Trends in the sample size, statistics, and contributions to the BrainMap database of activation likelihood estimation meta-analyses: An empirical study of 10-year data.

The literature of neuroimaging meta-analysis has been thriving for over a decade. A majority of them were coordinate-based meta-analyses, particularly the activation likelihood estimation (ALE) approach. A meta-evaluation of these meta-analyses was performed to qualitatively evaluate their design and reporting standards. The publications listed from the BrainMap website were screened. Six hundred and three ALE papers published during 2010-2019 were included and analysed. For reporting standards, most of the ALE papers reported their total number of Papers involved and mentioned the inclusion/exclusion criteria on Paper selection. However, most papers did not describe how data redundancy was avoided when multiple related Experiments were reported within one paper. The most prevalent repeated-measures correction methods were voxel-level FDR (54.4%) and cluster-level FWE (33.8%), with the latter quickly replacing the former since 2016. For study characteristics, sample size in terms of number of Papers included per ALE paper and number of Experiments per analysis seemed to be stable over the decade. One-fifth of the surveyed ALE papers failed to meet the recommendation of having >17 Experiments per analysis. For data sharing, most of them did not provide input and output data. In conclusion, the field has matured well in terms of rising dominance of cluster-level FWE correction, and slightly improved reporting on elimination of data redundancy and providing input data. The provision of Data and Code availability statements and flow chart of literature screening process, as well as data submission to BrainMap, should be more encouraged.

BrainMap VBM: An environment for structural meta-analysis.

The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches.

Key activities used by community based primary care practices to improve the quality of diabetes care in response to practice facilitation.

BACKGROUND: A recent systematic review suggests that practice facilitation (PF) is a robust intervention for implementing evidence-based preventive care guidelines in primary care, but the ability of PF to improve chronic illness care remains unclear. AIMS: To examine the specific activities and Chronic Care model (CCM) components that primary care practices implemented and sustained in response to a 12-month PF intervention. METHODS: The ABC trial tested the effectiveness of PF to improve care for diabetes in 40 small community-based primary care practices that were randomized to "initial" or "delayed" intervention arms. A trained facilitator met with each practice over 12-months. Facilitators used interactive consensus building to help practices implement one or more of quality improvement activities based on the CCM. Facilitators prospectively recorded implementation activities reported by practice teams during monthly meetings and confirmed which of these were sustained at the end of the intervention. RESULTS: 37 practices implemented and sustained a total of 43 unique activities [range 1-15, average 6.5 (SD=2.9)]. The number (%) of practices that implemented 1 or more key activities in each CCM component varied: Patient Self-Management Support: 37 (100%); Clinical Information Systems: 24 (64.9%), Delivery System Design: 14 (37.8%), Decision Support: 13 (35.1%), Community Linkages: 2 (5.4%); Healthcare System Support: 2 (2.7%). The majority of practices (59%) only implemented activities from 1 or 2 CCM components. The number of sustained activities was associated with the number of PF visits, but not with practice characteristics. CONCLUSIONS: In spite of the PF intervention, it was difficult for these small practices to implement comprehensive CCM changes. Although practices implemented and sustained a remarkable number and variety of key activities, the majority of these focused on patient self-management support, as opposed to other components of the CCM, such as clinical information systems, decision support, delivery system redesign, and community linkages.