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CAT – A Computational Anatomy Toolbox for the Analysis of Structural MRI Data

2022· preprint· en· 626 citations· W4282822545 on OpenAlex· 10.1101/2022.06.11.495736

Why is this work in the frame?

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

Canadian funderA Canadian agency funded it. The work may carry no Canadian affiliation at all.

No Canadian affiliation. An affiliation-only frame — the usual design — would never have seen this work. It is one of the works that make the case for inverting the frame.

Machine scores (provisional)

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Opus teacher head0.068
GPT teacher head0.353
Teacher spread
0.285 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Abstract

Abstract A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT) – a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface, but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams – illustrated on an example dataset – allow for voxel-based, surface-based, as well as region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT, while offering a citable standard for the neuroscience community.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

The record

Venue
bioRxiv (Cold Spring Harbor Laboratory)
Topic
Advanced Neuroimaging Techniques and Applications
Field
Medicine
Canadian institutions
Funders
National Institute on AgingNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchNational Institutes of HealthAlexander von Humboldt-StiftungGenentechIXICOH. Lundbeck A/SServierEisaiBristol-Myers SquibbRoyal SocietyNorthern California Institute for Research and EducationRoyal Society Te ApārangiPfizerBioClinicaBiogenF. Hoffmann-La RocheUniversity of AucklandEli Lilly and CompanyU.S. Department of DefenseMeso Scale DiagnosticsAlzheimer's Disease Neuroimaging InitiativeNovartis Pharmaceuticals CorporationAlzheimer's AssociationFoundation for the National Institutes of Health
Keywords
Computer scienceToolboxWorkflowSuitePreprocessorVisualizationUSableGraphical user interfaceData scienceHuman–computer interactionData miningArtificial intelligenceWorld Wide Web
Has abstract in OpenAlex
yes