DOI: 10.5061/dryad.pg4f4qs1j
title: Infrequent strong connections constrain connectomic predictions of neuronal function (1/3)

description: How does circuit wiring constrain neural computation? Recent work has
 leveraged connectomic datasets to predict the functions of cells and
 circuits in the brains of multiple species. However, many of these
 hypotheses have not been compared with physiological measurements,
 obscuring the limits of connectome-based functional predictions. To
 explore these limits, we characterized the visual responses of 43 cell
 types in the fruit fly and quantitatively compared them to connectomic
 predictions. We show that these predictions are accurate for some response
 properties, such as orientation tuning, but are surprisingly poor for
 other properties, such as receptive field size. Importantly, strong
 synaptic inputs are more functionally homogeneous than expected by chance
 and exert a disproportionately large influence on postsynaptic responses.
 Finally, we quantitatively define the subset of connections that best
 describe the functional differences between cell types. Our results
 establish a powerful set of constraints for improving the accuracy of
 connectomic predictions.
description: # Infrequent strong connections constrain connectomic predictions of
 neuronal function (1/3) This is a README file for the data deposition
 associated with the manuscript "Infrequent strong connections
 constrain connectomic predictions of neuronal function" by Timothy A.
 Currier and Thomas R. Clandinin (Cell, 2025). Any questions or comments
 can be directed to Tim
 ([currier@stanford.edu](mailto:currier@stanford.edu)) ## Dataset DOIs (3)
 The raw imaging data have been split over three Dryad depositions: \-
 (THIS DATASET) All files except ImagingData1
 [https://doi.org/10.5061/dryad.pg4f4qs1j](https://doi.org/10.5061/dryad.pg4f4qs1j) \- ImagingData1A [https://doi.org/10.5061/dryad.bnzs7h4ns](https://doi.org/10.5061/dryad.bnzs7h4ns) \- ImagingData1B [https://doi.org/10.5061/dryad.kh18932k1](https://doi.org/10.5061/dryad.kh18932k1) ## LOOKING FOR STRFS? If you are simply looking for STRFs for each recorded cell/cell type: \- Data S1 and Data S2 in the manuscript \- Download `ALL_RESPONSES.npy` here ## CONTENTS The deposition includes the following: \- Response summaries for all recorded neurons (.npy) \- Connectome data (various formats) \- Brain and Cell logs (.xls) \- A description of the logs (.pdf) \- Stimulus & ROI metadata files (.hdf5) \- High-resolution anatomical volumes (.nii) \- ROI annotations (.key) \- Functional scan volumes (.nii) ## WARNING! Python packages required to fully interact with this VERY RAW data: \- [Visanalysis](https://github.com/ClandininLab/visanalysis) \- [Original code for this manuscript](https://github.com/tacbox3/CurrierClandinin) ## DETAILED INFORMATION ### ALL\_RESPONSES.npy A compendium of all cells recorded for the study. The last dimension of each dict value is 571, the number of ROIs. The dict contains the following keys: * `'cell_IDs'`, the unique cell identifiers (ROIs with the same cell_ID were recorded from the same cell) * `'cell_types'`, the cell type assignment for each ROI (empty elements indicate that no identification was made) * `'blue_STRFs'`, the STRF for each ROI responding to blue noise (dims: x, y, t, ROI#) * `'uv_STRFs'`, the STRF for each ROI responding to UV noise (dims: x, y, t, ROI#) * `'STRF_time'`, the time vector for STRF data * `'flicker_responses'`, the dF/F response for each ROI responding to full-screen flicker at 0.1, 0.5, 1, or 2 Hz (dims: f, t, ROI#) * `'flicker_time'`, the time vector for flicker data ### brain\_log.xlsx * A list of the flies imaged for the study, including the recording date, the fly genotype, and an estimate of the number of cells in each fly. * This file is for housekeeping only (redundant with .hdf5 metadata files) ### ME0708\_full\_log\_snap.xls * A list of ROIs recorded for the study. My analysis scripts use this log as the primary source of metadata. * Includes information about the stimulu""""""""''"/s series # for blue, UV, and flicker presentation, as well as a cell type assignment, when an ID was possible. * ROIs from unique cells have a unique cell ID. When multiple ROIs come from the same neuron, the cell ID is repeated for those ROIs. * The weird file name is retained for compatibility with the GitHub repo. ### Description of Tables (pdf) * A detailed breakdown of each column in `brain_log.xlsx` and `ME0708_full_log_snap.xls` ### Stimulus & ROI metadata files (.hdf5) * Located in the StimData folder. Each recording date has one file. * Contains stimulus and ROI metadata for each fly recorded on the corresponding date. * To open, launch the Visanalysis `DataGUI.py` file and load the .hdf5 file. More details are available in the Visanalysis documentation. ### Connectome Data (various formats) * These are companion files to run a number of the analysis scripts in the GitHub repo; it is recommended to drop the unzipped folder into your cloned repo directory. * `complete_metrics` courtesy of the Reiser Lab, summary quantifications of neuronal morphology * Filenames that begin with `__EXTRACTED__` are the full type-to-type connectivity data from Nern et al., *Nature*, 2025 - used for calculating Euclidean connectivity distance. These numpy variables were extracted from `adj_ol_with_roi.csv`, which lists the pre- and post-synaptic cell ID (`bodyId`) and cell type (`instance`), along with the number of synapses (`weight`), for every individual connection in the male optic lobe dataset. * Filenames that begin with `Rsr_` are type-to-type weighted Jaccard distance matrices for the Nern et al. data * `FAFB_distances` courtesy of Sebastian Seung - type-to-type weighted Jaccard distances for flywire data, Matsliah et al., *Nature*, 2024. The `inputs only` version of the table only considers inputs when calculating weighted Jaccard distance, while the other version considers both inputs and outputs. * `HighN_InFracs...` scraped from the Reiser Lab's [Cell Type Explorer Website](https://reiserlab.github.io/male-drosophila-visual-system-connectome/). Contains type-to-type input fractions for cell types in the main dataset. ### Anatomical Scans (.nii) * Each file contains a high-resolution scan through the optic lobe of a recorded fly. For dates with multiple files, compare the volume to the snapshots in the ROI annotation files to determine the specific fly for each file. * Typically, lower-numbered anatomical scans on a specific date refer to lower-numbered fly IDs (see `brain_log.xls`). * When two channels are present for a specific scan, `channel_1` is myr::tdTomato and `channel_2` is GCaMP8m. When only one channel is present, it is GCaMP8m. * These scans were exclusively used for the anatomical identification of recorded cells. ### ROI Annotation Files (.key) * Each recording date has one annotation file, comprised of slides with snapshots of the high-resolution anatomical scan and numbers indicating the ROIs for each brain on that date. * Snapshots are ordered from most superficial (i.e., most posterior part of the brain) to most deep (most anterior). * This is meant to be a handy visual guide to direct users to more careful inspection in the full anatomical scan. The exact ROI position and shape can be seen in DataGUI. * Can be opened with Apple Keynote. On Windows, they can be viewed by uploading to iCloud Keynote. ### Functional Scans (.nii) These files are located at [https://doi.org/10.5061/dryad.bnzs7h4ns](https://doi.org/10.5061/dryad.bnzs7h4ns) and [https://doi.org/10.5061/dryad.kh18932k1](https://doi.org/10.5061/dryad.kh18932k1) * Located in the ImagingData folders. ImagingData1(A, B) contains the first ~300 cells that were largely collected over a 6-month period in late 2022 - early 2023. ImagingData2 contains an additional ~215 cells that were collected a few months later in Oct-Dec of 2023. * Each "series" (stimulus) has two associated files: the motion-corrected fluorescence data (`TSeries-*.nii`, xyzt data), and a volume with the fano factor of each pixel (`*_thresh_brain.nii`, xyz data). * The series number corresponding to blue noise, UV noise, or full-screen flicker presentation for any given recording date can be found in `ME0708_full_log_snap.xls`. * It is not recommended to open the fluorescence data files directly, as they are quite large. Visanalysis uses an ROI mask to extract raw fluorescence timecourses on which all subsequent analyses are based. * The fano factor volumes were used as a preliminary means of identifying noise-responsive cells for ROI curation.