Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging.

Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.

The influence of subcortical shortcuts on disordered sensory and cognitive processing.

The very earliest stages of sensory processing have the potential to alter how we perceive and respond to our environment. These initial processing circuits can incorporate subcortical regions, such as the thalamus and brainstem nuclei, which mediate complex interactions with the brain's cortical processing hierarchy. These subcortical pathways, many of which we share with other animals, are not merely vestigial but appear to function as 'shortcuts' that ensure processing efficiency and preservation of vital life-preserving functions, such as harm avoidance, adaptive social interactions and efficient decision-making. Here, we propose that functional interactions between these higher-order and lower-order brain areas contribute to atypical sensory and cognitive processing that characterizes numerous neuropsychiatric disorders.

Are we really Bayesian? Probabilistic inference shows sub-optimal knowledge transfer.

Numerous studies have found that the Bayesian framework, which formulates the optimal integration of the knowledge of the world (i.e. prior) and current sensory evidence (i.e. likelihood), captures human behaviours sufficiently well. However, there are debates regarding whether humans use precise but cognitively demanding Bayesian computations for behaviours. Across two studies, we trained participants to estimate hidden locations of a target drawn from priors with different levels of uncertainty. In each trial, scattered dots provided noisy likelihood information about the target location. Participants showed that they learned the priors and combined prior and likelihood information to infer target locations in a Bayes fashion. We then introduced a transfer condition presenting a trained prior and a likelihood that has never been put together during training. How well participants integrate this novel likelihood with their learned prior is an indicator of whether participants perform Bayesian computations. In one study, participants experienced the newly introduced likelihood, which was paired with a different prior, during training. Participants changed likelihood weighting following expected directions although the degrees of change were significantly lower than Bayes-optimal predictions. In another group, the novel likelihoods were never used during training. We found people integrated a new likelihood within (interpolation) better than the one outside (extrapolation) the range of their previous learning experience and they were quantitatively Bayes-suboptimal in both. We replicated the findings of both studies in a validation dataset. Our results showed that Bayesian behaviours may not always be achieved by a full Bayesian computation. Future studies can apply our approach to different tasks to enhance the understanding of decision-making mechanisms.

Bayesian accounts of perceptual decisions in the nonclinical continuum of psychosis: Greater imprecision in both top-down and bottom-up processes.

Neurocomputational accounts of psychosis propose mechanisms for how information is integrated into a predictive model of the world, in attempts to understand the occurrence of altered perceptual experiences. Conflicting Bayesian theories postulate aberrations in either top-down or bottom-up processing. The top-down theory predicts an overreliance on prior beliefs or expectations resulting in aberrant perceptual experiences, whereas the bottom-up theory predicts an overreliance on current sensory information, as aberrant salience is directed towards objectively uninformative stimuli. This study empirically adjudicates between these models. We use a perceptual decision-making task in a neurotypical population with varying degrees of psychotic-like experiences. Bayesian modelling was used to compute individuals' reliance on prior relative to sensory information. Across two datasets (discovery dataset n = 363; independent replication in validation dataset n = 782) we showed that psychotic-like experiences were associated with an overweighting of sensory information relative to prior expectations, which seem to be driven by decreased precision afforded to prior information. However, when prior information was more uncertain, participants with greater psychotic-like experiences encoded sensory information with greater noise. Greater psychotic-like experiences were associated with aberrant precision in the encoding both prior and likelihood information, which we suggest may be related to generally heightened perceptions of task instability. Our study lends empirical support to notions of both weaker bottom-up and weaker (rather than stronger) top-down perceptual processes, as well as aberrancies in belief updating that extend into the non-clinical continuum of psychosis.

Assessing the influence of dopamine and mindfulness on the formation of routines in visual search.

Given experience in cluttered but stable visual environments, our eye-movements form stereotyped routines that sample task-relevant locations, while not mixing-up routines between similar task-settings. Both dopamine signaling and mindfulness have been posited as factors that influence the formation of such routines, yet quantification of their impact remains to be tested in healthy humans. Over two sessions, participants searched through grids of doors to find hidden targets, using a gaze-contingent display. Within each session, door scenes appeared in either one of two colors, with each color signaling a differing set of likely target locations. We derived measures for how well target locations were learned (target-accuracy), how routine were sets of eye-movements (stereotypy), and the extent of interference between the two scenes (setting-accuracy). Participants completed two sessions, where they were administered either levodopa (dopamine precursor) or placebo (vitamin C), under double-blind counterbalanced conditions. Dopamine and trait mindfulness (assessed by questionnaire) interacted to influence both target-accuracy and stereotypy. Increasing dopamine improved accuracy and reduced stereotypy for high mindfulness scorers, but induced the opposite pattern for low mindfulness scorers. Dopamine also disrupted setting-accuracy invariant to mindfulness. Our findings show that mindfulness modulates the impact of dopamine on the target-accuracy and stereotypy of eye-movement routines, whereas increasing dopamine promotes interference between task-settings, regardless of mindfulness. These findings provide a link between non-human and human models regarding the influence of dopamine on the formation of task-relevant eye-movement routines and provide novel insights into behavior-trait factors that modulate the use of experience when building adaptive repertoires.

Temporal stability of Bayesian belief updating in perceptual decision-making.

Bayesian inference suggests that perception is inferred from a weighted integration of prior contextual beliefs with current sensory evidence (likelihood) about the world around us. The perceived precision or uncertainty associated with prior and likelihood information is used to guide perceptual decision-making, such that more weight is placed on the source of information with greater precision. This provides a framework for understanding a spectrum of clinical transdiagnostic symptoms associated with aberrant perception, as well as individual differences in the general population. While behavioral paradigms are commonly used to characterize individual differences in perception as a stable characteristic, measurement reliability in these behavioral tasks is rarely assessed. To remedy this gap, we empirically evaluate the reliability of a perceptual decision-making task that quantifies individual differences in Bayesian belief updating in terms of the relative precision weighting afforded to prior and likelihood information (i.e., sensory weight). We analyzed data from participants (n = 37) who performed this task twice. We found that the precision afforded to prior and likelihood information showed high internal consistency and good test-retest reliability (ICC = 0.73, 95% CI [0.53, 0.85]) when averaged across participants, as well as at the individual level using hierarchical modeling. Our results provide support for the assumption that Bayesian belief updating operates as a stable characteristic in perceptual decision-making. We discuss the utility and applicability of reliable perceptual decision-making paradigms as a measure of individual differences in the general population, as well as a diagnostic tool in psychiatric research.

Evidence for adaptive myelination of subcortical shortcuts for visual motion perception in healthy adults.

Conscious visual motion information follows a cortical pathway from the retina to the lateral geniculate nucleus (LGN) and on to the primary visual cortex (V1) before arriving at the middle temporal visual area (MT/V5). Alternative subcortical pathways that bypass V1 are thought to convey unconscious visual information. One flows from the retina to the pulvinar (PUL) and on to medial temporal visual area (MT); while the other directly connects the LGN to MT. Evidence for these pathways comes from non-human primates and modest-sized studies in humans with brain lesions. Thus, the aim of the current study was to reconstruct these pathways in a large sample of neurotypical individuals and to determine the degree to which these pathways are myelinated, suggesting information flow is rapid. We used the publicly available 7T (N = 98; 'discovery') and 3T (N = 381; 'validation') diffusion magnetic resonance imaging datasets from the Human Connectome Project to reconstruct the PUL-MT (including all subcompartments of the PUL) and LGN-MT pathways. We found more fibre tracts with greater density in the left hemisphere. Although the left PUL-MT path was denser, the bilateral LGN-MT tracts were more heavily myelinated, suggesting faster signal transduction. We suggest that this apparent discrepancy may be due to 'adaptive myelination' caused by more frequent use of the LGN-MT pathway that leads to greater myelination and faster overall signal transmission.

Anxiety attenuates learning advantages conferred by statistical stability and induces loss of volatility-attuning in brain activity.

Anxiety can alter an individual's perception of their external sensory environment. Previous studies suggest that anxiety can increase the magnitude of neural responses to unexpected (or surprising) stimuli. Additionally, surprise responses are reported to be boosted during stable compared to volatile environments. Few studies, however, have examined how learning is impacted by both threat and volatility. To investigate these effects, we used threat-of-shock to transiently increase subjective anxiety in healthy adults while they performed an auditory oddball task under stable and volatile environments and while undergoing functional Magnetic Resonance Imaging (fMRI) scanning. We then used Bayesian Model Selection (BMS) mapping to identify the brain areas where different models of anxiety displayed the highest evidence. Behaviourally, we found that threat-of-shock eliminated the accuracy advantage conferred by environmental stability over volatility. Neurally, we found that threat-of-shock led to attenuation and loss of volatility-attuning of brain activity evoked by surprising sounds across most subcortical and limbic regions including the thalamus, basal ganglia, claustrum, insula, anterior cingulate, hippocampal gyrus and the superior temporal gyrus. Taken together, our findings suggest that threat eliminates learning advantages conferred by statistical stability compared to volatility. Thus, we propose that anxiety disrupts behavioural adaptation to environmental statistics, and that multiple subcortical and limbic regions are implicated in this process.

Neural and computational processes of accelerated perceptual awareness and decisions: A 7T fMRI study.

Rapidly detecting salient information in our environments is critical for survival. Visual processing in subcortical areas like the pulvinar and amygdala has been shown to facilitate unconscious processing of salient stimuli. It is unknown, however, if and how these areas might interact with cortical regions to facilitate faster conscious perception of salient stimuli. Here we investigated these neural processes using 7T functional magnetic resonance imaging (fMRI) in concert with computational modelling while participants (n = 33) engaged in a breaking continuous flash suppression paradigm (bCFS) in which fearful and neutral faces are initially suppressed from conscious perception but then eventually 'breakthrough' into awareness. Participants reported faster breakthrough times for fearful faces compared with neutral faces. Drift-diffusion modelling suggested that perceptual evidence was accumulated at a faster rate for fearful faces compared with neutral faces. For both neutral and fearful faces, faster response times were associated with greater activity in the amygdala (specifically within its subregions, including superficial, basolateral and amygdalo-striatal transition area) and the insula. Faster rates of evidence accumulation coincided with greater activity in frontoparietal regions and occipital lobe, as well as the amygdala. A lower decision-boundary correlated with activity in the insula and the posterior cingulate cortex (PCC), but not with the amygdala. Overall, our findings suggest that hastened perceptual awareness of salient stimuli recruits the amygdala and, more specifically, is driven by accelerated evidence accumulation in fronto-parietal and visual areas. In sum, we have mapped distinct neural computations that accelerate perceptual awareness of visually suppressed faces.

Attention promotes the neural encoding of prediction errors.

The encoding of sensory information in the human brain is thought to be optimised by two principal processes: 'prediction' uses stored information to guide the interpretation of forthcoming sensory events, and 'attention' prioritizes these events according to their behavioural relevance. Despite the ubiquitous contributions of attention and prediction to various aspects of perception and cognition, it remains unknown how they interact to modulate information processing in the brain. A recent extension of predictive coding theory suggests that attention optimises the expected precision of predictions by modulating the synaptic gain of prediction error units. Because prediction errors code for the difference between predictions and sensory signals, this model would suggest that attention increases the selectivity for mismatch information in the neural response to a surprising stimulus. Alternative predictive coding models propose that attention increases the activity of prediction (or 'representation') neurons and would therefore suggest that attention and prediction synergistically modulate selectivity for 'feature information' in the brain. Here, we applied forward encoding models to neural activity recorded via electroencephalography (EEG) as human observers performed a simple visual task to test for the effect of attention on both mismatch and feature information in the neural response to surprising stimuli. Participants attended or ignored a periodic stream of gratings, the orientations of which could be either predictable, surprising, or unpredictable. We found that surprising stimuli evoked neural responses that were encoded according to the difference between predicted and observed stimulus features, and that attention facilitated the encoding of this type of information in the brain. These findings advance our understanding of how attention and prediction modulate information processing in the brain, as well as support the theory that attention optimises precision expectations during hierarchical inference by increasing the gain of prediction errors.

Correction: Attention promotes the neural encoding of prediction errors.

[This corrects the article DOI: 10.1371/journal.pbio.2006812.].

People with tobacco use disorder exhibit more prefrontal activity during preparatory control but reduced anterior cingulate activity during reactive control.

Reduced inhibitory control and a hypersensitivity to reward are key deficits in drug dependents; however, they tend to be studied in isolation. Here, we seek to understand the neural processes underlying control over reward and how this is different in people with a tobacco use disorder (pTUD). A novel variant of the monetary incentive delay task was performed by pTUD (n = 20) and non-smokers (n = 20), where we added a stop-signal component such that participants had to inhibit prepotent responses to earn a larger monetary reward. Brain activity was recorded using functional magnetic resonance imaging (fMRI). We estimated stop signal reaction times (SSRTs), an indicator of impulsivity, and correlated these with brain activity. Inhibitory accuracy scores did not differ between the control group and pTUD. However, pTUD had slower SSRTs, suggesting that they may find it harder to inhibit responses. Brain data revealed that pTUD had greater preparatory control activity in the middle frontal gyrus and inferior frontal gyrus prior to successful inhibitions over reward. In contrast, non-smokers had greater reactive control associated with more activity in the anterior cingulate cortex during these successful inhibitions. SSRT-brain activity correlations revealed that pTUD engaged more control-related prefrontal brain regions when SSRTs are slower. Overall, while the inhibition accuracy scores were similar between groups, differential neural processes and strategies were used to successfully inhibit a prepotent response. The findings suggest that increasing preparatory control in pTUD may be one possible treatment target in order to increase inhibitory control over reward.

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

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

Predicting subclinical psychotic-like experiences on a continuum using machine learning.

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants: A feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters; Regularisation of spatiotemporal maps of event-related potentials. Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.

Regional changes in muscle activity do not underlie the repeated bout effect in the human gastrocnemius muscle.

The repeated bout effect (RBE) confers protection following exercise-induced muscle damage. Typical signs of this protective effect are significantly less muscle soreness and faster recovery of strength after the second bout. The aim of this study was to compare regional changes in medial gastrocnemius (MG) muscle activity and mechanical hyperalgesia after repeated bouts of eccentric exercise. Twelve healthy male participants performed two bouts of eccentric heel drop exercise (separated by 7 days) while wearing a vest equivalent to 20% of their body weight. High-density MG electromyographic amplitude maps and topographical pressure pain sensitivity maps were created before, two hours (2H), and two days (2D) after both exercise bouts. Statistical parametric mapping was used to identify RBE effects on muscle activity and mechanical hyperalgesia, using pixel-level statistics when comparing maps. The results revealed a RBE, as a lower strength loss (17% less; P < .01) and less soreness (50% less; P < .01) were found after the second bout. However, different muscle regions were activated 2H and 2D after the initial bout but not following the repeated bout. Further, no overall changes in EMG distribution or mechanical hyperalgesia were found between bouts. These results indicate that muscle activation is unevenly distributed during the initial bout, possibly to maintain muscle function during localized mechanical fatigue. However, this does not reflect a strategy to confer protection during the repeated bout by activating undamaged/non-fatigued muscle areas.

Global effects of feature-based attention depend on surprise.

Recent studies have shown that prediction and attention can interact under various circumstances, suggesting that the two processes are based on interdependent neural mechanisms. In the visual modality, attention can be deployed to the location of a task-relevant stimulus ('spatial attention') or to a specific feature of the stimulus, such as colour or shape, irrespective of its location ('feature-based attention'). Here we asked whether predictive processes are influenced by feature-based attention outside the current spatial focus of attention. Across two experiments, we recorded neural activity with electroencephalography (EEG) as human observers performed a feature-based attention task at fixation and ignored a stream of peripheral stimuli with predictable or surprising features. Central targets were defined by a single feature (colour or orientation) and differed in salience across the two experiments. Task-irrelevant peripheral patterns usually comprised one particular conjunction of features (standards), but occasionally deviated in one or both features (deviants). Consistent with previous studies, we found reliable effects of feature-based attention and prediction on neural responses to task-irrelevant patterns in both experiments. Crucially, we observed an interaction between prediction and feature-based attention in both experiments: the neural effect of feature-based attention was larger for surprising patterns than it was for predicted patterns. These findings suggest that global effects of feature-based attention depend on surprise, and are consistent with a recent theory that suggests attention optimises the precision of predictions by modulating the gain of prediction errors.

Multi-dimensional predictions of psychotic symptoms via machine learning.

The diagnostic criteria for schizophrenia comprise a diverse range of heterogeneous symptoms. As a result, individuals each present a distinct set of symptoms despite having the same overall diagnosis. Whilst previous machine learning studies have primarily focused on dichotomous patient-control classification, we predict the severity of each individual symptom on a continuum. We applied machine learning regression within a multi-modal fusion framework to fMRI and behavioural data acquired during an auditory oddball task in 80 schizophrenia patients. Brain activity was highly predictive of some, but not all symptoms, namely hallucinations, avolition, anhedonia and attention. Critically, each of these symptoms was associated with specific functional alterations across different brain regions. We also found that modelling symptoms as an ensemble of subscales was more accurate, specific and informative than models which predict compound scores directly. In principle, this approach is transferrable to any psychiatric condition or multi-dimensional diagnosis.

Auditory white matter pathways are associated with effective connectivity of auditory prediction errors within a fronto-temporal network.

Auditory prediction errors, i.e. the mismatch between predicted, forthcoming auditory sensations and actual sensory input, trigger the detection of surprising auditory events in the environment. Auditory mismatches engage a hierarchical functional network of cortical sources, which are also interconnected by auditory white matter pathways. Hence it is plausible that these structural and functional networks are quantitatively related. The present study set out to investigate whether structural connectivity of auditory white matter pathways enables the effective connectivity underpinning auditory mismatch responses. Participants (N = 89) underwent diffusion weighted magnetic resonance imaging (MRI) and electroencephalographic (EEG) recordings. Anatomically-constrained tractography was used to extract auditory white matter pathways, namely the bilateral arcuate fasciculi, inferior fronto-occipital fasciculi (IFOF), and the auditory interhemispheric pathway, from which Apparent Fibre Density (AFD) was calculated. EEG data were recorded in the same participants during a stochastic oddball paradigm, which was used to elicit auditory prediction error responses. Dynamic causal modelling was used to investigate the effective connectivity underlying auditory mismatch responses generated in brain regions interconnected by the above mentioned auditory white matter pathways. Our results showed that brain areas interconnected by all auditory white matter pathways best explained the dynamics of auditory mismatch responses. Furthermore, AFD in the right arcuate fasciculus was significantly associated with the effective connectivity between the cortical regions that lie within it. Taken together, these findings indicate that auditory prediction errors recruit a fronto-temporal network of brain regions that are effectively and structurally connected by auditory white matter pathways.

White matter connectivity reductions in the pre-clinical continuum of psychosis: A connectome study.

Widespread white matter connectivity disruptions have commonly been reported in schizophrenia. However, it is questionable whether structural connectivity decline is specifically associated with schizophrenia or whether it extends along a continuum of psychosis into the healthy population. Elucidating brain structure changes associated with psychotic-like experiences in healthy individuals is insofar important as it is a necessary first step towards our understanding of brain pathology preceding florid psychosis. High resolution, multishell diffusion-weighted magnetic resonance images (MRI) were acquired from 89 healthy individuals. Whole-brain white matter fibre tracking was performed to quantify the strength of white matter connections. Network-based statistics were applied to white matter connections in a regression model in order to test for a linear relationship between streamline count and psychotic-like experiences. A significant subnetwork was identified whereby streamline count declined with increasing psychotic-like experiences. This network of structural connectivity reductions affected all cortical lobes, subcortical structures and the cerebellum and spanned along prominent association and commissural white matter pathways. A widespread network of linearly declining connectivity strength with increasing number of psychotic-like experiences was identified in healthy individuals. This finding is in line with white matter connectivity reductions reported from early to chronic stages of schizophrenia and might therefore aid the development of tools to identify individuals at risk of transitioning to psychosis.

Bayesian Mapping Reveals That Attention Boosts Neural Responses to Predicted and Unpredicted Stimuli.

Predictive coding posits that the human brain continually monitors the environment for regularities and detects inconsistencies. It is unclear, however, what effect attention has on expectation processes, as there have been relatively few studies and the results of these have yielded contradictory findings. Here, we employed Bayesian model comparison to adjudicate between 2 alternative computational models. The "Opposition" model states that attention boosts neural responses equally to predicted and unpredicted stimuli, whereas the "Interaction" model assumes that attentional boosting of neural signals depends on the level of predictability. We designed a novel, audiospatial attention task that orthogonally manipulated attention and prediction by playing oddball sequences in either the attended or unattended ear. We observed sensory prediction error responses, with electroencephalography, across all attentional manipulations. Crucially, posterior probability maps revealed that, overall, the Opposition model better explained scalp and source data, suggesting that attention boosts responses to predicted and unpredicted stimuli equally. Furthermore, Dynamic Causal Modeling showed that these Opposition effects were expressed in plastic changes within the mismatch negativity network. Our findings provide empirical evidence for a computational model of the opposing interplay of attention and expectation in the brain.