Drosophila central brain connectivity

See https://github.com/mhturner/SC-FC for analysis code and figure generation using this dataset<br><br>-StructuralMatrix_branson.csv and CorrelationMatrix_Branson.csv are the fine-grained Branson segmentation based structural and functional connectivity matrices shown in Fig. S3.<br>-body_ids.csv is the list of unique body ID numbers in the Hemibrain connectome that were used to compute structural connectivity.<br><br>data_TurnerMannClandinin:<br>Compressed directory containing these subdirectories (in <b>bold</b>):<b>-atlas_data</b> contains original Ito and Branson brain atlas/segmentation files<br><b></b><b>-ito_68_atlas</b> &amp; <b>branson_999_atlas</b> each contain .nii.gz image files containing the registered brain atlas mask for each fly. The mask numbers correspond to the regions as in Original_Index_panda_full.csv and atlas_roi_values<br><br><b>-ito_responses</b> &amp; <b>branson_responses</b> each contain pandas dataframes, one for each fly, describing raw fluorescence responses for each brain region in that atlas. The sampling rate is 1.2 Hz.<br>-<b>connectome_connectivity</b> contains computed pandas dataframe files for various structural connectivity metrics, for the regions included in the main paper analysis<br>-<b>hemi_2_atlas</b> contains results of structural connectivity computations from R code included in the corresponding GitHub repository.<br><br>-<b>subsample</b> contains numpy data the result of the region subsampling analysis<br>-<b>template_brains</b> contains template brain files in the original (JFRC2) brain template space as well as the same atlases transformed to JRC2018 space (in both .tif and .nii.gz format)<br>registration.xform.xipTransform file (CMTK format) to convert from JFRC2 template brain space, where the Ito atlas and Branson segmentation are, to JRC2018 template brain space<br><br>