DOI: 10.5061/dryad.ht76hdrsp
title: Autism-like atypical face processing in Shank3 mutant dogs

description: Atypical face processing is a neurocognitive basis of social deficits in
 autism spectrum disorder (ASD) and a candidate cognitive marker for the
 disease. Although hundreds of risk genes have been identified in ASD, it
 remains unclear whether mutations in a specific gene may cause ASD-like
 atypical face processing. Dogs have acquired exquisite face
 processing abilities during domestication and may serve as an effective
 animal model for studying genetic associations of ASD-like atypical face
 processing. Here, we showed that dogs with Shank3 mutations
 exhibited behavioral and attentional avoidance of faces, contrasting with
 wild-type controls. Moreover, neural responses specific to faces (versus
 objects) recorded from the electrodes over the temporal
 cortex were significantly decreased and delayed in
 Shank3 mutants compared to wild-type controls. Cortical responses
 in the frontal/parietal region underlying categorization of faces by
 species/breeds were reduced in Shank3 mutants. Our
 findings of atypical face processing in dogs with Shank3 mutations provide
 a useful animal model for studying ASD mechanisms and treatments.
description: <strong>Subjects</strong>
 Twenty-three WT (beagles) from Sinogene Ltd (Beijing, China) and
 fifteen <em>Shank3</em> mutant dogs (beagles) were tested in
 this study. The mean age did not differ significantly between WT controls
 and <em>Shank3 </em>mutants (WT, 19.3 ± 1.33 months of age
 (mean ± SEM); Mutants, 24.5 ± 2.75 months of age, U = 125.5, P = 0.164,
 see Tables S1 and S2 for detailed information about the subjects in each
 experiment). Four WT controls (80, 201138, 210755, 201115) and three
 <em>Shank3</em> mutants (201111, 201112, 201141) were tested
 in a previous behavioral study of dog-human interactions (20). Two
 <em>Shank3</em> mutants (190203, 190604) were tested in an
 ECoG study which required the subjects to passively listen to pure
 sinusoidal tones (27). None of these studies involved any training with
 food or employed stimuli of animal/human faces or houses. Three dogs
 tested in this work were littermates (i.e., WT(201115) and
 <em>Shank3</em> mutants (201111 and 201112)). WT(201115)
 participated in Experiment 1. <em>Shank3</em> mutants 201111
 participated in Experiments 1 and 4. <em>Shank3</em> mutants
 201112 participated in Experiment 4. The
 <em>Shank3</em> mutations generate frameshifts and truncated
 proteins disrupting the ANK domain and proline-rich domain of
 <em>Shank3</em> in mutant dogs (<em>20</em>). All
 mutant dogs showed a similarly reduced level of
 <em>Shank3</em> protein and similar autism-like social
 deficits, including social withdrawal and reduced social interactions with
 humans (<em>20</em>). Each dog was housed in a single cage and
 maintained on a 12-hour light/12-hour dark cycle with lights on at 7:00
 am. All subjects were submitted to ophthalmological and behavior
 evaluation to verify their health conditions before the study. No animal
 was sacrificed in these studies. All experimental procedures were approved
 by the Ethical Committee of the Institute of Genetics and Developmental
 Biology of the Chinese Academy of Sciences (AP2022033).
description: # Autism-like atypical face processing in Shank3 mutant dogs
 [https://doi.org/10.5061/dryad.ht76hdrsp
 ](https://doi.org/10.5061/dryad.ht76hdrsp) ## Description of the data and
 file structure * Files of ‘beagle&house, beagle&husky,
 car&house, dog&chimp, inverted beagle&inverted house’ are the
 data of behavioral preference data. - Each file contains the number of
 times and the proportion that wild-type dogs and mutant dogs respectively
 choose the corresponding category of pictures. * The file names that
 include "ECoG" are for the data of electrophysiological
 experiments. Different sheets in each file represent the results of
 different electroencephalographic components in the corresponding
 experiments. This data is mainly used for variance analysis. - The files
 with names including "eye-tracking" contain data from our
 eye-tracking experiments. It contains the results of each trial for each
 animal. * The statistical values mentioned in the text are obtained by
 averaging the trials for each animal into a single value for the purpose
 of statistical analysis. - The uploaded code file can be opened in the
 psychology software Presentation (0.71 09.24.03), and it is for an ECoG
 experiments. The file name corresponds to the experimental content.
 **Visual stimuli** The visual stimuli used in the present study included
 photos of 16 human faces, 16 beagle faces, 24 husky faces, 8 poodle faces,
 16 chimpanzee faces, 16 cars, and 16 houses. Black and white stimuli were
 used to control the potential effects of color differences between face
 and house stimuli. The details of the stimuli used for each experiment are
 shown in Tables S1 and S2. The human faces were adopted from the previous
 work (*28*). Chimpanzees, dog faces, and cars were collected from public
 Internet image resources. Faces of full-frontal views with eyes open,
 direct forward gaze, and neutral expression were used. All the images were
 unfamiliar to the subjects. Luminance and contrast were matched for images
 of each category using the SHINE toolbox in MATLAB and for images of
 different categories. All images were presented on a gray background (122
 cd/m2). The stimuli produced a 20.3 cm × 25.4 cm picture (resolution: 800
 ×1000 pixels) on the center of the screens, positioned 240 cm away from
 the subject in face preference tests. In the electrophysiological
 experiments, each stimulus with a resolution of 400×500 pixels was
 displayed on a 23.7-inch screen, positioned approximately 65 cm from the
 subject. **Behavioral preference test** The experimental set-up was
 modified from an approach-avoidance test in a previous study (*34*), as
 illustrated in Fig. 1A. The set-up included a food tray placed close to
 the bottom of a computer monitor on each side of the room. The two sets of
 food trays and monitors were separated by a board. Before the experiment,
 a dog was allowed to move freely in the test room for about 10 min.
 Thereafter, the experimenter turned the dog around with the back facing
 the two monitors. At the beginning of each trial, two identical dog snacks
 were placed on each of the trays. A pair of photos of two different
 categories were then randomly displayed on the left and right monitors
 (Fig. 1B). The photos of the two stimuli used in each trial varied
 randomly across trials. The test dog was guided to sit in the middle of
 the testing room facing the monitors for 3-5 seconds before being released
 to walk toward a food tray. After eating the snack on one food tray, the
 subject was led back to the release point for the next trial. A subject
 was tested for 16 trials in 15–45 min per day. If a test dog did not
 approach one of the two sides with different photo stimuli in a trial, the
 test was terminated. **Eye movement recording** The preferential viewing
 task was adapted from a previous study (*64*) for the eye tracking
 measurements after the calibration procedure using the EyeLink five-point
 calibration program. The experimenter guided the dogs through finger
 tapping or food cues to ensure their gaze fixation on the calibration
 point. The experimenter then repeated the five-point calibration to get
 the value of calibration accuracy. Subjects seated or stood approximately
 85 cm away from a 23.7-inch LCD monitor (1920×1080 pixels resolution) when
 viewing the stimuli. Eye movements were recorded at a sampling rate of 500
 Hz using an EyeLink 1000 Plus eye tracker (SR Research Ltd, Mississauga,
 Ontario, Canada). Each trial started with a moving circle that caught a
 subject’s attention toward the center of the monitor. Once a fixation on
 the circle lasted longer than 100 ms, two stimuli of different categories
 were presented simultaneously on the left and the right sides of the
 screen in a random order across trials for 3 seconds, similar to the
 paradigm used in previous studies of humans (*65*). Eye-tracking data
 during the presentation of the stimuli were analyzed. Eye-tracking
 measurements were conducted in Experiment 2 using photos of dog faces and
 houses and in Experiment 4 using photos of dog/human faces, dog/chimpanzee
 faces, and beagle/husky faces. In each experiment, a subject was tested in
 16 trials using each set of face stimuli. Additional trials were performed
 in case of failed trials to ensure the completion of 16 trials for each
 experiment. **ECoG data recording** ECoG signals were recorded from 32
 electrodes over the right hemisphere which covered the frontal, parietal,
 temporal, and occipital cortices, using the protocol described in previous
 research (Fig. S2) (*27*). Each electrode disc was 2.0 mm in diameter and
 spaced 5 mm apart. Electrode positions were verified using computer
 tomography scans. ECoG recordings were performed in a sound-attenuated
 room with dim light. Zeus data acquisition system (Zeus, Nanjing, China)
 was used to record ECoG signals with a sampling rate of 1 kHz. The subject
 was seated approximately 65 cm from the computer screen that was
 positioned to ensure the stimulus positions at the center of the screen.
 the stimuli. During ECoG recording, a dog was instructed to sit on the
 ground or on the experimenter’s lap. A camera was set to allow the
 experimenter to monitor the subject’s eye gaze. Each trial in Experiment 3
 started with the presentation of an image of a face or house for 1500 ms
 in the center of the gray background. This was followed by a fixation
 cross with a duration varying randomly from 250 to 550 ms. Each subject
 was tested in 4 to 6 sessions on separate days and there were 6 runs in
 each session. Each run consisted of 2 blocks of 24 trials. There was a 5s
 break between two consecutive blocks. In Experiments 6 and 7, ECoG was
 recorded in a repetition suppression paradigm in 5 sessions. There were 6
 runs in each session and there were 4 blocks of 16 trials in each run. The
 stimulus duration and inter-stimulus interval were the same as those in
 Experiment 3. **Behavioral data analysis** We quantified behavioral
 preferences for faces (against houses) as the percentage of trials in
 which a dog walked toward a face photo and preference for houses (against
 cars) as the percentage of trials in which a dog initially walked toward a
 house photo in Experiment 1. Behavioral preferences in Experiment 4 were
 quantified as the percentage of trials in which a dog walked toward for
 faces of its own species (against human or chimpanzee faces) or breed
 (against husky faces). One-sample t-tests were conducted to assess
 behavioral preferences for faces (or faces of a specific species) in
 Experiments 1 and 4. **Eye-tracking data analysis** We first quantified
 the ratio of time devoted to observing various categories of stimuli in
 relation to the total looking time in each trial. Trials with less than
 25% screening-looking time were considered invalid and excluded from data
 analyses. We defined the area on the left side of the screen, which
 encompasses the width subtracted from the fixation point to the center as
 an area of interest 1 (AOI-1), and the area on the right side of the
 screen, which encompasses the width subtracted from the fixation point to
 the center as AOI-2. The two AOIs independently represent the two
 different pictures in each trial. The AOIs were determined for faces and
 house stimuli in Experiment 2 and for faces of different species/breeds in
 Experiment 5. We also defined the area of eye region in Experiment 2 as
 AOI-3. By calculating the duration of all fixations falling inside each
 AOI in each trial, we obtained the total looking time on the target region
 for each trial. Because eye-tracking data do not follow a normal
 distribution, we used the nonparametric Mann-Whitney test to access
 statistical differences in behaviors between the two testing groups (i.e.,
 WT controls and *Shank3* mutants), and the nonparametric Wilcoxon signed
 rank test to assess differences in behaviors between two measures in one
 testing group (e.g., gaze fixations on faces versus house in WT controls).
 These statistical methods were applied similarly to individual-based data
 analyses (see results in the main text) and trial-based data analysis (see
 results in the Supplementary Materials, Fig. S1 and Fig. S5). Heatmap of
 the gaze distribution was drawn through a Python toolbox GazePointHeatMap
 (https://github.com/TobiasRoeddiger/GazePointHeatMap), based on the
 viewing time of the stimuli. **ECoG data analysis** ECoG data analyses
 were performed using MATLAB (version 2020b, Mathworks Inc., Natick, MA)
 and the EEGLAB toolbox. During pre-processing, the ECoG signals were
 re-referenced using a common average reference (CAR) montage and band-pass
 filtered from 0.1 to 30 Hz. ERPs in each condition were averaged
 separately offline with an epoch beginning 200 ms before stimulus onset
 and continuing for 600 ms after stimulus onset. Trials contaminated by
 noises exceeding ±200 μV at any electrode were excluded from the average
 analysis. The baseline for ERP measurements was the mean voltage across a
 200 ms prestimulus time window. The latency was measured relative to the
 stimulus onset. The time windows of peak responses were independently
 defined for each ERP component in each experiment (see Table S3 for
 details). The mean amplitudes and peak latencies of the P1, N1, and P2
 components in Experiment 3 were subjected to repeated-measures analyses
 (ANOVAs) with Stimulus category (human face vs. dog face vs. house) as a
 within-subjects factor and Testing group (WT vs. *Shank3* mutants) as a
 between-subjects factor. The mean P1 and N2 amplitude in Experiments 6 and
 7 were subject to ANOVAs with Stimulus categories (human vs. dog faces, or
 beagle versus husky) and Conditions (repetition vs. alternating
 conditions) as within-subjects factors and Group (WT vs. mutants) as a
 between-subjects factor. RS effects were defined as decreased amplitudes
 to faces of a category in the repetition compared to alternating
 conditions. To further verify the ERP results shown in ANOVAs by
 controlling the potential effects of within-subject correlations, we
 conducted linear mixed-effect model (LMM) analyses of the ERP data. The
 statistical analyses of single-trial ECoG amplitudes were conducted using
 the mixed-effects single-trial regression models, similar to the method of
 a previous work (*66*). The fixed-effect factors included Stimuli (first
 level: -0.67 for house, +0.33 for human, +0.33 for dog; second level:
 -0.33 for house, -0.33 for human, +0.67 for dog), Testing Group (-0.5 for
 WTs, +0.5 for *Shank3* mutants), and their interactions in Experiment 3.
 In Experiments 6 and 7, the fixed-effect factors included Stimuli (-0.5
 for dog/beagle faces, +0.5 for human/husky faces), Condition (-0.5 for the
 alternating condition, +0.5 for the repetition condition), and Testing
 Group (-0.5 for WTs, +0.5 for *Shank3* mutants), and their interactions.
 Subjects’ ID and the recording sessions of each subject were included as
 the nested random-effect factors.