Biology subjects

Lacin, M. E.

Publications and source records attributed to Lacin, M. E..

3 recordsLinked to original sources

bioRxiv2026

A Deterministically Synchronized Widefield Imaging and Virtual Reality Platform for Multimodal Brain Behavior Recording

ObjectiveSimultaneous recording of brain activity, behaviour, and virtual environments is essential for understanding large-scale neural dynamics during behaviour. However, existing systems often rely on software-based synchronization or post hoc alignment, introducing latency, jitter, and drift that obscure fast brain-behavior interactions. ApproachHere, we present a deterministically synchronized widefield calcium imaging platform that unifies neural imaging, high-speed behavioural monitoring, and closed-loop virtual reality (VR) under a shared hardware-defined clock. This system enables millisecond-precision temporal alignment across modalities, including dual-wavelength hemodynamic correction, pupil and orofacial tracking, locomotion sensing, and VR rendering. Main resultsThe platform achieves stable hardware-level synchronization across neural imaging, behavioural recordings, and VR rendering without reliance on software timestamps. It supports widefield imaging rates up to 100 Hz and integrates seamlessly with both ViRMEn and Blender VR engines, exhibiting a mean locomotion-to-VR update latency of [~]1.5 ms. Multimodal recordings during VR navigation demonstrate robust temporal alignment between cortical activity, facial dynamics, pupil signals, and locomotion. SignificanceThis system provides a deterministic multimodal framework for studying brain-behaviour relationships during active behaviour. By enabling millisecond-precision synchronization across neural imaging, behaviour, and virtual environments, this platform enables causal investigation of brain-behaviour interactions at millisecond precision and provides a foundation for next-generation closed-loop neuroengineering experiments.

neuroscience↗

bioRxiv2026

NeuroPupil: A generalization-first framework for scalable and biologically informative cross-species pupillometry

Quantitative pupillometry provides a noninvasive window into brain state and neurological function, but its broader use across experimental and clinical settings is limited by challenges in achieving accurate, scalable, and generalizable measurements. Here, we present NeuroPupil, a deep learning framework for high-throughput, cross-species pupillometry that emphasizes robust generalization across subjects, behavioral contexts, and imaging conditions. Through systematic benchmarking of training strategies and network architectures, we identify pooled multi-subject training combined with an optimized U-Net architecture as a key determinant of reliable and transferable pupil tracking performance. Across diverse mouse and human datasets, NeuroPupil achieves improved accuracy and substantial gains in computational efficiency compared to existing approaches, enabling practical analysis of large-scale datasets. We further demonstrate that improved pupil tracking fidelity enhances downstream biological inference: NeuroPupil-derived pupil features significantly improve prediction of distributed cortical activity in behaving mice and preserve diagnostically relevant temporal structure in human clinical recordings. These findings highlight the importance of precise and scalable measurement for linking pupil dynamics to brain activity and clinical phenotypes. By integrating benchmarking, scalability, and accessible software tools, NeuroPupil provides a reproducible framework for large-scale pupillometry and facilitates its application in systems and translational neuroscience.

neuroscience↗

bioRxiv2025

DeepFace: A High-Precision and Scalable Deep Learning Pipeline for Predicting Large-Scale Brain Activity from Facial Dynamics in Mice

We present DeepFace, a next-generation facial analysis pipeline that enhances orofacial tracking and cortical activity prediction in mice. Rather than replacing existing tools, DeepFace builds upon DeepLabCut and Facemap to address scalability bottlenecks and improve behavioral quantification. It offers high precision, keypoint customization, and robust performance across GCaMP6s, GCaMP6f, and jGCaMP8m lines. With scalable batch processing and high-performance computing compatibility, DeepFace enables high-throughput brain-behavior analysis in large-scale preclinical neuroscience.

neuroscience↗