Neural representation of prediction error signals in substance users

Aberrant neural computation of social controllability in nicotine-dependent humans

Social controllability, defined as the ability to exert influence when interacting with others, is crucial for optimal decision-making. Inability to do so might contribute to maladaptive behaviors such as drug use, which often takes place in social settings. Here, we examined nicotine-dependent humans using fMRI, as they made choices that could influence the proposals from simulated partners. Computational modeling revealed that smokers under-estimated the influence of their actions and self-reported a reduced sense of control, compared to non-smokers. These findings were replicated in a large independent sample of participants recruited online. Neurally, smokers showed reduced tracking of forward projected choice values in the ventromedial prefrontal cortex, and impaired computation of social prediction errors in the midbrain. These results demonstrate that smokers were less accurate in estimating their personal influence when the social environment calls for control, providing a neurocomputational account for the social cognitive deficits in this population.

Neural correlates of prediction error in patients with schizophrenia: evidence from an fMRI meta-analysis

Abnormal processes of learning from prediction errors, i.e. the discrepancies between expectations and outcomes, are thought to underlie motivational impairments in schizophrenia. Although dopaminergic abnormalities in the mesocorticolimbic reward circuit have been found in patients with schizophrenia, the pathway through which prediction error signals are processed in schizophrenia has yet to be elucidated. To determine the neural correlates of prediction error processing in schizophrenia, we conducted a meta-analysis of whole-brain neuroimaging studies that investigated prediction error signal processing in schizophrenia patients and healthy controls. A total of 14 studies (324 schizophrenia patients and 348 healthy controls) using the reinforcement learning paradigm were included. Our meta-analysis showed that, relative to healthy controls, schizophrenia patients showed increased activity in the precentral gyrus and middle frontal gyrus and reduced activity in the mesolimbic circuit, including the striatum, thalamus, amygdala, hippocampus, anterior cingulate cortex, insula, superior temporal gyrus, and cerebellum, when processing prediction errors. We also found hyperactivity in frontal areas and hypoactivity in mesolimbic areas when encoding prediction error signals in schizophrenia patients, potentially indicating abnormal dopamine signaling of reward prediction error and suggesting failure to represent the value of alternative responses during prediction error learning and decision making.

Listening to the Data: Computational Approaches to Addiction and Learning

Computational approaches hold great promise for identifying novel treatment targets and creating translational therapeutics for substance use disorders. From circuitries underlying decision-making to computationally derived neural markers of drug-cue reactivity, this review is a summary of the approaches to data presented at our 2023 Society for Neuroscience Mini-Symposium. Here, we highlight data- and hypothesis-driven computational approaches that recently afforded advancements in addiction and learning neuroscience. First, we discuss the value of hypothesis-driven algorithmic modeling approaches, which integrate behavioral, neural, and cognitive outputs to refine hypothesis testing. Then, we review the advantages of data-driven dimensionality reduction and machine learning methods for uncovering novel predictor variables and elucidating relationships in high-dimensional data. Overall, this review highlights recent breakthroughs in cognitive mapping, model-based analysis of behavior/risky decision-making, patterns of drug taking, relapse, and neuromarker discovery, and showcases the benefits of novel modeling techniques, across both preclinical and clinical data.

Causal Involvement of Medial Inferior Frontal Gyrus of Non-dominant Hemisphere in Higher Order Auditory Perception: A single case study

The medial side of the operculum is invisible from the lateral surface of cerebral cortex, and its functions remain largely unexplored using direct evidence. Non-invasive and invasive studies have proved functions on peri-sylvian area including the inferior frontal gyrus (IFG) and superior temporal gyrus within the language-dominant hemisphere for semantic processing during verbal communication. However, within the non-dominant hemisphere, there was less evidence of its functions except for pitch or prosody processing. Here we add direct evidence for the functions of the non-dominant hemisphere, the causal involvement of the medial IFG for subjective auditory perception, which is affected by the context of the condition, regarded as a contribution in higher order auditory perception. The phenomenon was clearly distinguished from absolute and invariant pitch perception which is regarded as lower order auditory perception. Electrical stimulation of the medial surface of pars triangularis of IFG in non-dominant hemisphere via depth electrode in an epilepsy patient rapidly and reproducibly elicited perception of pitch changes of auditory input. Pitches were perceived as either higher or lower than those given without stimulation and there was no selectivity for sound type. The patient perceived sounds as higher when she had greater control over the situation when her eyes were open and there were self-cues, and as lower when her eyes were closed and there were investigator-cues. Time-frequency analysis of electrocorticography signals during auditory naming demonstrated medial IFG activation, characterized by low-gamma band augmentation during her own vocal response. The overall evidence provides a neural substrate for altered perception of other vocal tones according to the condition context.

Translational models of addiction phenotypes to advance addiction pharmacotherapy

Alcohol and substance use disorders are heterogeneous conditions with limited effective treatment options. While there have been prior attempts to classify addiction subtypes, they have not been translated into clinical practice. In an effort to better understand heterogeneity in psychiatric disorders, the National Institute for Mental Health Research Domain Criteria (RDoC) has challenged scientists to think beyond diagnostic symptoms and to consider the underlying features of psychopathology from a neuroscience-based framework. The field of addiction has grappled with this approach by considering several key constructs with the potential to capture RDoC domains. This critical review will focus on the efforts to apply translational models of addiction phenomenology in human clinical samples, including their relative strengths and weaknesses. Opportunities for forward and reverse translation are also discussed. Deep behavioral phenotyping using neuroscience-informed batteries shows promise for a better understanding of the clinical neuroscience of addiction and advancing precision medicine for alcohol and substance use disorders.

Evaluating instrumental learning and striatal-cortical functional connectivity in adolescent alcohol and cannabis use

Adolescence is a vulnerable time for the acquisition of substance use disorders, potentially relating to ongoing development of neural circuits supporting instrumental learning. Striatal-cortical circuits undergo dynamic changes during instrumental learning and are implicated in contemporary addiction theory. Human studies have not yet investigated these dynamic changes in relation to adolescent substance use. Here, functional magnetic resonance imaging was used while 135 adolescents without (AUD-CUDLow ) and with significant alcohol (AUDHigh ) or cannabis use disorder symptoms (CUDHigh ) performed an instrumental learning task. We assessed how cumulative experience with instrumental cues altered cue selection preferences and functional connectivity strength between reward-sensitive striatal and cortical regions. Adolescents in AUDHigh and CUDHigh groups were slower in learning to select optimal instrumental cues relative to AUD-CUDLow adolescents. The relatively fast learning observed for AUD-CUDLow adolescents coincided with stronger functional connectivity between striatal and frontoparietal regions during early relative to later periods of task experience, whereas the slower learning for the CUDHigh group coincided with the opposite pattern. The AUDHigh group not only exhibited slower learning but also produced more instrumental choice errors relative to AUD-CUDLow adolescents. For the AUDHigh group, Bayesian analyses evidenced moderate support for no experience-related changes in striatal-frontoparietal connectivity strength during the task. Findings suggest that adolescent cannabis use is related to slowed instrumental learning and delays in peak functional connectivity strength between the striatal-frontoparietal regions that support this learning, whereas adolescent alcohol use may be more closely linked to broader impairments in instrumental learning and a general depression of the neural circuits supporting it.

Brain network dysfunctions in addiction: a meta-analysis of resting-state functional connectivity

Resting-state functional connectivity (rsFC) provides novel insights into variabilities in neural networks associated with the use of addictive drugs or with addictive behavioral repertoire. However, given the broad mix of inconsistent findings across studies, identifying specific consistent patterns of network abnormalities is warranted. Here we aimed at integrating rsFC abnormalities and systematically searching for large-scale functional brain networks in substance use disorder (SUD) and behavioral addictions (BA), through a coordinate-based meta-analysis of seed-based rsFC studies. A total of fifty-two studies are eligible in the meta-analysis, including 1911 SUD and BA patients and 1580 healthy controls. In addition, we performed multilevel kernel density analysis (MKDA) for the brain regions reliably involved in hyperconnectivity and hypoconnectivity in SUD and BA. Data from fifty-two studies showed that SUD was associated with putamen, caudate and middle frontal gyrus hyperconnectivity relative to healthy controls. Eight BA studies showed hyperconnectivity clusters within the putamen and medio-temporal lobe relative to healthy controls. Altered connectivity in salience or emotion-processing areas may be related to dysregulated affective and cognitive control-related networks, such as deficits in regulating elevated sensitivity to drug-related stimuli. These findings confirm that SUD and BA might be characterized by dysfunctions in specific brain networks, particularly those implicated in the core cognitive and affective functions. These findings might provide insight into the development of neural mechanistic biomarkers for SUD and BA.

Abnormal prediction error processing in schizophrenia and depression

To make adaptive decisions under uncertainty, individuals need to actively monitor the discrepancy between expected outcomes and actual outcomes, known as prediction errors. Reward‐based learning deficits have been shown in both depression and schizophrenia patients. For this study, we compiled studies that investigated prediction error processing in depression and schizophrenia patients and performed a series of meta‐analyses. In both groups, positive t‐maps of prediction error tend to yield striatum activity across studies. The analysis of negative t‐maps of prediction error revealed two large clusters within the right superior and inferior frontal lobes in schizophrenia and the medial prefrontal cortex and bilateral insula in depression. The concordant posterior cingulate activity was observed in both patient groups, more prominent in the depression group and absent in the healthy control group. These findings suggest a possible role in dopamine‐rich areas associated with the encoding of prediction errors in depression and schizophrenia.