Sub-second dopaminergic reinforcement orchestrates juvenile social play and is disrupted in Shank3 deficiency. Chen et al.
Description
This dataset accompanies a study testing whether nucleus accumbens dopamine (DA) acts as an action‑contingent reinforcement signal shaping juvenile social play in a sex‑divergent manner, and whether Shank3 deficiency disrupts this signal. We collected multimodal data from wild‑type (WT) and Shank3+/- (PND 35‑85) during same‑sex dyadic interactions. A nine‑camera volumetric system (120 fps) with segmentation‑first instance segmentation (Mask R‑CNN/YOLOv8) and DANNCE tracked 14 body keypoints. The Social‑Seq pipeline derived 32 kinematic/interaction features, segmented interactions into 800‑ms clips, and used Seq2seq RNN with perspective‑invariant active learning to generate 36 behavioral syllables (e.g., sniffing, chasing, pouncing, pinning, rearing). Simultaneously, fiber photometry recorded NAc DA dynamics via GRAB‑DA3m. In Shank3+/- males, a real‑time closed‑loop system (266‑ms latency) delivered 40‑Hz optogenetic stimulation to VTA‑NAc projections specifically upon proactive play initiation over 8 training days, followed by 10 stimulation‑free days. Key findings: In WT males, proactive play (pouncing, pinning) evoked DA surges, while forced submission suppressed DA. In WT females, DA increased during evasion and rearing but not contact‑heavy play. Shank3 mutants showed blunted DA during sniffing/chasing and, critically, a sign‑inverted DA response during pouncing , while solitary rearing produced exaggerated DA only in mutant males . A multi‑agent reinforcement learning model parameterized with empirical DA amplitudes reproduced mutant phenotypes (preserved sniffing, reduced play, disrupted transitions). Closed‑loop DA during play increased targeted play duration persistently and reduced non‑social leaving, establishing causal sufficiency. Data include raw 3D keypoint coordinates, behavioral syllable labels, synchronized DA ΔF/F traces, optogenetic timestamps, and summary metrics (frequencies, durations, transition matrices). Researchers can use these to replicate the ethogram, perform neural‑behavior alignment, parameterize computational models, or evaluate intervention outcomes. Key considerations: sex must be treated as a covariate; behavioral labels have quantitative operational definitions; the Shank3 model (exon 11‑21) is specific to Phelan‑McDermid syndrome; and optogenetic results demonstrate sufficiency, not exclusivity, of DA. This multi‑scale resource supports studies of developmental social reward and autism‑related motivation deficits.
Files
Steps to reproduce
After downloading, unzip it into a folder named Figshare. Installation Supports Windows, Linux, and MacOS. First, install the dependency environment via the uv tool. Warning Do not use uv and conda together. This will cause package installation conflicts. Before running the following commands, please run conda deactivate first. bash # Before running the following code, please download the FIGSHARE code and data. unzip Figshare_DATA_2025*.zip -d Figshare cd Figshare python3 -m pip install uv # UV is a lightweight Python package manager, similar to conda. uv sync --index-url https://pypi.tuna.tsinghua.edu.cn/simple # Optional Tsinghua mirror for users in China; otherwise installation may be very slow. uv run python --version # Verify Python version (should be 3.12) Install custom packages bash uv pip install git+https://github.com/chenxinfeng4/multiview_calib.git git clone https://github.com/chenxinfeng4/LILAB-py.git ../LILAB-py uv pip install -e ../LILAB-py uv run python -m lilab.multiview_scripts_dev.p1_checkboard_global -h # Verify installation Generate a single figure: Fig1C.pdf You can run the scripts in Fig*/Fig*.py one by one to generate figures. bash uv run python Fig1_S1/Fig1C.py # Results saved to Fig1_S1/result/Fig1C.pdf Note: If any error occurs, make sure all data files have been downloaded correctly. Generate all figures with one command bash uv run python main.py test This generates Fig1.pdf, FigS1.pdf, Fig2*.pdf, ... and saves the results to the corresponding folders: Fig*/result/*.pdf, e.g., Fig1_S1/result/Fig1E.pdf. Clean all results (restore to original state) bash #1. Delete all result data uv run python main.py clean #2. Or manually delete all result data rm Fig*/result/*pdf Fig*/result/*pkl Fig*/result/*png
Institutions
- Chinese Institute for Brain ResearchBeijing, Beijing