Published April 19, 2023
| Version 1.0
Dataset
Open
Multicenter dataset of neuroimaging features (part I)
Authors/Creators
- 1. Dept. of Statistics, Computer Science and Applications "Giuseppe Parenti", University of Florence
- 2. Dept. of Electrical, Electronic, and Information Engineering "Guglielmo Marconi", University of Bologna
Description
The CSV file contains the cortical thickness (CT) and fractal dimension (FD) estimated from the brain MR T1-weighted images contained in the following online repositories:
- Autism Brain Imaging Data Exchange (ABIDE)
- 1000 Functional Connectomes Project (FCP) – ICBM dataset
- Consortium for Reliability and Reproducibility (CoRR) - NKI 2 - Nathan Kline Institute (Milham)
Each CSV file contains the following columns:
- Subject: id of each subject
- SITE: imaging site label (ABIDEI or ABIDEII followed by the institution name that collected the images; ICBM; NKI2)
- Age: each subject's age, expressed in years
- Sex: 0=male; 1=female.
- cortex_CT (FD): CT (or FD) of the cerebral cortical gray matter (GM).
- lh_cortex_CT (FD), rh_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM.
- lh_frontal_cortex_CT (FD), rh_frontal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the frontal lobe.
- lh_temporal_cortex_CT (FD), rh_temporal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the temporal lobe.
- lh_parietal_cortex_CT (FD), rh_parietal_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the parietal lobe.
- lh_occipital_cortex_CT (FD), rh_occipital_cortex_CT (FD): CT (or FD) of the left (lh) and right (rh) cerebral cortical GM of the occipital lobe.
Files
multicenter_CT-FD_features_1.csv
Files
(214.3 kB)
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Additional details
Related works
- Is part of
- Journal article: 10.1038/s41598-020-73961-w (DOI)
- Journal article: 10.1109/ACCESS.2021.3077370 (DOI)
- Is supplemented by
- Dataset: 10.5281/zenodo.7845361 (DOI)
- Dataset: 10.5281/zenodo.7848840 (DOI)
- Dataset: 10.5281/zenodo.8119042 (DOI)
References
- Marzi, C., Giannelli, M., Barucci, A., Tessa, C., Mascalchi, M., & Diciotti, S. (2022). Efficacy of MRI data harmonization in the age of machine learning. A multicenter study across 36 datasets. arXiv preprint arXiv:2211.04125.