Heflin, M. | bioRxiv Science

bioRxiv2022

Nicotine-Related Beliefs Induce Dose-Dependent Responses in the Human Brain

Could non-pharmacological constructs, such as beliefs, impact brain activities in a dose-dependent manner as drugs do? While beliefs shape many aspects of our behavior and wellbeing, the precise mapping between subjective beliefs and neural substrates remains elusive. Here, nicotine-addicted humans were instructed to think that an electronic cigarette (e-cigarette) contained either "low", "medium", or "high" levels of nicotine, while nicotine content was kept constant. After vaping the e-cigarette, participants performed a decision-making task known to engage neural circuits affected by nicotine while being scanned by fMRI. Activity in the thalamus, a key binding site for nicotine, increased parametrically according to belief dosage. Furthermore, the functional coupling between thalamus and ventromedial prefrontal cortex, a region implicated in value and state representations, also scaled to belief dosage. These findings illustrate a dose-dependent relationship between a thalamic circuit and nicotine-related beliefs in humans, a mechanism previously known to only apply to pharmacological agents.

neuroscience↗

bioRxiv2021

An interpretable connectivity-based decoding model for classification of chronic marijuana use

BackgroundPsychiatric neuroimaging typically proceeds with one of two approaches: encoding models, which aim to model neural mechanisms, or decoding models, which aim to predict behavioral or clinical characteristics from brain imaging data. In this study, we seek to combine these aims by developing interpretable decoding models that offer both accurate prediction and novel neural insights. We demonstrate the effectiveness of this combined approach in a case study of chronic marijuana use. MethodsChronic marijuana (MJ) users (n=195) and non-using healthy controls (n=128) completed a cue-elicited craving task during functional magnetic resonance imaging. Linear machine learning methods were used to classify individuals into chronic MJ users and non-users based on task-evoked, whole-brain functional connectivity. We then used graph theoretic analyses to identify predictive functional connectivities among brain regions that contributed most substantially to the classification of chronic marijuana use. ResultsWe obtained high (~80% out-of-sample) accuracy across four different classification models, demonstrating that task-evoked, whole-brain functional connectivity can successfully differentiate chronic marijuana users from non-users. Subsequent network analyses revealed key predictive regions (e.g., anterior cingulate cortex, dorsolateral prefrontal cortex, and precuneus) that are often implicated in neuroimaging studies of substance use disorders, as well as some key exceptions. We also identified a core set of networks of brain regions that contributed to successful classification, comprised of many of the same predictive regions. ConclusionsOur dual aims of accurate prediction and interpretability were successful, producing a predictive model that also provides interpretability at the neural level. This novel approach may complement other predictive-exploratory approaches for a more complete understanding of neural mechanisms in drug use and other neuropsychiatric disorders.

neuroscience↗