Computational model of layer 2/3 in mouse primary visual cortex explains observed visuomotor mismatch response.

Activity in layer 2/3 of the mouse primary visual cortex has been shown to depend both on visual input and the mouse's locomotion. Moreover, this activity is altered by a mismatch between the observed visual flow and the predicted visual flow from locomotion. Here, I present a simple computational model that explains previously reported recordings from layer 2/3 neurons in mice. In my model, layer 2/3 encodes the velocity difference between the estimate from visual flow and the prediction from locomotion using a neural population code. Moreover, I describe a hypothesized mechanism for how the brain may carry out computations of variables encoded in population codes. This mechanism may point to a general principle for computing any mathematical function in the brain.

Corrigendum: The cerebellum and fear extinction: evidence from rodent and human studies.

[This corrects the article DOI: 10.3389/fnsys.2023.1166166.].

Distributed midbrain responses signal the content of positive identity prediction errors.

Recent work across species has shown that midbrain dopamine neurons signal not only errors in the prediction of reward value but also in the prediction of value-neutral sensory features. To support learning of associative structures in downstream areas, identity prediction errors (iPEs) should signal specific information about the mis-predicted outcome. Here, we used pattern-based analysis of functional magnetic resonance imaging (fMRI) data acquired during reversal learning to characterize the information content of iPE responses in the human midbrain. We find that fMRI responses to value-neutral identity errors contain information about the identity of the unexpectedly received reward (positive iPE+) but not about the identity of the omitted reward (negative iPE-). Exploratory analyses revealed representations of iPE- in the dorsomedial prefrontal cortex. These results demonstrate that ensemble midbrain responses to value-neutral identity errors convey information about the identity of unexpectedly received outcomes, which could shape the formation of novel stimulus-outcome associations that constitute cognitive maps.

A critical review of the use of R2 in risk equalization research.

Nearly all empirical studies that estimate the coefficients of a risk equalization formula present the value of the statistical measure R2. The R2-value is often (implicitly) interpreted as a measure of the extent to which the risk equalization payments remove the regulation-induced predictable profits and losses on the insured, with a higher R2-value indicating a better performance. In many cases, however, we do not know whether a model with R2 = 0.30 reduces the predictable profits and losses more than a model with R2 = 0.20. In this paper we argue that in the context of risk equalization R2 is hard to interpret as a measure of selection incentives, can lead to wrong and misleading conclusions when used as a measure of selection incentives, and is therefore not useful for measuring selection incentives. The same is true for related statistical measures such as the Mean Absolute Prediction Error (MAPE), Cumming's Prediction Measure (CPM) and the Payment System Fit (PSF). There are some exceptions where the R2 can be useful. Our recommendation is to either present the R2 with a clear, valid, and relevant interpretation or not to present the R2. The same holds for the related statistical measures MAPE, CPM and PSF.

Functional Movement Disorder Is Associated with Abnormal Interoceptive Brain Activity: A Task-based Functional MRI Study.

Background: Aberrant interoceptive processing has been hypothesized to contribute to the pathophysiology of functional neurological disorder, although findings have been inconsistent. Here, we utilized functional magnetic resonance imaging (fMRI) to examine neural correlates of interoceptive attention - the conscious focus and awareness of bodily sensations - in functional movement disorder (FMD). Methods: We used voxelwise analyses to compare blood oxygenation level-dependent responses between 13 adults with hyperkinetic FMD and 13 healthy controls (HCs) during a task requiring attention to different bodily sensations and to an exteroceptive stimulus. Additionally, we examined between-group differences in self-reported measures of interoception and evaluated their relationship with neural activity. Results: Interoceptive conditions (heartbeat, stomach and 'body', indicating sensations from the body part or limb affected in FMD participants) activated a network involving the precuneus, the posterior cingulate cortex (PCC) and caudate nucleus (CN) bilaterally, and the right anterior insula (aINS) (p <0.05, corrected). Group differences in brain activity were mainly driven by processing of disease-related interoceptive signals, which in the FMD group was associated with a broader neural activation than monitoring gastric interoception, while no group differences were detected during cardiac interoception. Differences based on interoceptive focus (body vs heartbeat and stomach) between FMD subjects and HCs were found in PCC, CN, angular gyrus, thalamus, and in the mid-insula (p <0.05, corrected). Conclusions: This is, to our knowledge, the first study showing that FMD is associated with abnormal interoceptive processing in regions involved in monitoring body state, attentional focus, and homeostatic inference.

Pavlovian safety learning: An integrative theoretical review.

Safety learning involves associating stimuli with the absence of threats, enabling the inhibition of fear and anxiety. Despite growing interest in psychology, psychiatry, and neuroscience, safety learning lacks a formal consensus definition, leading to inconsistent methodologies and varied results. Conceptualized as a form of inhibitory learning (conditioned inhibition), safety learning can be understood through formal learning theories, such as the Rescorla-Wagner and Pearce-Hall models. This review aims to establish a principled conceptualization of 'Pavlovian safety learning', identifying cognitive mechanisms that generate safety and the boundary conditions that constrain it. Based on these observations, we define Pavlovian safety learning as an active associative process, where surprising threat-omission (safety prediction error) acts as a salient reinforcing event. Instead of producing merely neutral or nonaversive states, safety learning endows stimuli with active positive associations to 'safety'. The resulting stimulus-safety memories counteract the influence of fear memories, promoting fear regulation, positive affect, and relief. We critically analyze traditional criteria of conditioned inhibition for their relevance to safety and propose areas for future innovation. A principled concept of Pavlovian safety learning may reduce methodological inconsistencies, stimulate translational research, and facilitate a comprehensive understanding of an indispensable psychological construct.

Survival analysis in breast cancer: evaluating ensemble learning techniques for prediction.

Breast cancer is most commonly faced with form of cancer amongst women worldwide. In spite of the fact that the breast cancer research and awareness have gained considerable momentum, there is still no one treatment due to disease heterogeneity. Survival data may be of specific interest in breast cancer studies to understand its dynamic and complex trajectories. This study copes with the most important covariates affecting the disease progression. The study utilizes the German Breast Cancer Study Group 2 (GBSG2) and the Molecular Taxonomy of Breast Cancer International Consortium dataset (METABRIC) datasets. In both datasets, interests lie in relapse of the disease and the time when the relapse happens. The three models, namely the Cox proportional hazards (PH) model, random survival forest (RSF) and conditional inference forest (Cforest) were employed to analyse the breast cancer datasets. The goal of this study is to apply these methods in prediction of breast cancer progression and compare their performances based on two different estimation methods: the bootstrap estimation and the bootstrap .632 estimation. The model performance was evaluated in concordance index (C-index) and prediction error curves (pec) for discrimination. The Cox PH model has a lower C-index and bigger prediction error compared to the RSF and the Cforest approach for both datasets. The analysis results of GBSG2 and METABRIC datasets reveal that the RSF and the Cforest algorithms provide non-parametric alternatives to Cox PH model for estimation of the survival probability of breast cancer patients.

Perceptual error based on Bayesian cue combination drives implicit motor adaptation.

The sensorimotor system can recalibrate itself without our conscious awareness, a type of procedural learning whose computational mechanism remains undefined. Recent findings on implicit motor adaptation, such as over-learning from small perturbations and fast saturation for increasing perturbation size, challenge existing theories based on sensory errors. We argue that perceptual error, arising from the optimal combination of movement-related cues, is the primary driver of implicit adaptation. Central to our theory is the increasing sensory uncertainty of visual cues with increasing perturbations, which was validated through perceptual psychophysics (Experiment 1). Our theory predicts the learning dynamics of implicit adaptation across a spectrum of perturbation sizes on a trial-by-trial basis (Experiment 2). It explains proprioception changes and their relation to visual perturbation (Experiment 3). By modulating visual uncertainty in perturbation, we induced unique adaptation responses in line with our model predictions (Experiment 4). Overall, our perceptual error framework outperforms existing models based on sensory errors, suggesting that perceptual error in locating one's effector, supported by Bayesian cue integration, underpins the sensorimotor system's implicit adaptation.

Prediction error determines how memories are organized in the brain.

How is new information organized in memory? According to latent state theories, this is determined by the level of surprise, or prediction error, generated by the new information: a small prediction error leads to the updating of existing memory, large prediction error leads to encoding of a new memory. We tested this idea using a protocol in which rats were first conditioned to fear a stimulus paired with shock. The stimulus was then gradually extinguished by progressively reducing the shock intensity until the stimulus was presented alone. Consistent with latent state theories, this gradual extinction protocol (small prediction errors) was better than standard extinction (large prediction errors) in producing long-term suppression of fear responses, and the benefit of gradual extinction was due to updating of the conditioning memory with information about extinction. Thus, prediction error determines how new information is organized in memory, and latent state theories adequately describe the ways in which this occurs.

Accuracy of intraocular lens power formulas in patients with average keratometry greater than 46 diopters.

OBJECTIVE: To compare the accuracy of Kane, Barrett Universal II, Haigis, and SRK-T formulas in eyes with average keratometry greater than 46 diopters (D). METHODS: A retrospective analysis was conducted on 101 eyes of 101 patients with average keratometry greater than 46 D. The absolute prediction error (EA) was obtained for each patient one month after surgery. The mean absolute prediction error (MEA), median absolute prediction error (MedEA) and the percentage of patients with absolute refractive error less than 0.25 D, 0.50 D, and 1.00 D were calculated for each formula analyzed. RESULTS: The Kane formula achieved the lowest MEA (0.53 ± 0.43) and the lowest MedEA (0.41), followed by Barrett Universal II (MEA: 0.56 ± 0.42, MedEA: 0.49), SRK-T (MEA: 0.59 ± 0.44, MedEA: 0.54), and Haigis (MEA: 0.77 ± 0.47, MedEA: 0.69), showing a significant difference in the results. It was also observed that the Kane formula was the most accurate, with the highest percentage of patients, with EA less than 0.25 D, 0.50 D, and 1.00 D (30.7%, 54.4%, and 86.1%, respectively), while the Haigis formula was the least accurate (12.9%, 33.7%, and 69.3%, respectively). CONCLUSION: In eyes with corneas having average keratometry greater than 46 D, the Kane formula proves to be a useful tool in intraocular lens (IOL) power calculation and demonstrates higher precision compared to the Barrett Universal II, SRK-T, and Haigis formulas.

Comparing algorithms to approximate accuracies for single-step genomic best linear unbiased predictor.

The exact accuracy of estimated breeding values can be calculated based on the prediction error variances obtained from the diagonal of the inverse of the left-hand side (LHS) of the mixed model equations (MME). However, inverting the LHS is not computationally feasible for large datasets, especially if genomic information is available. Thus, different algorithms have been proposed to approximate accuracies. This study aimed to: 1) compare the approximated accuracies from 2 algorithms implemented in the BLUPF90 suite of programs, 2) compare the approximated accuracies from the 2 algorithms against the exact accuracy based on the inversion of the LHS of MME, and 3) evaluate the impact of adding genotyped animals with and without phenotypes on the exact and approximated accuracies. Algorithm 1 approximates accuracies based on the diagonal of the genomic relationship matrix (G). In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data were provided by the American Angus Association and included 3 datasets of growth, carcass, and marbling traits. The genotype file contained 1,235,930 animals, and the pedigree file contained 12,492,581 animals. For the genomic evaluation, a multi-trait model was applied to the datasets. To ensure the feasibility of inverting the LHS of the MME, a subset of data under single-trait models was used to compare approximated and exact accuracies. The correlations between exact and approximated accuracies from algorithms 1 and 2 of genotyped animals ranged from 0.87 to 0.90 and 0.98 to 0.99, respectively. The intercept and slope of the regression of exact on approximated accuracies from algorithm 2 ranged from 0.00 to 0.01 and 0.82 to 0.87, respectively. However, the intercept and the slope for algorithm 1 ranged from -0.10 to 0.05 and 0.98 to 1.10, respectively. In more than 80% of the traits, algorithm 2 exhibited a smaller mean square error than algorithm 1. The correlation between the approximated accuracies obtained from algorithms 1 and 2 ranged from 0.56 to 0.74, 0.38 to 0.71, and 0.71 to 0.97 in the groups of genotyped animals, genotyped animals without phenotype, and proven genotyped sires, respectively. The approximated accuracy from algorithm 2 showed a closer behavior to the exact accuracy when including genotyped animals in the analysis. According to the results, algorithm 2 is recommended for genetic evaluations since it proved more precise.

The genomic estimated breeding value (GEBV) represents an animal's genetic merit calculated using a combination of phenotypes, pedigree, and genomic information through a procedure known as single-step genomic best linear unbiased prediction (ssGBLUP). The accuracy of a GEBV reflects how closely it correlates with the true breeding value. However, calculating accuracies is not computationally feasible for large datasets with genomic information. In this context, methods for approximating accuracies have been proposed and implemented into genetic evaluations. This study aimed to compare 2 algorithms to approximate accuracies for ssGBLUP. In algorithm 1, genomic contributions are based on the diagonal of the genomic relationship matrix (G), combined with contributions from animal records and pedigrees. In turn, algorithm 2 combines accuracies with and without genomic information through effective record contributions. The data for this study were provided by the American Angus Association and included datasets of growth, carcass, and marbling traits. Genotypes were available for 1,235,930 animals, and the pedigree had 12,492,581 animals. We showed that algorithm 2 is better suited for approximating accuracies, as its approximations closely matched the exact accuracy values obtained from the inverse of the mixed model equations.

Repetition-Dependent Adaptation and Prediction Error Signalling in Schizophrenia Patients With Auditory Hallucinations: A Roving Mismatch Negativity Study.

BACKGROUND: Dysregulated prediction error-signalling may explain auditory hallucinations in schizophrenia (SZ-AH). Roving mismatch negativity (rMMN) is an event-related potential (ERP) index where the deviant tone becomes the new standard with repetitions. Longer repetitions of standard stimuli yield a more positive sensory-adaptation response (Repetition Positivity-RP), elicit a stronger deviance-detection when interrupted (deviant negativity-DN), and the difference waveform between them reflects the strength of prediction-error signalling (mismatch negativity-MMN). METHODS: Twenty-three SZ-AH patients and twenty-three healthy controls (HC) underwent rMMN assessment. Various standard stimuli were repeated in sets of 3, 8 and 33 yielding three components for RP (RP3, RP8, RP33), DN (DN3, DN8, DN33), and MMN (MMN3, MMN8, MMN33). Amplitudes and latencies were compared across groups. Correlation between (a) rMMN amplitudes and latencies, and clinical variables in SZ-AH, and (b) the RP-DN amplitude pair for all three repetition sets (3, 8, 33) were also examined. RESULTS: All DN and MMN33 amplitudes were significantly suppressed in SZ-AH, while RP amplitudes were not. MMN33 latency was significantly longer in SZ-AH than HC. A few amplitudes and latencies significantly correlated with the frequency of AH. HC showed a significant positive correlation between RP-DN amplitude pairs for sets of 3 and 8 but not for 33; SZ-AH group's correlation profile was opposite to this. DISCUSSION: The link between repetition-dependent sensory-adaptation and deviance-detection is perturbed in SZ-AH. The unimpaired RP profile in SZ-AH is due to potential interference of AH with auditory information processing, and does not indicate a preserved short-term plasticity of the echoic memory trace.

Evaluation of the Accuracy of Intraocular Lens Power Calculation Formulas in Phacovitrectomy.

INTRODUCTION: The aim of this study was to evaluate the refractive error in patients undergoing combined phacovitrectomy with and without gas tamponade. METHODS: This was a retrospective chart review including patients undergoing phacoemulsification alone (Group 1), combined phacovitrectomy for epiretinal membrane (Group 2), and combined phacovitrectomy with gas tamponade for rhegmatogenous retinal detachment (RRD) (Group 3). Axial length and keratometry were measured using an optical biometric system (Argos, Alcon Laboratories. Inc.), and a three-piece intraocular lens (IOL; NX-70S) was implanted in all groups. In each group, the prediction error at 3 months was calculated using IOL power calculation formulas (SRK/T, Hill-RBF, Kane, and Barrett Universal II) for each eye. Outcome measures included the mean prediction error (MPE), its standard deviation (SD), and the mean absolute error (MAE). The change in IOL position at 3 months was also assessed using anterior segment optical coherence tomography. RESULTS: A total of 104 eyes were included (Group 1: 30; Group 2: 34; Group 3: 40 eyes). The MPE was -0.08 ± 0.37 diopters (D), -0.26 ± 0.32 D, and -0.59 ± 0.34 D in Group 1, Group 2, and Group 3, respectively, using the Barrett Universal II formula (P < 0.01, ANOVA). The movement forward in the IOL position was 0.95 ± 0.16 mm, 0.94 ± 0.12 mm, and 1.07 ± 0.20 mm in Group 1, Group 2, and Group 3, respectively (P < 0.01). No significant difference was shown in MPE among the four formulas after combined phacovitrectomy with gas (P = 0.531). CONCLUSIONS: Phacovitrectomy in RRD induced a significant myopic shift using any of the clinically available formulas. This suggests that myopic shift should be taken into consideration for better refractive outcomes in phacovitrectomy with gas tamponade in RRD.

Anterior Cingulate Cortex Causally Supports Meta-Learning.

In dynamic environments with volatile rewards the anterior cingulate cortex (ACC) is believed to determine whether a visual object is relevant and should be chosen. The ACC may achieve this by integrating reward information over time to estimate which objects are worth to explore and which objects should be avoided. Such a higher-order meta-awareness about which objects should be explored predicts that the ACC causally contributes to choices when the reward values of objects are unknown and must be inferred from ongoing exploration. We tested this suggestion in nonhuman primates using a learning task that varied the number of object features that could be relevant, and by controlling the motivational value of choosing objects. During learning the ACC was transiently micro-stimulated when subjects foveated the to-be-chosen stimulus. We found that stimulation selectively impaired learning when feature uncertainty and motivational value of choices were high, which was linked to a deficit in using reward outcomes for feature-specific credit assignment. Application of an adaptive reinforcement learning model confirmed a primary deficit in weighting prediction errors that led to a meta-learning impairment to adaptively increase exploration during learning and to an impaired use of working memory to support learning. These findings provide causal evidence that the reward history traces in ACC are essential for meta-adjusting the exploration-exploitation balance and the strength of working memory of object values during adaptive behavior.

Dorsal raphe neurons integrate the values of reward amount, delay, and uncertainty in multi-attribute decision-making.

The dorsal raphe nucleus (DRN) is implicated in psychiatric disorders that feature impaired sensitivity to reward amount, impulsivity when facing reward delays, and risk-seeking when confronting reward uncertainty. However, it has been unclear whether and how DRN neurons signal reward amount, reward delay, and reward uncertainty during multi-attribute value-based decision-making, where subjects consider these attributes to make a choice. We recorded DRN neurons as monkeys chose between offers whose attributes, namely expected reward amount, reward delay, and reward uncertainty, varied independently. Many DRN neurons signaled offer attributes, and this population tended to integrate the attributes in a manner that reflected monkeys' preferences for amount, delay, and uncertainty. After decision-making, in response to post-decision feedback, these same neurons signaled signed reward prediction errors, suggesting a broader role in tracking value across task epochs and behavioral contexts. Our data illustrate how the DRN participates in value computations, guiding theories about the role of the DRN in decision-making and psychiatric disease.

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.

Investigating sensitivity to multi-domain prediction errors in chronic auditory phantom perception.

The perception of a continuous phantom in a sensory domain in the absence of an external stimulus is explained as a maladaptive compensation of aberrant predictive coding, a proposed unified theory of brain functioning. If this were true, these changes would occur not only in the domain of the phantom percept but in other sensory domains as well. We confirm this hypothesis by using tinnitus (continuous phantom sound) as a model and probe the predictive coding mechanism using the established local-global oddball paradigm in both the auditory and visual domains. We observe that tinnitus patients are sensitive to changes in predictive coding not only in the auditory but also in the visual domain. We report changes in well-established components of event-related EEG such as the mismatch negativity. Furthermore, deviations in stimulus characteristics were correlated with the subjective tinnitus distress. These results provide an empirical confirmation that aberrant perceptions are a symptom of a higher-order systemic disorder transcending the domain of the percept.

Unsupervised Characterization of Prediction Error Markers in Unisensory and Multisensory Streams Reveal the Spatiotemporal Hierarchy of Cortical Information Processing.

Elicited upon violation of regularity in stimulus presentation, mismatch negativity (MMN) reflects the brain's ability to perform automatic comparisons between consecutive stimuli and provides an electrophysiological index of sensory error detection whereas P300 is associated with cognitive processes such as updating of the working memory. To date, there has been extensive research on the roles of MMN and P300 individually, because of their potential to be used as clinical markers of consciousness and attention, respectively. Here, we intend to explore with an unsupervised and rigorous source estimation approach, the underlying cortical generators of MMN and P300, in the context of prediction error propagation along the hierarchies of brain information processing in healthy human participants. The existing methods of characterizing the two ERPs involve only approximate estimations of their amplitudes and latencies based on specific sensors of interest. Our objective is twofold: first, we introduce a novel data-driven unsupervised approach to compute latencies and amplitude of ERP components accurately on an individual-subject basis and reconfirm earlier findings. Second, we demonstrate that in multisensory environments, MMN generators seem to reflect a significant overlap of "modality-specific" and "modality-independent" information processing while P300 generators mark a shift toward completely "modality-independent" processing. Advancing earlier understanding that multisensory contexts speed up early sensory processing, our study reveals that temporal facilitation extends to even the later components of prediction error processing, using EEG experiments. Such knowledge can be of value to clinical research for characterizing the key developmental stages of lifespan aging, schizophrenia, and depression.

Continuous-Time Model Identification of the Subglottal System.

Mathematical models that accurately simulate the physiological systems of the human body serve as cornerstone instruments for advancing medical science and facilitating innovative clinical interventions. One application is the modeling of the subglottal tract and neck skin properties for its use in the ambulatory assessment of vocal function, by enabling non-invasive monitoring of glottal airflow via a neck surface accelerometer. For the technique to be effective, the development of an accurate building block model for the subglottal tract is required. Such a model is expected to utilize glottal volume velocity as the input parameter and yield neck skin acceleration as the corresponding output. In contrast to preceding efforts that employed frequency-domain methods, the present paper leverages system identification techniques to derive a parsimonious continuous-time model of the subglottal tract using time-domain data samples. Additionally, an examination of the model order is conducted through the application of various information criteria. Once a low-order model is successfully fitted, an inverse filter based on a Kalman smoother is utilized for the estimation of glottal volume velocity and related aerodynamic metrics, thereby constituting the most efficient execution of these estimates thus far. Anticipated reductions in computational time and complexity due to the lower order of the subglottal model hold particular relevance for real-time monitoring. Simultaneously, the methodology proves efficient in generating a spectrum of aerodynamic features essential for ambulatory vocal function assessment.