{ "info": { "author": "Thomas W. D. M\u00f6bius", "author_email": "moebius@medinfo.uni-kiel.de", "bugtrack_url": null, "classifiers": [ "Development Status :: 3 - Alpha", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)", "Programming Language :: Python :: 3.7", "Topic :: Software Development :: Build Tools" ], "description": "Modelling the data and not the images in FMRI\n=============================================\n\nCurrent approaches to the analysis of functional magnetic resonance\nimaging (FMRI) data apply various preprocessing steps to the original\nFMRI. These preprocessings lead to a general underestimation of residual\nvariance in the downstream analysis. This negatively impacts the type I\nerror of statistical tests and increases the risk for reporting false\npositive results.\n\nThis is the first statistical software tool which implements the *model\nbased* (MB) estimator for FMRI data models. It is a new and original\nmethod for the statistical analysis of FMRI of brain scans. MB\nestimation combines all preprocessing steps of the standard approaches\ninto one single modelling step. Without altering the original 4D-image,\nthe method results in smooth fits of the underlying parameter fields.\nMore importantly, the method yields a trustworthy estimate of the\nuncertainty in BOLD effect estimation.\n\nThe availability of these uncertainty fields allows to model FMRI\nstudies by random effects meta regression models, acknowledging that\nindividual subjects are random entities, and that the certainty at which\nthe actual BOLD effect in an individual can be estimated from an FMRI\nvaries across the brain and between the subjects.\n\nMB estimation allows to process and report BOLD effects in ati units. In\nparticular multicentre studies gain power by its use: if an effect is\npresent in your data, you will be more likely to find it.\n\nCiting the MB estimator and this software:\n\n Thomas W. D. M\u00f6bius (2018) Modelling the data and not the images in\n FMRI, ArXiv e-prints, arXiv:1809.07232\n\n Thomas W. D. M\u00f6bius (2018) fmristats: Modelling the data and not the\n images in FMRI (Version 0.1.0) [Computer program]. Available at\n http://fmristats.github.io/\n\nThank you for citing this project.\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://fmristats.github.io/", "keywords": "fmri neuroimaging neuroscience statistics", "license": "GPLv3+", "maintainer": "", "maintainer_email": "", "name": "fmristats", "package_url": "https://pypi.org/project/fmristats/", "platform": "", "project_url": "https://pypi.org/project/fmristats/", "project_urls": { "Homepage": "https://fmristats.github.io/" }, "release_url": "https://pypi.org/project/fmristats/0.1.1/", "requires_dist": [ "numpy", "scipy", "scikit-image", "pandas", "statsmodels", "matplotlib", "nibabel", "nipype" ], "requires_python": "", "summary": "Modelling the data and not the images in FMRI", "version": "0.1.1" }, "last_serial": 5207520, "releases": { "0.0.1": [ { "comment_text": "", "digests": { "md5": 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