Visual Population Receptive Fields in People with Schizophrenia Have Reduced Inhibitory Surrounds

Visual surround suppression at the neural and perceptual levels

Surround suppression was initially identified as a phenomenon at the neural level in which stimuli outside the neuron's receptive field alone cannot activate responses but can modulate neural responses to stimuli covered inside the receptive field. Subsequent studies showed that surround suppression is not only a critical property of neurons across species and brain areas but also has been found in visual perceptions. More importantly, surround suppression varies across individuals and shows significant differences between normal controls and patients with certain mental disorders. Here, we combined results from related literature and summarized the findings derived from physiological and psychophysical evidence. We first outline the basic properties of surround suppression in the visual system and perceptions. Then, we mainly summarize the differences in perceptual surround suppression among different human subjects. Our review suggests that there is no consensus regarding whether the strength of perceptual surround suppression could be used as an effective index to distinguish particular populations. Then, we summarized the similar mechanisms for surround suppression and cognitive impairments to further explore the potential clinical applications of surround suppression. A clearer understanding of the mechanisms of surround suppression in neural responses and perceptions is necessary for facilitating its clinical applications.

Computational model links normalization to chemoarchitecture in the human visual system

A goal of cognitive neuroscience is to provide computational accounts of brain function. Canonical computations-mathematical operations used by the brain in many contexts-fulfill broad information-processing needs by varying their algorithmic parameters. A key question concerns the identification of biological substrates for these computations and their algorithms. Chemoarchitecture-the spatial distribution of neurotransmitter receptor densities-shapes brain function. Here, we propose that local variations in specific receptor densities implement algorithmic modulations of canonical computations. To test this hypothesis, we combine mathematical modeling of brain responses with chemoarchitecture data. We compare parameters of divisive normalization obtained from 7-tesla functional magnetic resonance imaging with receptor density maps obtained from positron emission tomography. We find evidence that serotonin and γ-aminobutyric acid receptor densities are the biological substrate for algorithmic modulations of divisive normalization in the human visual system. Our model links computational and biological levels of vision, explaining how canonical computations allow the brain to fulfill broad information-processing needs.

A neural modeling approach to study mechanisms underlying the heterogeneity of visual spatial frequency sensitivity in schizophrenia

Patients with schizophrenia exhibit abnormalities in spatial frequency sensitivity, and it is believed that these abnormalities indicate more widespread dysfunction and dysregulation of bottom-up processing. The early visual system, including the first-order Lateral Geniculate Nucleus of the thalamus (LGN) and the primary visual cortex (V1), are key contributors to spatial frequency sensitivity. Medicated and unmedicated patients with schizophrenia exhibit contrasting changes in spatial frequency sensitivity, thus making it a useful probe for examining potential effects of the disorder and antipsychotic medications in neural processing. We constructed a parameterized, rate-based neural model of on-center/off-surround neurons in the early visual system to investigate the impacts of changes to the excitatory and inhibitory receptive field subfields. By incorporating changes in both the excitatory and inhibitory subfields that are associated with pathophysiological findings in schizophrenia, the model successfully replicated perceptual data from behavioral/functional studies involving medicated and unmedicated patients. Among several plausible mechanisms, our results highlight the dampening of excitation and/or increase in the spread and strength of the inhibitory subfield in medicated patients and the contrasting decreased spread and strength of inhibition in unmedicated patients. Given that the model was successful at replicating results from perceptual data under a variety of conditions, these elements of the receptive field may be useful markers for the imbalances seen in patients with schizophrenia.

A neural model of modified excitation/inhibition and feedback levels in schizophrenia

Introduction

The strength of certain visual illusions, including contrast-contrast and apparent motion, is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms.

Methods

To investigate whether and how these factors influence the perceived contrast-contrast and apparent motion illusions in individuals with schizophrenia, we propose a two-layer network, with top-down feedback from layer 2 to layer 1 that can model visual receptive fields (RFs) and their inhibitory and excitatory subfields.

Results

Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia within certain clinical states and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups.

Discussion

We propose that the heterogeneity of visual and in general sensory processing in certain clinical states of schizophrenia can be largely explained by the degree of top-down feedback reduction, emphasizing the critical role of top-down feedback in illusion perception, and to a lesser extent on the imbalance of excitation/inhibition. Our neural model provides a mechanistic explanation for the modulated visual percepts of contrast-contrast and apparent motion in schizophrenia with findings that can explain a broad range of visual perceptual observations in previous studies. The two-layer motif of the current model provides a general framework that can be tailored to investigate subcortico-cortical (such as thalamocortical) and cortico-cortical networks, bridging neurobiological changes in schizophrenia and perceptual processing.

A neural model of modified excitation/inhibition and feedback levels in schizophrenia

The strength of certain visual illusions is weakened in individuals with schizophrenia. Such phenomena have been interpreted as the impaired integration of inhibitory and excitatory neural responses, and impaired top-down feedback mechanisms. To investigate whether and how these factors influence the perceived illusions in individuals with schizophrenia, we propose a two-layer network that can model visual receptive fields (RFs), their inhibitory and excitatory subfields, and the top-down feedback. Our neural model suggests that illusion perception changes in individuals with schizophrenia can be influenced by altered top-down mechanisms and the organization of the on-center off-surround receptive fields. Alteration of the RF inhibitory surround and/or the excitatory center can replicate the difference of illusion precepts between individuals with schizophrenia and normal controls. The results show that the simulated top-down feedback modulation enlarges the difference of the model illusion representations, replicating the difference between the two groups. We propose that the heterogeneity of visual and in general sensory processing in schizophrenia can be largely explained by the degree of top-down feedback reduction, emphasizing the critical role of top-down feedback in illusion perception, and to a lesser extent on the imbalance of excitation/inhibition. Our neural model provides a mechanistic explanation for the modulated visual percepts in schizophrenia with findings that can explain a broad range of visual perceptual observations in previous studies. The two-layer motif of the current model provides a general framework that can be tailored to investigate subcortico-cortical (such as thalamocortical) and cortico-cortical networks, bridging neurobiological changes in schizophrenia and perceptual processing.

The Psychosis Human Connectome Project: Design and rationale for studies of visual neurophysiology

Visual perception is abnormal in psychotic disorders such as schizophrenia. In addition to hallucinations, laboratory tests show differences in fundamental visual processes including contrast sensitivity, center-surround interactions, and perceptual organization. A number of hypotheses have been proposed to explain visual dysfunction in psychotic disorders, including an imbalance between excitation and inhibition. However, the precise neural basis of abnormal visual perception in people with psychotic psychopathology (PwPP) remains unknown. Here, we describe the behavioral and 7 tesla MRI methods we used to interrogate visual neurophysiology in PwPP as part of the Psychosis Human Connectome Project (HCP). In addition to PwPP (n = 66) and healthy controls (n = 43), we also recruited first-degree biological relatives (n = 44) in order to examine the role of genetic liability for psychosis in visual perception. Our visual tasks were designed to assess fundamental visual processes in PwPP, whereas MR spectroscopy enabled us to examine neurochemistry, including excitatory and inhibitory markers. We show that it is feasible to collect high-quality data across multiple psychophysical, functional MRI, and MR spectroscopy experiments with a sizable number of participants at a single research site. These data, in addition to those from our previously described 3 tesla experiments, will be made publicly available in order to facilitate further investigations by other research groups. By combining visual neuroscience techniques and HCP brain imaging methods, our experiments offer new opportunities to investigate the neural basis of abnormal visual perception in PwPP.

Intact and deficient contextual processing in schizophrenia patients

Schizophrenia patients are known to have deficits in contextual vision. However, results are often very mixed. In some paradigms, patients do not take the context into account and, hence, perform more veridically than healthy controls. In other paradigms, context deteriorates performance much more strongly in patients compared to healthy controls. These mixed results may be explained by differences in the paradigms as well as by small or biased samples, given the large heterogeneity of patients' deficits. Here, we show that mixed results may also come from idiosyncrasies of the stimuli used because in variants of the same visual paradigm, tested with the same participants, we found intact and deficient processing.

Bayesian connective field modeling using a Markov Chain Monte Carlo approach

The majority of neurons in the human brain process signals from neurons elsewhere in the brain. Connective Field (CF) modelling is a biologically-grounded method to describe this essential aspect of the brain's circuitry. It allows characterizing the response of a population of neurons in terms of the activity in another part of the brain. CF modelling translates the concept of the receptive field (RF) into the domain of connectivity by assessing, at the voxel level, the spatial dependency between signals in distinct cortical visual field areas. Thus, the approach enables to characterize the functional cortical circuitry of the human cortex. While already very useful, the present CF modelling approach has some intrinsic limitations due to the fact that it only estimates the model's explained variance and not the probability distribution associated with the estimated parameters. If we could resolve this, CF modelling would lend itself much better for statistical comparisons at the level of single voxels and individuals. This is important when trying to gain a detailed understanding of the neurobiology and pathophysiology of the visual cortex, notably in rare cases. To enable this, we present a Bayesian approach to CF modeling (bCF). Using a Markov Chain Monte Carlo (MCMC) procedure, it estimates the posterior probability distribution underlying the CF parameters. Based on this, bCF quantifies, at the voxel level, the uncertainty associated with each parameter estimate. This information can be used in various ways to increase confidence in the CF model predictions. We applied bCF to BOLD responses recorded in the early human visual cortex using 3T fMRI. We estimated both the CF parameters and their associated uncertainties and show they are only weakly correlated. Moreover, we show how bCF facilitates the use of effect size (beta) as a data-driven parameter that can be used to select the most reliable voxels for further analysis. Finally, to further illustrate the functionality gained by bCF, we apply it to perform a voxel-level comparison of a single, circular symmetric, Gaussian versus a Difference-of-Gaussian model. We conclude that our bCF framework provides a comprehensive tool to study human functional cortical circuitry in health and disease.

What you see is what you get: visual scanning failures of naturalistic social scenes in schizophrenia

BACKGROUND

Impairments in social cognition contribute significantly to disability in schizophrenia patients (SzP). Perception of facial expressions is critical for social cognition. Intact perception requires an individual to visually scan a complex dynamic social scene for transiently moving facial expressions that may be relevant for understanding the scene. The relationship of visual scanning for these facial expressions and social cognition remains unknown.

METHODS

In 39 SzP and 27 healthy controls (HC), we used eye-tracking to examine the relationship between performance on The Awareness of Social Inference Test (TASIT), which tests social cognition using naturalistic video clips of social situations, and visual scanning, measuring each individual's relative to the mean of HC. We then examined the relationship of visual scanning to the specific visual features (motion, contrast, luminance, faces) within the video clips.

RESULTS

TASIT performance was significantly impaired in SzP for trials involving sarcasm (p < 10-5). Visual scanning was significantly more variable in SzP than HC (p < 10-6), and predicted TASIT performance in HC (p = 0.02) but not SzP (p = 0.91), differing significantly between groups (p = 0.04). During the visual scanning, SzP were less likely to be viewing faces (p = 0.0001) and less likely to saccade to facial motion in peripheral vision (p = 0.008).

CONCLUSIONS

SzP show highly significant deficits in the use of visual scanning of naturalistic social scenes to inform social cognition. Alterations in visual scanning patterns may originate from impaired processing of facial motion within peripheral vision. Overall, these results highlight the utility of naturalistic stimuli in the study of social cognition deficits in schizophrenia.

Divisive normalization unifies disparate response signatures throughout the human visual hierarchy

A canonical neural computation is a mathematical operation applied by the brain in a wide variety of contexts and capable of explaining and unifying seemingly unrelated neural and perceptual phenomena. Here, we use a combination of state-of-the-art experiments (ultra-high-field functional MRI) and mathematical methods (population receptive field [pRF] modeling) to uniquely demonstrate the role of divisive normalization (DN) as the canonical neural computation underlying visuospatial responses throughout the human visual hierarchy. The DN pRF model provides a tool to investigate and interpret the computational processes underlying neural responses in human and animal recordings, but also in clinical and cognitive dimensions.

Neural processing is hypothesized to apply the same mathematical operations in a variety of contexts, implementing so-called canonical neural computations. Divisive normalization (DN) is considered a prime candidate for a canonical computation. Here, we propose a population receptive field (pRF) model based on DN and evaluate it using ultra-high-field functional MRI (fMRI). The DN model parsimoniously captures seemingly disparate response signatures with a single computation, superseding existing pRF models in both performance and biological plausibility. We observe systematic variations in specific DN model parameters across the visual hierarchy and show how they relate to differences in response modulation and visuospatial information integration. The DN model delivers a unifying framework for visuospatial responses throughout the human visual hierarchy and provides insights into its underlying information-encoding computations. These findings extend the role of DN as a canonical computation to neuronal populations throughout the human visual hierarchy.

Extremely fast pRF mapping for real-time applications

Population receptive field (pRF) mapping is a popular tool in computational neuroimaging that allows for the investigation of receptive field properties, their topography and interrelations in health and disease. Furthermore, the possibility to invert population receptive fields provides a decoding model for constructing stimuli from observed cortical activation patterns. This has been suggested to pave the road towards pRF-based brain-computer interface (BCI) communication systems, which would be able to directly decode internally visualized letters from topographically organized brain activity. A major stumbling block for such an application is, however, that the pRF mapping procedure is computationally heavy and time consuming. To address this, we propose a novel and fast pRF mapping procedure that is suitable for real-time applications. The method is built upon hashed-Gaussian encoding of the stimulus, which tremendously reduces computational resources. After the stimulus is encoded, mapping can be performed using either ridge regression for fast offline analyses or gradient descent for real-time applications. We validate our model-agnostic approach in silico, as well as on empirical fMRI data obtained from 3T and 7T MRI scanners. Our approach is capable of estimating receptive fields and their parameters for millions of voxels in mere seconds. This method thus facilitates real-time applications of population receptive field mapping.

Population Receptive Field Shapes in Early Visual Cortex Are Nearly Circular

The visual field region where a stimulus evokes a neural response is called the receptive field (RF). Analytical tools combined with functional MRI (fMRI) can estimate the RF of the population of neurons within a voxel. Circular population RF (pRF) methods accurately specify the central position of the pRF and provide some information about the spatial extent (diameter) of the RF. A number of investigators developed methods to further estimate the shape of the pRF, for example, whether the shape is more circular or elliptical. There is a report that there are many pRFs with highly elliptical pRFs in early visual cortex (V1-V3; Silson et al., 2018). Large aspect ratios (>2) are difficult to reconcile with the spatial scale of orientation columns or visual field map properties in early visual cortex. We started to replicate the experiments and found that the software used in the publication does not accurately estimate RF shape: it produces elliptical fits to circular ground-truth data. We analyzed an independent data set with a different software package that was validated over a specific range of measurement conditions, to show that in early visual cortex the aspect ratios are <2. Furthermore, current empirical and theoretical methods do not have enough precision to discriminate ellipses with aspect ratios of 1.5 from circles. Through simulation we identify methods for improving sensitivity that may estimate ellipses with smaller aspect ratios. The results we present are quantitatively consistent with prior assessments using other methodologies.SIGNIFICANCE STATEMENT We evaluated whether the shape of many population receptive fields (RFs) in early visual cortex is elliptical and differs substantially from circular. We evaluated two tools for estimating elliptical models of the pRF; one tool was valid over the measured compliance range. Using the validated tool, we found no evidence that confidently rejects circular fits to the pRF in visual field maps V1, V2, and V3. The new measurements and analyses are consistent with prior theoretical and experimental assessments in the literature.

Deficits and compensation: Attentional control cortical networks in schizophrenia

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Examined attention systems in SzP with resting-state connectivity and task fMRI.

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SzP have functional connectivity deficits in late visual cortex and prefrontal areas.

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Task performance correlated with ventral attention network deactivation in SzP only.

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This relationship is mediated by connectivity of key attentional control components.

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Results reveal deficits and potential compensation in SzP visual processing/attention.

Visual processing and attention deficits are responsible for a substantial portion of the disability caused by schizophrenia, but the source of these deficits remains unclear. In 35 schizophrenia patients (SzP) and 34 healthy controls (HC), we used a rapid serial visual presentation (RSVP) visual search task designed to activate/deactivate the cortical components of the attentional control system (i.e. the dorsal and ventral attention networks, lateral prefrontal regions in the frontoparietal network, and cingulo-opercular/salience networks), along with resting state functional connectivity, to examine the integrity of these components. While we find that behavioral performance and activation/deactivation of the RSVP task are largely similar between groups, SzP exhibited decreased functional connectivity within late visual components and between prefrontal and other components. We also find that performance correlates with the deactivation of the ventral attention network in SzP only. This relationship is mediated by the functional connectivity of critical components of the attentional control system. In summary, our results suggest that the attentional control system is potentially used to compensate for visual cortex deficits. Furthermore, prefrontal deficits in SzP may interfere with this compensatory use of the attentional control system. In addition to highlighting focal deficits and potential compensatory mechanisms in visual processing and attention, our findings point to the attentional control system as a potential target for rehabilitation and neuromodulation-based treatments for visual processing deficits in SzP.

A validation framework for neuroimaging software: The case of population receptive fields

Neuroimaging software methods are complex, making it a near certainty that some implementations will contain errors. Modern computational techniques (i.e., public code and data repositories, continuous integration, containerization) enable the reproducibility of the analyses and reduce coding errors, but they do not guarantee the scientific validity of the results. It is difficult, nay impossible, for researchers to check the accuracy of software by reading the source code; ground truth test datasets are needed. Computational reproducibility means providing software so that for the same input anyone obtains the same result, right or wrong. Computational validity means obtaining the right result for the ground-truth test data. We describe a framework for validating and sharing software implementations, and we illustrate its usage with an example application: population receptive field (pRF) methods for functional MRI data. The framework is composed of three main components implemented with containerization methods to guarantee computational reproducibility. In our example pRF application, those components are: (1) synthesis of fMRI time series from ground-truth pRF parameters, (2) implementation of four public pRF analysis tools and standardization of inputs and outputs, and (3) report creation to compare the results with the ground truth parameters. The framework was useful in identifying realistic conditions that lead to imperfect parameter recovery in all four pRF implementations, that would remain undetected using classic validation methods. We provide means to mitigate these problems in future experiments. A computational validation framework supports scientific rigor and creativity, as opposed to the oft-repeated suggestion that investigators rely upon a few agreed upon packages. We hope that the framework will be helpful to validate other critical neuroimaging algorithms, as having a validation framework helps (1) developers to build new software, (2) research scientists to verify the software's accuracy, and (3) reviewers to evaluate the methods used in publications and grants.

Mapping sequences can bias population receptive field estimates

Population receptive field (pRF) modelling is a common technique for estimating the stimulus-selectivity of populations of neurons using neuroimaging. Here, we aimed to address if pRF properties estimated with this method depend on the spatio-temporal structure and the predictability of the mapping stimulus. We mapped the polar angle preference and tuning width of voxels in visual cortex (V1-V4) of healthy, adult volunteers. We compared sequences sweeping orderly through the visual field or jumping from location to location employing stimuli of different width (45° vs 6°) and cycles of variable duration (8s vs 60s). While we did not observe any systematic influence of stimulus predictability, the temporal structure of the sequences significantly affected tuning width estimates. Ordered designs with large wedges and short cycles produced systematically smaller estimates than random sequences. Interestingly, when we used small wedges and long cycles, we obtained larger tuning width estimates for ordered than random sequences. We suggest that ordered and random mapping protocols show different susceptibility to other design choices such as stimulus type and duration of the mapping cycle and can produce significantly different pRF results.

Studying Cortical Plasticity in Ophthalmic and Neurological Disorders: From Stimulus-Driven to Cortical Circuitry Modeling Approaches

Unsolved questions in computational visual neuroscience research are whether and how neurons and their connecting cortical networks can adapt when normal vision is compromised by a neurodevelopmental disorder or damage to the visual system. This question on neuroplasticity is particularly relevant in the context of rehabilitation therapies that attempt to overcome limitations or damage, through either perceptual training or retinal and cortical implants. Studies on cortical neuroplasticity have generally made the assumption that neuronal population properties and the resulting visual field maps are stable in healthy observers. Consequently, differences in the estimates of these properties between patients and healthy observers have been taken as a straightforward indication for neuroplasticity. However, recent studies imply that the modeled neuronal properties and the cortical visual maps vary substantially within healthy participants, e.g., in response to specific stimuli or under the influence of cognitive factors such as attention. Although notable advances have been made to improve the reliability of stimulus-driven approaches, the reliance on the visual input remains a challenge for the interpretability of the obtained results. Therefore, we argue that there is an important role in the study of cortical neuroplasticity for approaches that assess intracortical signal processing and circuitry models that can link visual cortex anatomy, function, and dynamics.

Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

Background: Population receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Methods: Here, we compared pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries. We also tested the effect of low-pass filtering of the time series on pRF estimates. Results: As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Unsurprisingly, low-pass filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Conclusion: Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.

Comparison of human population receptive field estimates between scanners and the effect of temporal filtering

Background: Population receptive field (pRF) analysis with functional magnetic resonance imaging (fMRI) is an increasingly popular method for mapping visual field representations and estimating the spatial selectivity of voxels in human visual cortex. However, the multitude of experimental setups and processing methods used makes comparisons of results between studies difficult. Methods: Here, we compared pRF maps acquired in the same three individuals using comparable scanning parameters on a 1.5 and a 3 Tesla scanner located in two different countries. We also tested the effect of low-pass filtering of the time series on pRF estimates. Results: As expected, the signal-to-noise ratio for the 3 Tesla data was superior; critically, however, estimates of pRF size and cortical magnification did not reveal any systematic differences between the sites. Unsurprisingly, low-pass filtering enhanced goodness-of-fit, presumably by removing high-frequency noise. However, there was no substantial increase in the number of voxels containing meaningful retinotopic signals after low-pass filtering. Importantly, filtering also increased estimates of pRF size in the early visual areas which could substantially skew interpretations of spatial tuning properties. Conclusion: Our results therefore suggest that pRF estimates are generally comparable between scanners of different field strengths, but temporal filtering should be used with caution.

Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia

Psychotic experiences may be understood as altered information processing due to aberrant neural computations. A prominent example of such neural computations is the computation of prediction errors (PEs), which signal the difference between expected and experienced events. Among other areas showing PE coding, hippocampal-prefrontal-striatal neurocircuits play a prominent role in information processing. Dysregulation of dopaminergic signaling, often secondary to psychosocial stress, is thought to interfere with the processing of biologically important events (such as reward prediction errors) and result in the aberrant attribution of salience to irrelevant sensory stimuli and internal representations. Bayesian hierarchical predictive coding offers a promising framework for the identification of dysfunctional neurocomputational processes and the development of a mechanistic understanding of psychotic experience. According to this framework, mismatches between prior beliefs encoded at higher levels of the cortical hierarchy and lower-level (sensory) information can also be thought of as PEs, with important consequences for belief updating. Low levels of precision in the representation of prior beliefs relative to sensory data, as well as dysfunctional interactions between prior beliefs and sensory data in an ever-changing environment, have been suggested as a general mechanism underlying psychotic experiences. Translating the promise of the Bayesian hierarchical predictive coding into patient benefit will come from integrating this framework with existing knowledge of the etiology and pathophysiology of psychosis, especially regarding hippocampal-prefrontal-striatal network function and neural mechanisms of information processing and belief updating.

Linking resting-state networks and social cognition in schizophrenia and bipolar disorder

Individuals with schizophrenia and bipolar disorder show alterations in functional neural connectivity during rest. However, resting-state network (RSN) disruptions have not been systematically compared between the two disorders. Further, the impact of RSN disruptions on social cognition, a key determinant of functional outcome, has not been studied. Forty-eight individuals with schizophrenia, 46 with bipolar disorder, and 48 healthy controls completed resting-state functional magnetic resonance imaging. An atlas-based approach was used to examine functional connectivity within nine RSNs across the cortex. RSN connectivity was assessed via nonparametric permutation testing, and associations with performance on emotion perception, mentalizing, and emotion management tasks were examined. Group differences were observed in the medial and lateral visual networks and the sensorimotor network. Individuals with schizophrenia demonstrated reduced connectivity relative to healthy controls in all three networks. Individuals with bipolar disorder demonstrated reduced connectivity relative to controls in the medial visual network and connectivity within this network was significantly positively correlated with emotion management. In healthy controls, connectivity within the medial and lateral visual networks positively correlated with mentalizing. No significant correlations were found for either visual network in schizophrenia. Results highlight the role of altered early visual processing in social cognitive deficits in both schizophrenia and bipolar disorder. However, individuals with bipolar disorder appear to compensate for disrupted visual network connectivity on social cognitive tasks, whereas those with schizophrenia do not. The current study adds clarity on the neurophysiology underlying social cognitive deficits that result in impaired functioning in serious mental illness.

Data-driven models in human neuroscience and neuroengineering

Discoveries in modern human neuroscience are increasingly driven by quantitative understanding of complex data. Data-intensive approaches to modeling have promise to dramatically advance our understanding of the brain and critically enable neuroengineering capabilities. In this review, we provide an accessible primer to modern modeling approaches and highlight recent data-driven discoveries in the domains of neuroimaging, single-neuron and neuronal population responses, and device neuroengineering. Further, we suggest that meaningful progress requires the community to tackle open challenges in the realms of model interpretability and generalizability, training pipelines of data-fluent human neuroscientists, and integrated consideration of data ethics.

Integrated assessment of visual perception abnormalities in psychotic disorders and relationship with clinical characteristics

BACKGROUND

The visual system is recognized as an important site of pathology and dysfunction in schizophrenia. In this study, we evaluated different visual perceptual functions in patients with psychotic disorders using a potentially clinically applicable task battery and assessed their relationship with symptom severity in patients, and with schizotypal features in healthy participants.

METHODS

Five different areas of visual functioning were evaluated in patients with schizophrenia and schizoaffective disorder (n = 28) and healthy control subjects (n = 31) using a battery that included visuospatial working memory (VSWM), velocity discrimination (VD), contour integration, visual context processing, and backward masking tasks.

RESULTS

The patient group demonstrated significantly lower performance in VD, contour integration, and VSWM tasks. Performance did not differ between the two groups on the visual context processing task and did not differ across levels of interstimulus intervals in the backward masking task. Performances on VSWM, VD, and contour integration tasks were correlated with negative symptom severity but not with other symptom dimensions in the patient group. VSWM and VD performances were also correlated with negative sychizotypal features in healthy controls.

CONCLUSION

Taken together, these results demonstrate significant abnormalities in multiple visual processing tasks in patients with psychotic disorders, adding to the literature implicating visual abnormalities in these conditions. Furthermore, our results show that visual processing impairments are associated with the negative symptom dimension in patients as well as healthy individuals.

Altered topological characteristics of morphological brain network relate to language impairment in high genetic risk subjects and schizophrenia patients

OBJECTIVE

Evidence suggests relationships between abnormalities in various cortical and subcortical brain structures and language dysfunction in individuals with schizophrenia, and to some extent in those with increased genetic risk for this diagnosis. The topological features of the structural brain network at the systems-level and their impact on language function in schizophrenia and in those at high genetic risk has been less well studied.

METHOD

Single-subject morphological brain network was constructed in a total of 71 subjects (20 patients with schizophrenia, 19 individuals at high genetic risk for schizophrenia, and 32 controls). Among these 71 subjects, 56 were involved in our previous neuroimaging studies. Graphic Theoretical Techniques was applied to calculate the global and nodal topological characteristics of the morphological brain network of each participant. Index scores for five language-related cognitive tests were also attained from each participant.

RESULTS

Significantly smaller nodal degree in bilateral superior occipital gyri (SOG) were observed in individuals with schizophrenia, as compared to the controls and those at high risk; while significantly reduced nodal betweenness centrality (quantifying the level of a node in connecting other nodes in the network) in right middle frontal gyrus (MFG) was found in the high-risk group, relative to controls. The right MFG nodal efficiency and hub capacity (represented by both nodal degree and betweenness centrality) of the morphological brain network were negatively associated with the wide range achievement test (WRAT) standard performance score; while the right SOG nodal degree was positively associated with the WRAT standard performance score, in the entire study sample.

CONCLUSIONS

These findings enhance the understanding of structural brain abnormalities at the systems-level in individuals with schizophrenia and those at high genetic risk, which may serve as critical neural substrates for the origin of the language-related impairments and symptom manifestations of schizophrenia.

A review of visual aftereffects in schizophrenia

Psychosis-a cardinal symptom of schizophrenia-has been associated with a failure to appropriately create or use stored regularities about past states of the world to guide the interpretation of incoming information, which leads to abnormal perceptions and beliefs. The visual system provides a test bed for investigating the role of prior experience and prediction, as accumulated knowledge of the world informs our current perception. More specifically, the strength of visual aftereffects, illusory percepts that arise after prolonged viewing of a visual stimulus, can serve as a valuable measure of the influence of prior experience on current visual processing. In this paper, we review findings from a largely older body of work on visual aftereffects in schizophrenia, attempt to reconcile discrepant findings, highlight the role of antipsychotic medication, consider mechanistic interpretations for behavioral effects, and propose directions for future research.

Population receptive field tuning properties of visual cortex during childhood

Visuospatial abilities such as contrast sensitivity and Vernier acuity improve until late in childhood, but the neural mechanisms supporting these changes are poorly understood. We tested to which extent this development might reflect improved spatial sensitivity of neuronal populations in visual cortex. To do this, we measured BOLD-responses in areas V1-V4 and V3a, whilst 6- to 12-year-old children and adults watched large-field wedge and ring stimuli in the MRI scanner, and then fitted population receptive field (pRF) tuning functions to these data (Dumoulin and Wandell, 2008). Cortical magnification and pRF tuning width changed with eccentricity at all ages, as expected. However, there were no significant age differences in pRF size, shape, cortical magnification, or map consistency in any visual region. These findings thus strongly suggest that spatial vision in late childhood is not substantially limited by the spatial tuning of neuronal populations in early visual cortex. Instead, improvements in performance may reflect more efficient read-out of spatial information in early visual regions by higher-level processing stages, or prolonged tuning to more complex visual properties such as orientation. Importantly, this in-depth characterisation of the pRF tuning profiles across childhood, paves the way for in-vivo-testing of atypical visual cortex development and plasticity.

Adaptive sensory coding of gaze direction in schizophrenia

Schizophrenia has been associated with differences in how the visual system processes sensory input. A fundamental mechanism that regulates sensory processing in the brain is gain control, whereby the responses of sensory neurons to a given stimulus are modulated in accordance with the spatial and temporal context. Some studies indicate an impairment of certain cortical gain control mechanisms in schizophrenia in low-level vision, reflected, for instance, in how the visual appearance of a stimulus is affected by the presence of other stimuli around it. In the present study, we investigated higher-level, social vision in schizophrenia, namely the perception of other people's direction of gaze (i.e. a type of face processing). Recent computational modelling work indicates that perceptual aftereffects-changes in perception that occur following repeated exposure to faces that display a specific direction of gaze-are indicative of two distinct forms of gain control involved in the coding of gaze direction across sensory neurons. We find that individuals with schizophrenia display strong perceptual aftereffects following repeated exposure to faces with averted gaze, and a modelling analysis indicates similarly robust gain control in the form of (i) short-term adjustment of channel sensitivities in response to the recent sensory history and (ii) divisive normalization of the encoded gaze direction. Together, this speaks to the typical coding of other people's direction of gaze in the visual system in schizophrenia, including flexible gain control, despite the social-cognitive impairments that can occur in this condition.

Is the perception of illusions abnormal in schizophrenia?

There seems to be no common factor for visual perception, i.e., performance in visual tasks correlates only weakly with each other. Similar results were found with visual illusions. One may expect common visual factors for individuals suffering from pathologies that alter brain functioning, such as schizophrenia. For example, patients who are more severely affected by the disease, e.g., stronger positive symptoms, may show increased illusion magnitudes. Here, in the first experiment, we used a battery of seven visual illusions and a mental imagery questionnaire. Illusion magnitudes for the seven illusions did not differ significantly between the patients and controls. In addition, correlations between the different illusions and mental imagery were low. In the second experiment, we tested 59 patients (mostly outpatients) with ten visual illusions. As for the first experiment, patients and controls showed similar susceptibility to all but one visual illusion. Moreover, there were no significant correlations between different illusions, symptoms, or medication type. Thus, it seems that perception of visual illusions is mostly intact in schizophrenia.

How Visual Cortical Organization Is Altered by Ophthalmologic and Neurologic Disorders

Receptive fields are a core property of cortical organization. Modern neuroimaging allows routine access to visual population receptive fields (pRFs), enabling investigations of clinical disorders. Yet how the underlying neural circuitry operates is controversial. The controversy surrounds observations that measurements of pRFs can change in healthy adults as well as in patients with a range of ophthalmological and neurological disorders. The debate relates to the balance between plasticity and stability of the underlying neural circuitry. We propose that to move the debate forward, the field needs to define the implied mechanism. First, we review the pRF changes in both healthy subjects and those with clinical disorders. Then, we propose a computational model that describes how pRFs can change in healthy humans. We assert that we can correctly interpret the pRF changes in clinical disorders only if we establish the capabilities and limitations of pRF dynamics in healthy humans with mechanistic models that provide quantitative predictions.

A Bayesian model comparison approach to test the specificity of visual integration impairment in schizophrenia or psychosis

Impaired visual integration is well documented in schizophrenia and related to functional outcomes. However, it is unclear if this deficit is specific to schizophrenia, or characteristic of psychosis more broadly. To address this question, this study used a Bayesian model comparison approach to examine the evidence of three grouping models of visual integration performance in 116 individuals with schizophrenia (SZ), schizoaffective disorder (SA), bipolar disorder (BD) with or without a history of prominent psychosis (BDP+ and BDP-, respectively), or no psychiatric diagnosis (healthy controls; HC). We compared: (1) Psychosis Model (psychosis, non-psychosis), where the psychosis group included SZ, SA, and BDP+, and the non-psychosis group included BDP- and HC; (2) Schizophrenia Model (SZ, non-SZ); and (3) DSM Model (SZ, SA, BD, HC). The relationship between visual integration and general cognition was also explored. The Psychosis Model showed the strongest evidence, and visual integration was associated with general cognition in participants with psychosis. The results were consistent with the Research Domain Criteria (RDoC) framework, indicating that visual integration impairment is characteristic of psychosis and not specific to SZ or DSM categories, and may share similar disease pathways with observed neurocognitive deficits in psychotic disorders.

Development of the Thalamocortical Interactions: Past, Present and Future

For the past two decades, we have advanced in our understanding of the mechanisms implicated in the formation of brain circuits. The connection between the cortex and thalamus has deserved much attention, as thalamocortical connectivity is crucial for sensory processing and motor learning. Classical dye tracing studies in wild-type and knockout mice initially helped to characterize the developmental progression of this connectivity and revealed key transcription factors involved. With the recent advances in technical tools to specifically label subsets of projecting neurons, knock-down genes individually and/or modify their activity, the field has gained further understanding on the rules operating in thalamocortical circuit formation and plasticity. In this review, I will summarize the most relevant discoveries that have been made in this field, from development to early plasticity processes covering three major aspects: axon guidance, thalamic influence on sensory cortical specification, and the role of spontaneous thalamic activity. I will emphasize how the implementation of new tools has helped the field to progress and what I consider to be open questions and the perspective for the future.

Image identification from brain activity using the population receptive field model

A goal of computational models is not only to explain experimental data but also to make new predictions. A current focus of computational neuroimaging is to predict features of the presented stimulus from measured brain signals. These computational neuroimaging approaches may be agnostic about the underlying neural processes or may be biologically inspired. Here, we use the biologically inspired population receptive field (pRF) approach to identify presented images from fMRI recordings of the visual cortex, using an explicit model of the underlying neural response selectivity. The advantage of the pRF-model is its simplicity: it is defined by a handful of parameters, which can be estimated from fMRI data that was collected within half an hour. Using 7T MRI, we measured responses elicited by different visual stimuli: (i) conventional pRF mapping stimuli, (ii) semi-random synthetic images and (iii) natural images. The pRF mapping stimuli were used to estimate the pRF-properties of each cortical location in early visual cortex. Next, we used these pRFs to identify which synthetic or natural images was presented to the subject from the fMRI responses. We show that image identification using V1 responses is far above chance, both for the synthetic and natural images. Thus, we can identify visual images, including natural images, using the most fundamental low-parameter pRF model estimated from conventional pRF mapping stimuli. This allows broader application of image identification.

Assessing neural tuning for object perception in schizophrenia and bipolar disorder with multivariate pattern analysis of fMRI data

Introduction

Deficits in visual perception are well-established in schizophrenia and are linked to abnormal activity in the lateral occipital complex (LOC). Related deficits may exist in bipolar disorder. LOC contains neurons tuned to object features. It is unknown whether neural tuning in LOC or other visual areas is abnormal in patients, contributing to abnormal perception during visual tasks. This study used multivariate pattern analysis (MVPA) to investigate perceptual tuning for objects in schizophrenia and bipolar disorder.

Methods

Fifty schizophrenia participants, 51 bipolar disorder participants, and 47 matched healthy controls completed five functional magnetic resonance imaging (fMRI) runs of a perceptual task in which they viewed pictures of four different objects and an outdoor scene. We performed classification analyses designed to assess the distinctiveness of activity corresponding to perception of each stimulus in LOC (a functionally localized region of interest). We also performed similar classification analyses throughout the brain using a searchlight technique. We compared classification accuracy and patterns of classification errors across groups.

Results

Stimulus classification accuracy was significantly above chance in all groups in LOC and throughout visual cortex. Classification errors were mostly within-category confusions (e.g., misclassifying one chair as another chair). There were no group differences in classification accuracy or patterns of confusion.

Conclusions

The results show for the first time MVPA can be used successfully to classify individual perceptual stimuli in schizophrenia and bipolar disorder. However, the results do not provide evidence of abnormal neural tuning in schizophrenia and bipolar disorder.

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Abnormal visual perception exists in schizophrenia and, likely, bipolar disorder

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Neural processing abnormalities underlying those deficits are not well understood

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We used multivariate analyses of fMRI data to assess patients' visual processing

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We establish for the first time that such methods work well in these patient groups

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The analyses did not show group differences in neural processing of visual stimuli

Encoding of rapid time-varying information is impaired in poor readers

A characteristic set of eye movements and fixations are made during reading, so the position of words on the retinae is constantly being updated. Effective decoding of print requires this temporal stream of visual information to be segmented or parsed into its constituent units (e.g., letters or words). Poor readers' difficulties with word recognition could arise at the point of segmenting time-varying visual information, but the mechanisms underlying this process are little understood. Here, we used random-dot displays to explore the effects of reading ability on temporal segmentation. Thirty-eight adult readers viewed test stimuli that were temporally segmented by constraining either local motions or analogous form cues to oscillate back and fourth at each of a range of rates. Participants had to discriminate these segmented patterns from comparison stimuli containing the same motion and form cues but these were temporally intermingled. Results showed that the motion and form tasks could not be performed reliably when segment duration was shorter than a temporal resolution (acuity) limit. The acuity limits for both tasks were significantly and negatively correlated with reading scores. Importantly, the minimum segment duration needed to detect the temporally segmented stimuli was longer in relatively poor readers than relatively good readers. This demonstrates that adult poor readers have difficulty segmenting temporally changing visual input particularly at short segment durations. These results are consistent with evidence suggesting that precise encoding of rapid time-varying information is impaired in developmental dyslexia.