The dorsal visual pathway represents object-centered spatial relations for object recognition

Although there is mounting evidence that input from the dorsal visual pathway is crucial for object processes in the ventral pathway, the specific functional contributions of dorsal cortex to these processes remains poorly understood. Here, we hypothesized that dorsal cortex computes the spatial relations among an object's parts — a processes crucial for forming global shape percepts — and transmits this information to the ventral pathway to support object categorization. Using multiple functional localizers, we discovered regions in the intraparietal sulcus (IPS) that were selectively involved in computing object-centered part relations. These regions exhibited task-dependent functional connectivity with ventral cortex, and were distinct from other dorsal regions, such as those representing allocentric relations, 3D shape, and tools. In a subsequent experiment, we found that the multivariate response of posterior IPS, defined on the basis of part-relations, could be used to decode object category at levels comparable to ventral object regions. Moreover, mediation and multivariate connectivity analyses further suggested that IPS may account for representations of part relations in the ventral pathway. Together, our results highlight specific contributions of the dorsal visual pathway to object recognition. We suggest that dorsal cortex is a crucial source of input to the ventral pathway and may support the ability to categorize objects on the basis of global shape.

Examining Hospital Variation on Multiple Indicators of Stroke Quality of Care

Background: Provider profiling involves comparing the performance of hospitals on indicators of quality of care. Typically, provider profiling examines the performance of hospitals on each quality indicator in isolation. Consequently, one cannot formally examine whether hospitals that have poor performance on one indicator also have poor performance on a second indicator. Methods: We used Bayesian multivariate response random effects logistic regression model to simultaneously examine variation and covariation in multiple binary indicators across hospitals. We considered 7 binary patient-level indicators of quality of care for patients presenting to hospital with a diagnosis of acute stroke. We examined between-hospital variation in these 7 indicators across 86 hospitals in Ontario, Canada. Results: The number of patients eligible for each indicator ranged from 1321 to 14 079. There were 7 pairs of indicators for which there was a strong correlation between a hospital’s performance on each of the 2 indicators. Twenty-nine of the 86 hospitals had a probability higher than 0.90 of having worse performance than average on at least 4 of the 7 indicators. Seven of the 86 of hospitals had a probability higher than 0.90 of having worse performance than average on at least 5 indicators. Fourteen of the 86 of hospitals had a probability higher than 0.50 of having worse performance than average on at least 6 indicators. No hospitals had a probability higher than 0.50 of having worse performance than average on all 7 indicators. Conclusions: These findings suggest that there are a small number of hospitals that perform poorly on at least half of the quality indicators, and that certain indicators tend to cluster together. The described methods allow for targeting quality improvement initiatives at these hospitals.