continued use and relapse. However, the roles of craving and ETS exposure during the early stages of tobacco use in adolescence remain poorly understood. In this pre-registered study, we harness a recently developed fMRI marker of craving -the Neurobiological Craving Signature (NCS) -to compare craving-related brain responses to smoking versus social cues in adolescent Experimental Smokers (N=100) and Non-smokers (N=48) with varying levels of ETS exposure levels. Results showed that NCS responses to smoking cues compared to alternative social rewards were higher in Experimental Smokers compared to Non-smokers and predicted individual differences in self-reported craving and monthly smoking. Both smoking behavior and NCS responses were correlated with the relative amount of ETS exposure from peers compared to exposure from family members. Together, these findings indicate a heightened sensitivity of craving-related brain circuits already during experimental smoking and highlight the important role of peer social norms on craving and smoking initiation in the critical period of adolescence.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Maddalena Tamellini, Joyce Dieleman, Guillaume Sescousse, Maartje Luijten, Leonie Koban. 2026-01-05. Responses of the Neurobiological Craving Signature to smoking versus alternative social rewards predict craving and monthly smoking in adolescents. https://arxiv.org/abs/2601.02143

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Only what exists can cause: An intrinsic powers view of free will

This essay addresses the implications of integrated information theory (IIT) for free will. IIT is a theory of what consciousness is and of how its presence and quality can be accounted for in physical terms. According to IIT, the presence of consciousness is accounted for by a maximum of cause-effect power in the brain. Moreover, the way an experience feels is accounted for by how that cause-effect power is structured. If IIT is right, we do have free will in a genuine sense: we have alternatives, reasons, and values, we make decisions, and we-not our neurons or atoms-are the cause of our willed actions and bear responsibility for them. IIT's argument for genuine free will hinges on the proper understanding of consciousness as intrinsic existence, captured by its intrinsic powers ontology: what exists absolutely, in physical terms, are intrinsic entities, and only what exists can cause.

q-bio.NC

Seeing the imagined: latent functional alignment in visual imagery decoding from fMRI data

Recent progress in visual brain decoding from fMRI has been enabled by large-scale datasets such as the Natural Scenes Dataset (NSD) and powerful diffusion-based generative models. While current pipelines are primarily optimized for perception, their performance under mental-imagery remains less well understood. In this work, we study how a state-of-the-art (SOTA) perception decoder (DynaDiff) can be adapted to reconstruct imagined content from the NSD-Imagery benchmark. We propose a latent functional alignment (LFA) approach that maps imagery-evoked activity to the pretrained model's semantic content-enriched conditioning space, by adding a simple alignment module, while keeping the original remaining components frozen. To mitigate the limited amount of matched imagery-perception supervision, we further introduce a neural retrieval-based augmentation strategy that selects semantically related NSD perception trials from the same participants. Across four subjects, LFA consistently improves high-level semantic reconstruction metrics relative to the frozen pretrained baseline and a voxel-space ridge alignment baseline, and enables above-chance decoding from multiple cortical regions. These results suggest that semantic structure learned from perception can be leveraged to stabilize and improve visual imagery decoding under out-of-distribution conditions.

q-bio.NC

Deep Learning in Infant Functional Neuroimaging: Challenges, Advances, and Future Directions

Infancy is a critical developmental window characterized by rapid functional brain reorganization, during which large-scale networks emerge, individualized connectome signatures continue to form, and early deviations may shape long-term cognitive and clinical outcomes. Functional MRI (fMRI) offers an opportunity to study these processes in vivo, yet extracting developmentally meaningful information from it remains challenging due to comparatively short scan duration, structured motion artifacts, variable scan states, and rapid brain maturation. Amid these challenges, deep learning has expanded the capacity of computational neuroimaging by learning robust representations from noisy, high-dimensional data, integrating complex spatial and temporal information, and capturing the nonlinear and rapidly evolving organization of the developing brain. Here, we review recent advances in deep learning for infant functional neuroimaging, synthesizing progress across input representation formatting, population and individualized brain mapping, longitudinal trajectory forecasting, robust and explainable model evaluation, and biological translation. Collectively, these methodological advances mark a paradigm shift in infant functional neuroimaging from descriptive, group-level analyses toward reliable, individualized, and developmentally grounded models. Future progress will depend on larger and more diverse longitudinal datasets, developmentally appropriate model designs, rigorous and standardized evaluation, and integration of computational predictions with biological mechanisms towards clinically meaningful outcomes. Addressing these priorities will help establish deep learning as a robust framework for understanding early functional brain development, identifying developmental variation at the individual level, and ultimately supporting earlier and precise assessment of neurodevelopmental risk.

q-bio.NC