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Substance use disorders (SUDs) are seen as a continuum ranging from goal-directed and hedonic drug use to loss of control over drug intake with aversive consequences for mental and physical health and social functioning. The main goals of our interdisciplinary German collaborative research centre on Losing and Regaining Control over Drug Intake (ReCoDe) are (i) to study triggers (drug cues, stressors, drug priming) and modifying factors (age, gender, physical activity, cognitive functions, childhood adversity, social factors, such as loneliness and social contact/interaction) that longitudinally modulate the trajectories of losing and regaining control over drug consumption under real-life conditions. (ii) To study underlying behavioural, cognitive and neurobiological mechanisms of disease trajectories and drug-related behaviours and (iii) to provide non-invasive mechanism-based interventions. These goals are achieved by: (A) using innovative mHealth (mobile health) tools to longitudinally monitor the effects of triggers and modifying factors on drug consumption patterns in real life in a cohort of 900 patients with alcohol use disorder. This approach will be complemented by animal models of addiction with 24/7 automated behavioural monitoring across an entire disease trajectory; i.e. from a naïve state to a drug-taking state to an addiction or resilience-like state. (B) The identification and, if applicable, computational modelling of key molecular, neurobiological and psychological mechanisms (e.g., reduced cognitive flexibility) mediating the effects of such triggers and modifying factors on disease trajectories. (C) Developing and testing non-invasive interventions (e.g., Just-In-Time-Adaptive-Interventions (JITAIs), various non-invasive brain stimulations (NIBS), individualized physical activity) that specifically target the underlying mechanisms for regaining control over drug intake. Here, we will report on the most important results of the first funding period and outline our future research strategy.
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Transtornos Relacionados ao Uso de Substâncias , Humanos , Animais , Alemanha , Comportamento Aditivo , AlcoolismoRESUMO
BACKGROUND: Recent years have seen a growing interest in the use of digital tools for delivering person-centred mental health care. Experience Sampling Methodology (ESM), a structured diary technique for capturing moment-to-moment variation in experience and behaviour in service users' daily life, reflects a particularly promising avenue for implementing a person-centred approach. While there is evidence on the effectiveness of ESM-based monitoring, uptake in routine mental health care remains limited. The overarching aim of this hybrid effectiveness-implementation study is to investigate, in detail, reach, effectiveness, adoption, implementation, and maintenance as well as contextual factors, processes, and costs of implementing ESM-based monitoring, reporting, and feedback into routine mental health care in four European countries (i.e., Belgium, Germany, Scotland, Slovakia). METHODS: In this hybrid effectiveness-implementation study, a parallel-group, assessor-blind, multi-centre cluster randomized controlled trial (cRCT) will be conducted, combined with a process and economic evaluation. In the cRCT, 24 clinical units (as the cluster and unit of randomization) at eight sites in four European countries will be randomly allocated using an unbalanced 2:1 ratio to one of two conditions: (a) the experimental condition, in which participants receive a Digital Mobile Mental Health intervention (DMMH) and other implementation strategies in addition to treatment as usual (TAU) or (b) the control condition, in which service users are provided with TAU. Outcome data in service users and clinicians will be collected at four time points: at baseline (t0), 2-month post-baseline (t1), 6-month post-baseline (t2), and 12-month post-baseline (t3). The primary outcome will be patient-reported service engagement assessed with the service attachment questionnaire at 2-month post-baseline. The process and economic evaluation will provide in-depth insights into in-vivo context-mechanism-outcome configurations and economic costs of the DMMH and other implementation strategies in routine care, respectively. DISCUSSION: If this trial provides evidence on reach, effectiveness, adoption, implementation and maintenance of implementing ESM-based monitoring, reporting, and feedback, it will form the basis for establishing its public health impact and has significant potential to bridge the research-to-practice gap and contribute to swifter ecological translation of digital innovations to real-world delivery in routine mental health care. TRIAL REGISTRATION: ISRCTN15109760 (ISRCTN registry, date: 03/08/2022).
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Serviços de Saúde Mental , Humanos , Serviços de Saúde Mental/economia , Alemanha , Bélgica , Eslováquia , Transtornos Mentais/terapia , Transtornos Mentais/economia , Avaliação Momentânea Ecológica , Europa (Continente) , Análise Custo-Benefício/métodosRESUMO
This paper extends the concept and the visualization of vector field topology to vector fields with discontinuities. We address the non-uniqueness of flow in such fields by introduction of a time-reversible concept of equivalence. This concept generalizes streamlines to streamsets and thus vector field topology to discontinuous vector fields in terms of invariant streamsets. We identify respective novel critical structures as well as their manifolds, investigate their interplay with traditional vector field topology, and detail the application and interpretation of our approach using specifically designed synthetic cases and a simulated case from physics.
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Computational models in neuroscience usually take the form of systems of differential equations. The behaviour of such systems is the subject of dynamical systems theory. Dynamical systems theory provides a powerful mathematical toolbox for analysing neurobiological processes and has been a mainstay of computational neuroscience for decades. Recently, recurrent neural networks (RNNs) have become a popular machine learning tool for studying the non-linear dynamics of neural and behavioural processes by emulating an underlying system of differential equations. RNNs have been routinely trained on similar behavioural tasks to those used for animal subjects to generate hypotheses about the underlying computational mechanisms. By contrast, RNNs can also be trained on the measured physiological and behavioural data, thereby directly inheriting their temporal and geometrical properties. In this way they become a formal surrogate for the experimentally probed system that can be further analysed, perturbed and simulated. This powerful approach is called dynamical system reconstruction. In this Perspective, we focus on recent trends in artificial intelligence and machine learning in this exciting and rapidly expanding field, which may be less well known in neuroscience. We discuss formal prerequisites, different model architectures and training approaches for RNN-based dynamical system reconstructions, ways to evaluate and validate model performance, how to interpret trained models in a neuroscience context, and current challenges.
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Inteligência Artificial , Neurociências , Animais , Humanos , Redes Neurais de ComputaçãoRESUMO
The prefrontal cortex (PFC) enables a staggering variety of complex behaviors, such as planning actions, solving problems, and adapting to new situations according to external information and internal states. These higher-order abilities, collectively defined as adaptive cognitive behavior, require cellular ensembles that coordinate the tradeoff between the stability and flexibility of neural representations. While the mechanisms underlying the function of cellular ensembles are still unclear, recent experimental and theoretical studies suggest that temporal coordination dynamically binds prefrontal neurons into functional ensembles. A so far largely separate stream of research has investigated the prefrontal efferent and afferent connectivity. These two research streams have recently converged on the hypothesis that prefrontal connectivity patterns influence ensemble formation and the function of neurons within ensembles. Here, we propose a unitary concept that, leveraging a cross-species definition of prefrontal regions, explains how prefrontal ensembles adaptively regulate and efficiently coordinate multiple processes in distinct cognitive behaviors.
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Neurônios , Córtex Pré-Frontal , Córtex Pré-Frontal/fisiologia , Neurônios/fisiologia , Adaptação Psicológica , Plasticidade Neuronal/fisiologia , CogniçãoRESUMO
Short-term memory enables incorporation of recent experience into subsequent decision-making. This processing recruits both the prefrontal cortex and hippocampus, where neurons encode task cues, rules, and outcomes. However, precisely which information is carried when, and by which neurons, remains unclear. Using population decoding of activity in rat medial prefrontal cortex (mPFC) and dorsal hippocampal CA1, we confirm that mPFC populations lead in maintaining sample information across delays of an operant non-match to sample task, despite individual neurons firing only transiently. During sample encoding, distinct mPFC subpopulations joined distributed CA1-mPFC cell assemblies hallmarked by 4-5 Hz rhythmic modulation; CA1-mPFC assemblies re-emerged during choice episodes but were not 4-5 Hz modulated. Delay-dependent errors arose when attenuated rhythmic assembly activity heralded collapse of sustained mPFC encoding. Our results map component processes of memory-guided decisions onto heterogeneous CA1-mPFC subpopulations and the dynamics of physiologically distinct, distributed cell assemblies.
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Hipocampo , Rememoração Mental , Ratos , Animais , Hipocampo/fisiologia , Memória de Curto Prazo , Córtex Pré-Frontal/fisiologia , Neurônios/fisiologiaRESUMO
Our knowledge about neuronal activity in the sensorimotor cortex relies primarily on stereotyped movements that are strictly controlled in experimental settings. It remains unclear how results can be carried over to less constrained behavior like that of freely moving subjects. Toward this goal, we developed a self-paced behavioral paradigm that encouraged rats to engage in different movement types. We employed bilateral electrophysiological recordings across the entire sensorimotor cortex and simultaneous paw tracking. These techniques revealed behavioral coupling of neurons with lateralization and an anterior-posterior gradient from the premotor to the primary sensory cortex. The structure of population activity patterns was conserved across animals despite the severe under-sampling of the total number of neurons and variations in electrode positions across individuals. We demonstrated cross-subject and cross-session generalization in a decoding task through alignments of low-dimensional neural manifolds, providing evidence of a conserved neuronal code.
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Córtex Sensório-Motor , Ratos , Animais , Neurônios , Eletrofisiologia Cardíaca , Eletrodos , Generalização PsicológicaRESUMO
BACKGROUND: Novel approaches in mobile mental health (mHealth) apps that make use of Artificial Intelligence (AI), Ecological Momentary Assessments, and Ecological Momentary Interventions have the potential to support young people in the achievement of mental health and wellbeing goals. However, little is known on the perspectives of young people and mental health experts on this rapidly advancing technology. This study aims to investigate the subjective needs, attitudes, and preferences of key stakeholders towards an AI-informed mHealth app, including young people and experts on mHealth promotion and prevention in youth. METHODS: We used a convergent parallel mixed-method study design. Two semi-structured online focus groups (n = 8) and expert interviews (n = 5) to explore users and stakeholders perspectives were conducted. Furthermore a representative online survey was completed by young people (n = 666) to investigate attitudes, current use and preferences towards apps for mental health promotion and prevention. RESULTS: Survey results show that more than two-thirds of young people have experience with mHealth apps, and 60% make regular use of 1-2 apps. A minority (17%) reported to feel negative about the application of AI in general, and 19% were negative about the embedding of AI in mHealth apps. This is in line with qualitative findings, where young people displayed rather positive attitudes towards AI and its integration into mHealth apps. Participants reported pragmatic attitudes towards data sharing and safety practices, implying openness to share data if it adds value for users and if the data request is not too intimate, however demanded transparency of data usage and control over personalization. Experts perceived AI-informed mHealth apps as a complementary solution to on-site delivered interventions in future health promotion among young people. Experts emphasized opportunities in regard with low-threshold access through the use of smartphones, and the chance to reach young people in risk situations. CONCLUSIONS: The findings of this exploratory study highlight the importance of further participatory development of training components prior to implementation of a digital mHealth training in routine practice of mental health promotion and prevention. Our results may help to guide developments based on stakeholders' first recommendations for an AI-informed mHealth app.
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Background: The tendency to devaluate future options as a function of time, known as delay discounting, is associated with various factors such as psychiatric illness and personality. Under identical experimental conditions, individuals may therefore strongly differ in the degree to which they discount future options. In delay discounting tasks, this inter-individual variability inevitably results in an unequal number of discounted trials per subject, generating difficulties in linking delay discounting to psychophysiological and neural correlates. Many studies have therefore focused on assessing delay discounting adaptively. Here, we extend these approaches by developing an adaptive paradigm which aims at inducing more comparable and homogeneous discounting frequencies across participants on a dimensional scale. Method: The proposed approach probabilistically links a (common) discounting function to behavior to obtain a probabilistic model, and then exploits the model to obtain a formal condition which defines how to construe experimental trials so as to induce any desired discounting probability. We first infer subject-level models on behavior on a non-adaptive delay discounting task and then use these models to generate adaptive trials designed to evoke graded relative discounting frequencies of 0.3, 0.5, and 0.7 in each participant. We further compare and evaluate common models in the field through out-of-sample prediction error estimates, to iteratively improve the trial-generating model and paradigm. Results: The developed paradigm successfully increases discounting behavior during both reward and loss discounting. Moreover, it evokes graded relative choice frequencies in line with model-based expectations (i.e., 0.3, 0.5, and 0.7) suggesting that we can successfully homogenize behavior. Our model comparison analyses indicate that hyperboloid models are superior in predicting unseen discounting behavior to more conventional hyperbolic and exponential models. We report out-of-sample error estimates as well as commonalities and differences between reward and loss discounting, demonstrating for instance lower discounting rates, as well as differences in delay perception in loss discounting. Conclusion: The present work proposes a model-based framework to evoke graded responses linked to cognitive function at a single subject level. Such a framework may be used in the future to measure cognitive functions on a dimensional rather than dichotomous scale.
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Introduction: Interpretable latent variable models that probabilistically link behavioral observations to an underlying latent process have increasingly been used to draw inferences on cognition from observed behavior. The latent process usually connects experimental variables to cognitive computation. While such models provide important insights into the latent processes generating behavior, one important aspect has often been overlooked. They may also be used to generate precise and falsifiable behavioral predictions as a function of the modeled experimental variables. In doing so, they pinpoint how experimental conditions must be designed to elicit desired behavior and generate adaptive experiments. Methods: These ideas are exemplified on the process of delay discounting (DD). After inferring DD models from behavior on a typical DD task, the models are leveraged to generate a second adaptive DD task. Experimental trials in this task are designed to elicit 9 graded behavioral discounting probabilities across participants. Models are then validated and contrasted to competing models in the field by assessing the ouf-of-sample prediction error. Results: The proposed framework induces discounting probabilities on nine levels. In contrast to several alternative models, the applied model exhibits high validity as indicated by a comparably low prediction error. We also report evidence for inter-individual differences with respect to the most suitable models underlying behavior. Finally, we outline how to adapt the proposed method to the investigation of other cognitive processes including reinforcement learning. Discussion: Inducing graded behavioral frequencies with the proposed framework may help to highly resolve the underlying cognitive construct and associated neuronal substrates.
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Emerging studies corroborate the importance of neuroimaging biomarkers and machine learning to improve diagnostic classification of amyotrophic lateral sclerosis (ALS). While most studies focus on structural data, recent studies assessing functional connectivity between brain regions by linear methods highlight the role of brain function. These studies have yet to be combined with brain structure and nonlinear functional features. We investigate the role of linear and nonlinear functional brain features, and the benefit of combining brain structure and function for ALS classification. ALS patients (N = 97) and healthy controls (N = 59) underwent structural and functional resting state magnetic resonance imaging. Based on key hubs of resting state networks, we defined three feature sets comprising brain volume, resting state functional connectivity (rsFC), as well as (nonlinear) resting state dynamics assessed via recurrent neural networks. Unimodal and multimodal random forest classifiers were built to classify ALS. Out-of-sample prediction errors were assessed via five-fold cross-validation. Unimodal classifiers achieved a classification accuracy of 56.35-61.66%. Multimodal classifiers outperformed unimodal classifiers achieving accuracies of 62.85-66.82%. Evaluating the ranking of individual features' importance scores across all classifiers revealed that rsFC features were most dominant in classification. While univariate analyses revealed reduced rsFC in ALS patients, functional features more generally indicated deficits in information integration across resting state brain networks in ALS. The present work undermines that combining brain structure and function provides an additional benefit to diagnostic classification, as indicated by multimodal classifiers, while emphasizing the importance of capturing both linear and nonlinear functional brain properties to identify discriminative biomarkers of ALS.
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Esclerose Lateral Amiotrófica , Encéfalo , Conectoma , Aprendizado Profundo , Imageamento por Ressonância Magnética , Rede Nervosa , Adulto , Idoso , Esclerose Lateral Amiotrófica/classificação , Esclerose Lateral Amiotrófica/diagnóstico por imagem , Esclerose Lateral Amiotrófica/patologia , Esclerose Lateral Amiotrófica/fisiopatologia , Encéfalo/diagnóstico por imagem , Encéfalo/patologia , Encéfalo/fisiopatologia , Conectoma/métodos , Feminino , Humanos , Imageamento por Ressonância Magnética/métodos , Masculino , Pessoa de Meia-Idade , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/patologia , Rede Nervosa/fisiopatologiaRESUMO
Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology.
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Encéfalo/fisiologia , Memória de Curto Prazo/fisiologia , Rede Nervosa/fisiologia , Esquizofrenia/fisiopatologia , Adulto , Encéfalo/diagnóstico por imagem , Encéfalo/efeitos dos fármacos , Antagonistas dos Receptores de Dopamina D2/farmacologia , Feminino , Humanos , Imageamento por Ressonância Magnética , Masculino , Memória de Curto Prazo/efeitos dos fármacos , Pessoa de Meia-Idade , Rede Nervosa/diagnóstico por imagem , Rede Nervosa/efeitos dos fármacos , Córtex Pré-Frontal/diagnóstico por imagem , Córtex Pré-Frontal/efeitos dos fármacos , Córtex Pré-Frontal/metabolismo , Córtex Pré-Frontal/fisiologia , Receptores de Dopamina D1/genética , Receptores de Dopamina D1/metabolismo , Receptores de Dopamina D2/genética , Receptores de Dopamina D2/metabolismo , Esquizofrenia/diagnóstico por imagem , Esquizofrenia/genética , Esquizofrenia/metabolismo , Adulto JovemRESUMO
BACKGROUND: Public health measures to curb SARS-CoV-2 transmission rates may have negative psychosocial consequences in youth. Digital interventions may help to mitigate these effects. We investigated the associations between social isolation, COVID-19-related cognitive preoccupation, worries, and anxiety, objective social risk indicators, and psychological distress, as well as use of, and attitude toward, mobile health (mHealth) interventions in youth. METHODS: Data were collected as part of the "Mental Health And Innovation During COVID-19 Survey"-a cross-sectional panel study including a representative sample of individuals aged 16-25 years (N = 666; Mage = 21.3; assessment period: May 5, 2020 to May 16, 2020). RESULTS: Overall, 38% of youth met criteria for moderate or severe psychological distress. Social isolation worries and anxiety, and objective risk indicators were associated with psychological distress, with evidence of dose-response relationships for some of these associations. For instance, psychological distress was progressively more likely to occur as levels of social isolation increased (reporting "never" as reference group: "occasionally": adjusted odds ratio [aOR] 9.1, 95% confidence interval [CI] 4.3-19.1, p < 0.001; "often": aOR 22.2, CI 9.8-50.2, p < 0.001; "very often": aOR 42.3, CI 14.1-126.8, p < 0.001). There was evidence that psychological distress, worries, and anxiety were associated with a positive attitude toward using mHealth interventions, whereas psychological distress, worries, and anxiety were associated with actual use. CONCLUSIONS: Public health measures during pandemics may be associated with poor mental health outcomes in youth. Evidence-based digital interventions may help mitigate the negative psychosocial impact without risk of viral infection given there is an objective need and subjective demand.
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COVID-19 , Intervenção Baseada em Internet/estatística & dados numéricos , Saúde Mental , Quarentena , Isolamento Social/psicologia , Estresse Psicológico , Ansiedade/prevenção & controle , Ansiedade/psicologia , COVID-19/epidemiologia , COVID-19/prevenção & controle , COVID-19/psicologia , Estudos Transversais , Feminino , Alemanha/epidemiologia , Humanos , Masculino , Quarentena/métodos , Quarentena/psicologia , SARS-CoV-2 , Estresse Psicológico/etiologia , Estresse Psicológico/prevenção & controle , Telemedicina/métodos , Adulto JovemRESUMO
Extinction learning suppresses conditioned reward responses and is thus fundamental to adapt to changing environmental demands and to control excessive reward seeking. The medial prefrontal cortex (mPFC) monitors and controls conditioned reward responses. Abrupt transitions in mPFC activity anticipate changes in conditioned responses to altered contingencies. It remains, however, unknown whether such transitions are driven by the extinction of old behavioral strategies or by the acquisition of new competing ones. Using in vivo multiple single-unit recordings of mPFC in male rats, we studied the relationship between single-unit and population dynamics during extinction learning, using alcohol as a positive reinforcer in an operant conditioning paradigm. To examine the fine temporal relation between neural activity and behavior, we developed a novel behavioral model that allowed us to identify the number, onset, and duration of extinction-learning episodes in the behavior of each animal. We found that single-unit responses to conditioned stimuli changed even under stable experimental conditions and behavior. However, when behavioral responses to task contingencies had to be updated, unit-specific modulations became coordinated across the whole population, pushing the network into a new stable attractor state. Thus, extinction learning is not associated with suppressed mPFC responses to conditioned stimuli, but is anticipated by single-unit coordination into population-wide transitions of the internal state of the animal.SIGNIFICANCE STATEMENT The ability to suppress conditioned behaviors when no longer beneficial is fundamental for the survival of any organism. While pharmacological and optogenetic interventions have shown a critical involvement of the mPFC in the suppression of conditioned responses, the neural dynamics underlying such a process are still largely unknown. Combining novel analysis tools to describe behavior, single-neuron response, and population activity, we found that widespread changes in neuronal firing temporally coordinate across the whole mPFC population in anticipation of behavioral extinction. This coordination leads to a global transition in the internal state of the network, driving extinction of conditioned behavior.
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Comportamento Animal/fisiologia , Extinção Psicológica/fisiologia , Córtex Pré-Frontal/fisiologia , Recompensa , Animais , Condicionamento Operante , Aprendizagem/fisiologia , Masculino , Neurônios/fisiologia , Ratos , Ratos WistarRESUMO
This review provides a dynamical systems perspective on mental illness. After a brief introduction to the theory of dynamical systems, we focus on the common assumption in theoretical and computational neuroscience that phenomena at subcellular, cellular, network, cognitive, and even societal levels could be described and explained in terms of dynamical systems theory. As such, dynamical systems theory may also provide a framework for understanding mental illnesses. The review examines a number of core dynamical systems phenomena and relates each of these to aspects of mental illnesses. This provides an outline of how a broad set of phenomena in serious and common mental illnesses and neurological conditions can be understood in dynamical systems terms. It suggests that the dynamical systems level may provide a central, hublike level of convergence that unifies and links multiple biophysical and behavioral phenomena in the sense that diverse biophysical changes can give rise to the same dynamical phenomena and, vice versa, similar changes in dynamics may yield different behavioral symptoms depending on the brain area where these changes manifest. We also briefly outline current methodological approaches for inferring dynamical systems from data such as electroencephalography, functional magnetic resonance imaging, or self-reports, and we discuss the implications of a dynamical view for the diagnosis, prognosis, and treatment of psychiatric conditions. We argue that a consideration of dynamics could play a potentially transformative role in the choice and target of interventions.
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Transtornos Mentais , Neurociências , Encéfalo , Eletroencefalografia , Humanos , Imageamento por Ressonância MagnéticaRESUMO
Psychiatry today must gain a better understanding of the common and distinct pathophysiological mechanisms underlying psychiatric disorders in order to deliver more effective, person-tailored treatments. To this end, it appears that the analysis of 'small' experimental samples using conventional statistical approaches has largely failed to capture the heterogeneity underlying psychiatric phenotypes. Modern algorithms and approaches from machine learning, particularly deep learning, provide new hope to address these issues given their outstanding prediction performance in other disciplines. The strength of deep learning algorithms is that they can implement very complicated, and in principle arbitrary predictor-response mappings efficiently. This power comes at a cost, the need for large training (and test) samples to infer the (sometimes over millions of) model parameters. This appears to be at odds with the as yet rather 'small' samples available in psychiatric human research to date (n < 10,000), and the ambition of predicting treatment at the single subject level (n = 1). Here, we aim at giving a comprehensive overview on how we can yet use such models for prediction in psychiatry. We review how machine learning approaches compare to more traditional statistical hypothesis-driven approaches, how their complexity relates to the need of large sample sizes, and what we can do to optimally use these powerful techniques in psychiatric neuroscience.
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Aprendizado Profundo , Transtornos Mentais , Psiquiatria , Big Data , Humanos , Aprendizado de Máquina , Transtornos Mentais/terapiaRESUMO
The learning of stimulus-outcome associations allows for predictions about the environment. Ventral striatum and dopaminergic midbrain neurons form a larger network for generating reward prediction signals from sensory cues. Yet, the network plasticity mechanisms to generate predictive signals in these distributed circuits have not been entirely clarified. Also, direct evidence of the underlying interregional assembly formation and information transfer is still missing. Here we show that phasic dopamine is sufficient to reinforce the distinctness of stimulus representations in the ventral striatum even in the absence of reward. Upon such reinforcement, striatal stimulus encoding gives rise to interregional assemblies that drive dopaminergic neurons during stimulus-outcome learning. These assemblies dynamically encode the predicted reward value of conditioned stimuli. Together, our data reveal that ventral striatal and midbrain reward networks form a reinforcing loop to generate reward prediction coding.
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Dopamina/metabolismo , Neurônios Dopaminérgicos/efeitos dos fármacos , Neurônios Dopaminérgicos/metabolismo , Tubérculo Olfatório/efeitos dos fármacos , Animais , Dopamina/farmacologia , Masculino , Mesencéfalo/citologia , Camundongos , Modelos Teóricos , Estriado Ventral/efeitos dos fármacos , Estriado Ventral/metabolismoRESUMO
BACKGROUND: Motivational dysregulation represents a core vulnerability factor for bipolar disorder. Whether this also comprises aberrant learning of stimulus-reinforcer contingencies is less clear. METHODS: To answer this question, we compared healthy first-degree relatives of individuals with bipolar disorder (n = 42) known to convey an increased risk of developing a bipolar spectrum disorder and healthy individuals (n = 97). Further, we investigated the effects of the behavioral activation system (BAS) on reinforcement learning across the entire sample. All participants were assessed with a probabilistic learning task that distinguishes learning from positive and negative feedback. Main outcome measures included choice frequencies and learning rate parameters generated by computational reinforcement learning algorithms. RESULTS: First-degree relatives choose more rewarding stimuli more consistently and showed marginally reduced learning rates from unexpected negative feedback. Further, first-degree relatives had lower BAS scores than controls, which were negatively associated with learning rates from unexpected negative feedback. LIMITATIONS: However as probands also reported other mental disorders such as Attention-Deficit/Hyperactivity Disorder and substance abuse among their first-degree relatives, we cannot know, whether these findings are specific to the risk for bipolar disorder. CONCLUSION: The behavior of first-degree relatives of individuals with bipolar disorder, who also display increased BAS sensitivity, is less influenced by unexpected negative feedback. This reduced learning from unexpected negative feedback biases subsequent choices towards stimuli with higher probabilities for a reward. In sum, our results confirm the role of aberrant reinforcement learning in the pathophysiology of bipolar disorder.
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Transtorno do Deficit de Atenção com Hiperatividade , Transtorno Bipolar , Transtorno Bipolar/genética , Humanos , Motivação , Reforço Psicológico , RecompensaRESUMO
One of the major risk factors for global death and disability is alcohol, tobacco, and illicit drug use. While there is increasing knowledge with respect to individual factors promoting the initiation and maintenance of substance use disorders (SUDs), disease trajectories involved in losing and regaining control over drug intake (ReCoDe) are still not well described. Our newly formed German Collaborative Research Centre (CRC) on ReCoDe has an interdisciplinary approach funded by the German Research Foundation (DFG) with a 12-year perspective. The main goals of our research consortium are (i) to identify triggers and modifying factors that longitudinally modulate the trajectories of losing and regaining control over drug consumption in real life, (ii) to study underlying behavioral, cognitive, and neurobiological mechanisms, and (iii) to implicate mechanism-based interventions. These goals will be achieved by: (i) using mobile health (m-health) tools to longitudinally monitor the effects of triggers (drug cues, stressors, and priming doses) and modify factors (eg, age, gender, physical activity, and cognitive control) on drug consumption patterns in real-life conditions and in animal models of addiction; (ii) the identification and computational modeling of key mechanisms mediating the effects of such triggers and modifying factors on goal-directed, habitual, and compulsive aspects of behavior from human studies and animal models; and (iii) developing and testing interventions that specifically target the underlying mechanisms for regaining control over drug intake.
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Terapia Comportamental/métodos , Pesquisa Biomédica/métodos , Sinais (Psicologia) , Transtornos Relacionados ao Uso de Substâncias/fisiopatologia , Transtornos Relacionados ao Uso de Substâncias/terapia , Telemedicina/métodos , Animais , Comportamento Cooperativo , Modelos Animais de Doenças , Alemanha , Humanos , Recidiva , Transtornos Relacionados ao Uso de Substâncias/psicologiaRESUMO
A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the 'true' underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated 'ground-truth' dynamical systems as well as on experimental fMRI time series, and demonstrate that the learnt dynamics harbors task-related nonlinear structure that a linear dynamical model fails to capture. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research.