The EEG multiverse of schizophrenia.

Research on schizophrenia typically focuses on one paradigm for which clear-cut differences between patients and controls are established. Great efforts are made to understand the underlying genetical, neurophysiological, and cognitive mechanisms, which eventually may explain the clinical outcome. One tacit assumption of these "deep rooting" approaches is that paradigms tap into common and representative aspects of the disorder. Here, we analyzed the resting-state electroencephalogram (EEG) of 121 schizophrenia patients and 75 controls. Using multiple signal processing methods, we extracted 194 EEG features. Sixty-nine out of the 194 EEG features showed a significant difference between patients and controls, indicating that these features detect an important aspect of schizophrenia. Surprisingly, the correlations between these features were very low. We discuss several explanations to our results and propose that complementing "deep" with "shallow" rooting approaches might help in understanding the underlying mechanisms of the disorder.

Temporal windows of unconscious processing cannot easily be disrupted.

Conscious perception is preceded by long periods of unconscious processing. These periods are crucial for analyzing temporal information and for solving the many ill-posed problems of vision. An important question is what starts and ends these windows and how they may be interrupted. Most experimental paradigms do not offer the methodology required for such investigation. Here, we used the sequential metacontrast paradigm, in which two streams of lines, expanding from the center to the periphery, are presented, and participants are asked to attend to one of the motion streams. If several lines in the attended motion stream are offset, the offsets are known to integrate mandatorily and unconsciously, even if separated by up to 450 ms. Using this paradigm, we here found that external visual objects, such as an annulus, presented during the motion stream, do not disrupt mandatory temporal integration. Thus, if a window is started once, it appears to remain open even in the presence of disruptions that are known to interrupt visual processes normally. Further, we found that interrupting the motion stream with a gap disrupts temporal integration but does not terminate the overall unconscious processing window. Thus, while temporal integration is key to unconscious processing, not all stimuli in the same processing window are integrated together. These results strengthen the case for unconscious processing taking place in windows of sensemaking, during which temporal integration occurs in a flexible and perceptually meaningful manner.

[Impact of the COVID-19 pandemic on the diagnosis and treatment of patients with head and neck cancer in Brandenburg and Berlin]. / Einfluss der COVID-19-Pandemie auf die Diagnostik und Therapie bei Patienten mit Kopf-Hals-Tumoren in Brandenburg und Berlin.

BACKGROUND: The impact of the COVID-19 pandemic on potential limitations to the diagnosis and treatment of patients with head and neck tumours has not yet been adequately investigated. There are contradictory data on this subject. Data from larger patient collectives do not exist for Germany so far. OBJECTIVE: The aim of the survey was to clarify in a large cohort whether the COVID-19 pandemic had an influence on the diagnosis and treatment of patients with head and neck tumours. METHODS: A retrospective data analysis of the reporting data of the Clinical and Epidemiological Cancer Registry of Brandenburg and Berlin (Klinisch-epidemiologischen Krebsregisters Brandenburg-Berlin, KKRBB) of 4831 cases with head and neck tumours from 2018 to 2020 was performed. The period before April 01, 2020, was evaluated as a prepandemic cohort and compared with the cases of the pandemic cohort from April 1, 2020, until December 31, 2020, in terms of patient-related baseline data, tumour location, tumour stage, tumour board and treatments administered. RESULTS: No differences were observed between the prepandemic and pandemic cohorts with regard to patient-related baseline data, tumour localisation and tumour stage. Likewise, no temporal delay in diagnosis, tumour board and treatment was evident during the pandemic period. On the contrary, the time interval between diagnosis and start of therapy was shortened by an average of 2.7 days in the pandemic phase. Tumours with T4 stage were more frequently treated surgically during the pandemic compared to the period before (20.8% vs. 29.6%), whereas primary radio(chemo)therapy decreased during the pandemic (53.3% vs. 40.4%). For all other tumour stages and entities, there were no differences in treatment. CONCLUSION: Contrary to initial speculation that the COVID-19 pandemic may have led to a decrease in tumour cases, larger tumour stages at initial presentation and a delay in diagnosis and treatment, the cohort studied for Brandenburg and Berlin showed neither a delay in tumour treatment nor an increase in tumour size and stage at initial presentation. The treatments performed, however, were subject to a change in favour of surgery and it remains to be seen whether this trend will be maintained in the long term.

Pre-stimulus alpha activity modulates long-lasting unconscious feature integration.

Pre-stimulus alpha (α) activity can influence perception of shortly presented, low-contrast stimuli. The underlying mechanisms are often thought to affect perception exactly at the time of presentation. In addition, it is suggested that α cycles determine temporal windows of integration. However, in everyday situations, stimuli are usually presented for periods longer than ∼100 ms and perception is often an integration of information across space and time. Moving objects are just one example. Hence, the question is whether α activity plays a role also in temporal integration, especially when stimuli are integrated over several α cycles. Using electroencephalography (EEG), we investigated the relationship between pre-stimulus brain activity and long-lasting integration in the sequential metacontrast paradigm (SQM), where two opposite vernier offsets, embedded in a stream of lines, are unconsciously integrated into a single percept. We show that increases in α power, even 300 ms before the stimulus, affected the probability of reporting the first offset, shown at the very beginning of the SQM. This effect was mediated by the systematic slowing of the α rhythm that followed the peak in α power. No phase effects were found. Together, our results demonstrate a cascade of neural changes, following spontaneous bursts of α activity and extending beyond a single moment, which influences the sensory representation of visual features for hundreds of milliseconds. Crucially, as feature integration in the SQM occurs before a conscious percept is elicited, this also provides evidence that α activity is linked to mechanisms regulating unconscious processing.

Correction: Bayesian regression explains how human participants handle parameter uncertainty.

[This corrects the article DOI: 10.1371/journal.pcbi.1007886.].

No Common Factor Underlying Decline of Visual Abilities in Mild Cognitive Impairment.

INTRODUCTION: Recent work has shown an association between cognitive and visual impairments and two main theories were advanced, namely the sensory deprivation and the common cause theories. Most studies considered only basic visual functions such as visual acuity or visual field size and evaluated the association with dementia. OBJECTIVES: To reconcile between these theories and to test the link between visual and cognitive decline in mildly cognitive impaired people. METHODS: We employed a battery of 19 visual tasks on 39 older adults with mild cognitive impairment and 91 without any evidence of cognitive decline, as measured by the Montreal Cognitive Assessment. RESULTS: Our results show a strong association between visual impairment and mild cognitive impairment. In agreement with previous results with younger and healthy older adults, we found also only weak correlations between most tests in older adults with mild cognitive impairment. CONCLUSION: Our results suggest that visual and cognitive abilities decline simultaneously, but they do so independently across visual and cognitive functions and across participants.

Individual computer-assisted 3D planning for placement of auricular prosthesis anchors in combination with an implantable transcutaneous bone conduction hearing device in patients with aural atresia.

BACKGROUND: The simultaneous implantation of the Bonebridge (MED-EL, Innsbruck, Austria), a semi-implantable active transcutaneous bone conduction hearing device and anchors for auricular prostheses can be challenging as both implants contain magnets and compete for the narrow space in the designated implantation area. MATERIAL AND METHODS: A preoperative planning tool (virtual surgery) was used with individual 3D computer models of the skull and implants for finding optimal implant positions for both the floating mass transducer (FMT) and the anchors for the auricular prosthesis. The interaction between the magnetic prosthesis anchors and the FMT was measured by means of static magnetic forces. A retrospective data analysis was conducted to evaluate the surgical and audiological outcome. RESULTS: Between 2014 and 2021, a 3D planning of a simultaneous implantation of the Bonebridge with auricular prosthesis anchors was conducted on 6 ears of 5 patients (3 males, 2 females; age range 17-56 years). The individual preoperative planning was considered very useful for the optimal placement of bone anchors in combination with the Bonebridge. Audiological data showed a clear benefit for hearing 3 months and > 11 months after implantation. No adverse interactions between the magnetic prosthesis anchors and the FMT were observed. In two patients, revision surgery was carried out due to skin inflammation or wound healing problems. No long-term complications were observed 3-5 years after surgery. CONCLUSION: Preoperative 3D planning represents a clear benefit for the simultaneous audiological and esthetic rehabilitation using the Bonebridge and anchors for auricular prostheses.

[Individual computer-assisted 3D planning for placement of auricular prosthesis anchors in combination with an implantable transcutaneous bone conduction hearing device in patients with aural atresia. German version]. / Individuelle computergestützte 3D-Planung zur Platzierung von Epithesenankern in Kombination mit einem implantierbaren transkutanen Knochenleitungshörgerät bei Patienten mit Ohrfehlbildungen.

BACKGROUND: The simultaneous implantation of the Bonebridge (MED-EL, Innsbruck, Austria), a semi-implantable active transcutaneous bone conduction hearing device and anchors for auricular prostheses can be challenging as both implants contain magnets and compete for the narrow space in the designated implantation area. MATERIAL AND METHODS: A preoperative planning tool (virtual surgery) was used with individual 3D computer models of the skull and implants for finding optimal implant positions for both the floating mass transducer (FMT) and the anchors for the auricular prosthesis. The interaction between the magnetic prosthesis anchors and the FMT was measured by means of static magnetic forces. A retrospective data analysis was conducted to evaluate the surgical and audiological outcome. RESULTS: Between 2014 and 2021 a 3D planning of a simultaneous implantation of the Bonebridge with auricular prosthesis anchors was conducted on 6 ears of 5 patients (3 males, 2 females; age range 17-56 years). The individual preoperative planning was considered very useful for the optimal placement of bone anchors in combination with the Bonebridge. Audiological data showed a clear benefit for hearing 3 months and > 11 months after implantation. No adverse interactions between the magnetic prosthesis anchors and the FMT were observed. In two patients, revision surgery was carried out due to skin inflammation or wound healing problems. No long-term complications were observed 3-5 years after surgery. CONCLUSION: Preoperative 3D planning represents a clear benefit for the simultaneous audiological and esthetic rehabilitation using the Bonebridge and anchors for auricular prostheses.

A guideline for linking brain wave findings to the various aspects of discrete perception.

Brain waves, determined by electrical and magnetic brain recordings (e.g., EEG and MEG), and fluctuating behavioral responses, determined by response time or accuracy measures, are frequently taken to support discrete perception. For example, it has been proposed that humans experience only one conscious percept per brain wave (e.g., during one alpha cycle). However, the proposed link between brain waves and discrete perception is typically rather vague. More importantly, there are many models and aspects of discrete perception and it is often not apparent in what theoretical framework brain wave findings are interpreted and to what specific aspects of discrete perception they relate. Here, we review different approaches to discrete perception and highlight issues with particular interpretations. We then discuss how certain findings on brain waves may relate to certain aspects of discrete perception. The main purpose of this meta-contribution is to give a short overview of discrete models of perception and to illustrate the need to make explicit what aspects of discrete theories are addressed by what aspects of brain wave findings.

Shrinking Bouma's window: How to model crowding in dense displays.

In crowding, perception of a target deteriorates in the presence of nearby flankers. Traditionally, it is thought that visual crowding obeys Bouma's law, i.e., all elements within a certain distance interfere with the target, and that adding more elements always leads to stronger crowding. Crowding is predominantly studied using sparse displays (a target surrounded by a few flankers). However, many studies have shown that this approach leads to wrong conclusions about human vision. Van der Burg and colleagues proposed a paradigm to measure crowding in dense displays using genetic algorithms. Displays were selected and combined over several generations to maximize human performance. In contrast to Bouma's law, only the target's nearest neighbours affected performance. Here, we tested various models to explain these results. We used the same genetic algorithm, but instead of selecting displays based on human performance we selected displays based on the model's outputs. We found that all models based on the traditional feedforward pooling framework of vision were unable to reproduce human behaviour. In contrast, all models involving a dedicated grouping stage explained the results successfully. We show how traditional models can be improved by adding a grouping stage.

Novelty is not surprise: Human exploratory and adaptive behavior in sequential decision-making.

Classic reinforcement learning (RL) theories cannot explain human behavior in the absence of external reward or when the environment changes. Here, we employ a deep sequential decision-making paradigm with sparse reward and abrupt environmental changes. To explain the behavior of human participants in these environments, we show that RL theories need to include surprise and novelty, each with a distinct role. While novelty drives exploration before the first encounter of a reward, surprise increases the rate of learning of a world-model as well as of model-free action-values. Even though the world-model is available for model-based RL, we find that human decisions are dominated by model-free action choices. The world-model is only marginally used for planning, but it is important to detect surprising events. Our theory predicts human action choices with high probability and allows us to dissociate surprise, novelty, and reward in EEG signals.

First-person experience cannot rescue causal structure theories from the unfolding argument.

We recently put forward an argument, the Unfolding Argument (UA), that integrated information theory (IIT) and other causal structure theories are either already falsified or unfalsifiable, which provoked significant criticism. It seems that we and the critics agree that the main question in this debate is whether first-person experience, independent of third-person data, is a sufficient foundation for theories of consciousness. Here, we argue that pure first-person experience cannot be a scientific foundation for IIT because science relies on taking measurements, and pure first-person experience is not measurable except through reports, brain activity, and the relationship between them. We also argue that pure first-person experience cannot be taken as ground truth because science is about backing up theories with data, not about asserting that we have ground truth independent of data. Lastly, we explain why no experiment based on third-person data can test IIT as a theory of consciousness. IIT may be a good theory of something, but not of consciousness. We conclude by exposing a deeper reason for the above conclusions: IIT's consciousness is by construction fully dissociated from any measurable thing and, for this reason, IIT implies that both the level and content of consciousness are epiphenomenal, with no causal power. IIT and other causal structure theories end up in a form of dissociative epiphenomenalism, in which we cannot even trust reports about first-person experiences. But reports about first-person experiences are taken as ground truth and the foundation for IIT's axioms. Therefore, accepting IIT leads to rejecting its own axioms. We also respond to several other criticisms against the UA.

Course of social support and associations with distress after partial laryngectomy.

PURPOSE: Social support has been shown to be positively associated with quality of life and adjustment after a cancer diagnosis. The present study investigates the course of social support up to one year after partial laryngectomy and its association with distress. DESIGN: Longitudinal questionnaire study. SAMPLE: A total of 428 patients after partial laryngectomy (mean age: 64, SD = 11, 91% male). METHODS: Patients completed questionnaires before treatment (t1), one week after a partial laryngectomy (t2), 3 months (t3), and one year (t4) thereafter. Social support was evaluated at t2, t3, and t4 using a brief version of the Social Support Questionnaire. Distress was measured at t2, t3, and t4 using the HADS. Descriptive statistics for social support were computed across the three measurement points. Changes were analyzed by Wilcoxon signed-rank tests. Associations with distress were identified using linear regression analyses. FINDINGS: Social support increased between t2 and t3 and decreased to baseline level between t3 and t4. Distress at t2 was associated with social support at t2 (B = -0.15, p < 0.01) and distress at t3 with social support at t3 (B = -0.19, p < 0.01). Distress at t4 was related to social support at t2 (B = -0.10, p = 0.05). CONCLUSIONS: Although perceived social support increases after partial laryngectomy, it decreases again during the course of aftercare. IMPLICATIONS FOR PSYCHOSOCIAL PROVIDERS OR POLICY: Social support resources should be assessed to identify patients at risk for worse psychological well-being.

Unraveling brain interactions in vision: The example of crowding.

Crowding, the impairment of target discrimination in clutter, is the standard situation in vision. Traditionally, crowding is explained with (feedforward) models, in which only neighboring elements interact, leading to a "bottleneck" at the earliest stages of vision. It is with this implicit prior that most functional magnetic resonance imaging (fMRI) studies approach the identification of the "neural locus" of crowding, searching for the earliest visual area in which the blood-oxygenation-level-dependent (BOLD) signal is suppressed under crowded conditions. Using this classic approach, we replicated previous findings of crowding-related BOLD suppression starting in V2 and increasing up the visual hierarchy. Surprisingly, under conditions of uncrowding, in which adding flankers improves performance, the BOLD signal was further suppressed. This suggests an important role for top-down connections, which is in line with global models of crowding. To discriminate between various possible models, we used dynamic causal modeling (DCM). We show that recurrent interactions between all visual areas, including higher-level areas like V4 and the lateral occipital complex (LOC), are crucial in crowding and uncrowding. Our results explain the discrepancies in previous findings: in a recurrent visual hierarchy, the crowding effect can theoretically be detected at any stage. Beyond crowding, we demonstrate the need for models like DCM to understand the complex recurrent processing which most likely underlies human perception in general.

Capsule networks as recurrent models of grouping and segmentation.

Classically, visual processing is described as a cascade of local feedforward computations. Feedforward Convolutional Neural Networks (ffCNNs) have shown how powerful such models can be. However, using visual crowding as a well-controlled challenge, we previously showed that no classic model of vision, including ffCNNs, can explain human global shape processing. Here, we show that Capsule Neural Networks (CapsNets), combining ffCNNs with recurrent grouping and segmentation, solve this challenge. We also show that ffCNNs and standard recurrent CNNs do not, suggesting that the grouping and segmentation capabilities of CapsNets are crucial. Furthermore, we provide psychophysical evidence that grouping and segmentation are implemented recurrently in humans, and show that CapsNets reproduce these results well. We discuss why recurrence seems needed to implement grouping and segmentation efficiently. Together, we provide mutually reinforcing psychophysical and computational evidence that a recurrent grouping and segmentation process is essential to understand the visual system and create better models that harness global shape computations.

Bayesian regression explains how human participants handle parameter uncertainty.

Accumulating evidence indicates that the human brain copes with sensory uncertainty in accordance with Bayes' rule. However, it is unknown how humans make predictions when the generative model of the task at hand is described by uncertain parameters. Here, we tested whether and how humans take parameter uncertainty into account in a regression task. Participants extrapolated a parabola from a limited number of noisy points, shown on a computer screen. The quadratic parameter was drawn from a bimodal prior distribution. We tested whether human observers take full advantage of the given information, including the likelihood of the quadratic parameter value given the observed points and the quadratic parameter's prior distribution. We compared human performance with Bayesian regression, which is the (Bayes) optimal solution to this problem, and three sub-optimal models, which are simpler to compute. Our results show that, under our specific experimental conditions, humans behave in a way that is consistent with Bayesian regression. Moreover, our results support the hypothesis that humans generate responses in a manner consistent with probability matching rather than Bayesian decision theory.

Depth of sedation during drug induced sedation endoscopy monitored by BiSpectral Index® and Cerebral State Index®.

PURPOSE: Drug induced sedation endoscopy (DISE) is performed to investigate patterns and sites of obstruction in patients with sleep-disordered breathing (SDB). During DISE the patients are sedated to obtain a muscular relaxation of the upper airway which mimics the relaxation during natural sleep. Different sleep stages are intended to be simulated by drug induced sedation, and it is helpful to measure the depth of sedation. The BiSpectral Index® (BIS) is often used for this procedure. Besides the BIS, other means of sedation depth monitoring exist in anaesthesiology but have not yet been investigated with respect to DISE. Monitoring of the Cerebral State Index® (CSI) is one of these methods. The aim of the study was to compare the BIS and CSI for sedation depth monitoring during DISE. METHODS: Sixty patients underwent DISE monitored by the BIS and CSI in parallel. The BIS and CSI values were compared using the Bland-Altman analysis. RESULTS: The BIS and CSI values differed during the course of sedation during DISE by a mean of - 6.07. At light sedation (BIS 60-80), lower values by 10 scale points of CSI compared with BIS were detectable. At deeper sedation levels (BIS 40-50), the CSI turned to present equal and even higher values compared with the BIS. CONCLUSION: Sedation depth measurement during DISE can be performed by the BIS or CSI, but the differences should be interpreted carefully as comparable data for sleep stages in natural sleep are available only for BIS.

Features integrate along a motion trajectory when object integrity is preserved.

Information about a moving object is usually poor at each retinotopic location because photoreceptor activation is short, noisy, and affected by shadows, reflections of other objects, and so on. Integration across the motion trajectory may yield a much better estimate about the objects' features. Using the sequential metacontrast paradigm, we have shown previously that features, indeed, integrate along a motion trajectory in a long-lasting window of unconscious processing. In the sequential metacontrast paradigm, a percept of two diverging streams is elicited by the presentation of a central line followed by a sequence of flanking pairs of lines. When several lines are spatially offset, the offsets integrate mandatorily for several hundreds of milliseconds along the motion trajectory of the streams. We propose that, within these long-lasting windows, stimuli are first grouped based on Gestalt principles of grouping. These processes establish reference frames that are used to attribute features. Features are then integrated following their respective reference frame. Here using occlusion and bouncing effects, we show that indeed such grouping operations are in place. We found that features integrate only when the spatiotemporal integrity of the object is preserved. Moreover, when several moving objects are present, only features belonging to the same object integrate. Overall, our results show that feature integration is a deliberate strategy of the brain and long-lasting windows of processing can be seen as periods of sense making.

A comparative biology approach to DNN modeling of vision: A focus on differences, not similarities.

Deep neural networks (DNNs) have revolutionized computer science and are now widely used for neuroscientific research. A hot debate has ensued about the usefulness of DNNs as neuroscientific models of the human visual system; the debate centers on to what extent certain shortcomings of DNNs are real failures and to what extent they are redeemable. Here, we argue that the main problem is that we often do not understand which human functions need to be modeled and, thus, what counts as a falsification. Hence, not only is there a problem on the DNN side, but there is also one on the brain side (i.e., with the explanandum-the thing to be explained). For example, should DNNs reproduce illusions? We posit that we can make better use of DNNs by adopting an approach of comparative biology by focusing on the differences, rather than the similarities, between DNNs and humans to improve our understanding of visual information processing in general.

Dissecting (un)crowding.

In crowding, perception of a target deteriorates in the presence of nearby flankers. Surprisingly, perception can be rescued from crowding if additional flankers are added (uncrowding). Uncrowding is a major challenge for all classic models of crowding and vision in general, because the global configuration of the entire stimulus is crucial. However, it is unclear which characteristics of the configuration impact (un)crowding. Here, we systematically dissected flanker configurations and showed that (un)crowding cannot be easily explained by the effects of the sub-parts or low-level features of the stimulus configuration. Our modeling results suggest that (un)crowding requires global processing. These results are well in line with previous studies showing the importance of global aspects in crowding.