DOI: 10.5061/dryad.2rbnzs81t
title: Visual objects refine head direction coding

description: Animals use visual objects to guide navigation-related behaviors, from
 hunting prey to escaping predators and exploring the world. However,
 little is known about where visual objects are encoded in the mouse brain
 or how they impact processing in the spatial navigation system. Using
 functional ultrasound imaging, we conducted a brain-wide screen and
 identified areas preferentially activated by images of objects compared to
 scrambled versions. While visual cortical areas did not show a significant
 preference, brain areas associated with spatial navigation were
 preferentially activated by visual objects. Electrophysiological
 recordings in the postsubiculum, the primary cortical area of the head
 direction (HD) system, confirmed a preference for visual objects in both
 HD cells and fast-spiking interneurons. Finally, we found that visual
 objects dynamically modulated HD cells in freely moving animals,
 selectively increasing firing rates of HD cells aligned with a visual
 object while decreasing activity in HD cells coding for other directions.
description: # Visual objects refine head direction coding Dataset
 DOI: [10.5061/dryad.2rbnzs81t](http://10.0.19.197/dryad.2rbnzs81t) ###
 Description of the data and file structure All preprocessed data and code
 (without raw data) can be found in this
 file: Visual_objects_refine_head_direction_coding.tar Raw data can be
 found in this
 file: Visual_objects_refine_head_direction_coding_rawData.tar Data files
 are stored in MATLAB (.mat) or comma-separated value (.csv) format and
 scripts as MATLAB scripts (.m) or Jupyter notebooks (.ipynb). **Files and
 variables** Scripts are named after the Figure panel(s) they produce. Data
 required to produce the figures are stored in the folder “preprocessed
 data”, and scripts required to for panel production are stored in the
 folder “codes”. Data and scripts are stored in subfolders depending on the
 technique they are associated with ("fUS" (Figure 1) or
 electrophysiology ("ephys", Figure 2-5)). "epyhs" is
 further separated in either acutely inserted electrodes
 ("acute", Figure 2) or chronically implanted electrodes
 ("chronic", Figures 3-5). Only for Data, "chronic" is
 further subdivided by experimental design in either data coming from
 sessions with an arena with a single cue and subsequent head-fixation
 ("one cue and head-fixed", Figure 3-5) or sessions with two cues
 present in the arena ("two cues", Figure 5). Raw data can be
 found in the folder “raw data” of the file
 Visual_objects_refine_head_direction_coding_rawData.tar and is stored in
 different subfolders depending on whether the data was collected in
 anesthetized (“anes”) or awake (“awake”) mice. These subfolders are
 further subdivided based on animal identity and recording day. In each
 folder of a recording day, one raw fUS dataset (.mat, variable “I”) can be
 found with the corresponding PsychoPy output file (.csv) containing the
 stimulus information. In the subfolder “fus”, the text file “stimulus and
 setup information.txt” describes general details of the stimulus timing
 and setup configuration. **Description of scripts:** Fig1bef_S1b_S2cd.m:
 In this script, the region-segmented fUS data is loaded, and temporal
 dynamics of specific or all regions are visualized. This script also
 produces “TableS1_pValues_segmented_anes.xls” and
 “TableS1_pValues_segmented_awake.xls” stored in “figures/final
 panels/Tables” that contain the p-values associated with the panels Fig.
 1e,g and Fig. S2c,e. Fig1c_S1d_S2a.m: This script loads the voxel-wise GLM
 results of the fUS data and overlays them on the Allen brain atlas.
 Fig1d.m: This script visualizes the Object-Preference indices of the fUS
 data at the voxel-level. Fig1gh_S4ab_S2ef.m: In this script, the
 region-segmented fUS data is further analyzed in respect to
 literature-defined brain networks. FigS1a.m: This script plots all stimuli
 used in the fUS experiments. FigS1c_2b.m: This script plots the
 region-segmented GLM results of the fUS data on top of the Allen Brain
 Atlas. FigS3abc.m: In this script the region-segmented fUS data is further
 analyzed by different clustering approaches. FigS3de.m: In this script,
 the voxel-wise fUS data is further analyzed by k-means clustering.
 FigS13.m: This script plots the pupil responses recorded during awake fUS
 experiments in response to object and control images. Fig2cdefgh.m: This
 script analyzes the electrophysiological recordings performed in five
 different regions of anesthetized mice in response to object and control
 stimuli. FigS6.m: This script visualizes all objects and control stimuli
 used in awake and anesthetized electrophysiological recordings. FigS7a.m,
 FigS7b.m and FigS7cd.m: These three scripts describe the analysis for the
 electrophysiological recordings performed in five different regions of
 anesthetized mice in response to stimuli aiming to map receptive field
 size (S7a), temporal frequency tuning (S7b), and spatial frequency tuning
 (S7cd). FigS9bcdefg.m: In this script, differences between
 electrophysiological recording in Postsubiculum from awake and
 anesthetized mice are analyzed. FigS9hi.m: This script analyzes the object
 sensitivity for the ON and OFF responses observed in electrophysiological
 recordings of the Postsubiculum of anesthetized mice. Fig3abcdefghij.m: In
 this script, the analysis of the electrophysiological data recorded in the
 Postsubiculum of awake mice is provided. Here, the mice were first allowed
 to freely explore an arena with one landmark, and subsequently, the mice
 were head-fixed and object and control images were presented to them.
 Fig4abcdef_FigS10abcdefghij.m: This script provides further analysis of
 the above-described data with emphasis on the head-direction cells and
 their relationship between head-direction coding and visual stimulus
 responses. Fig5abcd_S12.m: In this script, the freely-moving epoch of the
 above-described dataset is further analyzed with emphasis on how the
 visual landmark influences head-direction coding. Fig5efg.m: This script
 provides the analysis of electrophysiological data recorded in the
 Postsubiculum of awake mice. In this case, the mice were allowed to freely
 move in an arena containing two landmarks (an image of an object and an
 image of a scrambled version of this object). **Description of Figures:**
 Figure 1 (and associated Supplemental Figures S1-S4 and S13): Images of
 objects and scrambled control stimuli were shown to anesthetized or awake
 mice while whole-brain blood volume changes were recorded using functional
 ultrasound imaging. Figure 2 (and associated Supplemental Figures S5-S7
 and S9): Acute electrophysiological recordings in five brain regions were
 performed in anesthetized mice while showing images of objects and two
 different types of scrambled control stimuli. Figures 3-4 (and associated
 Supplemental Figures S8 and S10-S11): Chronic electrophysiological
 recordings in Postsubiculum of awake mice; First, the mice were allowed to
 freely move around in an arena with a single landmark and secondly, the
 mice were head-fixed, and the same stimuli as in Figure 2 were shown to
 the mice (images of objects and control stimuli). Panels Figure 4H-L are
 based on an *in silico* model described in the script
 “Fig4hijkl_FigS11.ipynb”. Figure 5 (and associated Supplemental Figure
 S12): Further analysis of chronic electrophysiological recordings in
 Postsubiculum of awake mice. In Figure 5A-D (script “Fig5abcd_S12.m”), the
 same epoch as described above, where the mice freely moved around in an
 arena with a single landmark, was analyzed. In Figure 5E-G (script
 “Fig5efg.m” the mice were placed in an arena with two landmarks (an object
 and a scrambled version of the same object). For this Dryad upload, three
 images of human faces were removed (“PC4_1”, PC4_2”, PC4_3”) from
 “ephys/all object stimuli” and “fUS/stimuli/PCs”. The original files were
 obtained from
 [https://fei.edu.br/~cet/facedatabase.html](https://fei.edu.br/~cet/facedatabase.html). ### Code/software Matlab and the following toolboxes are required: Curve Fitting Toolbox, Image Processing Toolbox, Statistics and Machine Learning Toolbox, Wavelet Toolbox, Bioinformatics Toolbox, Signal Processing Toolbox