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Reuter, B.

Publications and source records attributed to Reuter, B..

2 recordsLinked to original sources

Impaired differential learning of fear versus safety signs in obsessive-compulsive disorder

Pavlovian learning mechanisms are of great importance both for models of psychiatric disorders and treatment approaches, but understudied in obsessive-compulsive disorder (OCD). Using an established Pavlovian fear conditioning and reversal procedure, we studied skin conductance responses (SCRs) in 41 patients with OCD and in 32 matched healthy control participants. Within both groups, fear acquisition and reversal effects were evident. When comparing groups, patients showed impaired differential learning of threatening and safe stimuli, consistent with previous research. In contrast to prior findings, differential learning impairments were restricted to fear acquisition, and not observed in the reversal stage of the experiment. As previous and present fear reversal experiments in OCD differed in the use of color coding to facilitate stimulus discrimination, the studies converge to suggest that differential learning of threatening versus safe stimuli is impaired in OCD, but manifests itself differently depending on the difficulty of the association to be learned. When supported by the addition of color, patients with OCD previously appeared to acquire an association early but failed to reverse it according to changed contingencies. In absence of such color coding of stimuli, our data suggest that patients with OCD already show differential learning impairments during fear acquisition, which may relate to findings of altered coping with uncertainty previously observed in OCD. Impaired differential learning of threatening versus safe stimuli should be studied further in OCD, in order to determine whether impairments in differential learning predict CBT treatment outcomes in patients, and whether they are etiologically relevant for OCD.

neuroscience

CellRank for directed single-cell fate mapping

Computational trajectory inference enables the reconstruction of cell-state dynamics from single-cell RNA sequencing experiments. However, trajectory inference requires that the direction of a biological process is known, largely limiting its application to differentiating systems in normal development. Here, we present CellRank (https://cellrank.org) for mapping the fate of single cells in diverse scenarios, including perturbations such as regeneration or disease, for which direction is unknown. Our approach combines the robustness of trajectory inference with directional information from RNA velocity, derived from ratios of spliced to unspliced reads. CellRank takes into account both the gradual and stochastic nature of cellular fate decisions, as well as uncertainty in RNA velocity vectors. On data from pancreas development, we show that it automatically detects initial, intermediate and terminal populations, predicts fate potentials and visualizes continuous gene expression trends along individual lineages. CellRank also predicts a novel dedifferentiation trajectory during regeneration after lung injury, which we follow up experimentally by confirming the existence of previously unknown intermediate cell states.

bioinformatics