To select a consensus-based set of relevant and feasible indicators for monitoring and improving the quality of regional ICU network collaboratives.
A three-round Delphi study was conducted in the Netherlands between April and July 2022. A multidisciplinary expert panel prioritized potentially relevant and feasible indicators in two questionnaire rounds with two consensus meetings between both rounds. The RAND/UCLA appropriateness method was used to categorize indicators and synthesize results. A core set of highest ranked indicators with consensus-based levels of relevance and feasibility were finally tested in two ICU networks to assess their measurability.
Twenty-four indicators were deemed as relevant and feasible. Seven indicators were selected for the core set measuring the standardized mortality rate in the region (n = 1) and evaluating the presence, content and/or follow-up of a formal plan describing network structures and policy agreements (n = 3), a long-term network vision statement (n = 1), and network meetings to reflect on and learn from outcome data (n = 2). The practice tests led to minor reformulations.
This study generated relevant and feasible indicators for monitoring and improving the quality of ICU network collaboratives based on the collective opinion of various experts. The indicators may help to effectively govern such networks.

Many recent developments surrounding the functional network organization of the human brain have focused on data that have been averaged across groups of individuals. While such group-level approaches have shed considerable light on the brain\'s large-scale distributed systems, they conceal individual differences in network organization, which recent work has demonstrated to be common and widespread. This individual variability produces noise in group analyses, which may average together regions that are part of different functional systems across participants, limiting interpretability. However, cost and feasibility constraints may limit the possibility for individual-level mapping within studies. Here our goal was to leverage information about individual-level brain organization to probabilistically map common functional systems and identify locations of high inter-subject consensus for use in group analyses. We probabilistically mapped 14 functional networks in multiple datasets with relatively high amounts of data. All networks show \"core\" (high-probability) regions, but differ from one another in the extent of their higher-variability components. These patterns replicate well across four datasets with different participants and scanning parameters. We produced a set of high-probability regions of interest (ROIs) from these probabilistic maps; these and the probabilistic maps are made publicly available, together with a tool for querying the network membership probabilities associated with any given cortical location. These quantitative estimates and public tools may allow researchers to apply information about inter-subject consensus to their own fMRI studies, improving inferences about systems and their functional specializations.

In a network, a distributed consensus algorithm is fully characterized by its weighting matrix. Although there exist numerical methods for obtaining the optimal weighting matrix, we have not found an in-network implementation of any of these methods that works for all network topologies. In this paper, we propose an in-network algorithm for finding such an optimal weighting matrix.

A role for the cerebellum in causing ataxia, a disorder characterized by uncoordinated movement, is widely accepted. Recent work has suggested that alterations in activity, connectivity, and structure of the cerebellum are also associated with dystonia, a neurological disorder characterized by abnormal and sustained muscle contractions often leading to abnormal maintained postures. In this manuscript, the authors discuss their views on how the cerebellum may play a role in dystonia. The following topics are discussed: The relationships between neuronal/network dysfunctions and motor abnormalities in rodent models of dystonia. Data about brain structure, cerebellar metabolism, cerebellar connections, and noninvasive cerebellar stimulation that support (or not) a role for the cerebellum in human dystonia. Connections between the cerebellum and motor cortical and sub-cortical structures that could support a role for the cerebellum in dystonia. Overall points of consensus include: Neuronal dysfunction originating in the cerebellum can drive dystonic movements in rodent model systems. Imaging and neurophysiological studies in humans suggest that the cerebellum plays a role in the pathophysiology of dystonia, but do not provide conclusive evidence that the cerebellum is the primary or sole neuroanatomical site of origin.