MGRD：用于方差感知跨域神经突预测的紧凑形态门控残差扩散
MGRD: Compact morphology-gated residual diffusion for variance-aware cross-domain neurite forecasting
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中文总结 AI 辅助
提出紧凑随机扩散模型MGRD，从十帧观测预测二十帧神经突形态，以更低参数和计算开销在跨域数据集上提升预测精度，并提供方差评分辅助案例筛选。
中文摘要 AI 辅助
追踪神经突形态随时间的变化有助于表征神经元发育和退化过程中的结构变化，但长期延时成像资源密集且难以扩展。预测未来形态可减轻这一负担。现有的神经突数字孪生模型（如门控时空注意力gSTA）产生单一确定性预测，无法表示合理未来之间的变异性。我们提出形态门控残差扩散（MGRD），一种紧凑的随机替代模型，从十个观测帧联合预测未来二十个神经突形态帧，同时以最新观测中提取的形态特征为条件。在受控相场轨迹上，MGRD相对于匹配对照组将轨迹级平均MAE降低9.7%，同时更新参数减少4.46倍。在人类iPSC衍生神经元显微镜数据上，MGRD在gSTA报告的所有四项指标上均有提升，包括轨迹级平均MAE降低39.6%，骨架F1提高45.3%。无需小鼠域重训练或微调，MGRD在10-40分钟采样间隔及超过13小时的预测时域上，也改善了小鼠皮层神经球显微镜数据的MAE和骨架F1。重复采样提供案例级方差评分，用于排序预测难度。保留约60%最低方差案例可将iPSC显微镜数据平均MAE降低17.6%，模拟数据降低16.8%。MGRD使用gSTA参数的1.01%，训练更新所需时间不足其十分之一，且在形态特征缓存时生成50步DDIM轨迹速度快7.9%。这些结果确立了MGRD作为神经突形态预测和案例优先级排序的紧凑随机替代模型，适用于模拟和显微镜数据集。
英文摘要
Tracking neurite morphology over time helps characterize structural changes during neuronal development and deterioration, but long-term time-lapse imaging is resource-intensive and difficult to scale. Forecasting future morphology could reduce this burden. Existing neurite digital-twin models such as gated spatiotemporal attention (gSTA) produce a single deterministic forecast without representing variability among plausible futures. We introduce Morphology-Gated Residual Diffusion (MGRD), a compact stochastic surrogate that jointly forecasts twenty future neurite-morphology frames from ten observed frames while conditioning on morphology features derived from the latest observation. On controlled phase-field trajectories, MGRD reduces trajectory-wise mean MAE by 9.7% relative to a matched control while updating 4.46 times fewer parameters. On human iPSC-derived neuron microscopy, MGRD improves all four reported metrics over gSTA, including a 39.6% reduction in trajectory-wise mean MAE and a 45.3% increase in skeleton F1. Without mouse-domain retraining or fine-tuning, MGRD also improves MAE and skeleton F1 on mouse cortical-neurosphere microscopy across 10-40-min sampling intervals and forecast horizons beyond 13 hours. Repeated sampling provides a case-level variance score for ranking forecast difficulty. Retaining approximately 60% of the lowest-variance cases reduces mean MAE by 17.6% on iPSC microscopy and 16.8% on simulation data. MGRD uses 1.01% of gSTA's parameters, requires less than one tenth of its training-update time, and generates a 50-step DDIM trajectory 7.9% faster when morphology features are cached. These results establish MGRD as a compact stochastic surrogate for neurite-morphology forecasting and case prioritization across simulation and microscopy datasets.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
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