Biology subjects

Fiore, V. G.

Publications and source records attributed to Fiore, V. G..

12 recordsLinked to original sources

bioRxiv2025

Dopamine and serotonin transients predict depressive symptom relief following deep brain stimulation of human subcallosal cingulate cortex

Recent advances in deep brain stimulation (DBS) of the subcallosal cingulate (SCC) show promise in mitigating the symptoms of treatment-resistant depression (TRD) in humans1-3. Monoamines, such as dopamine and serotonin, mediate the effects of pharmacological treatments of depression. However, their roles in recovery following DBS remain elusive, largely due to technical limitations of measuring these neurotransmitters in the living human brain. Here, by leveraging machine learning-enhanced electrochemistry4-7, we show that dopamine and serotonin signaling following DBS to the SCC predicted later depressive symptom relief in humans with TRD. We found that both dopamine and serotonin levels increased following subtherapeutic intraoperative SCC stimulation, with each neurotransmitter showing selective responses to distinct decision-making tasks. Furthermore, acute dopamine increases predicted later mood improvements during a social decision-making task, while serotonin enhancement predicted faster responses during a non-social learning task longitudinally. Critically, changes in dopamine and serotonin levels during the social decision-making task jointly predicted depressive symptom remission at 6-month follow-up. These findings illustrate the contribution of both dopamine and serotonin signaling in predicting behavioral improvement and depressive symptom remission in humans with TRD. Such neurochemical plasticity may serve as potential mechanistic biomarkers for SCC DBS mechanism and TRD treatment response. Significance statementO_LIDopamine and serotonin levels increased following acute DBS to the SCC in humans. C_LIO_LIAcute dopamine and serotonin changes predicted later mood and response speed changes. C_LIO_LISustained TRD recovery was predicted by acute increases in both dopamine and serotonin estimates. C_LI

neuroscience↗

bioRxiv2025

Multi-Class Classification of Cannabis and Alcohol Use Disorder: Identifying Common and Substance-Specific Neural Circuits.

Machine learning approaches have advanced the identification of neural signatures of substance use, particularly through case-control comparisons and network-level analyses. However, most studies have focused on single substances in isolation, making it difficult to directly compare shared and distinct network computations and limiting their generalizability, given that many individuals engage in polysubstance use. Here, we introduce an explainable, connectivity-based multiclass classification framework that models distributed brain network organization to delineate both shared and substance-specific mechanisms of addiction. This design enables direct comparison among cannabis users, alcohol users, and controls, revealing network computations that uniquely differentiate each group. Using functional connectivity features from cue-induced craving tasks, we classified cannabis users (n=166), alcohol users (n=101), and healthy controls (n=238), achieving high out-of-sample accuracy (87% for cannabis, 69% for alcohol, and 73% for controls). Functional connectivity-based models consistently outperformed activation-based models, highlighting the importance of inter-regional network properties for biomarker development. Network analysis further revealed dorsal prefrontal, cingulate, and precuneus hubs consistent with meta-analytic craving networks and substance-specific connectivity motifs enhanced prefrontal coupling in cannabis users and greater insular-striatal integration in alcohol users. These results demonstrate that distinct network configurations define different substance-use profiles, advancing interpretable biomarkers for addiction neuroscience.

neuroscience↗

bioRxiv2023

A generalizable computational mechanism underlying the interaction between momentary craving and decision-making

Substance craving and maladaptive choices are intertwined across addictive disorders. However, the computational mechanisms connecting craving and decision-making remain elusive. Here, we tested a hypothesis that momentary craving and value-based decision-making influence each other during substance-related reinforcement learning. We measured momentary craving as two groups of human participants (alcohol drinkers and cannabis users; total n=132) performed a reinforcement learning task in which they received group-specific addictive cue or monetary rewards. Using computational modeling, we found that, across both groups, momentary craving biased learning rate related to substance-associated prediction errors (RPEs), but not monetary RPEs. Additionally, expected values and RPEs jointly influenced elicited craving across reward types and participant groups. Alcohol and cannabis users also differed in the extent to which their craving and decision-making influenced each other, suggesting important computational divergence between the two groups. Finally, regressions incorporating model-derived parameters best predicted substance use severity in the alcohol, but not cannabis group, supporting the utility of using these model-based parameters in making clinical predictions for selective substance groups. Together, these findings provide a computational mechanism for the interaction between substance craving and maladaptive choices that is generalizable across addictive domains.

neuroscience↗

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.

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bioRxiv2020

An insula-driven network computes decision uncertainty and promotes abstinence in chronic cocaine users

The anterior insular cortex (AIC) and its interconnected brain regions have been associated with both addiction and decision-making under uncertainty. However, the causal interactions in this uncertainty-encoding neurocircuitry and how these neural dynamics impact relapse remain elusive. Here, we used model-based fMRI to measure choice uncertainty in a motor decision task in 61 individuals with cocaine use disorder (CUD) and 25 healthy controls. CUD participants were assessed before discharge from a residential treatment program and followed for up to 24 weeks. We found that choice uncertainty was tracked by the AIC, dorsal anterior cingulate cortex (dACC), and ventral striatum (VS), across participants. Stronger activations in these regions measured pre-discharge predicted longer abstinence after discharge in individuals with CUD. Dynamic causal modelling revealed an AIC-to-dACC directed connectivity modulated by uncertainty in controls, but a dACC-to-AIC connectivity in CUD participants. This reversal was mostly driven by early-relapsers (<30 days). Furthermore, CUD individuals who displayed a stronger AIC-to-dACC excitatory connection during uncertainty encoding remained abstinent for longer periods. These findings reveal a critical role of an AIC-driven, uncertainty-encoding neurocircuitry in protecting against relapse and promoting abstinence.

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bioRxiv2019

Context-invariant neural dynamics underlying the encoding of Bayesian uncertainty, but not confidence

Classic decision theories, such as reinforcement learning, typically require the presence of explicit outcomes for learning and belief updating. However, ecological environments are often opaque and explicit feedback, such as those based on values, might not be immediately accessible. It remains unclear whether the neural dynamics underlying belief updating in absence of outcomes differ from those responsible for decision-making based on accessible outcomes. Here, we investigated this question in healthy humans (n=28) using Bayesian modeling and two multi-option fMRI tasks, one with and one without immediate outcome. Model-based fMRI analysis revealed two opposing networks encoding belief updating regardless of the presence of immediate outcomes. A "confidence-building" network including the hippocampus, amygdala, and medial prefrontal cortex (mPFC) became more active as beliefs about action-outcome probabilities were confirmed by newly acquired information. Meanwhile, an "uncertainty-building" network including the anterior insular (AIC), dorsal anterior cingulate (dACC), and dorsolateral prefrontal (dlPFC) cortices became more active as new evidence conflicted with action-outcome estimates. Interestingly, dynamic causal modeling revealed that the confidence network was driven either by the hippocampus when outcomes were not available, or by the mPFC and amygdala when value-based outcomes were immediately accessible. Convsersely, the AIC always drove the activities of dACC and dlPFC, under the modulation of increasing uncertainty, independent of outcome availability. These findings reveal similar network compositions but distinct neural dynamics underlying belief updating in changing environments with and without explicit outcomes, highlighting an asymmetric relationship between decision confidence and uncertainty computation across levels of analysis. HighlightsO_LIWe investigated belief updating in two tasks, with and without explicit feedback. C_LIO_LIModel-based fMRI analysis revealed similar neural responses across tasks. C_LIO_LIThe anterior insula drove an uncertainty-encoding network, across tasks. C_LIO_LIThe anterior hippocampus drove a confidence-encoding network, w/o feedbacks. C_LIO_LIThe medial PFC and amygdala drove a confidence-encoding network, with feedbacks. C_LI

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bioRxiv2019

Humans Use Future Thinking to Exert Social Control

Social control, the ability to exert influence over others, is critical in interpersonal interactions yet uninvestigated. Here, we used functional neuroimaging and a social exchange paradigm in which peoples current choices either did, or did not, influence their partners proposals in the future. Computational modeling revealed that participants used forward thinking and calculated the downstream effects of their current actions regardless of the controllability of the social environment. Furthermore, greater levels of estimated control correlated with better performance in controllable interactions and less illusory beliefs about control in uncontrollable interactions. Neural instantiation of trial-by-trial values of social controllability were tracked in the ventromedial prefrontal cortex (vmPFC), striatum, and insula for controllable interactions, but only in vmPFC for uncontrollable interactions. These findings demonstrate that humans use forward thinking, a strategy similar to model-based planning, to guide social choices; and that subjective beliefs about social controllability might not be grounded in reality.

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bioRxiv2017

Value Encoding in the Globus Pallidus: fMRI reveals an interaction effect between reward and dopamine drive

The external part of the globus pallidus (GPe) is a core nucleus of the basal ganglia (BG) whose activity is disrupted under conditions of low dopamine release, as in Parkinsons disease. Current models assume decreased dopamine release in the dorsal striatum results in deactivation of dorsal GPe, which in turn affects motor expression via a regulatory effect on other nuclei of the BG. However, recent studies in healthy and pathological animal models have reported neural dynamics that do not match with this view of the GPe as a relay in the BG circuit. Thus, the computational role of the GPe in the BG is still to be determined. We previously proposed a neural model that revisits the functions of the nuclei of the BG, and this model predicts that GPe encodes values which are amplified under a condition of low striatal dopaminergic drive. To test this prediction, we used an fMRI paradigm involving a within-subject placebo-controlled design, using the dopamine antagonist risperidone, wherein healthy volunteers performed a motor selection and maintenance task under low and high reward conditions. ROI-based fMRI analysis revealed an interaction between reward and dopamine drive manipulations, with increased BOLD activity in GPe in a high compared to low reward condition, and under risperidone compared to placebo. These results confirm the core prediction of our computational model, and provide a new perspective on neural dynamics in the BG and their effects on motor selection and motor disorders.

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bioRxiv2017

A Multilevel Computational Characterization of Endophenotypes in Addiction

Substance use disorders are characterized by a profound intersubject (phenotypic) variability in the expression of addictive symptomatology and propensity to relapse following treatment. However, laboratory investigations have primarily focused on common neural substrates in addiction, and have not yet been able to identify mechanisms that can account for the multifaceted phenotypic behaviors reported in literature. To investigate this knowledge gap theoretically, here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning. These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by drug consumption. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted U-shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. Furthermore, endophenotypes with opposite asymmetrical biases among cortico-striatal circuits expressed similar addiction behaviors, but responded differently to simulated treatments, suggesting personalized treatment development could rely on endophenotypic rather than phenotypic differentiations. We propose our simulated results, confirmed across neural and algorithmic levels of analysis, inform on a fundamental and, to date, neglected quantitative method to characterize clinical heterogeneity in addiction.

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bioRxiv2017

Addiction in a Bounded Rational Model: Role of Environment and its Exploration

Several decision-making vulnerabilities have been identified as underlying causes for addictive behaviours, or the repeated execution of stereotyped actions despite their adverse consequences. These vulnerabilities are mostly associated with brain alterations caused by the consumption of substances of abuse. However, addiction can also happen in the absence of a pharmacological component, such as seen in pathological gambling and videogaming. We use a new reinforcement learning model to highlight a previously neglected vulnerability that we suggest interacts with those already identified, whilst playing a prominent role in non-pharmacological forms of addiction. Specifically, we show that a duallearning system (i.e. combining model-based and model-free) can be vulnerable to highly rewarding, but suboptimal actions, that are followed by a complex ramification of stochastic adverse effects. This phenomenon is caused by the overload of the capabilities of an agent, as time and cognitive resources required for exploration, deliberation, situation recognition, and habit formation, all increase as a function of the depth and richness of detail of an environment. Furthermore, the cognitive overload can be aggravated due to alterations (e.g. caused by stress) in the bounded rationality, i.e. the limited amount of resources available for the model-based component, in turn increasing the agents chances to develop or maintain addictive behaviours. Our study demonstrates that, independent of drug consumption, addictive behaviours can arise in the interaction between the environmental complexity and the biologically finite resources available to explore and represent it.

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bioRxiv2017

A lineage-related reciprocal inhibition circuitry for sensory-motor action selection

The insect central complex and vertebrate basal ganglia are forebrain centres involved in selection and maintenance of behavioural actions. However, little is known about the formation of the underlying circuits, or how they integrate sensory information for motor actions. Here, we show that paired embryonic neuroblasts generate central complex ring neurons that mediate sensory-motor transformation and action selection in Drosophila. Lineage analysis resolves four ring neuron subtypes, R1-R4, that form GABAergic inhibition circuitry among inhibitory sister cells. Genetic manipulations, together with functional imaging, demonstrate subtype-specific R neurons mediate the selection and maintenance of behavioural activity. A computational model substantiates genetic and behavioural observations suggesting that R neuron circuitry functions as salience detector using competitive inhibition to amplify, maintain or switch between activity states. The resultant gating mechanism translates facilitation, inhibition and disinhibition of behavioural activity as R neuron functions into selection of motor actions and their organisation into action sequences.

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