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The retina transforms patterns of light into visual feature representations supporting behaviour. These representations are distributed across various types of retinal ganglion cells (RGCs), whose spatial and temporal tuning properties have been studied extensively in many model organisms, including the mouse. However, it has been difficult to link the potentially nonlinear retinal transformations of natural visual inputs to specific ethological purposes. Here, we discover a nonlinear selectivity to chromatic contrast in an RGC type that allows the detection of changes in visual context. We trained a convolutional neural network (CNN) model on large-scale functional recordings of RGC responses to natural mouse movies, and then used this model to search in silico for stimuli that maximally excite distinct types of RGCs. This procedure predicted centre colour opponency in transient suppressed-by-contrast (tSbC) RGCs, a cell type whose function is being debated. We confirmed experimentally that these cells indeed responded very selectively to Green-OFF, UV-ON contrasts. This type of chromatic contrast was characteristic of transitions from ground to sky in the visual scene, as might be elicited by head or eye movements across the horizon. Because tSbC cells performed best among all RGC types at reliably detecting these transitions, we suggest a role for this RGC type in providing contextual information (i.e. sky or ground) necessary for the selection of appropriate behavioural responses to other stimuli, such as looming objects. Our work showcases how a combination of experiments with natural stimuli and computational modelling allows discovering novel types of stimulus selectivity and identifying their potential ethological relevance.

Abstract Background This study aimed to describe sequences of vocational rehabilitation services among individuals with approved vocational rehabilitation in Germany and to identify typical service sequences. Methods We used administrative data on vocational rehabilitation services and questionnaire data on health and work ability to describe frequencies and sequences of vocational rehabilitation services financed by the Federal German Pension Insurance. Through sequence analysis, we were able to map the service sequences. We did cluster analyses to identify typical different service sequences. Results Our sample included 1,652 individuals with 2,584 services. Integration services and two-year vocational retraining were the most common services. We could identify three different service clusters around integration services: shorter ones, followed by employer benefits and without employer benefits. We found two different clusters around two-year vocational retraining: shorter and longer clusters. Two-year vocational retraining was more often initiated by preparatory services and followed by employer benefits than integration services. Longer services in both clusters were associated with better baseline data for physical health, work ability, risk of future work disability, and younger age than shorter services. People in two-year-vocational retraining reported at baseline better general health, better work ability, low risk of future work disability, and less mental illness compared to people in integration services. Conclusions Multiple services, that is, sequences of services, were more likely to occur among individuals with more complex services like two-year vocational retraining. Utilization of complex services and longer services was influenced by health, age, risk of future work disability, and education. Trial registration German Clinical Trials Register DRKS00009910, registration 25/01/2016.

Abstract Purpose Psycho-oncological treatment is recommended in cancer rehabilitation as it improves fatigue, anxiety, depression, and quality of life in breast cancer patients. The aim of our study was to compare a structured short-term psychotherapy and a non-specific group discussion provided during breast cancer rehabilitation. Methods Breast cancer patients were randomly assigned to structured group short-term psychotherapy or a non-specific group discussion during breast cancer rehabilitation. The patients completed questionnaires at the beginning and end of rehabilitation and three months after rehabilitation. The primary outcome was anxiety. Secondary outcomes were depression, distress, fatigue and health-related quality of life domains. Results In total, 160 patients (80 in both groups) were recruited and included in the analysis. There was no significant difference between both groups in the primary outcome anxiety at the end of rehabilitation (difference = -0.2; 95% CI -1.2 to 0.7) and three months after rehabilitation (difference = 0.2; 95% CI -0.9 to 1.3) and in any secondary outcome. Patients in the short-term psychotherapy group with high anxiety levels at baseline reported fewer depressive symptoms at the end of rehabilitation. Conclusions Our study showed no difference between structured short-term psychotherapy and a non-specific group discussion. Patients with high baseline anxiety levels were more likely to benefit from short-term structured psychotherapy. Early identification of this subgroup and symptoms of mental illness should occur after initial treatment in breast cancer patients in order to offer a structured treatment for anxiety and depressive symptoms during rehabilitation. Trial registration German Clinical Trials Register (DRKS00017571; 08/07/2019).

Abstract Background Depression, generalized and cardiac anxiety, and posttraumatic stress disorder negatively affect disease severity, participation, and mortality in patients with cardiovascular disease. Psychological treatments within cardiac rehabilitation may improve the outcomes of these patients. We therefore developed a cognitive-behavioral rehabilitation program for patients with cardiovascular disease and mild or moderate mental illness or stress or exhaustion. In Germany, similar programs are well established in musculoskeletal rehabilitation and cancer rehabilitation. However, no randomized controlled trials have evaluated if such programs achieve better outcomes in patients with cardiovascular disease compared with standard cardiac rehabilitation. Methods Our randomized controlled trial compares cognitive-behavioral cardiac rehabilitation with standard cardiac rehabilitation. The cognitive-behavioral program complements standard cardiac rehabilitation with additional psychological and exercise interventions. Both rehabilitation programs last for four weeks. We enroll 410 patients with cardiovascular disease and mild or moderate mental illness or stress or exhaustion aged 18 to 65 years. Half of the individuals are randomly assigned to cognitive-behavioral rehabilitation and the other half to standard cardiac rehabilitation. Our primary outcome is cardiac anxiety 12 months after the end of rehabilitation. Cardiac anxiety is assessed with the German 17-item version of the Cardiac Anxiety Questionnaire. Secondary outcomes cover outcomes assessed by clinical examinations and medical assessments and a range of patient-reported outcome measures. Discussion This randomized controlled trial is designed to determine the effectiveness of cognitive-behavioral rehabilitation at decreasing cardiac anxiety in patients with cardiovascular disease and mild or moderate mental illness or stress or exhaustion. Trial registration German Clinical Trials Register (DRKS00029295, June 21, 2022).

Abstract Background Our analyses examined the extent to which the use of rehabilitation for patients with mental disorders decreased due to the COVID-19 pandemic in Germany. Methods We used monthly cross-sectional administrative data on rehabilitation utilisation due to mental disorders in 2019 and 2020 and estimated a difference-in-differences model to determine the reduction in rehabilitation utilisation attributable to the pandemic. Results We included 151,775 rehabilitations in 2019 and 123,229 rehabilitations in 2020 in our analysis. The number of rehabilitations decreased from April to December by 14.2% due to the pandemic (March to December: 21.8%). The decline was more pronounced for women than for men and varied regionally. Temporal and regional differences in utilisation were moderately associated with the decrease in mobility in the pandemic year. In the first phase of the pandemic, i.e., March and April 2020, the decline was strongly associated with the regional incidence of SARS-CoV-2 infection. Conclusion Due to the pandemic, significantly fewer rehabilitations due to mental disorders occurred in Germany in 2020 than in 2019. The likely increasing need for rehabilitation for people with mental disorders should be addressed by making rehabilitation access and delivery more flexible.

Abstract Background Effective care services for people whose work participation is at risk require low-threshold access, a comprehensive diagnostic clarification of intervention needs, a connection to the workplace and job demands, and interdisciplinary collaboration between key stakeholders at the interface of rehabilitation and occupational medicine. We have developed a comprehensive diagnostic service to clarify intervention needs for employees with health restrictions and limited work ability: this service is initiated by occupational health physicians. Methods/design Our randomized controlled trial tests the effectiveness of a comprehensive diagnostic service for clarifying intervention needs (GIBI: Comprehensive clarification of the need for intervention for people whose work participation is at risk). The comprehensive intervention comprises three elements: initial consultation, two-day diagnostics at a rehabilitation center and follow-up consultations. We will include 210 employees with health restrictions and limited work ability, who are identified by occupational health physicians. All individuals will receive an initial consultation with their occupational health physician to discuss their health, work ability and job demands. After this, half the individuals are randomly assigned to the intervention group and the other half to the waiting-list control group. Individuals in the intervention group start two-day diagnostics, carried out by a multi-professional rehabilitation team in a rehabilitation center, shortly after the initial consultation. The diagnostics will allow first recommendations for improving work participation. The implementation of these recommendations is supported by an occupational health physician in four follow-up consultations. The control group will receive the comprehensive two-day diagnostic service and subsequent follow-up consultations six months after the initial consultation. The primary outcome of the randomized controlled trial is self-rated work ability assessed using the Work Ability Score (0 to 10 points) six months after study inclusion. Secondary outcomes include a range of patient-reported outcomes regarding physical and mental health, impairment, and the physical and mental demands of jobs. Discussion This randomized controlled trial is designed to test the effects of a new complex intervention involving a comprehensive clarification of intervention needs in order to promote work participation and prevent the worsening of health and work disability. Trial registration German Clinical Trials Register (DRKS00027577, February 01, 2022).

BackgroundChronic illnesses can have an unfavorable impact on the participation opportunities of children and adolescents. The German health care system offers medical rehabilitation in order to prevent negative effects, however, migrant children and adolescents make use of this option less frequently than their peers without a migrant background. A multimodal information campaign was developed to increase the use of medical rehabilitation by children and adolescents with a migrant background, and to reduce disparities in health care.MethodsThe process evaluation will examine the implementation of a multimodal information campaign intended to increase the use of medical rehabilitation by migrant children and adolescents. The information campaign follows a low-threshold participatory approach. In a first step, persons from different migrant communities in Berlin and Hamburg are trained to become transcultural health mediators. These mediators then share their knowledge about chronic illnesses and medical rehabilitation with other families at information events held in their native language. The transcultural mediators also support migrant families in applying for medical rehabilitation. The effectiveness of the intervention will be tested by a trend study with repeated cross-sectional surveys. For this purpose, all families in the project regions of Berlin and Hamburg whose child has received medical rehabilitation are surveyed annually in order to be able to map changes in the proportions of children and adolescents with a migrant background over the course of the project.DiscussionThe study protocol describes a complex intervention to increase the use of medical rehabilitation by migrant children and adolescents, and the accompanying process evaluation and trend study. The intervention is intended to contribute to reducing health inequalities in Germany.ConclusionThe study described in this protocol will provide extensive data on the multimodal information campaign and can thus help organizations and institutions adapt or further develop similar measures for other regions.Clinical trial registrationGerman Clinical Trials Register (DRKS00019090).

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body’s state. The canonical role of proprioception is representing the body state. We hypothesize that the proprioceptive system could also be critical for high-level tasks such as action recognition. To test this theory, we pursued a task-driven modeling approach, which allowed us to isolate the study of proprioception. We generated a large synthetic dataset of human arm trajectories tracing characters of the Latin alphabet in 3D space, together with muscle activities obtained from a musculoskeletal model and model-based muscle spindle activity. Next, we compared two classes of tasks: trajectory decoding and action recognition, which allowed us to train hierarchical models to decode either the position and velocity of the end-effector of one’s posture or the character (action) identity from the spindle firing patterns. We found that artificial neural networks could robustly solve both tasks, and the networks’ units show tuning properties similar to neurons in the primate somatosensory cortex and the brainstem. Remarkably, we found uniformly distributed directional selective units only with the action-recognition-trained models and not the trajectory-decoding-trained models. This suggests that proprioceptive encoding is additionally associated with higher-level functions such as action recognition and therefore provides new, experimentally testable hypotheses of how proprioception aids in adaptive motor control.

Abstract Background Performance of functional capacity evaluation (FCE) may affect patients, self-efficacy to complete physical activity tasks. First evidence from a diagnostic before-after study indicates a significant increase of patient-reported functional ability. Our study set out to test the reproducibility of these results. Methods Patients with musculoskeletal trauma and an unclear return to work prognosis were recruited in a trauma rehabilitation center in Lower Austria. We included patient cohorts of three consecutive years (2016: n = 161, 2017: n = 140; 2018: n = 151). Our primary outcome was patient-reported functional ability, measured using the Spinal Function Sort (SFS). SFS scores were assessed before and after performing an FCE to describe the change in patient-reported functional ability (cohort study). We investigated whether the change in SFS scores observed after performing an FCE in our first cohort could be replicated in subsequent cohorts. Results Demographic data (gender, age and time after trauma) did not differ significantly between the three patient cohorts. Correlation analysis showed highly associated before and after SFS scores in each cohort (2016: rs = 0.84, 95% CI: 0.79 to 0.89; 2017: rs = 0.85, 95% CI: 0.81 to 0.91; 2018: rs = 0.86, 95% CI: 0.82 to 0.91). Improvements in SFS scores were consistent across the cohorts, with overlapping 95% confidence intervals (2016: 14.8, 95% CI: 11.3 to 18.2; 2017: 14.8, 95% CI: 11.5 to 18.0; 2018: 15.2, 95% CI: 12.0 to 18.4). Similarity in SFS scores and SFS differences were also supported by non-significant Kruskal–Wallis H tests (before FCE: p = 0.517; after FCE: p = 0.531; SFS differences: p = 0.931). Conclusions A significant increase in patient-reported functional ability after FCE was found in the original study and the results could be reproduced in two subsequent cohorts.

Abstract Background German social law offers services from various providers and agencies for people with chronic disease or disability. Claiming services requires a high level of information and communication. Dealing with a multitude of actors, such as pension insurance agencies, job centers, employers, physicians, and psychotherapists, and coordinating between and with these actors can delay or prevent the utilization of necessary measures like medical or vocational rehabilitation. We developed a case management intervention for people at high risk of receiving a disability pension to address these challenges. Methods/design Our randomized controlled trial tests the effectiveness of the multi-component AktiFAME strategy (Active access, counseling, and case management for people at high risk of permanent work disability). This strategy is comprised of four modules: screening and postal contact, telephone counseling, an initial one-on-one interview, and case management. The randomized controlled trial is complemented by an observational study that exclusively involves the individuals participating in case management and accompanies the implementation of the case management intervention. We enroll 9000 individuals aged 18 to 65 years who were identified as being at increased risk for receiving a disability pension based on administrative data from the German Pension Insurance North but who have not yet applied for rehabilitation. Half of the individuals are randomly assigned to the intervention group and the other half to the control group. The intervention group is contacted and informed about a case management intervention, whereas the control group is not. The primary outcome of the randomized controlled trial is the utilization of rehabilitation services from the German Pension Insurance North (medical or vocational rehabilitation). Data on rehabilitation will be provided by the German Pension Insurance North 1 year after random allocation. Secondary and tertiary outcomes cover additional administrative data (e.g., employment and welfare benefits) and a range of patient-reported outcome measures. Discussion This randomized controlled trial is designed to determine the effectiveness of a newly implemented multi-component strategy to promote participation and rehabilitation coverage among individuals at high risk of receiving a disability pension. Trial registration German Clinical Trials Register (DRKS00024648, April 27, 2021).

Neural system identification aims at learning the response function of neurons to arbitrary stimuli using experimentally recorded data, but typically does not leverage normative principles such as efficient coding of natural environments. Visual systems, however, have evolved to efficiently process input from the natural environment. Here, we present a normative network regularization for system identification models by incorporating, as a regularizer, the efficient coding hypothesis, which states that neural response properties of sensory representations are strongly shaped by the need to preserve most of the stimulus information with limited resources. Using this approach, we explored if a system identification model can be improved by sharing its convolutional filters with those of an autoencoder which aims to efficiently encode natural stimuli. To this end, we built a hybrid model to predict the responses of retinal neurons to noise stimuli. This approach did not only yield a higher performance than the "stand-alone" system identification model, it also produced more biologically plausible filters, meaning that they more closely resembled neural representation in early visual systems. We found these results applied to retinal responses to different artificial stimuli and across model architectures. Moreover, our normatively regularized model performed particularly well in predicting responses of direction-of-motion sensitive retinal neurons. The benefit of natural scene statistics became marginal, however, for predicting the responses to natural movies. In summary, our results indicate that efficiently encoding environmental inputs can improve system identification models, at least for noise stimuli, and point to the benefit of probing the visual system with naturalistic stimuli.

Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.

Objective Synthesising evidence of the effects of interventions to improve work participation among people with health problems is currently difficult due to heterogeneity in outcome measurements. A core outcome set for work participation is needed.Study design and setting Following the Core Outcome Measures in Effectiveness Trials methodology, we used a five-step approach to reach international multistakeholder consensus on a core outcome set for work participation. Five subgroups of stakeholders took part in two rounds of discussions and completed two Delphi voting rounds on 26 outcomes. A consensus of ≥80% determined core outcomes and 50%–80% consensus was required for candidate outcomes.Results Fifty-eight stakeholders took part in the Delphi rounds. Core outcomes were: ‘any type of employment including self-employment’, ‘proportion of workers that return to work after being absent because of illness’ and ‘time to return to work’. Ten candidate outcomes were proposed, among others: ‘sustainable employment’, ‘work productivity’ and ‘workers’ perception of return to work’.Conclusion As a minimum, all studies evaluating the impact of interventions on work participation should include one employment outcome and two return to work outcomes if workers are on sick leave prior to the intervention.

Abstract Background Various rehabilitation services and return-to-work programs have been developed in order to reduce sickness absence and increase sustainable return-to-work. To ensure that people with a high risk of not returning to work can participate in working life, the model of work-related medical rehabilitation was developed in Germany. The efficacy of these programs in patients with mental disorders has been tested in only a few trials with very specific intervention approaches. To date, there is no clear evidence of the effectiveness of work-related medical rehabilitation implemented in real-care practice. Methods/design Our randomized controlled trial will be conducted in six rehabilitation centers across Germany. Within 15 months, 1800 patients with mental disorders (300 per rehabilitation center) will be recruited and assigned one-to-one either to a work-related medical rehabilitation program or to a conventional psychosomatic rehabilitation program. Participants will be aged 18–60 years. The control group will receive a conventional psychosomatic rehabilitation program without additional work-related components. The intervention group will receive a work-related medical rehabilitation program that contains at least 11 h of work-related treatment modules. Follow-up data will be assessed at the end of the rehabilitation and 3 and 12 months after completing the rehabilitation program. The primary outcome is a stable return to work. Secondary outcomes cover several dimensions of health, functioning and coping strategies. Focus groups and individual interviews supplement our study with qualitative data. Discussion This study will determine the relative effectiveness of a complex and newly implemented work-related rehabilitation strategy for patients with mental disorders. Trial registration German Clinical Trials Register ( DRKS00023175 , September 29 2020).

Summary: Background: Medical rehabilitation (MR) by the German Pension Insurance is approved to maintain and to restore work ability and to avoid disability pensions. Studies on the rehabilitation utilization by people with a migration background (PMB) compared to people without a migration background (non-PMB) showed heterogeneous results, which may be partly due to different definitions of migration status. The aim of this paper was to test whether there are differences in utilization of MR between employed PMB and non-PMB with self-reported back pain. Methods: We used data from a large German cohort study that analyzed the effectiveness of MR for individuals with back pain and was conducted between 1st January 2017 and 31st December 2019. Employees aged 45 to 59 years who reported back pain in the last three months completed the baseline questionnaire in 2017. We used four definitions of migration background (MB) to differentiate by first- and second-generation migration, by one- and two-sided migration background, by language, or by nationality. Data on rehabilitation utilization was extracted from administrative records covering the period until the end of 2018. Findings: Data of 6,713 participants were included, and 514 individuals utilized MR during follow-up. Adjusted analyses showed a decreased risk of rehabilitation utilization in people with a first-generation MB (HR = 0·46; 95% CI 0·29; 0·72), people with a two-sided MB (HR = 0·47; 95% CI 0·31; 0·72), people whose native language was not German (HR = 0·52; 95% CI 0·30; 0·91), and people without German nationality (HR = 0·29; 95% CI 0·12; 0·72) when compared to non-PMB. Interpretation: This study showed that employees with a MB reporting back pain had a significantly reduced risk for utilization of rehabilitation services. This underutilization could be observed considering different definitions of MB. Future research on rehabilitation utilization by PMB should consider the impact of different definitions on the results. Funding: The study was funded by the German Research Foundation (grant numbers: BE 5885/2–1; MA 6981/2–1). The German Research Foundation functions as a self-governing institution for the promotion of science and research in Germany.

Objective: Longitudinal studies on barriers to applying for rehabilitation in Germany are lacking in light of the suspected underutilization of rehabilitation services. The aim of this study was to examine application behaviour in persons with disabling back pain and to identify relevant predictors for making an application. Design: A prospective cohort study with randomized sampling of insurants in the German Pension Insurance, using a questionnaire at baseline and follow-up with linked administrative data for 1.5 years. Subjects/patients: Employed persons (age range 45–59 years) with a high degree of limitations due to back pain and a self-reported risk of permanent work disability (not applied for disability pension, no medical rehabilitation within the last 4 years). Methods: Multivariable Cox regression was used to examine the influence of pre-selected variables on making an application in the follow-up period. Results: Of 690 persons, only 12% applied for rehabilitation. Predictors for making an application were: support from physicians (hazard ratio (HR)=2.24; 95% confidence interval (95% CI) 1.32–3.80), family, and friends (HR=1.67; 95% CI 1.02–2.73), more pain-related disability days (HR=1.02; 95% CI 1.01–1.03), and worse work ability (HR=0.86; 95% CI 0.75–0.97). An intention to apply at baseline mediated the effect of family and physician support on the application. Conclusion: The low number of applications for rehabilitation despite disabling back pain indicates access barriers to, and underuse of, medical rehabilitation.

Objectives: In Germany, behavioural medical rehabilitation programmes have been implemented for patients with musculoskeletal disorders and additional mental health comorbidity. The aim of this cohort study is to assess the relative effectiveness of behavioural medical rehabilitation under real-life conditions. Design: Participants received either a common or behavioural medical rehabilitation programme. Propensity score matching was used to provide balanced samples of both groups (German Clinical Trials Register: DRKS00016404). Participants: A total of 360 patients treated in behavioural medical rehabilitation were compared with 360 matched controls. The mean age of study participants was approximately 53.5 years (standard deviation (SD)=7.0 years) and 74.0% were women. Results: No significant and clinical meaningful differences were found in return to work, applications for disability pension, and the number of patients receiving social security benefits in the year after rehabilitation. However, participants in behavioural medical rehabilitation reported better self-rated work ability, physical functioning and self-management skills, and decreased pain disability and fear-avoidance beliefs 10 months after rehabilitation. Standardized effect sizes were between 0.13 and 0.22. Conclusion: Behavioural medical rehabilitation had no clinical meaningful effect on maintaining and restoring work ability. However, behavioural medical rehabilitation affected pain and disease management skills 10 months after completing the rehabilitation programme.

Objectives To develop a work-related medical rehabilitation (WMR) program for cancer patients based on the best available evidence, the expertise of rehabilitation professionals and the perspective of the patients, to ensure the fidelity of its implementation and to prepare its subsequent outcome evaluation. Material and Methods The implementation study was based on organizational ethnography and action research, and followed a multimethod, participatory and iterative approach to data collection and analysis. The authors carried out observations in 4 rehabilitation centers and conducted focus groups with rehabilitation professionals and patients. The obtained data were subjected to qualitative content analysis. All findings were discussed promptly with the rehabilitation centers at feedback meetings that contributed to the further development of the program. Results The following WMR modules were defined based on the findings: additional work-related diagnostics, multi-professional team meetings, an introductory session, work-related functional capacity training, work-related psychological groups and intensified social counseling. Process descriptions for the subsequent evaluation of the program via a cluster-randomized trial were also developed, containing, e.g., instructions for patient information and recruitment. Conclusions Implementation studies can help to prepare for valid trials as they facilitate ensuring the feasibility, acceptability and fidelity of program implementation and evaluation. Organizational ethnography and action research are suitable methods for carrying out such studies. Int J Occup Med Environ Health. 2019;32(2):217–28

Divisive normalization (DN) is a prominent computational building block in the brain that has been proposed as a canonical cortical operation. Numerous experimental studies have verified its importance for capturing nonlinear neural response properties to simple, artificial stimuli, and computational studies suggest that DN is also an important component for processing natural stimuli. However, we lack quantitative models of DN that are directly informed by measurements of spiking responses in the brain and applicable to arbitrary stimuli. Here, we propose a DN model that is applicable to arbitrary input images. We test its ability to predict how neurons in macaque primary visual cortex (V1) respond to natural images, with a focus on nonlinear response properties within the classical receptive field. Our model consists of one layer of subunits followed by learned orientation-specific DN. It outperforms linear-nonlinear and wavelet-based feature representations and makes a significant step towards the performance of state-of-the-art convolutional neural network (CNN) models. Unlike deep CNNs, our compact DN model offers a direct interpretation of the nature of normalization. By inspecting the learned normalization pool of our model, we gained insights into a long-standing question about the tuning properties of DN that update the current textbook description: we found that within the receptive field oriented features were normalized preferentially by features with similar orientation rather than non-specifically as currently assumed.

This study provides insights into the longitudinal relation between multimorbidity, mental wellbeing, and social support. The analysis used the German Sociomedical Panel of Employees, a study of the German working population aged 40 to 54. In the context of multimorbidity, this population has been little studied.Multimorbidity is significantly associated with reduced mental wellbeing and social support, whereas social support increases mental wellbeing. We argue that, especially among the working population, multimorbidity reduces perceived social support and decreases mental wellbeing. We elaborate on the mediation process empirically by comparing two distinct structural equation models: a cross-lagged panel mediation model that models a potential reverse-causality between social support and mental wellbeing; and a synchronous mediation model that allows for more immediate mediation.Both models estimated significant mediation. The relative size of the mediation effect, however, varied widely based on the added mediational paths (8.57% vs. 28%). Fit statistics for both models were good, and the comparison did not favour either model.We conclude that theoretical reasoning must prevail over empirical testing. The cross-lagged model implies a more longitudinal (lagged) mediation process for social support. However, we suggest an immediate, flexible mediation as more plausible. Nevertheless, we suggest that cross-lagged models, when given a data structure and time gaps, reflect the social processes adequately.

Objectives: To analyse the association between self-reported prognosis of employability and health-related measures, and to clarify which determinants influence the intention to apply for medical rehabil-itation. Design: Cross-sectional study of a random sample of German employees. Participants: A total of 6,654 participants (58% female) aged 45–59 years with back pain during the last 3 months. Results: Out of a total of 6,654 persons, 4,838 had a positive self-reported prognosis of employability. Persons with positive and negative prognoses clearly differ with regard to health-related measures. Of 1,816 persons who reported a negative prognosis, 26% stated an intention to apply for rehabilitation. Intention was determined mainly by perceived social support from family and friends (odds ratio (OR) 1.87; 95% confidence interval (95% CI) 1.66–2.10), as well as physicians and therapists (OR 1.64; 95% CI 1.41–1.90). Conclusion: A negative self-reported prognosis of employability is associated with self-reported health restrictions that may determine the need for rehabilitation interventions. A considerable proportion of persons with self-reported health restrictions do not plan to use medical rehabilitation. Perceived social support is an important facilitator of intention to apply for rehabilitation. However, this study needs to be replicated in other populations combining self-reported and administrative data.

Attention reduces correlated variability in population activity, however the effect of fluctuations in attentional state has not been studied. Here, the authors report in a novel visual task that fluctuations in attentional allocation have a pronounced effect on correlated variability at longer timescales.

Abstract Background Back pain is one of the most common chronic diseases in Germany and has a major impact on work ability and social participation. The German Pension Insurance (GPI) is the main provider of medical rehabilitation to improve work ability and prevent disability pensions in Germany. However, over half of the persons granted a disability pension have never used a medical rehabilitation service. Furthermore, evidence on the effects of medical rehabilitation in Germany is inconclusive. Consequently, this study has two aims: first, to determine barriers to using rehabilitation services, and second, to examine the effectiveness of medical rehabilitation in German residents with chronic back pain. Methods In 2017 a postal questionnaire will be sent to 45,000 persons aged 45 to 59 years whose pension insurance contributions are managed by the GPI North or the GPI Central Germany. In 2019 respondents who report back pain in the first survey (n = 5760 expected) will be sent a second questionnaire. Individuals will be eligible for the first survey if they are employed, have neither used nor applied for a rehabilitation programme during the last 4 years and neither received nor applied for a disability pension. The sample will be drawn randomly from the registers of the GPI North (n = 22,500) and the GPI Central Germany (n = 22,500) and stratified by sex and duration of sickness absence benefits. Barriers to rehabilitation services will be related to socio-demographic and social characteristics, pain and attitudes to pain, health and health behaviour, healthcare utilisation, experiences and cognitions about rehabilitation services and job conditions. Propensity score matched analyses will be used to examine the effectiveness of rehabilitation services. Data on use of medical rehabilitation will be extracted from administrative records. The primary outcome is pain disability. Secondary outcomes are pain intensity and days of disability, pain self-efficacy, fear avoidance beliefs, self-rated health, depression, healthcare utilisation, self-rated work ability and subjective prognosis of employability, sickness absence benefits, and disability pensions. Discussion This study identifies barriers to use of rehabilitation services and determines the effectiveness of medical rehabilitation for patients with chronic back pain. Trial registration German Clinical Trials Register ( DRKS00011554 , January 26, 2017).

We subjectively perceive our visual field with high fidelity, yet peripheral distortions can go unnoticed and peripheral objects can be difficult to identify (crowding). Prior work showed that humans could not discriminate images synthesised to match the responses of a mid-level ventral visual stream model when information was averaged in receptive fields with a scaling of about half their retinal eccentricity. This result implicated ventral visual area V2, approximated ‘Bouma’s Law’ of crowding, and has subsequently been interpreted as a link between crowding zones, receptive field scaling, and our perceptual experience. However, this experiment never assessed natural images. We find that humans can easily discriminate real and model-generated images at V2 scaling, requiring scales at least as small as V1 receptive fields to generate metamers. We speculate that explaining why scenes look as they do may require incorporating segmentation and global organisational constraints in addition to local pooling.

Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals.

Abstract Background Musculoskeletal disorders are one of the most important causes of work disability. Various rehabilitation services and return-to-work programs have been developed in order to reduce sickness absence and increase sustainable return-to-work. As the effects of conventional medical rehabilitation programs on sickness absence duration were shown to be slight, work-related medical rehabilitation programs have been developed and tested. While such studies proved the efficacy of work-related medical rehabilitation compared with conventional medical rehabilitation in well-conducted randomized controlled trials, its effectiveness under real-life conditions has yet to be proved. Methods/Design The cohort study will be performed under real-life conditions with two parallel groups. Participants will receive either a conventional or a work-related medical rehabilitation program. Propensity score matching will be used to identify controls that are comparable to treated work-related medical rehabilitation patients. Over a period of three months, about 18,000 insured patients with permission to undergo a musculoskeletal rehabilitation program will be contacted. Of these, 15,000 will receive a conventional and 3,000 a work-related medical rehabilitation. We expect a participation rate of 40 % at baseline. Patients will be aged 18 to 65 years and have chronic musculoskeletal disorders, usually back pain. The control group will receive a conventional medical rehabilitation program without any explicit focus on work, work ability and return to work in diagnostics and therapy. The intervention group will receive a work-related medical rehabilitation program that in addition to common rehabilitation treatments contains 11 to 25 h of work-related treatment modules. Follow-up data will be assessed three and ten months after patients’ discharge from the rehabilitation center. Additionally, department characteristics will be assessed and administrative data records used. The primary outcomes are sick leave duration, stable return to work and subjective work ability. Secondary outcomes cover several dimensions of health, functioning and coping strategies. Discussion This study will determine the relative effectiveness of a complex, newly implemented work-related rehabilitation strategy for patients with musculoskeletal disorders. Trial registration German Clinical Trials Register ( DRKS00009780 , February 10, 2016).

OBJECTIVES: Graded work exposure is deemed to have a therapeutic effect. In Germany, graded return-to-work (GRTW) is therefore frequently used following a rehabilitation program if workers are still unable to perform full job duties. The aim of the analyses was to determine long-term effects on disability pension and regular employment. METHODS: Analyses were performed with longitudinal administrative data. Patients aged 18–60 years who attended an orthopedic, cardiac, oncological, or psychosomatic rehabilitation between January and June 2007 were eligible to participate in a GRTW scheme. The effects of GRTW were analyzed by a propensity-score-matched comparison of patients with and without GRTW. Outcomes were disability pension rates, regular income, and the duration of receiving welfare benefits due to sickness absence and unemployment up to the end of 2009. RESULTS: The propensity-score-matched sample comprised 1875 patients on GRTW and 1875 matched controls not undergoing GRTW. The probability of a disability pension was decreased by about 40% among GRTW patients [5.4% versus 8.6%; hazard rate ratio (HR) 0.62, 95% confidence interval (95% CI) 0.49–0.80]. The three-year income (2007–2009) was EUR12 920 higher (95% CI EUR10 054–15 786) in the GRTW group. The duration of receiving welfare benefits due to sickness absence and unemployment was significantly reduced. CONCLUSIONS: Graded work exposure supports labor participation and reduces the risk of permanent work disability.

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

The rise of large-scale recordings of neuronal activity has fueled the hope to gain new insights into the collective activity of neural ensembles. How can one link the statistics of neural population activity to underlying principles and theories? One attempt to interpret such data builds upon analogies to the behaviour of collective systems in statistical physics. Divergence of the specific heat-a measure of population statistics derived from thermodynamics-has been used to suggest that neural populations are optimized to operate at a "critical point". However, these findings have been challenged by theoretical studies which have shown that common inputs can lead to diverging specific heat. Here, we connect "signatures of criticality", and in particular the divergence of specific heat, back to statistics of neural population activity commonly studied in neural coding: firing rates and pairwise correlations. We show that the specific heat diverges whenever the average correlation strength does not depend on population size. This is necessarily true when data with correlations is randomly subsampled during the analysis process, irrespective of the detailed structure or origin of correlations. We also show how the characteristic shape of specific heat capacity curves depends on firing rates and correlations, using both analytically tractable models and numerical simulations of a canonical feed-forward population model. To analyze these simulations, we develop efficient methods for characterizing large-scale neural population activity with maximum entropy models. We find that, consistent with experimental findings, increases in firing rates and correlation directly lead to more pronounced signatures. Thus, previous reports of thermodynamical criticality in neural populations based on the analysis of specific heat can be explained by average firing rates and correlations, and are not indicative of an optimized coding strategy. We conclude that a reliable interpretation of statistical tests for theories of neural coding is possible only in reference to relevant ground-truth models.

The statistical analysis and modeling of natural images is an important branch of statistics with applications in image signaling, image compression, computer vision, and human perception. Because the space of all possible images is too large to be sampled exhaustively, natural image models must inevitably make assumptions in order to stay tractable. Subsequent model comparison can then ?lter out those models that best capture the statistical regularities in natural images. Proper model comparison, however, often requires that the models and the preprocessing of the data match down to the implementation details. Here we present the Natter, a statistical software toolbox for natural images models, that can provide such consistency. The Natter includes powerful but tractable baseline model as well as standardized data preprocessing steps. It has an extensive test suite to ensure correctness of its algorithms, it interfaces to the modular toolkit for data processing toolbox MDP, and provides simple ways to log the results of numerical experiments. Most importantly, its modular structure can be extended by new models with minimal coding e?ort, thereby providing a platform for the development and comparison of probabilistic models for natural image data.

Following earlier studies which showed that a sparse coding principle may explain the receptive field properties of complex cells in primary visual cortex, it has been concluded that the same properties may be equally derived from a slowness principle. In contrast to this claim, we here show that slowness and sparsity drive the representations towards substantially different receptive field properties. To do so, we present complete sets of basis functions learned with slow subspace analysis (SSA) in case of natural movies as well as translations, rotations, and scalings of natural images. SSA directly parallels independent subspace analysis (ISA) with the only difference that SSA maximizes slowness instead of sparsity. We find a large discrepancy between the filter shapes learned with SSA and ISA. We argue that SSA can be understood as a generalization of the Fourier transform where the power spectrum corresponds to the maximally slow subspace energies in SSA. Finally, we investigate the trade-off between slowness and sparseness when combined in one objective function.

Generalized linear models (GLMs) represent a popular choice for the probabilistic characterization of neural spike responses. While GLMs are attractive for their computational tractability, they also impose strong assumptions and thus only allow for a limited range of stimulus-response relationships to be discovered. Alternative approaches exist that make only very weak assumptions but scale poorly to high-dimensional stimulus spaces. Here we seek an approach which can gracefully interpolate between the two extremes. We extend two frequently used special cases of the GLM-a linear and a quadratic model-by assuming that the spike-triggered and non-spike-triggered distributions can be adequately represented using Gaussian mixtures. Because we derive the model from a generative perspective, its components are easy to interpret as they correspond to, for example, the spike-triggered distribution and the interspike interval distribution. The model is able to capture complex dependencies on high-dimensional stimuli with far fewer parameters than other approaches such as histogram-based methods. The added flexibility comes at the cost of a non-concave log-likelihood. We show that in practice this does not have to be an issue and the mixture-based model is able to outperform generalized linear and quadratic models.

A key hypothesis in sensory system neuroscience is that sensory representations are adapted to the statistical regularities in sensory signals and thereby incorporate knowledge about the outside world. Supporting this hypothesis, several probabilistic models of local natural image regularities have been proposed that reproduce neural response properties. Although many such physiological links have been made, these models have not been linked directly to visual sensitivity. Previous psychophysical studies of sensitivity to natural image regularities focus on global perception of large images, but much less is known about sensitivity to local natural image regularities. We present a new paradigm for controlled psychophysical studies of local natural image regularities and compare how well such models capture perceptually relevant image content. To produce stimuli with precise statistics, we start with a set of patches cut from natural images and alter their content to generate a matched set whose joint statistics are equally likely under a probabilistic natural image model. The task is forced choice to discriminate natural patches from model patches. The results show that human observers can learn to discriminate the higher-order regularities in natural images from those of model samples after very few exposures and that no current model is perfect for patches as small as 5 by 5 pixels or larger. Discrimination performance was accurately predicted by model likelihood, an information theoretic measure of model efficacy, indicating that the visual system possesses a surprisingly detailed knowledge of natural image higher-order correlations, much more so than current image models. We also perform three cue identification experiments to interpret how model features correspond to perceptually relevant image features.

Divisive normalization in primary visual cortex has been linked to adaptation to natural image statistics in accordance to Barlow's redundancy reduction hypothesis. Using recent advances in natural image modeling, we show that the previously studied static model of divisive normalization is rather inefficient in reducing local contrast correlations, but that a simple temporal contrast adaptation mechanism of the half-saturation constant can substantially increase its efficiency. Our findings reveal the experimentally observed temporal dynamics of divisive normalization to be critical for redundancy reduction.

We present a probabilistic model for natural images that is based on mixtures of Gaussian scale mixtures and a simple multiscale representation. We show that it is able to generate images with interesting higher-order correlations when trained on natural images or samples from an occlusion-based model. More importantly, our multiscale model allows for a principled evaluation. While it is easy to generate visually appealing images, we demonstrate that our model also yields the best performance reported to date when evaluated with respect to the cross-entropy rate, a measure tightly linked to the average log-likelihood. The ability to quantitatively evaluate our model differentiates it from other multiscale models, for which evaluation of these kinds of measures is usually intractable.

Several studies have reported optimal population decoding of sensory responses in two-alternative visual discrimination tasks. Such decoding involves integrating noisy neural responses into a more reliable representation of the likelihood that the stimuli under consideration evoked the observed responses. Importantly, an ideal observer must be able to evaluate likelihood with high precision and only consider the likelihood of the two relevant stimuli involved in the discrimination task. We report a new perceptual bias suggesting that observers read out the likelihood representation with remarkably low precision when discriminating grating spatial frequencies. Using spectrally filtered noise, we induced an asymmetry in the likelihood function of spatial frequency. This manipulation mainly affects the likelihood of spatial frequencies that are irrelevant to the task at hand. Nevertheless, we find a significant shift in perceived grating frequency, indicating that observers evaluate likelihoods of a broad range of irrelevant frequencies and discard prior knowledge of stimulus alternatives when performing two-alternative discrimination.

Generalized Linear Models (GLMs) are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli. When the dimension of the parameter space is large, strong regularization has to be used in order to fit GLMs to datasets of realistic size without overfitting. By imposing properly chosen priors over parameters, Bayesian inference provides an effective and principled approach for achieving regularization. Here we show how the posterior distribution over model parameters of GLMs can be approximated by a Gaussian using the Expectation Propagation algorithm. In this way, we obtain an estimate of the posterior mean and posterior covariance, allowing us to calculate Bayesian confidence intervals that characterize the uncertainty about the optimal solution. From the posterior we also obtain a different point estimate, namely the posterior mean as opposed to the commonly used maximum a posteriori estimate. We systematically compare the different inference techniques on simulated as well as on multi-electrode recordings of retinal ganglion cells, and explore the effects of the chosen prior and the performance measure used. We find that good performance can be achieved by choosing an Laplace prior together with the posterior mean estimate.

The timing of action potentials in spiking neurons depends on the temporal dynamics of their inputs and contains information about temporal fluctuations in the stimulus. Leaky integrate-and-fire neurons constitute a popular class of encoding models, in which spike times depend directly on the temporal structure of the inputs. However, optimal decoding rules for these models have only been studied explicitly in the noiseless case. Here, we study decoding rules for probabilistic inference of a continuous stimulus from the spike times of a population of leaky integrate-and-fire neurons with threshold noise. We derive three algorithms for approximating the posterior distribution over stimuli as a function of the observed spike trains. In addition to a reconstruction of the stimulus we thus obtain an estimate of the uncertainty as well. Furthermore, we derive a `spike-by-spike' online decoding scheme that recursively updates the posterior with the arrival of each new spike. We use these decoding rules to reconstruct time-varying stimuli represented by a Gaussian process from spike trains of single neurons as well as neural populations.

Orientation selectivity is the most striking feature of simple cell coding in V1 that has been shown to emerge from the reduction of higher-order correlations in natural images in a large variety of statistical image models. The most parsimonious one among these models is linear Independent Component Analysis (ICA), whereas second-order decorrelation transformations such as Principal Component Analysis (PCA) do not yield oriented filters. Because of this finding, it has been suggested that the emergence of orientation selectivity may be explained by higher-order redundancy reduction. To assess the tenability of this hypothesis, it is an important empirical question how much more redundancy can be removed with ICA in comparison to PCA or other second-order decorrelation methods. Although some previous studies have concluded that the amount of higher-order correlation in natural images is generally insignificant, other studies reported an extra gain for ICA of more than 100%. A consistent conclusion about the role of higher-order correlations in natural images can be reached only by the development of reliable quantitative evaluation methods. Here, we present a very careful and comprehensive analysis using three evaluation criteria related to redundancy reduction: In addition to the multi-information and the average log-loss, we compute complete rate-distortion curves for ICA in comparison with PCA. Without exception, we find that the advantage of the ICA filters is small. At the same time, we show that a simple spherically symmetric distribution with only two parameters can fit the data significantly better than the probabilistic model underlying ICA. This finding suggests that, although the amount of higher-order correlation in natural images can in fact be significant, the feature of orientation selectivity does not yield a large contribution to redundancy reduction within the linear filter bank models of V1 simple cells.