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Calcium imaging in visual cortex during fidget behaviors in mice


Ramadan, Mahdi (2023), Calcium imaging in visual cortex during fidget behaviors in mice , Dryad, Dataset,


Multiple recent studies have shown that motor activity greatly impacts the activity of primary sensory areas like V1. Yet, the role of this motor related activity in sensory processing is still unclear. 

Here we dissect how these behavior signals are broadcast to different layers and areas of the visual cortex. To do so, we leveraged a standardized and spontaneous behavioral fidget event in passively viewing mice. Importantly, this behavior event had no relevance to any ongoing task allowing us to compare its neuronal correlates with visually relevant behaviors (e.g running). 

A large two-photon Ca2+ imaging database of neuronal responses uncovered four neural response types during fidgets that were consistent in their proportion and response patterns across all visual areas and layers of the visual cortex. Indeed, the layer and area identity could not be decoded above chance level based only on neuronal recordings. In contrast to running behavior, fidget evoked neural responses that were independent to visual processing. 

The broad availability of visually orthogonal standardized behavior signals could be a key component in how the cortex selects, learns and binds local sensory information with motor outputs. Contrary to behaviorally relevant motor outputs, irrelevant motor signals could project to separate local neural subspaces.


This dataset is a collection of fidget aligned neural events collected from large scale calcium imaging in the mouse brain.

Transgenic mice

All animal procedures were approved by the Institutional Animal Care and Use Committee (IACUC) at the Allen Institute for Brain Science. Triple transgenic mice (Ai93, tTA, Cre) were generated by first crossing Ai93 mice with Camk2a-tTA mice, which preferentially express tTA in forebrain excitatory neurons. Double transgenic mice were then crossed with a Cre driver line to generate mice in which GCaMP6f expression is induced in the specific populations of neurons that express both Cre and tTA.

Rorb-IRES2-Cre;Cam2a-tTA;Ai93 (n=10) exhibit GCaMP6f in excitatory neurons in cortical layer 4 (dense patches) and layers 5,6 (sparse). Cux2-CreERT2;Camk2a-tTA;Ai93 (n=16) expression is regulated by the tamoxifen-inducible Cux2 promoter, induction of which results in Cre-mediated expression of GCaMP6f predominantly in superficial cortical layers 2, 3 and 4. Slc17a7-IRES2-Cre;Camk2a-tTA;Ai93 (n=2) is a pan-excitatory line and shows expression throughout all cortical layers. Scnn1a-Tg3-Cre;Camk2a-tTA;Ai93 (n=5) exhibit GCaMP6f in excitatory neurons in cortical layer 4 and in restricted areas within the cortex, in particular primary sensory cortices. Nr5a1-Cre;Camk2a-tTA;Ai93 (n=1) exhibit GCaMP6f in excitatory neurons in cortical layer 4. Rbp4-Cre;Camk2a-tTA;Ai93 (n=11) exhibit GCaMP6f in excitatory neurons in cortical layer 5. Ntsr1-Cre_GN220;Ai148 (n=1) exhibit CaMP6f in excitatory corticothalamic neurons in cortical layer 6.

Animal head-implants and cortical window implantation

Transgenic mice expressing GCaMP6f were weaned and genotyped at ~p21, and surgery was performed between p37 and p63. Surgical protocols were described in previous publications associated with the two-photon datasets4.

Intrinsic imaging and mapping of the visual cortex

Retinotopic mapping was used to delineate functionally defined visual area boundaries and enable targeting of the in vivo two-photon calcium imaging to retinotopically defined locations in primary and secondary visual areas. Retinotopic mapping protocols were described in previous publications associated with the two-photon datasets4.

In vivo two-photon chronic imaging

Calcium imaging was performed using a two-photon-imaging instrument, Nikon A1R MP+. The Nikon system was adapted to provide space to accommodate the behavior apparatus). Laser excitation was provided by a Ti:Sapphire laser (ChameleonVision – Coherent) at 910 nm. Pre-compensation was set at ~10,000 fs2. Movies were recorded at 30Hz using resonant scanners over a 400 μm field of view.

Mice were head-fixed on top of a rotating disk and free to walk at will. The disk was covered with a layer of removable foam (Super-Resilient Foam, 86375K242, McMaster) to alleviate motion-induced artifacts during imaging sessions.

An experiment container consisted of three imaging sessions (60 min each) at a given field of view during which mice passively observed three different stimuli. The same location was targeted for imaging on all three recording days to allow repeat comparison of the same neurons across sessions. One imaging session was performed per day, for a maximum of 16 sessions for each mouse.

On the first day of imaging at a new field of view, the ISI targeting map was used to select spatial coordinates. A comparison of surface vasculature patterns was used to verify the appropriate location by imaging over a field of view of ~800 μm using epi-fluorescence and blue light illumination. Once a cortical region was selected, the imaging objective was shrouded from stray light from the stimulus screen using opaque black tape. In two-photon imaging mode, the desired depth of imaging was set to record from a specific cortical depth. On subsequent imaging days, we returned to the same location by matching (1) the pattern of vessels in epi-fluorescence with (2) the pattern of vessels in two photon imaging and (3) the pattern of cellular labelling in two photon imaging at the previously recorded location.

Calcium imaging data was collected at the four cortical depths of 175, 275, 350 and 375 micrometers. Throughout our analysis, data from the cortical depth of 175 micrometers were classified as layer 2/3, 275 and 350 micrometers as layer 4, and 375 as layer 5.

Processing of two-photon calcium imaging movies

For each two-photon imaging session, the image processing pipeline performed: (1) spatial or temporal calibration, (2) motion correction, (3) image normalization to minimize confounding random variations between sessions, (4) segmentation of connected shapes and (5) classification of soma-like shapes from remaining clutter.

The motion correction algorithm relied on phase correlation and only corrected for rigid translational errors. Each movie was partitioned into 400 consecutive frame blocks, representing 13.3 s of video. Each block was registered iteratively to its own average three times. A second stage of registration integrated the periodic average frames themselves into a single global average frame through six additional iterations. The global average frame served as the reference image for the final resampling of every raw frame in the video.

Fluorescence movies were processed using a segmentation algorithm to identify somatic regions of interest (ROIs) that was described previously2. Segmented ROIs were matched across imaging sessions. For each ROI, events were detected from ΔF/F by using an L0-regularized algorithm. For each neuron, we z-score ΔF/F trial activity and compute the mean z-scored response of each neuron aligned to the time of fidget onset (0 seconds).

To determine the significance of neural activity modulation post-fidget, we apply several threshold criteria to clustered neuronal activity. The threshold criteria were used in previous calcium imaging literature, and here we present two that gave very different results: One criteria where the mean ΔF/F is larger than 6%, and one criteria where the maximum ΔF/F during the post-fidget period is greater than 5%.

Fidget neuronal response analysis

Neural responses were aligned to the onset of the fidget behavior and cropped to keep 100 frames (~ 3 seconds) preceding the initiation of the fidget and 200 frames (~ 6 seconds) post fidget initiation.

Neural activity was normalized on a trial-by-trial basis by subtracting the mean activity of the 100 frames (~3 seconds) of baseline neural activity preceding the initiation of the fidget response and dividing by the standard deviation of activity. Across trial z-scored neural activity was then averaged to get the mean z-scored activity for each neuron.

Usage notes

Matlab and/or Python are needed to open data. This data submission includes 144 mat files. They can be accessed with the matlab or python function "load". Files are named Fidget_Fluorescence_XXXXXXXX.mat where XXXXXXXX represents the session number. 


Allen Institute