Search

Objective To examine the extent to which medical rehabilitation requests decreased because of the pandemic in Germany. Design Data were retrieved from the German Pension Insurance, which is the main provider for rehabilitation of working-age people in Germany. Our data represented all medical rehabilitation requests in 2019 and 2020. These requests have to be approved to use a rehabilitation program. We used a difference-in-differences model to determine the reduction in rehabilitation requests attributable to the pandemic. Setting General community. Participants We included 1,621,840 rehabilitation requests from working-age people across Germany in 2019 and 1,391,642 rehabilitation requests in 2020 (N=3,013,482). Intervention Medical rehabilitation in inpatient or outpatient facilities. Main Outcome Measures Number of medical rehabilitation requests. Results The number of medical rehabilitation requests decreased by 14.5% because of the pandemic (incidence rate ratio, 0.855; 95% confidence interval, 0.851-0.859). The decline in requests was more pronounced among women and in Western Germany than among men and in Eastern Germany. The reduction in requests affected non-postacute rehabilitations more clearly than postacute rehabilitation services. After the pandemic declaration by the German Bundestag in March 2020, the reduction in requests was initially strongly associated with the regional incidence of infection. This association weakened in the following months. Conclusions The reduction in requests will have a significant effect on the number of completed rehabilitation services. For many people with chronic diseases, failure to provide medical rehabilitation increases the risk of disease progression.

We study modeling and inference with the Elliptical Gamma Distribution (EGD). We consider maximum likelihood (ML) estimation for EGD scatter matrices, a task for which we develop new fixed-point algorithms. Our algorithms are efficient and converge to global optima despite nonconvexity. Moreover, they turn out to be much faster than both a well-known iterative algorithm of Kent & Tyler (1991) and sophisticated manifold optimization algorithms. Subsequently, we invoke our ML algorithms as subroutines for estimating parameters of a mixture of EGDs. We illustrate our methods by applying them to model natural image statistics---the proposed EGD mixture model yields the most parsimonious model among several competing approaches., 23 pages, 11 figures

Summary In the mammalian retina, 10–12 different cone bipolar cell (BC) types decompose the photoreceptor signal into parallel channels [1–8], providing the basis for the functional diversity of retinal ganglion cells (RGCs) [9]. BCs differing in their temporal properties appear to project to different strata of the retina's inner synaptic layer [10, 11], based on somatic recordings of BCs [1, 2, 4, 12–14] and excitatory synaptic currents measured in RGCs [10]. However, postsynaptic currents in RGCs are influenced by dendritic morphology [15, 16] and receptor types [17], and the BC signal can be transformed at the axon terminals both through interactions with amacrine cells [18, 19] and through the generation of all-or-nothing spikes [20–24]. Therefore, the temporal properties of the BC output have not been analyzed systematically across different types of mammalian BCs. We recorded calcium signals directly within axon terminals using two-photon imaging [25, 26] and show that BCs can be divided into ≥eight functional clusters. The temporal properties of the BC output were directly reflected in their anatomical organization within the retina's inner synaptic layer: faster cells stratified closer to the border between ON and OFF sublamina. Moreover, ≥three fastest groups generated clear all-or-nothing spikes. Therefore, the systematic projection pattern of BCs provides distinct temporal "building blocks" for the feature extracting circuits of the inner retina.

A striking feature of cortical organization is that the encoding of many stimulus features, for example orientation or direction selectivity, is arranged into topographic maps. Functional imaging methods such as optical imaging of intrinsic signals, voltage sensitive dye imaging or functional magnetic resonance imaging are important tools for studying the structure of cortical maps. As functional imaging measurements are usually noisy, statistical processing of the data is necessary to extract maps from the imaging data. We here present a probabilistic model of functional imaging data based on Gaussian processes. In comparison to conventional approaches, our model yields superior estimates of cortical maps from smaller amounts of data. In addition, we obtain quantitative uncertainty estimates, i.e. error bars on properties of the estimated map. We use our probabilistic model to study the coding properties of the map and the role of noise-correlations by decoding the stimulus from single trials of an imaging experiment.

The light intensities of natural images exhibit a high degree of redundancy. Knowing the exact amount of their statistical dependencies is important for biological vision as well as compression and coding applications but estimating the total amount of redundancy, the multi-information, is intrinsically hard. The common approach is to estimate the multi-information for patches of increasing sizes and divide by the number of pixels. Here, we show that the limiting value of this sequence—the multi-information rate—can be better estimated by using another limiting process based on measuring the mutual information between a pixel and a causal neighborhood of increasing size around it. Although in principle this method has been known for decades, its superiority for estimating the multi-information rate of natural images has not been fully exploited yet. Either method provides a lower bound on the multi-information rate, but the mutual information based sequence converges much faster to the multi-information rate than the conventional method does. Using this fact, we provide improved estimates of the multi-information rate of natural images and a better understanding of its underlying spatial structure.

Introduction/Background Work is a central resource for cancer survivors impacting health and quality of life. Effective rehabilitation programs supporting return to work become increasingly relevant for cancer survivors. In Germany, such programs were established as work-related medical rehabilitation, comprising work-related diagnostics, functional capacity training, psychosocial groups and intensified social counseling. The study investigated the effectiveness of work-related medical rehabilitation compared to conventional medical rehabilitation. Material and method We conducted a cluster randomized multicenter trial. 484 cancer patients aged 18 to 60 years were recruited in four inpatient rehabilitation centers. Patients of a center starting their rehabilitation in the same week represented a cluster. These clusters were randomly assigned with computer-generated randomization schedules to intervention (IG) or control group (CG). The primary outcome was the role functioning scale of the European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire as assessed at the 12-month follow-up. Secondary outcomes were other quality of life domains, disease coping and return to work. Effects were estimated using random effects models. The study was registered in the German Clinical Trials Register (DRKS00007770). Results Analysis of delivered dose of treatments indicated a successful implementation of work-related medical rehabilitation. 379 patients, 197 in IG, were included in the analysis of the 12-month follow-up. There was no significant difference between IG and CG in role functioning (b = 3.69; 95% CI: −2.01;9.39; P = 0.204) and any secondary outcome. Results indicate effects in favor of those patients with a high risk for early retirement in IG regarding role functioning (b = 10.54; 95% CI: 1.68–19.40; P = 0.020) and physical functioning (b = 7.84; 95% CI: 2.60–13.08; P = 0.003). Conclusion Work-related medical rehabilitation had no significant effects on primary and secondary outcomes compared to conventional medical rehabilitation. However, participants with an increased risk for health-related early retirement may benefit from a work-related medical rehabilitation program.

The need for a neuronal coding scheme that is robust against the corruption of action potentials seems to support the idea of population rate coding, where the relevance of a single spike decreases proportional to the increase of population size. In order to test this intuition, we here investigate the efficiency and robustness of a population rate coding scheme in comparison to a place coding scheme using identical noise model. It turns out that the efficiency of population rate coding is substantially worse than that of place coding even if the generation or propagation of spikes are highly unreliable processes.

While there are many experiments providing evidence for synchronized neuronal activity, there is little agreement about its functional role. Since many proposals rely on the assumption that neuronal activity can be modulated by top-down or feedback signals in a multiplicative way, it is a critical question how the dynamics of neurons may account for a selective control of their gain. In this paper we present a novel gain control mechanism based on the interplay of synaptic depression and synchronous inhibition. From simulations of a two-layered model of populations of integrate-and-fire neurons connected by stochastic depressing synapses, we conclude that synchronous inhibition can act as a selective gain control signal, which may be relevant, in particular when sensory processing reflects an ongoing process of hypotheses testing.

Spike-frequency adaptation is a common feature of neural dynamics. Here we present a low-dimensional phenomenological model whose parameters can be easily determined from experimental data. We test the model on intracellular recordings from auditory receptor neurons of locusts and demonstrate that the temporal variation of discharge rate is predicted with high accuracy. We relate the model to biophysical descriptions of adaptation in conduc- tance-based models and analyze its implications for neural computation. 2001 Elsevier Science B.V. All rights reserved.

Activity-dependent synaptic depression is a striking feature of synaptic transmission between neocortical pyramidal neurons. It has been shown that this kind of synaptic dynamics permits the transmission of rate changes rather than the DC part of presynaptic activities. In this paper, we show that activity-dependent depression makes synapses sensitive to reductions of presynaptic activity which are brief compared to the recovery time scale of the synapse. This surprising finding suggests that the synchronous lack of activity is potentially relevant for neuronal information processing. We present a mathematical analysis and an intuitive explanation of this paradoxical phenomenon.