Kragel 270 participant dataset (18 studies x 15 participants) from Kragel et al. 2018 Nature Neuroscience

<br><br>This dataset is an fmri_data object class object created using CANlab tools (canlab.github.io). It contains 270 single-participant fMRI contrast maps across 18 studies (with 15 participants per study). Studies are grouped into three domains: Pain, Cognitive Control, and Negative Emotion, with 9 studies each. Each domain includes 3 subdomains, with 3 studies each. <br>The dataset is from Kragel et al. 2018, Nature Neuroscience. Kragel, P. A., Kano, M., Van Oudenhove, L., Ly, H. G., Dupont, P., Rubio, A., Delon-Martin, C., Bonaz, B. L., Manuck, S. B., Gianaros, P. J., Ceko, M., Losin, R. E. A., Woo, C. W., Nichols, T. E., Wager, T. D. (2018). Generalizable representations of pain, cognitive control, and negative emotion in medial frontal cortex. <i>Nature neuroscience</i>. 21(2): 283-289. PMCID: PMC5801068.https://www.nature.com/articles/s41593-017-0051-7it's also stored on Neurovault.org as collection #504. Using CANlab tools , you could get it using:[files_on_disk, url_on_neurovault, mycollection, myimages] = retrieve_neurovault_collection(504);This file also has metadata that is not on Neurovault, in data_obj.metadata_table. If you save this file somewhere on your Matlab path, you'll be able to reload and reuse the dataset easily.<pre>[test_images, names] = load_image_set('kragel18_alldata', 'noverbose');<br></pre><br>270×6 tableDomain Subdomain imagenames Studynumber Orig_Studynumber StudyCodes_______________ ___________ __________________________ ___________ ________________ {'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }{'Pain' } {'Thermal'} {'ThermalPain1' } 1 10 {'Atlas_2010_EXP' }.... etcNote: Individual participant images are <i>not</i> on the same magnitude scale across studies. For most applications, you will want to normalize the scale (e.g., L2 norm or z-score images).e.g., in CANlab tools:data_obj = rescale(data_obj, 'l2norm_images');A .nii file format image is also included. These images are rescaled by dividing by the L2 norm of each image. <br>