neurocaps
importedsoftware/neurocaps
A Python package for performing Co-Activation Patterns (CAPs) analyses on resting-state and task-based fMRI data.
Machine-generated from the listed sources and not yet reviewed by a human.
- Category
- Software & Systems
- Subcategory
- unknown
- License
- MIT(osi)
- Status
- active
- Maturity
- deployed
- Organization
- unknown
- Country
- unknown
- Homepage
- neurocaps.readthedocs.io
- Repository
- github.com/donishadsmith/neurocaps
- Documentation
- unknown
- Tags
- co-activation-patterns · dynamic-functional-connectivity · fmri · fmriprep · kmeans · neuroimaging · python
- Regulatory
- unknown
Top contributors by commit count, from the project’s public repository. Avatars are served by their origin, not stored here. To be removed from this list, open an issue.
Computed from shared tags, weighted so a rare tag counts for more than a common one. These are suggestions, not curated relationships.
- FCEstdynamic-functional-connectivity · fmri · neuroimaging
Methods for estimating time-varying functional connectivity (TVFC)
- FCEst-benchmarkingdynamic-functional-connectivity · fmri · neuroimaging
Benchmarks for functional connectivity estimators and FCEst Python package
- biomed-researchfmri · fmriprep · neuroimaging
Modular, YAML-configured Python pipeline for multi-scale fMRI functional brain network analysis. Validated on OpenNeuro ds007318 with Schaefer-200 parcellation.
- multi-FRAMEfmri · fmriprep · neuroimaging
Package for multivariate fMRI analyses
- BHS-AuditoryMultimodalfmri · fmriprep
Combine fMRI/EEG to learn about music/auditory processing
- ABC_neuroimagingfmri · neuroimaging
A sample neuroimaging pipeline for the fMRI data collected by the Aging Brain Cohort.
- api.github.com/repos/donishadsmith/neurocapsretrieved 2026-08-05 · via github-api
Machine-imported from GitHub search. Last push 2026-07-31, 19 stars, license reported as MIT. Category and schematic were assigned by keyword heuristics and are unreviewed.
Not yet verified by a human. Correct this record →
/v1/entries/29.json→ .entries["neurocaps"]
Entries are sharded 64 ways by a stable hash of the id, so a consumer can find any record without an index.