Data from: Multiplexed subspaces route neural activity across brain-wide networks
Data files
Mar 05, 2025 version files 13.61 GB
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Mouse331_06_11_2021.zip
156.66 MB
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Mouse331_06_12_2021.zip
123.60 MB
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Mouse331_RestingState_NP_06_11_2021_1dff_combined_processed.mat
2.09 GB
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Mouse331_RestingState_NP_06_12_2021_1dff_combined_processed.mat
2.10 GB
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Mouse332_06_07_2021.zip
163 MB
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Mouse332_06_08_2021.zip
110.80 MB
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Mouse332_RestingState_NP_06_07_2021_1dff_combined_processed.mat
2.14 GB
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Mouse332_RestingState_NP_06_08_2021_1dff_combined_processed.mat
2.14 GB
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Mouse334_06_09_2021.zip
169.18 MB
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Mouse334_06_10_2021.zip
109.09 MB
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Mouse334_RestingState_NP_06_09_2021_1dff_combined_processed.mat
2.15 GB
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Mouse334_RestingState_NP_06_10_2021_1dff_combined_processed.mat
2.16 GB
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README.md
4.32 KB
Abstract
Cognition is flexible, allowing behavior to change on a moment-by-moment basis. Such flexibility relies on the brain’s ability to route information through different networks of brain regions in order to perform different cognitive computations. However, the mechanisms that determine which network of regions is active are not well understood. Here, we combined cortex-wide calcium imaging with high-density electrophysiological recordings in mice to understand the interactions between networks of brain regions. We found that different dimensions within the activity of each region were functionally connected with different cortex-wide ‘subspace networks’. These subspace networks were multiplexed; each brain region was functionally connected with multiple independent, yet overlapping, networks. The subspace network that was active changed from moment-to-moment. These changes were associated with changes in the geometric relationship between the neural response within a region and the subspace dimensions: when neural responses were aligned with (i.e., projected along) a subspace dimension, neural activity was increased in the associated regions. Together, our results suggest that changing the geometry of neural representations within a brain region may allow the brain to flexibly engage different brain-wide networks, thereby supporting cognitive flexibility.
https://doi.org/10.5061/dryad.gxd2547x8
Data is from 6 different sessions that combine cortex-wide calcium imaging and high-density electrophysiological recordings in eight cortical and subcortical regions of mice.
Description of the data and file structure
All files contain data collected from a different experiment stored in Matlab file format. Filenames indicate mouse by ID number and data.
Imaging Files:
- Mouse331_RestingState_NP_06_11_2021_1dff_combined_processed.mat
- Mouse331_RestingState_NP_06_12_2021_1dff_combined_processed.mat
- Mouse332_RestingState_NP_06_07_2021_1dff_combined_processed.mat
- Mouse332_RestingState_NP_06_08_2021_1dff_combined_processed.mat
- Mouse334_RestingState_NP_06_09_2021_1dff_combined_processed.mat
- Mouse334_RestingState_NP_06_10_2021_1dff_combined_processed.mat
Electrophysiology Files:
- Mouse331_06_12_2021.zip
- Mouse332_06_08_2021.zip
- Mouse331_06_11_2021.zip
- Mouse332_06_07_2021.zip
- Mouse334_06_10_2021.zip
- Mouse334_06_09_2021.zip
Files are organized by the mouse (sorted by ID number, 331, 332, and 334) and day of experiment.
Description of the data and file structure
Imaging Data:
Each mat files contains the following variables:
- data_norm: a pixel x time normalized fluoresence trace.
- data_train and data_test: alternating chunks of recording time on which to train and then test motifs
- gp: a list of general processing parameters used when processing the imaging recordings and fitting motifs.
- nanpxs: a vector of non-active pixels (e.g., those outside the field of view of the brain or that correspond to known vasculature) that are to be excluded form analysis
- opts: additional preprocessing opts
- num_chunks: the number of chunks into which to spike data_norm in order to create data_train and data_test.
Functions to load and work with the data along with a demo can be found here: https://github.com/cmacdow/fpCNMF
Electrophysiology Data:
Each zip files contains 5 .mat files for a given recording.
- AP_opts: a file containing a matlab structure of preprocessing options used when processing the electrophysiological recording. These are included for basic reference (e.g., at what frequencies the raw data was filtered, sample rates, etc.). The nomenclature of these options correspond to standardized Neuropixel preprocessing software (Kilosort v2.5, Cat_GT, and TPrime)
- AP_Probe1,AP_Probe2,AP_Probe3,AP_Probe4: a mat file containing electrophysiological data for each probe.
- Clust_Info is a structure containing data for the actional potentials identified along the probe. Nomenclature follows Kilosort (link below) and Phy (link below). Clust_Info contains two sub-objects:
- spike_times contains the timing of all spikes i.e. final value is the time (in seconds) of the last spike in the recording.
- spike_cluster contains the corresponding spike_cluster (i.e., multi or single unit) associated with those spike times.
- vert_depth is the vertical depth along the probe that each spike cluster is located
- Clust_Info is a structure containing data for the actional potentials identified along the probe. Nomenclature follows Kilosort (link below) and Phy (link below). Clust_Info contains two sub-objects:
See Kilosort (v2.5) and Phy for variable definitions and units of measurement https://github.com/MouseLand/Kilosort and https://phy.readthedocs.io/en/latest/sorting_user_guide/
Sharing/Access information
Data was generated for this study through experiments.
Code/Software
Data is stored in Matlab format (versions 2019-2023). Code for generation of figures is available at: https://doi.org/10.5281/zenodo.14721950. Data supporting figures is available with the manuscript.
Initial functions to load and work with the imaging data along with a demo can be found here: https://github.com/cmacdow/fpCNMF
Initial functions to load and work with ephys data and allign to imaging data with a demo can be found here: https://github.com/cmacdow/LoadEphysDemo
See also: https://github.com/MouseLand/Kilosort
Data is a combination of cortex-wide calcium imaging (*dff_combined_processed.mat) and high-density electrophysiological recordings (included in zipped files for each animal) in eight cortical and subcortical regions of mice.
Experiments were performed on three adult (>8 weeks old) male (N=2) and female (N=1) mice expressing GCaMP6f in cortical excitatory neurons (Thy1-GCaMP6f). Each mouse was recorded twice for a total of n=6 recordings.
Widefield imaging was performed using an Optimos CMOS Camera (Photometrics) through back-to-back 50 mm objective lens (Leica, 0.63x and 1x magnification), separated by a 495nm dichroic mirror (Semrock Inc, FF495-Di03-50x70). Excitation light (470nm, 0.4mW/mm2) was delivered through the objective lens from an LED (Luxeon, 470nm Rebel LED, part #SP-03-B4) with a 470/22 clean-up bandpass filter (Semrock, FF01-470/22-25). Fluorescence was captured at 30 frames per second (FPS; 33.3ms exposure) using Micro-Manager software (V1.4) at 980x540 resolution (~34um/pixel) for 90 minutes. Neuropixel recordings and imaging were aligned to the exposure out signal of the CMOS camera (captured on a NIDAQ PXIe-8381 in SpikeGLX; aligned with Tprime v1.6 available from https://billkarsh.github.io/SpikeGLX/#tprime).
Electrophysiological recordings used four Neuropixels20 1.0 probes (phase 3B2), inserted simultaneously. Electrodes were inserted under 2x magnification with micromanipulators (Siskiyou, part# MX-1131). Probes were coated with DiI (ThermoFisher Scientific, item #V22885) prior to insertion to allow for post-hoc histological reconstruction. Probes were lowered to desired depths (1.5-5mm, pre-determined from modeling described above) and allowed to settle for >30 minutes before starting recording.
Recordings were 90 minutes in length. Data was acquired using SpikeGLX (v3.0 available from https://billkarsh.github.io/SpikeGLX/), with tip reference mode and high-pass filtered at 300Hz. After collection, data was re-referenced by subtracting the global average across all channels using CatGT (v2), and spike-sorted using Kilosort54 (v2.5). Automatically identified units were then manually curated with Phy72 into well-isolated single units and 'multiunits' that may have aggregated the activity of multiple neurons.
Recorded neurons were grouped by anatomical location, as labeled in the Allen Brain Atlas Common Coordinates Framework73 (CCF v3). Prelimbic (PL; n=1527) included neurons from CCF parent regions Prelimbic (PL), Infralimbic (ILA), Dorsal Anterior Cingulate Area (ACAd). Frontal Motor (FMR; n=1257) included neurons from Secondary Motor Area (Mos). Visual (VIS; n=716) included neurons from Posteromedial (VISpm), Anterior (VISa), and Anteromedial (VISam) visual areas. Somatosensory (SS; n=833) included neurons from nose (SSp-n) mouth (SSp-m) and unassigned (SSp-un) primary somatosensory areas. Whisker (WHS; n=805) included neurons from Primary Somatosensory Barrel Field area (SSp-bfd). Retrosplenial (RSP; 640) included neurons from Dorsal and Lateral Agranular Retrosplenial areas (RSPd and RSPagl, respectively). Hippocampus (HPC; n=353) included neurons from Dentate Gyrus (DG) and Ammon's horn (CA). Thalamus (TH, n=389) included neurons from the Lateral Group (LAT), Medial Group (MED), and Intralaminar nuclei (ILM) of the dorsal Thalamus and Epithalamus (EPI).