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We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets. Measurement(s) brain white matter Technology Type(s) Diffusion Weighted Magnetic Resonance Imaging Sample Characteristic - Organism Homo sapiens Sample Characteristic - Location contiguous United States of America [ABSTRACT FROM AUTHOR]

The list of members of the Fibr Community Science Consortium in the original version of the paper incorrectly omitted consortium member Matthew D. Sacchet, Massachusetts General Hospital, Harvard Medical School, Boston, USA. The corrected membership list has replaced the incorrect version in the pdf and HTML versions of the article. [Extracted from the article]

The corrected membership list has replaced the incorrect version in the pdf and HTML versions of the article. Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01695-7, published online 12 October 2022 The list of members of the Fibr Community Science Consortium in the original version of the paper incorrectly omitted consortium member Christian K. Tamnes, University of Oslo, Norway. [Extracted from the article]

Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01695-7, published online 12 October 2022 In this article the affiliation details for Bárbara Avelar-Pereira were incorrectly given as "University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland" but should have been "Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA". These authors contributed equally: Adam Richie-Halford, Matthew Cieslak. [Extracted from the article]

Recent progress in multiplexed tissue imaging is advancing the study of tumor microenvironments to enhance our understanding of treatment response and disease progression. Cellular neighborhood analysis is a popular computational approach for these complex image data. Despite its popularity, there are significant challenges, including high computational demands that limit feasibility for large-scale applications and the lack of a principled strategy for integrative analysis across images. This absence hampers the precise and consistent identification of spatial features and tracking of their dynamics over disease progression. To overcome these challenges, we introduce SpatialTopic , a spatial topic model designed to decode high-level spatial architecture across multiplexed tissue images. SpatialTopic integrates both cell type and spatial information within a topic modelling framework, originally developed for natural language processing and adapted for computer vision. Spatial information is incorporated into the flexible design of documents, representing densely overlapping regions in images. We employ an efficient collapsed Gibbs sampling algorithm for model inference. We benchmarked the performance against five state-of-the-art algorithms through various case studies using different single-cell spatial transcriptomic and proteomic imaging platforms across different tissue types. We show that SpatialTopic is highly scalable on large-scale image datasets with millions of cells, along with high precision and interpretability. Our findings demonstrate that SpatialTopic consistently identifies biologically and clinically significant spatial "topics" such as tertiary lymphoid structures (TLSs) and tracks dynamic changes in spatial features over disease progression. Its computational efficiency and broad applicability across various molecular imaging platforms will enhance the analysis of large-scale tissue imaging datasets., Competing Interests: J.W.S. Research funding—IO Biotech (Inst), Regeneron (Inst), Daiichi Sankyo (Inst); Consulting or advisory role—IO Biotech; M.A.P. Consulting or Advisory Role - Bristol-Myers Squibb; Cancer Expert Now; Chugai Pharma; Eisai; Erasca, Inc; Intellisphere; Merck; MJH Associates; Nektar; Novartis; Pfizer; WebMD; Research Funding - Array BioPharma (Inst); Bristol-Myers Squibb (Inst); Infinity Pharmaceuticals (Inst); Merck (Inst); Novartis (Inst); Rgenix (Inst); K.S.P. Stock ownership in 23 and Me, Vincerx, Eyepoint, & Kyverna; C.E.K, Stock ownership in Johnson & Johnson; M.K.C. BMS– Research support (Inst), advisory role/consulting; Medimmune - advisory role/consulting; Immunocore–advisory role/consulting; Merus–family member employee; X.P., J.L., M.Y., M.B., R.S., F.D.E. No disclosures;