DOI: 10.5061/dryad.41ns1rnh2
title: Dopamine activity in the tail of the striatum, DeepLabCut and MoSeq during novel object exploration

description: In this study, we characterized dynamics of novelty exploration using
 multi-point tracking (DeepLabCut) and behavioral segmentation (MoSeq).
 Mice were habituated in an arena, and then a object was placed at the
 corner of the arena. We compared 4 groups of mice: one with presentation
 of a novel object (stimulus novelty), one with a presentation of a
 familiar object (contextual novelty), one with presentation of a
 novel object after ablation of dopamine neuorns that project to the tail
 of the striatum (TS), and one with presentation of a novel object
 after sham surgery. With a separate group of mice, dopamine activity in TS
 was recorded during novelty exploration.
description: Novelty testing sessions consisted of animals exploring a single
 novel object within the behavioral arena. Object was placed in the corner
 of a behavioral arena (taped to floor to prevent animal from moving it,
 ~12-15cm from either wall). Sessions lasted for 25 minutes per animal per
 day for 4-12 days, and mice were run in the same order as habituation each
 day. One object was used per animal for duration of experiment and the
 objects were not shared between animals. Before each session, object would
 be submerged in soiled bedding (mixture of bedding from each mouse’s cage
 in current round, 6 animals) and wiped off with dry kimwipe to remove
 excess bedding dust. Objects were wiped with ethanol after each day and
 allowed to air out overnight before use. For body part
 tracking, we used DeepLabCut version 1.0 (Mathis et al., 2018). Separate
 networks were used for different experimental settings: namely for mice
 without fiber implants (network A) and mice with fiber implants (network
 B). Both networks were run using a ResNet-50-based neural network (He et
 al., 2016; Insafutdinov et al., 2016) with default parameters for
 1,030,000 training iterations. We provided manually labeled locations of
 four mouse body parts within video frames for training: nose, left ear
 base, right ear base, and tail base. For network A: We labeled 1760 frames
 taken from 64 videos. For network B: We labeled 540 frames taken from 17
 videos. For both networks, 95% of labeled frames were then used for
 training. Raw imaging data was collected from the depth
 camera, pre-processed (filtered, background subtracted, and parallax
 corrected), and submitted to a machine learning algorithm that evaluates
 the pose dynamics over time (Wiltschko et al., 2015). During video
 extraction (moseq2-extract), 900 frames were trimmed from the beginning of
 the video to correct for time between when video was started and when the
 mouse was placed in arena. During model learning (moseq2-model), a
 hyperparameter was set to the total number of frames in the training set
 (kappa=2,711,134, 52 sessions, 52 animals). This exceeds the recommended
 &gt;=1 million frames (at 30 frames per second) needed to ensure
 quality MoSeq modeling. DA sensor (green) and tdTomato
 (red) signals were collected as voltage measurements from current
 pre-amplifiers (SR570, Stanford Research Systems, CA). Green and red
 signals were cleaned by removing 60 Hz noise with bandstop FIR filter
 58-62 Hz and smoothing with a moving average of signals in 50 ms. The
 global change within a session was normalized using a moving median of 100
 s. Then, the correlation between green and red signals was examined by
 linear regression. If the correlation was significant (p&lt;0.05), the
 fitted red signals were subtracted from green signals. Responses aligned
 at a behavioral event were calculated by subtracting the average baseline
 activity (-3s to -1s before the event) from the average activity of the
 target window (0-1s after the event).
description: # Dopamine activity in the tail of the striatum, DeepLabCut and MoSeq
 during novel object exploration
 There are data for 5
 animal groups. Two groups of mice were presented with stimulus novelty or
 contextual novelty. Different two groups of mice were presented with
 stimulus novelty and dopamine neurons that project to the tail of the
 striatum (TS) were ablated or sham surgery was operated. A different group
 of mice were presented with stimulus novelty and dopamine activity in TS
 was recorded. All mice were recorded in videos and location of body parts
 was tracked with DeepLabCut. Some videos were also analyzed with Moseq. ##
 Description of the data and file structure # Data structure DLC
 (DeepLabCut) data is stored in .mat files. Given .mat files were named
 based on the following file structure:   . +-- group1_name |   +--
 animal1_name |       +-- session1_name |           -- DLClabel.mat |      
 +-- session2_name |       +-- session3_name |   +-- animal2_name |   +--
 animal3_name |   -- bout.mat |   -- bout_multi.mat +-- group2_name +--
 group3_name \-- akiti_miceID_231129.xlsx   There is a separate .mat file
 for each session. DLClabel.mat contains the variable "Labels"
 which has the following format:   Labels(:,2) Nose x (pixel) Labels(:,3)
 Nose y (pixel) Labels(:,5) Leftear x (pixel) Labels(:,6) Leftear y (pixel)
 Labels(:,8) Rightear x (pixel) Labels(:,9) Rightear y (pixel) Labels(:,11)
 Tailbase x (pixel) Labels(:,12) Tailbase y (pixel) Labels(:,14)
 Tailmidpoint x (pixel) Labels(:,15) Tailmidpoint y (pixel) Labels(:,17)
 Tailtip x (pixel) Labels(:,18) Tailtip y (pixel) Labels(:,20) Head x
 (pixel) 'average of nose, leftear and rightear' Labels(:,21)
 Head y (pixel) Labels(:,22) Body x (pixel) 'average of head and tail
 base' Labels(:,23) Body y (pixel) Labels(:,24) Tail x (pixel)
 'average of tailtip, midpoint and base' Labels(:,25) Tail y
 (pixel) Labels(:,26) head speed (pixel) Labels(:,27) head accerelation
 (pixel) Labels(:,28) head jerk (pixel) Labels(:,29) body speed (pixel)
 Labels(:,30) body accerelation (pixel) Labels(:,31) body jerk (pixel)
 Labels(:,32) nose distance from object (pixel) Labels(:,33) head distance
 from object (pixel) Labels(:,34) tailbase distance from object (pixel)
 Labels(:,35) body length (pixel) Labels(:,36) head speed related to object
 (pixel) Labels(:,37) head speed unrelated to object (pixel) Labels(:,38)
 tail-base from wall (pixel)   MoSeq data is stored in
 `MoSeq_MiceIndex_wLabels_combine3L.mat`. This file contains the variable
 "Mice" which has the following format:   Mice.name: mouse name
 Mice.novelty: stimulus novelty ‘S’ or contextual novelty ‘C’ Mice.ExpDay:
 experiment date     Photometry data (Dopamine sensor in the tail of the
 striatum, TS) is stored in files named ` *_approach_start.mat`,
 `*_retreat.mat`, and `*_retreat_end.mat` (3 files for the first day of
 novelty for each animal).   Original videos are stored in files named
 ‘_rgb.mp4’.   Information about each mouse is stored in
 akiti_miceID_231129.xlsx.   There are two types of sessions: ·     
 "novel"1, 2, 3, ... : an object was presented. 1, 2, 3 indicates
 day. For example, novel1 means that the data was obtained on the first day
 of object presentation. A same object was presented on following days.
 ·      "hab"1, 2 : habituation sessions with no object, before
 novelty day 1.   There are 5 groups of mice: 1.     "FP_all":
 recording of dopamine sensor signals in TS on novelty day 1 2.    
 "stimulus": a novel object was presented (a different object was
 presented before habituation day 1 in a home cage) 3.    
 "contextual": an unexpected familiar object was presented (a
 same object was presented before habituation day 1 in a home cage) 4.    
 “saline”: sham surgery (vehicle injection), a novel object was presented
 5.     “6OHDA”: TS-projecting dopamine neurons were ablated, a novel
 object was presented   MoSeq data was obtained using data of groups 2 to 5
 on novelty day 1.Sharing/Access information ## Code/Software Matlab code
 files using this dataset are available on GitHub
 (