NeuroTD: A Time-Frequency Based Multimodal Learning Approach to Analyze Time Delays in Neural Activities
Studying the temporal dynamics of neural activities is essential for understanding how neurons function. These dynamics often involve temporal delays between neurons that vary over time, revealing both their functions and how they interact within circuits. Recent techniques such as Neuropixels, depth electrodes, and Patch-seq enable time-series recordings of neural activity at various scales, ranging from single neurons to large populations. However, inferring such time-varying delays remains challenging due to noise, high sampling rates, and complex temporal patterns. To address these challenges, we developed NeuroTD, a novel computational approach based on sliding windows to align time-series datasets and infer time-varying delays. Particularly, NeuroTD integrates adaptive window-size tuning to obtain optimal and robust delay estimates. We first benchmarked NeuroTD in simulation studies, demonstrating its robustness and outperformance. Then we applied it to two emerging real-world datasets: (i) intracranial multi-channel electrophysiological recordings from depth electrodes across medial temporal lobe regions in humans, showing that hippocampal signals recorded via depth electrodes exhibited consistently significantly longer time delays than other regions during working memory tasks, and (ii) Patch-seq data in the mouse motor cortex, revealing intrinsic electro-physiological time-delays of excitatory neurons correlated with gene expression and highlighting pathways related to ion transport and neuronal excitability. Finally, NeuroTD is open-source and available at https://github.com/daifengwanglab/NeuroTD for general use.