Predictions Shape Confidence in Right Inferior Frontal Gyrus

Common threads: Altered interoceptive processes across affective and anxiety disorders

There is growing attention towards atypical brain-body interactions and interoceptive processes and their potential role in psychiatric conditions, including affective and anxiety disorders. This paper aims to synthesize recent developments in this field. We present emerging explanatory models and focus on brain-body coupling and modulations of the underlying neurocircuitry that support the concept of a continuum of affective disorders. Grounded in theoretical frameworks like peripheral theories of emotion and predictive processing, we propose that altered interoceptive processes might represent transdiagnostic mechanisms that confer common vulnerability traits across multiple disorders. A deeper understanding of the interplay between bodily states and neural processing is essential for a holistic conceptualization of mental disorders.

Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.

Neural mechanisms underpinning metacognitive shifts driven by non-informative predictions

Humans constantly make predictions and such predictions allow us to prepare for future events. Yet, such benefits may come with drawbacks as premature predictions may potentially bias subsequent judgments. Here we examined how prediction influences our perceptual decisions and subsequent confidence judgments, on scenarios where the predictions were arbitrary and independent of the identity of the upcoming stimuli. We defined them as invalid and non-informative predictions. Behavioral results showed that, such non-informative predictions biased perceptual decisions in favor of the predicted choice, and such prediction-induced perceptual bias further increased the metacognitive efficiency. The functional MRI results showed that activities in the medial prefrontal cortex (mPFC) and subgenual anterior cingulate cortex (sgACC) encoded the response consistency between predictions and perceptual decisions. Activity in mPFC predicted the strength of this congruency bias across individuals. Moreover, the parametric encoding of confidence in putamen was modulated by prediction-choice consistency, such that activity in putamen was negatively correlated with confidence rating after inconsistent responses. These findings suggest that predictions, while made arbitrarily, orchestrate the neural representations of choice and confidence judgment.

Assigning a social status from face adornments: an fMRI study

For at least 150,000 years, the human body has been culturally modified by the wearing of personal ornaments and probably by painting with red pigment. The present study used functional magnetic resonance imaging to explore the brain networks involved in attributing social status from face decorations. Results showed the fusiform gyrus, orbitofrontal cortex, and salience network were involved in social encoding, categorization, and evaluation. The hippocampus and parahippocampus were activated due to the memory and associative skills required for the task, while the inferior frontal gyrus likely interpreted face ornaments as symbols. Resting-state functional connectivity analysis clarified the interaction between these regions. The study highlights the importance of these neural interactions in the symbolic interpretation of social markers on the human face, which were likely active in early Homo species and intensified with Homo sapiens populations as more complex technologies were developed to culturalize the human face.

Knowing that you know that you know? An extreme-confidence heuristic can lead to above-chance discrimination of metacognitive performance

In daily life, we can not only estimate confidence in our inferences ('I'm sure I failed that exam'), but can also estimate whether those feelings of confidence are good predictors of decision accuracy ('I feel sure I failed, but my feeling is probably wrong; I probably passed'). In the lab, by using simple perceptual tasks and collecting trial-by-trial confidence ratings visual metacognition research has repeatedly shown that participants can successfully predict the accuracy of their perceptual choices. Can participants also successfully evaluate 'confidence in confidence' in these tasks? This is the question addressed in this study. Participants performed a simple, two-interval forced choice numerosity task framed as an exam. Confidence judgements were collected in the form of a 'predicted exam grade'. Finally, we collected 'meta-metacognitive' reports in a two-interval forced-choice design: trials were presented in pairs, and participants had to select that in which they thought their confidence (predicted grade) best matched their accuracy (actual grade), effectively minimizing their quadratic scoring rule (QSR) score. Participants successfully selected trials on which their metacognition was better when metacognitive performance was quantified using area under the type 2 ROC (AUROC2) but not when using the 'gold-standard' measure m-ratio. However, further analyses suggested that participants selected trials on which AUROC2 is lower in part via an extreme-confidence heuristic, rather than through explicit evaluation of metacognitive inferences: when restricting analyses to trials on which participants gave the same confidence rating AUROC2 no longer differed as a function of selection, and likewise when we excluded trials on which extreme confidence ratings were given. Together, our results show that participants are able to make effective metacognitive discriminations on their visual confidence ratings, but that explicit 'meta-metacognitive' processes may not be required.

Right prefrontal activation associated with deviations from expected lipstick texture assessed with functional near-infrared spectroscopy

Introduction

There is a continuous consumer demand for ever superior cosmetic products. In marketing, various forms of sensory evaluation are used to measure the consumer experience and provide data with which to improve cosmetics. Nonetheless, potential downsides of existing approaches have led to the exploration of the use of neuroimaging methods, such as functional near-infrared spectroscopy (fNIRS), to provide addition information about consumers' experiences with cosmetics. The aim of the present study was to investigate the feasibility of a real-time brain-based product evaluation method which detects the incongruency between a product, in this case lipstick, and a consumer's expectations.

Method

Thirty healthy, female, habitual lipstick users were asked to apply six different lipsticks varying in softness and to rate the softness of and their willingness to pay (WTP) for each lipstick. Cerebral hemodynamic responses in frontal areas were measured with fNIRS during lipstick application and analyzed using the general linear model (GLM). Incongruency scores between softness and expectation were calculated in order to understand how far removed each lipstick was from a participant's optimal softness preference. The correlation between brain activation (beta scores) during the application of each lipstick and the respective incongruency scores from each participant were acquired using semi-partial correlation analysis, controlling for the effects of WTP.

Results

We revealed a significant intra-subject correlation between incongruency scores and activation in the right inferior frontal gyrus (IFG). This confirms that as the texture incongruency scores increased for the lipstick samples, activation in each individual's right IFG also increased.

Conclusion

The correlation observed between incongruency perceived by participants and activation of the right IFG not only suggests that the right IFG may play an important role in detecting incongruity when there is a discrepancy between the perceived texture and the consumer's expectations but also that measuring activity in the IFG may provide a new objective measurement of the consumer experience, thus contributing to the development of superior cosmetics.

Differential effects of specific emotions on spatial decision-making: evidence from cross-frequency functionally independent brain networks

Emotions significantly shape the way humans make decisions. However, the underlying neural mechanisms of this influence remain elusive. In this study, we designed an experiment to investigate how emotions (specifically happiness, fear, and sadness) impact spatial decision-making, utilizing EEG data. To address the inherent limitations of sensor-level investigations previously conducted, we employed standard low-resolution brain electromagnetic tomography and functional independent component analysis to analyze the EEG data at the cortical source level. Our findings showed that across various spectral-spatial networks, positive emotion activated the decision-making network in the left middle temporal gyrus and inferior temporal gyrus, in contrast to negative emotions. We also identified the common spectral-spatial networks and observed significant differences in network strength across emotions. These insights further revealed the important role of the gamma-band prefrontal network. Our research provides a basis for deciphering the roles of brain networks in the impact of emotions on decision-making.

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.

Boundary updating as a source of history effect on decision uncertainty

When sorting a sequence of stimuli into binary classes, current choices are often negatively correlated with recent stimulus history. This phenomenon-dubbed the repulsive bias-can be explained by boundary updating, a process of shifting the class boundary to previous stimuli. This explanation implies that recent stimulus history can also influence "decision uncertainty," the probability of making incorrect decisions, because it depends on the location of the boundary. However, there have been no previous efforts to elucidate the impact of previous stimulus history on decision uncertainty. Here, from the boundary-updating process that accounts for the repulsive bias, we derived a prediction that decision uncertainty increases as current choices become more congruent with previous stimuli. We confirmed this prediction in behavioral, physiological, and neural correlates of decision uncertainty. Our work demonstrates that boundary updating offers a principled account of how previous stimulus history concurrently relates to choice bias and decision uncertainty.

Total sleep deprivation reduces the table tennis anticipation performance of young men: A functional magnetic resonance imaging study

This study explored whether and how sleep deprivation (SD) affects sport-related anticipation. Twenty table tennis players and 28 non-athletes completed a table tennis anticipation task before and after 36 h SD. Functional magnetic resonance imaging (fMRI) data were acquired simultaneously. The results showed that, compared with the non-athletes, table tennis players had higher neural efficiency, manifested by their higher anticipation accuracy and lower frontal lobe activation. SD impaired anticipation performance, accompanied by decreased activation of the occipital and temporal lobes. Compensatory activation occurred in the left hippocampus and orbital part of the right inferior frontal gyrus (IFG) after SD in the table tennis player group, but not in the non-athlete group. The decreased accuracy of non-athletes was positively correlated with decreased activation of orbital part of the right IFG. This study's findings improve the understanding of the cognitive neuroscience mechanisms by which SD affects sport-related anticipation.

Neural correlates of hierarchical predictive processes in autistic adults

Bayesian theories of autism spectrum disorders (ASD) suggest that atypical predictive mechanisms could underlie the autistic symptomatology, but little is known about their neural correlates. Twenty-six neurotypical (NT) and 26 autistic adults participated in an fMRI study where they performed an associative learning task in a volatile environment. By inverting a model of perceptual inference, we characterized the neural correlates of hierarchically structured predictions and prediction errors in ASD. Behaviorally, the predictive abilities of autistic adults were intact. Neurally, predictions were encoded hierarchically in both NT and ASD participants and biased their percepts. High-level predictions were following activity levels in a set of regions more closely in ASD than NT. Prediction errors yielded activation in shared regions in NT and ASD, but group differences were found in the anterior cingulate cortex and putamen. This study sheds light on the neural specificities of ASD that might underlie atypical predictive processing.

Trial-by-trial predictions of subjective time from human brain activity

Human experience of time exhibits systematic, context-dependent deviations from clock time; for example, time is experienced differently at work than on holiday. Here we test the proposal that differences from clock time in subjective experience of time arise because time estimates are constructed by accumulating the same quantity that guides perception: salient events. Healthy human participants watched naturalistic, silent videos of up to 24 seconds in duration and estimated their duration while fMRI was acquired. We were able to reconstruct trial-by-trial biases in participants’ duration reports, which reflect subjective experience of duration, purely from salient events in their visual cortex BOLD activity. By contrast, salient events in neither of two control regions–auditory and somatosensory cortex–were predictive of duration biases. These results held despite being able to (trivially) predict clock time from all three brain areas. Our results reveal that the information arising during perceptual processing of a dynamic environment provides a sufficient basis for reconstructing human subjective time duration.

Our perception of time isn’t like a clock; it varies depending on other aspects of experience, such as what we see and hear in that moment. Previous studies have shown that differences in simple features, such as an image being larger or smaller, or brighter or dimmer, can change how we perceive time for those experiences. But in everyday life, the properties of these simple features can change frequently, presenting a challenge to understanding real-world time perception based on simple lab experiments. To overcome this problem, we developed a computational model of human time perception based on tracking changes in neural activity across brain regions involved in sensory processing (using non-invasive brain imaging). By measuring changes in brain activity patterns across these regions, our approach accommodates the different and changing feature combinations present in natural scenarios, such as walking on a busy street. Our model reproduces people’s duration reports for natural videos (up to almost half a minute long) and, most importantly, predicts whether a person reports a scene as relatively shorter or longer–the biases in time perception that reflect how natural experience of time deviates from clock time.

I remember it like it was yesterday: Age-related differences in the subjective experience of remembering

It has been frequently described that older adults subjectively report the vividness of their memories as being as high, or even higher, than young adults, despite poorer objective memory performance. Here, we review studies that examined age-related differences in the subjective experience of memory vividness. By examining vividness calibration and resolution, studies using different types of approaches converge to suggest that older adults overestimate the intensity of their vividness ratings relative to young adults, and that they rely on retrieved memory details to a lesser extent to judge vividness. We discuss potential mechanisms underlying these observations. Inflation of memory vividness with regard to the richness of memory content may stem from age-differences in vividness criterion or scale interpretation and psycho-social factors. The reduced reliance on episodic memory details in older adults may stem from age-related differences in how they monitor these details to make their vividness ratings. Considered together, these findings emphasize the importance of examining age-differences in memory vividness using different analytical methods and they provide valuable evidence that the subjective experience of remembering is more than the reactivation of memory content. In this vein, we recommend that future studies explore the links between memory vividness and other subjective memory scales (e.g., ratings of details or memory confidence) in healthy aging and/or other populations, as it could be used as a window to better characterize the cognitive processes that underpin the subjective assessment of the quality of recollected events.

Prediction learning in adults with autism and its molecular correlates

Background

According to Bayesian hypotheses, individuals with Autism Spectrum Disorder (ASD) have difficulties making accurate predictions about their environment. In particular, the mechanisms by which they assign precision to predictions or sensory inputs would be suboptimal in ASD. These mechanisms are thought to be mostly mediated by glutamate and GABA. Here, we aimed to shed light on prediction learning in ASD and on its neurobiological correlates.

Methods

Twenty-six neurotypical and 26 autistic adults participated in an associative learning task where they had to learn a probabilistic association between a tone and the rotation direction of two dots, in a volatile context. They also took part in magnetic resonance spectroscopy (MRS) measurements to quantify Glx (glutamate and glutamine), GABA + and glutathione in a low-level perceptual region (occipital cortex) and in a higher-level region involved in prediction learning (inferior frontal gyrus).

Results

Neurotypical and autistic adults had their percepts biased by their expectations, and this bias was smaller for individuals with a more atypical sensory sensitivity. Both groups were able to learn the association and to update their beliefs after a change in contingency. Interestingly, the percentage of correct predictions was correlated with the Glx/GABA + ratio in the occipital cortex (positive correlation) and in the right inferior frontal gyrus (negative correlation). In this region, MRS results also showed an increased concentration of Glx in the ASD group compared to the neurotypical group.

Limitations

We used a quite restrictive approach to select the MR spectra showing a good fit, which led to the exclusion of some MRS datasets and therefore to the reduction of the sample size for certain metabolites/regions.

Conclusions

Autistic adults appeared to have intact abilities to make predictions in this task, in contrast with the Bayesian hypotheses of ASD. Yet, higher ratios of Glx/GABA + in a frontal region were associated with decreased predictive abilities, and ASD individuals tended to have more Glx in this region. This neurobiological difference might contribute to suboptimal predictive mechanisms in ASD in certain contexts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13229-021-00470-6.

Causal Inference Gates Corticostriatal Learning

Attributing outcomes to your own actions or to external causes is essential for appropriately learning which actions lead to reward and which actions do not. Our previous work showed that this type of credit assignment is best explained by a Bayesian reinforcement learning model which posits that beliefs about the causal structure of the environment modulate reward prediction errors (RPEs) during action value updating. In this study, we investigated the brain networks underlying reinforcement learning that are influenced by causal beliefs using functional magnetic resonance imaging while human participants (n = 31; 13 males, 18 females) completed a behavioral task that manipulated beliefs about causal structure. We found evidence that RPEs modulated by causal beliefs are represented in dorsal striatum, while standard (unmodulated) RPEs are represented in ventral striatum. Further analyses revealed that beliefs about causal structure are represented in anterior insula and inferior frontal gyrus. Finally, structural equation modeling revealed effective connectivity from anterior insula to dorsal striatum. Together, these results are consistent with a possible neural architecture in which causal beliefs in anterior insula are integrated with prediction error signals in dorsal striatum to update action values.SIGNIFICANCE STATEMENT Learning which actions lead to reward-a process known as reinforcement learning-is essential for survival. Inferring the causes of observed outcomes-a process known as causal inference-is crucial for appropriately assigning credit to one's own actions and restricting learning to effective action-outcome contingencies. Previous studies have linked reinforcement learning to the striatum, and causal inference to prefrontal regions, yet how these neural processes interact to guide adaptive behavior remains poorly understood. Here, we found evidence that causal beliefs represented in the prefrontal cortex modulate action value updating in posterior striatum, separately from the unmodulated action value update in ventral striatum posited by standard reinforcement learning models.

Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.

Critical review of the use of the Rorschach in European courts

In relation to the admissibility of evidence obtained using projective personality tests arose in F v. Bevándorlási és Állampolgársági Hivatam (2018). The Court of Justice of the European Union has held that an expert's report can only be accepted if it is based on the international scientific community's standards, but has refrained from stipulating what these standards are. It appears timely for European psychologists to decide what standards should be applied to determine whether or not a test is appropriate for psycholegal use. We propose standards and then apply them to the Rorschach because it was used in this case and is an exemplar of projective tests. We conclude that the Rorschach does not meet the proposed standards and that psychologists should abstain from using it in legal proceedings even in the absence of a clear judicial prohibition.

Meta-analytic evidence of differential prefrontal and early sensory cortex activity during non-social sensory perception in autism

To date, neuroimaging research has had a limited focus on non-social features of autism. As a result, neurobiological explanations for atypical sensory perception in autism are lacking. To address this, we quantitively condensed findings from the non-social autism fMRI literature in line with the current best practices for neuroimaging meta-analyses. Using activation likelihood estimation (ALE), we conducted a series of robust meta-analyses across 83 experiments from 52 fMRI studies investigating differences between autistic (n = 891) and typical (n = 967) participants. We found that typical controls, compared to autistic people, show greater activity in the prefrontal cortex (BA9, BA10) during perception tasks. More refined analyses revealed that, when compared to typical controls, autistic people show greater recruitment of the extrastriate V2 cortex (BA18) during visual processing. Taken together, these findings contribute to our understanding of current theories of autistic perception, and highlight some of the challenges of cognitive neuroscience research in autism.

Repetitive transcranial magnetic stimulation changes cognitive/motor tasks performance: An absolute alpha and beta power study

The voluntary movement demands integration between cognitive and motor functions. During the initial stages of motor learning until mastery of a new motor task, and during a demanding task that is not automatic, cognitive and motor functions can be perceived as independent from each other. Areas used for actually performing motor tasks are essentially the same used by Motor Imagery (MI). The main objective of this study was to investigate inhibition effects on cognitive functions of motor skills induced by low-frequency (1 Hz) Repetitive Transcranial Magnetic Stimulation (rTMS) at the sensory-motor integration site (Cz). In particular, the goal was to examine absolute alpha and beta power changes on frontal regions during Execution, Action observation, and Motor Imagery of finger movement tasks. Eleven healthy, right-handed volunteers of both sexes (5 males, 6 females; mean age 28 ± 5 years), with no history of psychiatric or neurological disorders, participated in the experiment. The execution task consisted of the subject flexing and extending the index finger. The action observation task involved watching a video of the same movement. The motor imagery task was imagining the flexion and extension of the index finger movement. After performing the tasks randomly, subjects were submitted to 15 min of low-frequency rTMS and performed the tasks again. All tasks were executed simultaneously with EEG signals recording. Our results demonstrated a significant interaction between rTMS and the three tasks in almost all analyzed regions showing that rTMS can affect the frontal region regarding Execution, Action observation, and Motor Imagery tasks.

Learning to predict: Neuronal signatures of auditory expectancy in human event-related potentials

Learning to anticipate future states of the world based on statistical regularities in the environment is a key component of perception and is vital for the survival of many organisms. Such statistical learning and prediction are crucial for acquiring language and music appreciation. Importantly, learned expectations can be implicitly derived from exposure to sensory input, without requiring explicit information regarding contingencies in the environment. Whereas many previous studies of statistical learning have demonstrated larger neuronal responses to unexpected versus expected stimuli, the neuronal bases of the expectations themselves remain poorly understood. Here we examined behavioral and neuronal signatures of learned expectancy via human scalp-recorded event-related brain potentials (ERPs). Participants were instructed to listen to a series of sounds and press a response button as quickly as possible upon hearing a target noise burst, which was either reliably or unreliably preceded by one of three pure tones in low-, mid-, and high-frequency ranges. Participants were not informed about the statistical contingencies between the preceding tone ‘cues’ and the target. Over the course of a stimulus block, participants responded more rapidly to reliably cued targets. This behavioral index of learned expectancy was paralleled by a negative ERP deflection, designated as a neuronal contingency response (CR), which occurred immediately prior to the onset of the target. The amplitude and latency of the CR were systematically modulated by the strength of the predictive relationship between the cue and the target. Re-averaging ERPs with respect to the latency of behavioral responses revealed no consistent relationship between the CR and the motor response, suggesting that the CR represents a neuronal signature of learned expectancy or anticipatory attention. Our results demonstrate that statistical regularities in an auditory input stream can be implicitly learned and exploited to influence behavior. Furthermore, we uncover a potential ‘prediction signal’ that reflects this fundamental learning process.

Disrupted Functional Network Connectivity Predicts Cognitive Impairment in Presbycusis Patients

Purpose: Individuals with presbycusis often show deficits in cognitive function, however, the exact neurophysiological mechanisms are not well understood. This study explored the alterations in intra- and inter-network functional connectivity (FC) of multiple networks in presbycusis patients, and further correlated FC with cognitive assessment scores to assess their ability to predict cognitive impairment.

Methods: Resting-state functional magnetic resonance imaging (rs-fMRI) was performed in 40 presbycusis patients and 40 matched controls, and 12 resting-state networks (RSNs) were identified by independent component analysis (ICA) approach. A two-sample t-test was carried out to detect the intra-network FC differences, and functional network connectivity (FNC) was calculated to compare the inter-network FC differences. Pearson or Spearman correlation analysis was subsequently used to explore the correlation between altered FC and cognitive assessment scores.

Results: Our study demonstrated that patients with presbycusis showed significantly decreased FC in the subcortical limbic network (scLN), default mode network (DMN), executive control network (ECN), and attention network (AN) compared with the control group. Moreover, the connectivity for scLN-AUN (auditory network) and VN (visual network)-DMN were found significantly increased while AN-DMN was found significantly decreased in presbycusis patients. Ultimately, this study revealed the intra- and inter-network alterations associated with some cognitive assessment scores.

Conclusion: This study observed intra- and inter-network FC alterations in presbycusis patients, and investigated that presbycusis can lead to abnormal connectivity of RSNs and plasticity compensation mechanism, which may be the basis of cognitive impairment, suggesting that FNC can be used to predict potential cognitive impairment in their early stage.

Ventral stream hierarchy underlying perceptual organization in adolescents with autism

Object recognition relies on a hierarchically organized ventral visual stream, with both bottom-up and top-down processes. Here, we aimed at investigating the neural underpinnings of perceptual organization along the ventral visual stream in Autism Spectrum Disorders (ASD), and at determining whether this would be associated with decreased top-down processing in ASD. Nineteen typically developing (TD) adolescents and sixteen adolescents with ASD participated in an fMRI study where they had to detect visual objects. Five conditions displayed Gabor patterns (defined by texture and/or contour) with increasing levels of perceptual organization. In each condition, both groups showed similar abilities. In line with the expected cortical hierarchy, brain activity patterns revealed a progressive involvement of regions, from low-level occipital regions to higher-level frontal regions, when stimuli became more and more organized. The brain patterns were generally similar in both groups, but the ASD group showed greater activation than TD participants in the middle occipital gyrus and lateral occipital complex when perceiving fully organized everyday objects. Effective connectivity analyses suggested that top-down functional connections between the lower levels of the cortical hierarchy were less influenced by the meaning carried by the stimuli in the ASD group than in the TD group. We hypothesize that adolescents with ASD may have been less influenced by top-down processing when perceiving recognizable objects.

Centro-parietal EEG potentials index subjective evidence and confidence during perceptual decision making

Recent studies suggest that a centro-parietal positivity (CPP) in the EEG signal tracks the absolute (unsigned) strength of accumulated evidence for choices that require the integration of noisy sensory input. Here, we investigated whether the CPP might also reflect the evidence for decisions based on a quantitative comparison between two sequentially presented stimuli (a signed quantity). We recorded EEG while participants decided whether the latter of two vibrotactile frequencies was higher or lower than the former in six variants of this task (n = 116). To account for biases in sequential comparisons, we applied a behavioral model based on Bayesian inference that estimated subjectively perceived frequency differences. Immediately after the second stimulus, parietal ERPs reflected the signed value of subjectively perceived differences and afterwards their absolute value. Strikingly, the modulation by signed difference was evident in trials without any objective evidence for either choice and correlated with choice-selective premotor beta band amplitudes. Modulations by the absolute strength of subjectively perceived evidence - a direct indicator of task difficulty - exhibited all features of statistical decision confidence. Together, our data suggest that parietal EEG signals first index subjective evidence, and later include a measure of confidence in the context of perceptual decision making.

Connectomics of bipolar disorder: a critical review, and evidence for dynamic instabilities within interoceptive networks

The notion that specific cognitive and emotional processes arise from functionally distinct brain regions has lately shifted toward a connectivity-based approach that emphasizes the role of network-mediated integration across regions. The clinical neurosciences have likewise shifted from a predominantly lesion-based approach to a connectomic paradigm-framing disorders as diverse as stroke, schizophrenia (SCZ), and dementia as "dysconnection syndromes". Here we position bipolar disorder (BD) within this paradigm. We first summarise the disruptions in structural, functional and effective connectivity that have been documented in BD. Not surprisingly, these disturbances show a preferential impact on circuits that support emotional processes, cognitive control and executive functions. Those at high risk (HR) for BD also show patterns of connectivity that differ from both matched control populations and those with BD, and which may thus speak to neurobiological markers of both risk and resilience. We highlight research fields that aim to link brain network disturbances to the phenotype of BD, including the study of large-scale brain dynamics, the principles of network stability and control, and the study of interoception (the perception of physiological states). Together, these findings suggest that the affective dysregulation of BD arises from dynamic instabilities in interoceptive circuits which subsequently impact on fear circuitry and cognitive control systems. We describe the resulting disturbance as a "psychosis of interoception".

Diffuse brain damage in normal tension glaucoma

Brain changes within and beyond the visual system have been demonstrated in primary open angle glaucoma (POAG), the most common type of glaucoma. These changes have been often interpreted as a neurodegenerative process due, at least partially, to the raised intraocular pressure (IOP). In this context, normal tension glaucoma (NTG), a form of POAG with IOP <21 mm Hg despite the typical glaucomatous findings, represents the most suitable model of glaucoma to test the validity of this hypothesis. We acquired multimodal brain MRI in NTG patients (n = 17) and compared them with demographically matched groups of POAG patients with raised IOP (n = 17) and normal controls (NC, n = 29). Voxelwise statistics was performed with nonparametric permutation testing. Both NTG and POAG patients showed, compared to NC, significantly more gray matter atrophy in both the visual system and in nonvisual brain regions and altered diffusion tensor imaging-derived anatomical connectivity (AC; lower fractional anisotropy and/or higher diffusivities). Compared with NTG, POAG had both more atrophic visual cortex and higher axial diffusivity in nonvisual regions. Functional connectivity (FC) with respect to NC was altered in NTG at short-range level [visual network (VN), ventral attention network] and in POAG at long-range level (between secondary VN and limbic network). FC of POAG was higher than NTG in both VN and executive network. This study provides further evidence that diffuse structural and functional abnormalities across glaucoma brain may be, at least partially, independent of raised IOP and the consequent retinal degeneration. This further defines glaucoma as a condition with neurodegeneration spreading. Hum Brain Mapp 39:532-541, 2018. © 2017 Wiley Periodicals, Inc.

Accumulation of non-numerical evidence during nonsymbolic number processing in the brain: An fMRI study

Behavioral evidence has shown that when performing a nonsymbolic number comparison task (e.g., deciding which of two dot arrays contains more dots), participants' responses are sensitive to affected by both numerical (e.g., number of items) and non-numerical magnitudes (i.e., area, density, etc.). Thus far it is unclear what brain circuits support this process of accumulating non-numerical variables during nonsymbolic number processing. To investigate this, 21 adult participants were asked to engage in a dot comparison task. To measure the neural correlates of accumulating numerical and non-numerical variables, we manipulated the number of the non-numerical magnitudes that were congruent (correlated with number) or incongruent (anticorrelated with number). In a control task, participants were asked to choose the darker of two gray rectangles (brightness task). The tasks were matched in terms of their difficulty. The results of a whole brain analysis for regions sensitive to the congruity of numerical and non-numerical magnitudes revealed a region in the right inferior frontal gyrus (rIFG). Activation in this region was found to be correlated with the relative congruency of numerical and non-numerical magnitudes. In contrast, this region was not modulated by difficulty of the brightness control task. Accordingly in view of these findings, we suggest that the rIFG supports the accumulation of non-numerical magnitudes that are positively correlated with number. Therefore taken together, this study reveals a brain region whose pattern of activity is influenced by the congruency between numerical and non-numerical variables during nonsymbolic number judgments. Hum Brain Mapp 38:4908-4921, 2017. © 2017 Wiley Periodicals, Inc.

Dysfunctions in striatal microstructure can enhance perceptual decision making through deficits in predictive coding

An important brain function is to predict upcoming events on the basis of extracted regularities of previous inputs. These predictive coding processes can disturb performance in concurrent perceptual decision-making and are known to depend on fronto-striatal circuits. However, it is unknown whether, and if so, to what extent striatal microstructural properties modulate these processes. We addressed this question in a human disease model of striosomal dysfunction, i.e. X-linked dystonia-parkinsonism (XDP), using high-density EEG recordings and source localization. The results show faster and more accurate perceptual decision-making performance during distraction in XDP patients compared to healthy controls. The electrophysiological data show that sensory memory and predictive coding processes reflected by the mismatch negativity related to lateral prefrontal brain regions were weakened in XDP patients and thus induced less cognitive conflict than in controls as reflected by the N2 event-related potential (ERP). Consequently, attentional shifting (P3a ERP) and reorientation processes (RON ERP) were less pronounced in the XDP group. Taken together, these results suggests that striosomal dysfunction is related to predictive coding deficits leading to a better performance in concomitant perceptual decision-making, probably because predictive coding does not interfere with perceptual decision-making processes. These effects may reflect striatal imbalances between the striosomes and the matrix compartment.