Recent Visual Experience Reshapes V4 Neuronal Activity and Improves Perceptual Performance.

Recent visual experience heavily influences our visual perception, but how neuronal activity is reshaped to alter and improve perceptual discrimination remains unknown. We recorded from populations of neurons in visual cortical area V4 while two male rhesus macaque monkeys performed a natural image change detection task under different experience conditions. We found that maximizing the recent experience with a particular image led to an improvement in the ability to detect a change in that image. This improvement was associated with decreased neural responses to the image, consistent with neuronal changes previously seen in studies of adaptation and expectation. We found that the magnitude of behavioral improvement was correlated with the magnitude of response suppression. Furthermore, this suppression of activity led to an increase in signal separation, providing evidence that a reduction in activity can improve stimulus encoding. Within populations of neurons, greater recent experience was associated with decreased trial-to-trial shared variability, indicating that a reduction in variability is a key means by which experience influences perception. Taken together, the results of our study contribute to an understanding of how recent visual experience can shape our perception and behavior through modulating activity patterns in the mid-level visual cortex.

Integration of Prior Expectations and Suppression of Prediction Errors During Expectancy-Induced Pain Modulation: The Influence of Anxiety and Pleasantness.

Pain perception arises from the integration of prior expectations with sensory information. Although recent work has demonstrated that treatment expectancy effects (e.g., placebo hypoalgesia) can be explained by a Bayesian integration framework incorporating the precision level of expectations and sensory inputs, the key factor modulating this integration in stimulus expectancy-induced pain modulation remains unclear. In a stimulus expectancy paradigm combining emotion regulation in healthy male and female adults, we found that participants' voluntary reduction in anticipatory anxiety and pleasantness monotonically reduced the magnitude of pain modulation by negative and positive expectations, respectively, indicating a role of emotion. For both types of expectations, Bayesian model comparisons confirmed that an integration model using the respective emotion of expectations and sensory inputs explained stimulus expectancy effects on pain better than using their respective precision. For negative expectations, the role of anxiety is further supported by our fMRI findings that (1) functional coupling within anxiety-processing brain regions (amygdala and anterior cingulate) reflected the integration of expectations with sensory inputs and (2) anxiety appeared to impair the updating of expectations via suppressed prediction error signals in the anterior cingulate, thus perpetuating negative expectancy effects. Regarding positive expectations, their integration with sensory inputs relied on the functional coupling within brain structures processing positive emotion and inhibiting threat responding (medial orbitofrontal cortex and hippocampus). In summary, different from treatment expectancy, pain modulation by stimulus expectancy emanates from emotion-modulated integration of beliefs with sensory evidence and inadequate belief updating.

Threat Expectation Does Not Improve Perceptual Discrimination despite Causing Heightened Priority Processing in the Frontoparietal Network.

Threat cues have been widely shown to elicit increased sensory and attentional neural processing. However, whether this enhanced recruitment leads to measurable behavioral improvements in perception is still in question. Here, we adjudicate between two opposing theories: that threat cues do or do not enhance perceptual sensitivity. We created threat stimuli by pairing one direction of motion in a random dot kinematogram with an aversive sound. While in the MRI scanner, 46 subjects (both men and women) completed a cued (threat/safe/neutral) perceptual decision-making task where they indicated the perceived motion direction of each moving dot stimulus. We found strong evidence that threat cues did not increase perceptual sensitivity compared with safe and neutral cues. This lack of improvement in perceptual decision-making ability occurred despite the threat cue resulting in widespread increases in frontoparietal BOLD activity, as well as increased connectivity between the right insula and the frontoparietal network. These results call into question the intuitive claim that expectation automatically enhances our perception of threat and highlight the role of the frontoparietal network in prioritizing the processing of threat-related environmental cues.

Concurrent Encoding of Sequence Predictability and Event-Evoked Prediction Error in Unfolding Auditory Patterns.

Human listeners possess an innate capacity to discern patterns within rapidly unfolding sensory input. Core questions, guiding ongoing research, focus on the mechanisms through which these representations are acquired and whether the brain prioritizes or suppresses predictable sensory signals. Previous work, using fast auditory sequences (tone-pips presented at a rate of 20 Hz), revealed sustained response effects that appear to track the dynamic predictability of the sequence. Here, we extend the investigation to slower sequences (4 Hz), permitting the isolation of responses to individual tones. Stimuli were 50 ms tone-pips, ordered into random (RND) and regular (REG; a repeating pattern of 10 frequencies) sequences; Two timing profiles were created: in "fast" sequences, tone-pips were presented in direct succession (20 Hz); in "slow" sequences, tone-pips were separated by a 200 ms silent gap (4 Hz). Naive participants (N = 22; both sexes) passively listened to these sequences, while brain responses were recorded using magnetoencephalography (MEG). Results unveiled a heightened magnitude of sustained brain responses in REG when compared to RND patterns. This manifested from three tones after the onset of the pattern repetition, even in the context of slower sequences characterized by extended pattern durations (2,500 ms). This observation underscores the remarkable implicit sensitivity of the auditory brain to acoustic regularities. Importantly, brain responses evoked by single tones exhibited the opposite pattern-stronger responses to tones in RND than REG sequences. The demonstration of simultaneous but opposing sustained and evoked response effects reveals concurrent processes that shape the representation of unfolding auditory patterns.

Expectation-Maximization enables Phylogenetic Dating under a Categorical Rate Model.

Dating phylogenetic trees to obtain branch lengths in time units is essential for many downstream applications but has remained challenging. Dating requires inferring substitution rates that can change across the tree. While we can assume to have information about a small subset of nodes from the fossil record or sampling times (for fast-evolving organisms), inferring the ages of the other nodes essentially requires extrapolation and interpolation. Assuming a distribution of branch rates, we can formulate dating as a constrained maximum likelihood (ML) estimation problem. While ML dating methods exist, their accuracy degrades in the face of model misspecification, where the assumed parametric statistical distribution of branch rates vastly differs from the true distribution. Notably, most existing methods assume rigid, often unimodal, branch rate distributions. A second challenge is that the likelihood function involves an integral over the continuous domain of the rates, often leading to difficult non-convex optimization problems. To tackle both challenges, we propose a new method called Molecular Dating using Categorical-models (MD-Cat). MD-Cat uses a categorical model of rates inspired by non-parametric statistics and can approximate a large family of models by discretizing the rate distribution into k categories. Under this model, we can use the Expectation-Maximization algorithm to co-estimate rate categories and branch lengths in time units. Our model has fewer assumptions about the true distribution of branch rates than parametric models such as Gamma or LogNormal distribution. Our results on two simulated and real datasets of Angiosperms and HIV and a wide selection of rate distributions show that MD-Cat is often more accurate than the alternatives, especially on datasets with exponential or multimodal rate distributions.

Flexible model-based non-negative matrix factorization with application to mutational signatures.

Somatic mutations in cancer can be viewed as a mixture distribution of several mutational signatures, which can be inferred using non-negative matrix factorization (NMF). Mutational signatures have previously been parametrized using either simple mono-nucleotide interaction models or general tri-nucleotide interaction models. We describe a flexible and novel framework for identifying biologically plausible parametrizations of mutational signatures, and in particular for estimating di-nucleotide interaction models. Our novel estimation procedure is based on the expectation-maximization (EM) algorithm and regression in the log-linear quasi-Poisson model. We show that di-nucleotide interaction signatures are statistically stable and sufficiently complex to fit the mutational patterns. Di-nucleotide interaction signatures often strike the right balance between appropriately fitting the data and avoiding over-fitting. They provide a better fit to data and are biologically more plausible than mono-nucleotide interaction signatures, and the parametrization is more stable than the parameter-rich tri-nucleotide interaction signatures. We illustrate our framework in a large simulation study where we compare to state of the art methods, and show results for three data sets of somatic mutation counts from patients with cancer in the breast, Liver and urinary tract.

How side effects can improve treatment efficacy: a randomized trial.

While treatment side effects may adversely impact patients, they could also potentially function as indicators for effective treatment. In this study, we investigated whether and how side effects can trigger positive treatment expectations and enhance treatment outcomes. In this pre-registered trial (DRKS00026648), 77 healthy participants were made to believe that they will receive fentanyl nasal sprays before receiving thermal pain in a controlled experimental setting. However, nasal sprays did not contain fentanyl, rather they either contained capsaicin to induce a side effect (mild burning sensation) or saline (inert). After the first session, participants were randomized to two groups and underwent functional MRI. One group continued to believe that the nasal sprays could contain fentanyl while the other group was explicitly informed that no fentanyl was included. This allowed for the independent manipulation of the side effects and the expectation of pain relief. Our results revealed that nasal sprays with a side effect lead to lower pain than inert nasal sprays without side effects. The influence of side effects on pain was dependent on individual beliefs about how side effects are related to treatment outcome, as well as on expectations about received treatment. Functional MRI data indicated an involvement of the descending pain modulatory system including the anterior cingulate cortex and the periaqueductal gray during pain after experiencing a nasal spray with side effects. In summary, our data show that mild side effects can serve as a signal for effective treatment thereby influencing treatment expectations and outcomes, which is mediated by the descending pain modulatory system. Using these mechanisms in clinical practice could provide an efficient way to optimize treatment outcome. In addition, our results indicate an important confound in clinical trials, where a treatment (with potential side effects) is compared to placebo.

Expectation effects in working memory training.

There is a growing body of research focused on developing and evaluating behavioral training paradigms meant to induce enhancements in cognitive function. It has recently been proposed that one mechanism through which such performance gains could be induced involves participants' expectations of improvement. However, no work to date has evaluated whether it is possible to cause changes in cognitive function in a long-term behavioral training study by manipulating expectations. In this study, positive or negative expectations about cognitive training were both explicitly and associatively induced before either a working memory training intervention or a control intervention. Consistent with previous work, a main effect of the training condition was found, with individuals trained on the working memory task showing larger gains in cognitive function than those trained on the control task. Interestingly, a main effect of expectation was also found, with individuals given positive expectations showing larger cognitive gains than those who were given negative expectations (regardless of training condition). No interaction effect between training and expectations was found. Exploratory analyses suggest that certain individual characteristics (e.g., personality, motivation) moderate the size of the expectation effect. These results highlight aspects of methodology that can inform future behavioral interventions and suggest that participant expectations could be capitalized on to maximize training outcomes.

Stimulus-Specific Prediction Error Neurons in Mouse Auditory Cortex.

Comparing expectation with experience is an important neural computation performed throughout the brain and is a hallmark of predictive processing. Experiments that alter the sensory outcome of an animal's behavior reveal enhanced neural responses to unexpected self-generated stimuli, indicating that populations of neurons in sensory cortex may reflect prediction errors (PEs), mismatches between expectation and experience. However, enhanced neural responses to self-generated stimuli could also arise through nonpredictive mechanisms, such as the movement-based facilitation of a neuron's inherent sound responses. If sensory prediction error neurons exist in sensory cortex, it is unknown whether they manifest as general error responses, or respond with specificity to errors in distinct stimulus dimensions. To answer these questions, we trained mice of either sex to expect the outcome of a simple sound-generating behavior and recorded auditory cortex activity as mice heard either the expected sound or sounds that deviated from expectation in one of multiple distinct dimensions. Our data reveal that the auditory cortex learns to suppress responses to self-generated sounds along multiple acoustic dimensions simultaneously. We identify a distinct population of auditory cortex neurons that are not responsive to passive sounds or to the expected sound but that encode prediction errors. These prediction error neurons are abundant only in animals with a learned motor-sensory expectation, and encode one or two specific violations rather than a generic error signal. Together, these findings reveal that cortical predictions about self-generated sounds have specificity in multiple simultaneous dimensions and that cortical prediction error neurons encode specific violations from expectation.SIGNIFICANCE STATEMENT Audette et. al record neural activity in the auditory cortex while mice perform a sound-generating forelimb movement and measure neural responses to sounds that violate an animal's expectation in different ways. They find that predictions about self-generated sounds are highly specific across multiple stimulus dimensions and that a population of typically nonsound-responsive neurons respond to sounds that violate an animal's expectation in a specific way. These results identify specific prediction error (PE) signals in the mouse auditory cortex and suggest that errors may be calculated early in sensory processing.

Conceptual Associations Generate Sensory Predictions.

A crucial ability of the human brain is to learn and exploit probabilistic associations between stimuli to facilitate perception and behavior by predicting future events. Although studies have shown how perceptual relationships are used to predict sensory inputs, relational knowledge is often between concepts rather than percepts (e.g., we learned to associate cats with dogs, rather than specific images of cats and dogs). Here, we asked if and how sensory responses to visual input may be modulated by predictions derived from conceptual associations. To this end we exposed participants of both sexes to arbitrary word-word pairs (e.g., car-dog) repeatedly, creating an expectation of the second word, conditional on the occurrence of the first. In a subsequent session, we exposed participants to novel word-picture pairs, while measuring fMRI BOLD responses. All word-picture pairs were equally likely, but half of the pairs conformed to the previously formed conceptual (word-word) associations, whereas the other half violated this association. Results showed suppressed sensory responses throughout the ventral visual stream, including early visual cortex, to pictures that corresponded to the previously expected words compared with unexpected words. This suggests that the learned conceptual associations were used to generate sensory predictions that modulated processing of the picture stimuli. Moreover, these modulations were tuning specific, selectively suppressing neural populations tuned toward the expected input. Combined, our results suggest that recently acquired conceptual priors are generalized across domains and used by the sensory brain to generate category-specific predictions, facilitating processing of expected visual input.SIGNIFICANCE STATEMENT Perceptual predictions play a crucial role in facilitating perception and the integration of sensory information. However, little is known about whether and how the brain uses more abstract, conceptual priors to form sensory predictions. In our preregistered study, we show that priors derived from recently acquired arbitrary conceptual associations result in category-specific predictions that modulate perceptual processing throughout the ventral visual hierarchy, including early visual cortex. These results suggest that the predictive brain uses prior knowledge across various domains to modulate perception, thereby extending our understanding of the extensive role predictions play in perception.

Learning-Induced Odor Modulation of Neuronal Activity in Auditory Cortex.

Sensory cortices, even of primary regions, are not purely unisensory. Rather, cortical neurons in sensory cortex show various forms of multisensory interactions. While some multisensory interactions naturally co-occur, the combination of others will co-occur through experience. In real life, learning and experience will result in conjunction with seemingly disparate sensory information that ultimately becomes behaviorally relevant, impacting perception, cognition, and action. Here we describe a novel auditory discrimination task in mice, designed to manipulate the expectation of upcoming trials using olfactory cues. We show that, after learning, female mice display a transient period of several days during which they exploit odor-mediated expectations for making correct decisions. Using two-photon calcium imaging of single neurons in auditory cortex (ACx) during behavior, we found that the behavioral effects of odor-mediated expectations are accompanied by an odor-induced modulation of neuronal activity. Further, we find that these effects are manifested differentially, based on the response preference of individual cells. A significant portion of effects, but not all, are consistent with a predictive coding framework. Our data show that learning novel odor-sound associations evoke changes in ACx. We suggest that behaviorally relevant multisensory environments mediate contextual effects as early as ACx.SIGNIFICANCE STATEMENT Natural environments are composed of multisensory objects. It remains unclear whether and how animals learn the regularities of congruent multisensory associations and how these may impact behavior and neural activity. We tested how learned odor-sound associations affected single-neuron responses in auditory cortex. We introduce a novel auditory discrimination task for mice in which odors set different contexts of expectation to upcoming trials. We show that, although the task can be solved purely by sounds, odor-mediated expectation impacts performance. We further show that odors cause a modulation of neuronal activity in auditory cortex, which is correlated with behavior. These results suggest that learning prompts an interaction of odor and sound information as early as sensory cortex.

EEG Decoding Reveals Neural Predictions for Naturalistic Material Behaviors.

Material properties, such as softness or stickiness, determine how an object can be used. Based on our real-life experience, we form strong expectations about how objects should behave under force, given their typical material properties. Such expectations have been shown to modulate perceptual processes, but we currently do not know how expectation influences the temporal dynamics of the cortical visual analysis for objects and their materials. Here, we tracked the neural representations of expected and unexpected material behaviors using time-resolved EEG decoding in a violation-of-expectation paradigm, where objects fell to the ground and deformed in expected or unexpected ways. Participants were 25 men and women. Our study yielded three key results: First, both objects and materials were represented rapidly and in a temporally sustained fashion. Second, objects exhibiting unexpected material behaviors were more successfully decoded than objects exhibiting expected behaviors within 190 ms after the impact, which might indicate additional processing demands when expectations are unmet. Third, general signals of expectation fulfillment that generalize across specific objects and materials were found within the first 150 ms after the impact. Together, our results provide new insights into the temporal neural processing cascade that underlies the analysis of real-world material behaviors. They reveal a sequence of predictions, with cortical signals progressing from a general signature of expectation fulfillment toward increased processing of unexpected material behaviors.SIGNIFICANCE STATEMENT In the real world, we can make accurate predictions about how an object's material shapes its behavior: For instance, we know that cups are typically made of porcelain and shatter when we accidentally drop them. Here, we use EEG to experimentally test how expectations about material behaviors impact neural processing. We showed our participants videos of objects that exhibited expected material behaviors (e.g., a glass shattering when falling to the ground) or unexpected material behaviors (e.g., a glass melting on impact). Our results reveal a hierarchy of predictions in cortex: The visual system rapidly generates signals that index whether expectations about material behaviors are met. These signals are followed by increased processing of objects displaying unexpected material behaviors.

MR-BOIL: Causal inference in one-sample Mendelian randomization for binary outcome with integrated likelihood method.

Mendelian randomization is a statistical method for inferring the causal relationship between exposures and outcomes using an economics-derived instrumental variable approach. The research results are relatively complete when both exposures and outcomes are continuous variables. However, due to the noncollapsing nature of the logistic model, the existing methods inherited from the linear model for exploring binary outcome cannot take the effect of confounding factors into account, which leads to biased estimate of the causal effect. In this article, we propose an integrated likelihood method MR-BOIL to investigate causal relationships for binary outcomes by treating confounders as latent variables in one-sample Mendelian randomization. Under the assumption of a joint normal distribution of the confounders, we use expectation maximization algorithm to estimate the causal effect. Extensive simulations demonstrate that the estimator of MR-BOIL is asymptotically unbiased and that our method improves statistical power without inflating type I error rate. We then apply this method to analyze the data from Atherosclerosis Risk in Communications Study. The results show that MR-BOIL can better identify plausible causal relationships with high reliability, compared with the unreliable results of existing methods. MR-BOIL is implemented in R and the corresponding R code is provided for free download.

Mediation analysis of multiple mediators with incomplete omics data.

There is an increasing interest in using multiple types of omics features (e.g., DNA sequences, RNA expressions, methylation, protein expressions, and metabolic profiles) to study how the relationships between phenotypes and genotypes may be mediated by other omics markers. Genotypes and phenotypes are typically available for all subjects in genetic studies, but typically, some omics data will be missing for some subjects, due to limitations such as cost and sample quality. In this article, we propose a powerful approach for mediation analysis that accommodates missing data among multiple mediators and allows for various interaction effects. We formulate the relationships among genetic variants, other omics measurements, and phenotypes through linear regression models. We derive the joint likelihood for models with two mediators, accounting for arbitrary patterns of missing values. Utilizing computationally efficient and stable algorithms, we conduct maximum likelihood estimation. Our methods produce unbiased and statistically efficient estimators. We demonstrate the usefulness of our methods through simulation studies and an application to the Metabolic Syndrome in Men study.

Updating predictions in a complex repertoire of actions and its neural representation.

Even though actions we observe in everyday life seem to unfold in a continuous manner, they are automatically divided into meaningful chunks, that are single actions or segments, which provide information for the formation and updating of internal predictive models. Specifically, boundaries between actions constitute a hub for predictive processing since the prediction of the current action comes to an end and calls for updating of predictions for the next action. In the current study, we investigated neural processes which characterize such boundaries using a repertoire of complex action sequences with a predefined probabilistic structure. Action sequences consisted of actions that started with the hand touching an object (T) and ended with the hand releasing the object (U). These action boundaries were determined using an automatic computer vision algorithm. Participants trained all action sequences by imitating demo videos. Subsequently, they returned for an fMRI session during which the original action sequences were presented in addition to slightly modified versions thereof. Participants completed a post-fMRI memory test to assess the retention of original action sequences. The exchange of individual actions, and thus a violation of action prediction, resulted in increased activation of the action observation network and the anterior insula. At U events, marking the end of an action, increased brain activation in supplementary motor area, striatum, and lingual gyrus was indicative of the retrieval of the previously encoded action repertoire. As expected, brain activation at U events also reflected the predefined probabilistic branching structure of the action repertoire. At T events, marking the beginning of the next action, midline and hippocampal regions were recruited, reflecting the selected prediction of the unfolding action segment. In conclusion, our findings contribute to a better understanding of the various cerebral processes characterizing prediction during the observation of complex action repertoires.

Adaptation patterns and their associations with mismatch negativity: An electroencephalogram (EEG) study with controlled expectations.

Adaptation refers to the decreased neural response that occurs after repeated exposure to a stimulus. While many electroencephalogram (EEG) studies have investigated adaptation by using either single or multiple repetitions, the adaptation patterns under controlled expectations manifested in the two main auditory components, N1 and P2, are still largely unknown. Additionally, although multiple repetitions are commonly used in mismatch negativity (MMN) experiments, it is unclear how adaptation at different time windows contributes to this phenomenon. In this study, we conducted an EEG experiment with 37 healthy adults using a random stimulus arrangement and extended tone sequences to control expectations. We tracked the amplitudes of the N1 and P2 components across the first 10 tones to examine adaptation patterns. Our findings revealed an L-shaped adaptation pattern characterised by a significant decrease in N1 amplitude after the first repetition (N1 initial adaptation), followed by a continuous, linear increase in P2 amplitude after the first repetition (P2 subsequent adaptation), possibly indicating model adjustment. Regression analysis demonstrated that the peak amplitudes of both the N1 initial adaptation and the P2 subsequent adaptation significantly accounted for variance in MMN amplitude. These results suggest distinct adaptation patterns for multiple repetitions across different components and indicate that the MMN reflects a combination of two processes: the initial adaptation in the N1 and a continuous model adjustment effect in the P2. Understanding these processes separately could have implications for models of cognitive processing and clinical disorders.

BML: a versatile web server for bipartite motif discovery.

Motif discovery and characterization are important for gene regulation analysis. The lack of intuitive and integrative web servers impedes the effective use of motifs. Most motif discovery web tools are either not designed for non-expert users or lacking optimization steps when using default settings. Here we describe bipartite motifs learning (BML), a parameter-free web server that provides a user-friendly portal for online discovery and analysis of sequence motifs, using high-throughput sequencing data as the input. BML utilizes both position weight matrix and dinucleotide weight matrix, the latter of which enables the expression of the interdependencies of neighboring bases. With input parameters concerning the motifs are given, the BML achieves significantly higher accuracy than other available tools for motif finding. When no parameters are given by non-expert users, unlike other tools, BML employs a learning method to identify motifs automatically and achieve accuracy comparable to the scenario where the parameters are set. The BML web server is freely available at http://motif.t-ridership.com/ (https://github.com/Mohammad-Vahed/BML).

SC-MEB: spatial clustering with hidden Markov random field using empirical Bayes.

Spatial transcriptomics has been emerging as a powerful technique for resolving gene expression profiles while retaining tissue spatial information. These spatially resolved transcriptomics make it feasible to examine the complex multicellular systems of different microenvironments. To answer scientific questions with spatial transcriptomics and expand our understanding of how cell types and states are regulated by microenvironment, the first step is to identify cell clusters by integrating the available spatial information. Here, we introduce SC-MEB, an empirical Bayes approach for spatial clustering analysis using a hidden Markov random field. We have also derived an efficient expectation-maximization algorithm based on an iterative conditional mode for SC-MEB. In contrast to BayesSpace, a recently developed method, SC-MEB is not only computationally efficient and scalable to large sample sizes but is also capable of choosing the smoothness parameter and the number of clusters. We performed comprehensive simulation studies to demonstrate the superiority of SC-MEB over some existing methods. We applied SC-MEB to analyze the spatial transcriptome of human dorsolateral prefrontal cortex tissues and mouse hypothalamic preoptic region. Our analysis results showed that SC-MEB can achieve a similar or better clustering performance to BayesSpace, which uses the true number of clusters and a fixed smoothness parameter. Moreover, SC-MEB is scalable to large 'sample sizes'. We then employed SC-MEB to analyze a colon dataset from a patient with colorectal cancer (CRC) and COVID-19, and further performed differential expression analysis to identify signature genes related to the clustering results. The heatmap of identified signature genes showed that the clusters identified using SC-MEB were more separable than those obtained with BayesSpace. Using pathway analysis, we identified three immune-related clusters, and in a further comparison, found the mean expression of COVID-19 signature genes was greater in immune than non-immune regions of colon tissue. SC-MEB provides a valuable computational tool for investigating the structural organizations of tissues from spatial transcriptomic data.

The Causes and Consequences of Drifting Expectations.

Awaiting news of uncertain outcomes is distressing because the news might be disappointing. To prevent such disappointments, people often "brace for the worst," pessimistically lowering expectations before news arrives to decrease the possibility of surprising disappointment (a negative prediction error, or PE). Computational decision-making research commonly assumes that expectations do not drift within trials, yet it is unclear whether expectations pessimistically drift in real-world, high-stakes settings, what factors influence expectation drift, and whether it effectively buffers emotional responses to goal-relevant outcomes. Moreover, individuals learn from PEs to accurately anticipate future outcomes, but it is unknown whether expectation drift also impedes PE-based learning. In a sample of students awaiting exam grades (N = 625), we found that expectations often drift and tend to drift pessimistically. We demonstrate that bracing is preferentially modulated by uncertainty; it transiently buffers the initial emotional impact of negative PEs but impairs PE-based learning, counterintuitively sustaining uncertainty into the future.

Association between expectations and clinical outcomes in online v. face-to-face therapy - an individual participant data meta-analysis.

BACKGROUND: Online treatments are increasing in number and are currently available for a wide range of clinical problems. To date little is known about the role of treatment expectations and other placebo-like mechanisms in online settings compared to traditional face-to-face treatment. To address this knowledge gap, we analyzed individual participant data from randomized clinical trials that compared online and face-to-face psychological interventions. METHODS: MEDLINE (Ovid) and PsycINFO (Ovid) were last searched on 2 February 2021. Randomized clinical trials of therapist guided online v. face-to-face psychological interventions for psychiatric or somatic conditions using a randomized controlled design were included. Titles, abstracts, and full texts of studies were independently screened by multiple observers. The Preferred Reporting Items for Systematic Reviews and Meta-analyses guideline was followed. Authors of the matching trials were contacted for individual participant data. Ratings from the Credibility and Expectancy Questionnaire and the primary outcome measure from each trial were used to estimate the association between expectation ratings and treatment outcomes in online v. face-to-face interventions, using a mixed-effects model. RESULTS: Of 7045 screened studies, 62 full-text articles were retrieved whereof six studies fulfilled the criteria and provided individual participant data (n = 491). Overall, CEQ ratings predicted clinical outcomes (ß = 0.27) at end of treatment with no moderating effect of treatment modality (online v. face-to-face). CONCLUSIONS: Online treatment appears to be equally susceptible to expectancy effects as face-to-face therapy. This furthers our understanding of the importance of placebo-like factors in online treatment and may aid the improvement of healthcare in online settings.