Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

Predicting treatment response to ketamine in treatment-resistant depression using auditory mismatch negativity: Study protocol

BACKGROUND

Ketamine has recently attracted considerable attention for its rapid effects on patients with major depressive disorder, including treatment-resistant depression (TRD). Despite ketamine's promising results in treating depression, a significant number of patients do not respond to the treatment, and predicting who will benefit remains a challenge. Although its antidepressant effects are known to be linked to its action as an antagonist of the N-methyl-D-aspartate (NMDA) receptor, the precise mechanisms that determine why some patients respond and others do not are still unclear.

OBJECTIVE

This study aims to understand the computational mechanisms underlying changes in the auditory mismatch negativity (MMN) response following treatment with intravenous ketamine. Moreover, we aim to link the computational mechanisms to their underlying neural causes and use the parameters of the neurocomputational model to make individual treatment predictions.

METHODS

This is a prospective study of 30 patients with TRD who are undergoing intravenous ketamine therapy. Prior to 3 out of 4 ketamine infusions, EEG will be recorded while patients complete the auditory MMN task. Depression, suicidality, and anxiety will be assessed throughout the study and a week after the last ketamine infusion. To translate the effects of ketamine on the MMN to computational mechanisms, we will model changes in the auditory MMN using the hierarchical Gaussian filter, a hierarchical Bayesian model. Furthermore, we will employ a conductance-based neural mass model of the electrophysiological data to link these computational mechanisms to their neural causes.

CONCLUSION

The findings of this study may improve understanding of the mechanisms underlying response and resistance to ketamine treatment in patients with TRD. The parameters obtained from fitting computational models to EEG recordings may facilitate single-patient treatment predictions, which could provide clinically useful prognostic information.

TRIAL REGISTRATION

Clinicaltrials.gov NCT05464264. Registered June 24, 2022.

Ketamine reduces the neural distinction between self- and other-produced affective touch: a randomized double-blind placebo-controlled study

A coherent sense of self is crucial for social functioning and mental health. The N-methyl-D-aspartate antagonist ketamine induces short-term dissociative experiences and has therefore been used to model an altered state of self-perception. This randomized double-blind placebo-controlled cross-over study investigated the mechanisms for ketamine's effects on the bodily sense of self in the context of affective touch. Thirty healthy participants (15 females/15 males, age 19-39) received intravenous ketamine or placebo while performing self-touch and receiving touch by someone else during functional MRI - a previously established neural measure of tactile self-other-differentiation. Afterwards, tactile detection thresholds during self- and other-touch were assessed, as well as dissociative states, interoceptive awareness, and social touch attitudes. Compared to placebo, ketamine administration elicited dissociation and reduced neural activity associated with self-other-differentiation in the right temporoparietal cortex, which was most pronounced during other-touch. This reduction correlated with ketamine-induced reductions in interoceptive awareness. The temporoparietal cortex showed higher connectivity to somatosensory cortex and insula during other- compared to self-touch. This difference was augmented by ketamine, and correlated with dissociation strength for somatosensory cortex. These results demonstrate that disrupting the self-experience through ketamine administration affects neural activity associated with self-other-differentiation in a region involved in touch perception and social cognition, especially with regard to social touch by someone else. This process may be driven by ketamine-induced effects on top-down signaling, rendering the processing of predictable self-generated and unpredictable other-generated touch more similar. These findings provide further evidence for the intricate relationship of the bodily self with the tactile sense.

The influence of psilocybin on subconscious and conscious emotional learning

Serotonergic psychedelics hold promise as a treatment modality for various psychiatric disorders and are currently applied in psychedelic-assisted psychotherapy. We investigated the learning effects of the serotonin receptor agonist psilocybin in a probabilistic cue-reward task with emotional cues in the form of neutral or fearful faces, presented either consciously or subconsciously. This study represents the first investigation into reinforcement learning with psilocybin. Across different dosages, psilocybin preserved learning effects and was statistically noninferior compared to placebo, while suggesting a higher exploratory behavior. Notably, the 20 mg group exhibited significantly better learning rates against the placebo group. Psilocybin induced inferior results with subconscious cues compared to placebo, and better results with conscious neutral cues in some conditions. These findings suggest that modulating serotonin signaling in the brain with psilocybin sufficiently preservers reinforcement learning.

Tracking the habituation of the event-related EEG potential in automatic change detection using an auditory two-tone oddball paradigm

The mismatch negativity and the P3a of the event-related EEG potential reflect the electrocortical response to a deviant stimulus in a series of stimuli. Although both components have been investigated in various paradigms, these paradigms usually incorporate many repetitions of the same deviant, thus leaving open whether both components vary as a function of the deviant's position in a series of deviant stimuli-i.e. whether they are subject to qualitative/quantitative habituation from one instantiation of a deviant to the next. This is so because the detection of mismatch negativity/P3a in the event-related EEG potential requires an averaging over dozens or hundreds of stimuli, i.e. over many instantiations of the deviant per participant. The present study addresses this research gap. We used a two-tone oddball paradigm implementing only a small number of (deviant) stimuli per participant, but applying it to a large number of participants (n > 230). Our data show that the mismatch negativity amplitude exhibits no decrease as a function of the deviant's position in a series of (standard and) deviant stimuli. Importantly, only after the very first deviant stimulus, a distinct P3a could be detected, indicative of an orienting reaction and an attention shift, and thus documenting a dissociation of mismatch negativity and P3a.

A multimodal cortical network of sensory expectation violation revealed by fMRI

The brain is subjected to multi-modal sensory information in an environment governed by statistical dependencies. Mismatch responses (MMRs), classically recorded with EEG, have provided valuable insights into the brain's processing of regularities and the generation of corresponding sensory predictions. Only few studies allow for comparisons of MMRs across multiple modalities in a simultaneous sensory stream and their corresponding cross-modal context sensitivity remains unknown. Here, we used a tri-modal version of the roving stimulus paradigm in fMRI to elicit MMRs in the auditory, somatosensory and visual modality. Participants (N = 29) were simultaneously presented with sequences of low and high intensity stimuli in each of the three senses while actively observing the tri-modal input stream and occasionally reporting the intensity of the previous stimulus in a prompted modality. The sequences were based on a probabilistic model, defining transition probabilities such that, for each modality, stimuli were more likely to repeat (p = .825) than change (p = .175) and stimulus intensities were equiprobable (p = .5). Moreover, each transition was conditional on the configuration of the other two modalities comprising global (cross-modal) predictive properties of the sequences. We identified a shared mismatch network of modality general inferior frontal and temporo-parietal areas as well as sensory areas, where the connectivity (psychophysiological interaction) between these regions was modulated during mismatch processing. Further, we found deviant responses within the network to be modulated by local stimulus repetition, which suggests highly comparable processing of expectation violation across modalities. Moreover, hierarchically higher regions of the mismatch network in the temporo-parietal area around the intraparietal sulcus were identified to signal cross-modal expectation violation. With the consistency of MMRs across audition, somatosensation and vision, our study provides insights into a shared cortical network of uni- and multi-modal expectation violation in response to sequence regularities.

Time-resolved dynamic computational modeling of human EEG recordings reveals gradients of generative mechanisms for the MMN response

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models' ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

Aberrant Hierarchical Prediction Errors Are Associated With Transition to Psychosis: A Computational Single-Trial Analysis of the Mismatch Negativity

BACKGROUND

Mismatch negativity reductions are among the most reliable biomarkers for schizophrenia and have been associated with increased risk for conversion to psychosis in individuals who are at clinical high risk for psychosis (CHR-P). Here, we adopted a computational approach to develop a mechanistic model of mismatch negativity reductions in CHR-P individuals and patients early in the course of schizophrenia.

METHODS

Electroencephalography was recorded in 38 CHR-P individuals (15 converters), 19 patients early in the course of schizophrenia (≤5 years), and 44 healthy control participants during three different auditory oddball mismatch negativity paradigms including 10% duration, frequency, or double deviants, respectively. We modeled sensory learning with the hierarchical Gaussian filter and extracted precision-weighted prediction error trajectories from the model to assess how the expression of hierarchical prediction errors modulated electroencephalography amplitudes over sensor space and time.

RESULTS

Both low-level sensory and high-level volatility precision-weighted prediction errors were altered in CHR-P individuals and patients early in the course of schizophrenia compared with healthy control participants. Moreover, low-level precision-weighted prediction errors were significantly different in CHR-P individuals who later converted to psychosis compared with nonconverters.

CONCLUSIONS

Our results implicate altered processing of hierarchical prediction errors as a computational mechanism in early psychosis consistent with predictive coding accounts of psychosis. This computational model seems to capture pathophysiological mechanisms that are relevant to early psychosis and the risk for future psychosis in CHR-P individuals and may serve as predictive biomarkers and mechanistic targets for the development of novel treatments.

Can't catch the beat: Failure to find simple repetition effects in three types of temporal judgements

More experience results in better performance, usually. In most tasks, the more chances to learn we have, the better we are at it. This does not always appear to be the case in time perception however. In the current article, we use three different methods to investigate the role of the number of standard example durations presented on performance on three timing tasks: rhythm continuation, deviance detection, and final stimulus duration judgement. In Experiments 1a and 1b, rhythms were produced with the same accuracy whether one, two, three, or four examples of the critical duration were presented. In Experiment 2, participants were required to judge which of four stimuli had a different duration from the other three. This judgement did not depend on which of the four stimuli was the deviant one. In Experiments 3a and 3b, participants were just as accurate at judging the duration of a final stimulus in comparison to the prior stimuli regardless of the number of standards presented prior to the final stimulus. In summary, we never found any systematic effect of the number of standards presented on performance on any of the three timing tasks. In the discussion, we briefly relate these findings to three theories of time perception.

Subanaesthetic doses of ketamine reduce but do not eliminate predictive coding responses: implications for mechanisms of sensory disconnection

BACKGROUND

Sensory disconnection is a key feature of sleep and anaesthesia. We have proposed that predictive coding offers a framework for understanding the mechanisms of disconnection. Low doses of ketamine that do not induce disconnection should thus diminish predictive coding, but not abolish it.

METHODS

Ketamine was administered to 14 participants up to a blood concentration of 0.3 μg ml-1 Participants were played a series of tones comprising a roving oddball sequence while electroencephalography evoked response potentials were recorded. We fit a Bayesian observer model to the tone sequence, correlating neural activity with the prediction errors generated by the model using linear mixed effects models and cluster-based statistics.

RESULTS

Ketamine modulated prediction errors associated with the transition of one tone to the next (transitional probability), but not how often tones changed (environmental volatility), of the system. Transitional probability was reduced when blood concentrations of ketamine were increased to 0.2-0.3 μg ml-1 (96-208 ms, P=0.003); however, correlates of prediction error were still evident in the electroencephalogram (124-168 ms, P=0.003). Prediction errors related to environmental volatility were associated with electroencephalographic activity before ketamine (224-284 ms, P=0.028) and during 0.2-0.3 μg ml-1 ketamine (108-248 ms, P=0.003). At this subanaesthetic dose, ketamine did not exert a dose-dependent modulation of prediction error.

CONCLUSIONS

Subanaesthetic dosing of ketamine reduced correlates of predictive coding but did not eliminate them. Future studies should evaluate whether states of sensory disconnection, including anaesthetic doses of ketamine, are associated with a complete absence of predictive coding responses.

CLINICAL TRIAL REGISTRATION

NCT03284307.

Why mismatch negativity continues to hold potential in probing altered brain function in schizophrenia

The brain potential known as mismatch negativity (MMN) is one of the most studied indices of altered brain function in schizophrenia. This review looks at what has been learned about MMN in schizophrenia over the last three decades and why the level of interest and activity in this field of research remains strong. A diligent consideration of available evidence suggests that MMN can serve as a biomarker in schizophrenia, but perhaps not the kind of biomarker that early research supposed. This review concludes that MMN measurement is likely to be most useful as a monitoring and response biomarker enabling tracking of an underlying pathology and efficacy of interventions, respectively. The role of, and challenges presented by, pre-clinical models is discussed as well as the merits of different methodologies that can be brought to bear in pursuing a deeper understanding of pathophysiology that might explain smaller MMN in schizophrenia.

The impact of spatial variance on precision estimates in an auditory oddball paradigm

Predictive processing theories suggest that a principal function of the brain is to reduce the surprise of incoming sensory information by creating accurate and precise models of the environment. These models are commonly explored by looking at the prediction errors elicited when experience departs from predictions. One such prediction error is the mismatch negativity (MMN). Using this component, it is possible to examine the effect of external noise on the precision of the developed model. Recent studies have shown that the brain may not update its model every time there is a change in the environment, rather it will only update it when doing so will increase precision and or accuracy of the model. The current study examined this process using oddball sound sequences with high and low spatial variability and examining how this affected the elicited MMN to a duration deviant sound. The results showed a strong null effect of spatial variance both at a local and sequence levels. These results indicate that variability in the sound sequence will not invariably affect model precision estimates and thus the amplitude of the MMN component.

Suicide prevention and ketamine: insights from computational modeling

Suicide is a pressing public health issue, with over 700,000 individuals dying each year. Ketamine has emerged as a promising treatment for suicidal thoughts and behaviors (STBs), yet the complex mechanisms underlying ketamine's anti-suicidal effect are not fully understood. Computational psychiatry provides a promising framework for exploring the dynamic interactions underlying suicidality and ketamine's therapeutic action, offering insight into potential biomarkers, treatment targets, and the underlying mechanisms of both. This paper provides an overview of current computational theories of suicidality and ketamine's mechanism of action, and discusses various computational modeling approaches that attempt to explain ketamine's anti-suicidal effect. More specifically, the therapeutic potential of ketamine is explored in the context of the mismatch negativity and the predictive coding framework, by considering neurocircuits involved in learning and decision-making, and investigating altered connectivity strengths and receptor densities targeted by ketamine. Theory-driven computational models offer a promising approach to integrate existing knowledge of suicidality and ketamine, and for the extraction of model-derived mechanistic parameters that can be used to identify patient subgroups and personalized treatment approaches. Future computational studies on ketamine's mechanism of action should optimize task design and modeling approaches to ensure parameter reliability, and external factors such as set and setting, as well as psychedelic-assisted therapy should be evaluated for their additional therapeutic value.

What came before: Assimilation effects in the categorization of time intervals

Assimilation is the process by which one judgment tends to approach some aspect of another stimulus or judgment. This effect has been known for over half a century in various domains such as the judgment of weight or sound intensity. However, the assimilation of judgments of durations have been relatively unexplored. In the current article, we present the results of five experiments in which participant s were required to judge the duration of a visual stimulus on each trial. In each experiment, we manipulated the pattern of durations they experienced in order to systematically separate the effects of the objective and subjective duration of stimuli on subsequent judgments. We found that duration judgments were primarily driven by prior judgments, with little, if any, effect of the prior objective stimulus duration. This is in contrast to the findings previously reported in regards to non-temporal judgments. We propose two mechanist explanations of this effect; a representational account in which judgments represent the speed of an underlying pacemaker, and an assimilation account in which judgment is based in prior experience. We further discuss results in terms of predictive coding, in which the previous rating is representative of a prior expectation, which is modified by current experience.

Anterior cingulate and medial prefrontal cortex oscillations underlie learning alterations in trait anxiety in humans

Anxiety has been linked to altered belief formation and uncertainty estimation, impacting learning. Identifying the neural processes underlying these changes is important for understanding brain pathology. Here, we show that oscillatory activity in the medial prefrontal, anterior cingulate and orbitofrontal cortex (mPFC, ACC, OFC) explains anxiety-related learning alterations. In a magnetoencephalography experiment, two groups of human participants pre-screened with high and low trait anxiety (HTA, LTA: 39) performed a probabilistic reward-based learning task. HTA undermined learning through an overestimation of volatility, leading to faster belief updating, more stochastic decisions and pronounced lose-shift tendencies. On a neural level, we observed increased gamma activity in the ACC, dmPFC, and OFC during encoding of precision-weighted prediction errors in HTA, accompanied by suppressed ACC alpha/beta activity. Our findings support the association between altered learning and belief updating in anxiety and changes in gamma and alpha/beta activity in the ACC, dmPFC, and OFC.

Modulation of Motor Vigor by Expectation of Reward Probability Trial-by-Trial Is Preserved in Healthy Ageing and Parkinson's Disease Patients

Motor improvements, such as faster movement times or increased velocity, have been associated with reward magnitude in deterministic contexts. Yet whether individual inferences on reward probability influence motor vigor dynamically remains undetermined. We investigated how dynamically inferring volatile action-reward contingencies modulated motor performance trial-by-trial. We conducted three studies that coupled a reversal learning paradigm with a motor sequence task and used a validated hierarchical Bayesian model to fit trial-by-trial data. In Study 1, we tested healthy younger [HYA; 37 (24 females)] and older adults [HOA; 37 (17 females)], and medicated Parkinson's disease (PD) patients [20 (7 females)]. We showed that stronger predictions about the tendency of the action-reward contingency led to faster performance tempo, commensurate with movement time, on a trial-by-trial basis without robustly modulating reaction time (RT). Using Bayesian linear mixed models, we demonstrated a similar invigoration effect on performance tempo in HYA, HOA, and PD, despite HOA and PD being slower than HYA. In Study 2 [HYA, 39 (29 females)], we additionally showed that retrospective subjective inference about credit assignment did not contribute to differences in motor vigor effects. Last, Study 3 [HYA, 33 (27 females)] revealed that explicit beliefs about the reward tendency (confidence ratings) modulated performance tempo trial-by-trial. Our study is the first to reveal that the dynamic updating of beliefs about volatile action-reward contingencies positively biases motor performance through faster tempo. We also provide robust evidence for a preserved sensitivity of motor vigor to inferences about the action-reward mapping in aging and medicated PD.SIGNIFICANCE STATEMENT Navigating a world rich in uncertainty relies on updating beliefs about the probability that our actions lead to reward. Here, we investigated how inferring the action-reward contingencies in a volatile environment modulated motor vigor trial-by-trial in healthy younger and older adults, and in Parkinson's disease (PD) patients on medication. We found an association between trial-by-trial predictions about the tendency of the action-reward contingency and performance tempo, with stronger expectations speeding the movement. We additionally provided evidence for a similar sensitivity of performance tempo to the strength of these predictions in all groups. Thus, dynamic beliefs about the changing relationship between actions and their outcome enhanced motor vigor. This positive bias was not compromised by age or Parkinson's disease.

Bayesian surprise shapes neural responses in somatosensory cortical circuits

Numerous psychophysical studies show that Bayesian inference governs sensory decision-making; however, the specific neural circuitry underlying this probabilistic mechanism remains unclear. We record extracellular neural activity along the somatosensory pathway of mice while delivering sensory stimulation paradigms designed to isolate the response to the surprise generated by Bayesian inference. Our results demonstrate that laminar cortical circuits in early sensory areas encode Bayesian surprise. Systematic sensitivity to surprise is not identified in the somatosensory thalamus, rather emerging in the primary (S1) and secondary (S2) somatosensory cortices. Multiunit spiking activity and evoked potentials in layer 6 of these regions exhibit the highest sensitivity to surprise. Gamma power in S1 layer 2/3 exhibits an NMDAR-dependent scaling with surprise, as does alpha power in layers 2/3 and 6 of S2. These results show a precise spatiotemporal neural representation of Bayesian surprise and suggest that Bayesian inference is a fundamental component of cortical processing.

Anaesthetics and time perception: A review

Consciousness requires subjective experience in the "now." Establishing "now," however, necessitates temporal processing. In the current article, we review one method of altering consciousness, anaesthetic drug administration, and its effects on perceived duration. We searched PubMed, PsycInfo, and ScienceDirect databases, and article reference sections, for combinations of anaesthetic drugs and time perception tasks, finding a total of 36 articles which met our inclusion criteria. We categorised these articles with regard to whether they altered the felt passage of time, short or long interval timing, or were motor timing tasks. We found that various drugs alter the perceived passage of time; ketamine makes time subjectively slow down while GABAergic drugs make time subjectively speed up. At a short interval there is little established evidence of a shift in time perception, though temporal estimates appear more variable. Similarly, when asked to use time to optimise responses (i.e., in motor timing tasks), various anaesthetic agents make timing more variable. Longer durations are estimated as lasting longer than their objective duration, though there is some variation across articles in this regard. We conclude by proposing further experiments to examine time perception under altered states of consciousness and ask whether it is possible to perceive the passage of time of events which do not necessarily reach the level of conscious perception. The variety of methods used raises the need for more systematic investigations of time perception under anaesthesia. We encourage future investigations into the overlap of consciousness and time perception to advance both fields.

Atypical prediction error learning is associated with prodromal symptoms in individuals at clinical high risk for psychosis

Reductions in the auditory mismatch negativity (MMN) have been well-demonstrated in schizophrenia rendering it a promising biomarker for understanding the emergence of psychosis. According to the predictive coding theory of psychosis, MMN impairments may reflect disturbances in hierarchical information processing driven by maladaptive precision-weighted prediction errors (pwPEs) and enhanced belief updating. We applied a hierarchical Bayesian model of learning to single-trial EEG data from an auditory oddball paradigm in 31 help-seeking antipsychotic-naive high-risk individuals and 23 healthy controls to understand the computational mechanisms underlying the auditory MMN. We found that low-level sensory and high-level volatility pwPE expression correlated with EEG amplitudes, coinciding with the timing of the MMN. Furthermore, we found that prodromal positive symptom severity was associated with increased expression of sensory pwPEs and higher-level belief uncertainty. Our findings provide support for the role of pwPEs in auditory MMN generation, and suggest that increased sensory pwPEs driven by changes in belief uncertainty may render the environment seemingly unpredictable. This may predispose high-risk individuals to delusion-like ideation to explain this experience. These results highlight the value of computational models for understanding the pathophysiological mechanisms of psychosis.

Evaluation of Early Ketamine Effects on Belief-Updating Biases in Patients With Treatment-Resistant Depression

IMPORTANCE

Clinical research has shown that persistent negative beliefs maintain depression and that subanesthetic ketamine infusions induce rapid antidepressant responses.

OBJECTIVE

To evaluate whether ketamine alters belief updating and how such cognitive effects are associated with the clinical effects of ketamine.

DESIGN, SETTING, AND PARTICIPANTS

This study used an observational case-control protocol with a mixed-effects design that nested 2 groups by 2 testing time points. Observers were not blinded. Patients with treatment-resistant depression (TRD) and healthy volunteer participants aged 34 to 68 years were included. Patients with TRD were diagnosed with major depressive disorder or bipolar depression, had a Montgomery-Åsberg Depression Rating Scale score greater than 20, a Maudsley Staging Method score greater than 7, and failed to respond to at least 2 prior antidepressant trials. Exclusion criteria were any other psychiatric, neurological, or neurosurgical comorbidities, substance use or addictive disorders, and recreational ketamine consumption. Data were collected from January to February 2019 and from May to December 2019, and data were analyzed from January 2020 to July 2021.

EXPOSURES

Patients with TRD were observed 24 hours before single ketamine infusion, 4 hours after the infusion, and 4 hours after the third infusion, which was 1 week after the first infusion. Healthy control participants were observed twice 1 week apart without ketamine exposure.

MAIN OUTCOMES AND MEASURES

Montgomery-Åsberg Depression Rating Scale score and belief updating after belief updating when patients received good news and bad news measured by a cognitive belief-updating task and mathematically formalized by a computational reinforcement learning model.

RESULTS

Of 56 included participants, 29 (52%) were male, and the mean (SEM) age was 52.3 (1.2) years. A total of 26 patients with TRD and 30 control participants were included. A significant group × testing time point × news valence interaction showed that patients with TRD updated their beliefs more after good than bad news following a single ketamine infusion (controlled for age and education: β = -0.91; 95% CI, -1.58 to -0.24; t216 = -2.67; P = .008) than controls. Computational modeling showed that this effect was associated with asymmetrical learning rates (LRs) after ketamine treatment (good news LRs after ketamine, 0.51 [SEM, 0.04]; bad news LRs after ketamine 0.36 [SEM, 0.03], t25 = 3.8; P < .001) and partially mediated early antidepressant responses (path a*b: β = -1.00 [SEM, 0.66]; t26 = -1.53; z = -1.98; P = .04).

CONCLUSIONS AND RELEVANCE

These findings provide novel insights into the cognitive mechanisms of the action of ketamine in patients with TRD, with promising perspectives for augmented psychotherapy for individuals with mood disorders.

The neurophysiological effect of NMDA-R antagonism of frontotemporal lobar degeneration is conditional on individual GABA concentration

There is a pressing need to accelerate therapeutic strategies against the syndromes caused by frontotemporal lobar degeneration, including symptomatic treatments. One approach is for experimental medicine, coupling neurophysiological studies of the mechanisms of disease with pharmacological interventions aimed at restoring neurochemical deficits. Here we consider the role of glutamatergic deficits and their potential as targets for treatment. We performed a double-blind placebo-controlled crossover pharmaco-magnetoencephalography study in 20 people with symptomatic frontotemporal lobar degeneration (10 behavioural variant frontotemporal dementia, 10 progressive supranuclear palsy) and 19 healthy age- and gender-matched controls. Both magnetoencephalography sessions recorded a roving auditory oddball paradigm: on placebo or following 10 mg memantine, an uncompetitive NMDA-receptor antagonist. Ultra-high-field magnetic resonance spectroscopy confirmed lower concentrations of GABA in the right inferior frontal gyrus of people with frontotemporal lobar degeneration. While memantine showed a subtle effect on early-auditory processing in patients, there was no significant main effect of memantine on the magnitude of the mismatch negativity (MMN) response in the right frontotemporal cortex in patients or controls. However, the change in the right auditory cortex MMN response to memantine (vs. placebo) in patients correlated with individuals' prefrontal GABA concentration. There was no moderating effect of glutamate concentration or cortical atrophy. This proof-of-concept study demonstrates the potential for baseline dependency in the pharmacological restoration of neurotransmitter deficits to influence cognitive neurophysiology in neurodegenerative disease. With changes to multiple neurotransmitters in frontotemporal lobar degeneration, we suggest that individuals' balance of excitation and inhibition may determine drug efficacy, with implications for drug selection and patient stratification in future clinical trials.

The promise of layer-specific neuroimaging for testing predictive coding theories of psychosis

Predictive coding potentially provides an explanatory model for understanding the neurocognitive mechanisms of psychosis. It proposes that cognitive processes, such as perception and inference, are implemented by a hierarchical system, with the influence of each level being a function of the estimated precision of beliefs at that level. However, predictive coding models of psychosis are insufficiently constrained-any phenomenon can be explained in multiple ways by postulating different changes to precision at different levels of processing. One reason for the lack of constraint in these models is that the core processes are thought to be implemented by the function of specific cortical layers, and the technology to measure layer specific neural activity in humans has until recently been lacking. As a result, our ability to constrain the models with empirical data has been limited. In this review we provide a brief overview of predictive processing models of psychosis and then describe the potential for newly developed, layer specific neuroimaging techniques to test and thus constrain these models. We conclude by discussing the most promising avenues for this research as well as the technical and conceptual challenges which may limit its application.

Auditory mismatch responses are differentially sensitive to changes in muscarinic acetylcholine versus dopamine receptor function

The auditory mismatch negativity (MMN) has been proposed as a biomarker of NMDA receptor (NMDAR) dysfunction in schizophrenia. Such dysfunction may be caused by aberrant interactions of different neuromodulators with NMDARs, which could explain clinical heterogeneity among patients. In two studies (N = 81 each), we used a double-blind placebo-controlled between-subject design to systematically test whether auditory mismatch responses under varying levels of environmental stability are sensitive to diminishing and enhancing cholinergic vs. dopaminergic function. We found a significant drug × mismatch interaction: while the muscarinic acetylcholine receptor antagonist biperiden delayed and topographically shifted mismatch responses, particularly during high stability, this effect could not be detected for amisulpride, a dopamine D2/D3 receptor antagonist. Neither galantamine nor levodopa, which elevate acetylcholine and dopamine levels, respectively, exerted significant effects on MMN. This differential MMN sensitivity to muscarinic versus dopaminergic receptor function may prove useful for developing tests that predict individual treatment responses in schizophrenia.

Dynamics of Oddball Sound Processing: Trial-by-Trial Modeling of ECoG Signals

Recent computational models of perception conceptualize auditory oddball responses as signatures of a (Bayesian) learning process, in line with the influential view of the mismatch negativity (MMN) as a prediction error signal. Novel MMN experimental paradigms have put an emphasis on neurophysiological effects of manipulating regularity and predictability in sound sequences. This raises the question of the contextual adaptation of the learning process itself, which on the computational side speaks to the mechanisms of gain-modulated (or precision-weighted) prediction error. In this study using electrocorticographic (ECoG) signals, we manipulated the predictability of oddball sound sequences with two objectives: (i) Uncovering the computational process underlying trial-by-trial variations of the cortical responses. The fluctuations between trials, generally ignored by approaches based on averaged evoked responses, should reflect the learning involved. We used a general linear model (GLM) and Bayesian Model Reduction (BMR) to assess the respective contributions of experimental manipulations and learning mechanisms under probabilistic assumptions. (ii) To validate and expand on previous findings regarding the effect of changes in predictability using simultaneous EEG-MEG recordings. Our trial-by-trial analysis revealed only a few stimulus-responsive sensors but the measured effects appear to be consistent over subjects in both time and space. In time, they occur at the typical latency of the MMN (between 100 and 250 ms post-stimulus). In space, we found a dissociation between time-independent effects in more anterior temporal locations and time-dependent (learning) effects in more posterior locations. However, we could not observe any clear and reliable effect of our manipulation of predictability modulation onto the above learning process. Overall, these findings clearly demonstrate the potential of trial-to-trial modeling to unravel perceptual learning processes and their neurophysiological counterparts.

Classic Psychedelic Drugs: Update on Biological Mechanisms

Renewed interest in the effects of psychedelics in the treatment of psychiatric disorders warrants a better understanding of the neurobiological mechanisms underlying the effects of these substances. During the past two decades, state-of-the-art studies of animals and humans have yielded new important insights into the molecular, cellular, and systems-level actions of psychedelic drugs. These efforts have revealed that psychedelics affect primarily serotonergic receptor subtypes located in cortico-thalamic and cortico-cortical feedback circuits of information processing. Psychedelic drugs modulate excitatory-inhibitory balance in these circuits and can participate in neuroplasticity within brain structures critical for the integration of information relevant to sensation, cognition, emotions, and the narrative of self. Neuroimaging studies showed that characteristic dimensions of the psychedelic experience obtained through subjective questionnaires as well as alterations in self-referential processing and emotion regulation obtained through neuropsychological tasks are associated with distinct changes in brain activity and connectivity patterns at multiple-system levels. These recent results suggest that changes in self-experience, emotional processing, and social cognition may contribute to the potential therapeutic effects of psychedelics.

Automatic Sensory Predictions: A Review of Predictive Mechanisms in the Brain and Their Link to Conscious Processing

The human brain has the astonishing capacity of integrating streams of sensory information from the environment and forming predictions about future events in an automatic way. Despite being initially developed for visual processing, the bulk of predictive coding research has subsequently focused on auditory processing, with the famous mismatch negativity signal as possibly the most studied signature of a surprise or prediction error (PE) signal. Auditory PEs are present during various consciousness states. Intriguingly, their presence and characteristics have been linked with residual levels of consciousness and return of awareness. In this review we first give an overview of the neural substrates of predictive processes in the auditory modality and their relation to consciousness. Then, we focus on different states of consciousness - wakefulness, sleep, anesthesia, coma, meditation, and hypnosis - and on what mysteries predictive processing has been able to disclose about brain functioning in such states. We review studies investigating how the neural signatures of auditory predictions are modulated by states of reduced or lacking consciousness. As a future outlook, we propose the combination of electrophysiological and computational techniques that will allow investigation of which facets of sensory predictive processes are maintained when consciousness fades away.

An active inference perspective on the negative symptoms of schizophrenia

Predictive coding has played a transformative role in the study of psychosis, casting delusions and hallucinations as statistical inference in a system with abnormal precision. However, the negative symptoms of schizophrenia, such as affective blunting, avolition, and asociality, remain poorly understood. We propose a computational framework for emotional expression based on active inference-namely that affective behaviours such as smiling are driven by predictions about the social consequences of smiling. Similarly to how delusions and hallucinations can be explained by predictive uncertainty in sensory circuits, negative symptoms naturally arise from uncertainty in social prediction circuits. This perspective draws on computational principles to explain blunted facial expressiveness and apathy-anhedonia in schizophrenia. Its phenomenological consequences also shed light on the content of paranoid delusions and indistinctness of self-other boundaries. Close links are highlighted between social prediction, facial affect mirroring, and the fledgling study of interoception. Advances in automated analysis of facial expressions and acoustic speech patterns will allow empirical testing of these computational models of the negative symptoms of schizophrenia.

Electrophysiological Signatures of Hierarchical Learning

Human perception and learning is thought to rely on a hierarchical generative model that is continuously updated via precision-weighted prediction errors (pwPEs). However, the neural basis of such cognitive process and how it unfolds during decision-making remain poorly understood. To investigate this question, we combined a hierarchical Bayesian model (i.e., Hierarchical Gaussian Filter [HGF]) with electroencephalography (EEG), while participants performed a probabilistic reversal learning task in alternatingly stable and volatile environments. Behaviorally, the HGF fitted significantly better than two control, nonhierarchical, models. Neurally, low-level and high-level pwPEs were independently encoded by the P300 component. Low-level pwPEs were reflected in the theta (4-8 Hz) frequency band, but high-level pwPEs were not. Furthermore, the expressions of high-level pwPEs were stronger for participants with better HGF fit. These results indicate that the brain employs hierarchical learning and encodes both low- and high-level learning signals separately and adaptively.

Stronger Top-Down and Weaker Bottom-Up Frontotemporal Connections During Sensory Learning Are Associated With Severity of Psychotic Phenomena

Recent theories in computational psychiatry propose that unusual perceptual experiences and delusional beliefs may emerge as a consequence of aberrant inference and disruptions in sensory learning. The current study investigates these theories and examines the alterations that are specific to schizophrenia spectrum disorders vs those that occur as psychotic phenomena intensify, regardless of diagnosis. We recruited 66 participants: 22 schizophrenia spectrum inpatients, 22 nonpsychotic inpatients, and 22 nonclinical controls. Participants completed the reversal oddball task with volatility manipulated. We recorded neural responses with electroencephalography and measured behavioral errors to inferences on sound probabilities. Furthermore, we explored neural dynamics using dynamic causal modeling (DCM). Attenuated prediction errors (PEs) were specifically observed in the schizophrenia spectrum, with reductions in mismatch negativity in stable, and P300 in volatile, contexts. Conversely, aberrations in connectivity were observed across all participants as psychotic phenomena increased. DCM revealed that impaired sensory learning behavior was associated with decreased intrinsic connectivity in the left primary auditory cortex and right inferior frontal gyrus (IFG); connectivity in the latter was also reduced with greater severity of psychotic experiences. Moreover, people who experienced more hallucinations and psychotic-like symptoms had decreased bottom-up and increased top-down frontotemporal connectivity, respectively. The findings provide evidence that reduced PEs are specific to the schizophrenia spectrum, but deficits in brain connectivity are aligned on the psychosis continuum. Along the continuum, psychotic experiences were related to an aberrant interplay between top-down, bottom-up, and intrinsic connectivity in the IFG during sensory uncertainty. These findings provide novel insights into psychosis neurocomputational pathophysiology.

TAPAS: An Open-Source Software Package for Translational Neuromodeling and Computational Psychiatry

Psychiatry faces fundamental challenges with regard to mechanistically guided differential diagnosis, as well as prediction of clinical trajectories and treatment response of individual patients. This has motivated the genesis of two closely intertwined fields: (i) Translational Neuromodeling (TN), which develops "computational assays" for inferring patient-specific disease processes from neuroimaging, electrophysiological, and behavioral data; and (ii) Computational Psychiatry (CP), with the goal of incorporating computational assays into clinical decision making in everyday practice. In order to serve as objective and reliable tools for clinical routine, computational assays require end-to-end pipelines from raw data (input) to clinically useful information (output). While these are yet to be established in clinical practice, individual components of this general end-to-end pipeline are being developed and made openly available for community use. In this paper, we present the Translational Algorithms for Psychiatry-Advancing Science (TAPAS) software package, an open-source collection of building blocks for computational assays in psychiatry. Collectively, the tools in TAPAS presently cover several important aspects of the desired end-to-end pipeline, including: (i) tailored experimental designs and optimization of measurement strategy prior to data acquisition, (ii) quality control during data acquisition, and (iii) artifact correction, statistical inference, and clinical application after data acquisition. Here, we review the different tools within TAPAS and illustrate how these may help provide a deeper understanding of neural and cognitive mechanisms of disease, with the ultimate goal of establishing automatized pipelines for predictions about individual patients. We hope that the openly available tools in TAPAS will contribute to the further development of TN/CP and facilitate the translation of advances in computational neuroscience into clinically relevant computational assays.

A continuum hypothesis of psychotomimetic rapid antidepressants

Ketamine, classical psychedelics and sleep deprivation are associated with rapid effects on depression. Interestingly, these interventions also have common psychotomimetic actions, mirroring aspects of psychosis such as an altered sense of self, perceptual distortions and distorted thinking. This raises the question whether these interventions might be acute antidepressants through the same mechanisms that underlie some of their psychotomimetic effects. That is, perhaps some symptoms of depression can be understood as occupying the opposite end of a spectrum where elements of psychosis can be found on the other side. This review aims at reviewing the evidence underlying a proposed continuum hypothesis of psychotomimetic rapid antidepressants, suggesting that a range of psychotomimetic interventions are also acute antidepressants as well as trying to explain these common features in a hierarchical predictive coding framework, where we hypothesise that these interventions share a common mechanism by increasing the flexibility of prior expectations. Neurobiological mechanisms at play and the role of different neuromodulatory systems affected by these interventions and their role in controlling the precision of prior expectations and new sensory evidence will be reviewed. The proposed hypothesis will also be discussed in relation to other existing theories of antidepressants. We also suggest a number of novel experiments to test the hypothesis and highlight research areas that could provide further insights, in the hope to better understand the acute antidepressant properties of these interventions.

State anxiety biases estimates of uncertainty and impairs reward learning in volatile environments

Clinical and subclinical (trait) anxiety impairs decision making and interferes with learning. Less understood are the effects of temporary anxious states on learning and decision making in healthy populations, and whether these can serve as a model for clinical anxiety. Here we test whether anxious states in healthy individuals elicit a pattern of aberrant behavioural, neural, and physiological responses comparable with those found in anxiety disorders-particularly when processing uncertainty in unstable environments. In our study, both a state anxious and a control group learned probabilistic stimulus-outcome mappings in a volatile task environment while we recorded their electrophysiological (EEG) signals. By using a hierarchical Bayesian model of inference and learning, we assessed the effect of state anxiety on Bayesian belief updating with a focus on uncertainty estimates. State anxiety was associated with an underestimation of environmental uncertainty, and informational uncertainty about the reward tendency. Anxious individuals' beliefs about reward contingencies were more precise (had smaller uncertainty) and thus more resistant to updating, ultimately leading to impaired reward-based learning. State anxiety was also associated with greater uncertainty about volatility. We interpret this pattern as evidence that state anxious individuals are less tolerant to informational uncertainty about the contingencies governing their environment and more willing to be uncertain about the level of stability of the world itself. Further, we tracked the neural representation of belief update signals in the trial-by-trial EEG amplitudes. In control participants, lower-level precision-weighted prediction errors (pwPEs) about reward tendencies were represented in the ERP signals across central and parietal electrodes peaking at 496 ms, overlapping with the late P300 in classical ERP analysis. The state anxiety group did not exhibit a significant representation of low-level pwPEs, and there were no significant differences between the groups. Smaller variance in low-level pwPE about reward tendencies in state anxiety could partially account for the null results. Expanding previous computational work on trait anxiety, our findings establish that temporary anxious states in healthy individuals impair reward-based learning in volatile environments, primarily through changes in uncertainty estimates, which play a central role in current Bayesian accounts of perceptual inference and learning.

Statistical Learning and Inference Is Impaired in the Nonclinical Continuum of Psychosis

Our perceptions result from the brain's ability to make inferences, or predictive models, of sensory information. Recently, it has been proposed that psychotic traits may be linked to impaired predictive processes. Here, we examine the brain dynamics underlying statistical learning and inference in stable and volatile environments, in a population of healthy human individuals (N = 75; 36 males, 39 females) with a range of psychotic-like experiences. We measured prediction error responses to sound sequences with electroencephalography, gauged sensory inference explicitly by behaviorally recording sensory statistical learning errors, and used dynamic causal modeling to tap into the underlying neural circuitry. We discuss the findings that were robust to replication across the two experiments (Discovery dataset, N = 31; Validation dataset, N = 44). First, we found that during stable conditions, participants demonstrated greater precision in their predictive model, reflected in a larger prediction error response to unexpected sounds, and decreased statistical learning errors. Moreover, individuals with attenuated prediction errors in stable conditions were found to make greater incorrect predictions about sensory information. Critically, we show that greater errors in statistical learning and inference are related to increased psychotic-like experiences. These findings link neurophysiology to behavior during statistical learning and prediction formation, as well as providing further evidence for the idea of a continuum of psychosis in the healthy, nonclinical population.SIGNIFICANCE STATEMENT While perceiving the world, we make inferences by learning the statistics present in the sensory environment. It has been argued that psychosis may emerge because of a failure to learn sensory statistics, resulting in an impaired representation of the world. Recently, it has been proposed that psychosis exists on a continuum; however, there is conflicting evidence on whether sensory learning deficits align on the nonclinical end of the psychosis continuum. We found that statistical learning of sensory events is associated with the magnitude of mismatch negativity and, critically, is impaired in healthy people who report more psychotic-like experiences. We replicated these findings in an independent sample, demonstrating strengthened credibility to support the continuum of psychosis that extends into the nonclinical population.