Monitoring Drug-Induced Brain Activity Changes with Functional Ultrasound Imaging and Convolutional Neural Networks

Functional ultrasound imaging (fUSI) is a cutting-edge technology that measures changes in cerebral blood volume (CBV) by detecting backscattered echoes from red blood cells moving within its field of view (FOV). It offers high spatiotemporal resolution and sensitivity, allowing for detailed visualization of cerebral blood flow dynamics. While fUSI has been utilized in preclinical drug development studies to explore the mechanisms of action of various drugs targeting the central nervous system, many of these studies have primarily focused on predetermined regions of interest (ROIs). This focus may overlook relevant brain activity outside these specific areas, which could influence the results. To address this limitation, we combined convolutional neural networks (CNNs) with fUSI to comprehensively understand the pharmacokinetic process of Dizocilpine, also known as MK-801, a drug that blocks the N-Methyl-D-aspartate (NMDA) receptor in the central nervous system. CNN and class activation mapping (CAM) revealed the spatiotemporal effects of MK-801, which originated in the cortex and propagated to the hippocampus, demonstrating the ability to detect dynamic drug effects over time. Additionally, CNN and CAM assessed the impact of anesthesia on the spatiotemporal hemodynamics of the brain, revealing no distinct patterns between early and late stages. The integration of fUSI and CNN provides a powerful tool to gain insights into the spatiotemporal dynamics of drug action in the brain. This combination enables a comprehensive and unbiased assessment of drug effects on brain function, potentially accelerating the development of new therapies in neuropharmacological studies.

Adaptive Environment-Aware Robotic Arm Reaching Based on a Bio-Inspired Neurodynamical Computational Framework

Bio-inspired robotic systems are capable of adaptive learning, scalable control, and efficient information processing. Enabling real-time decision-making for such systems is critical to respond to dynamic changes in the environment. We focus on dynamic target tracking in open areas using a robotic six-degree-of-freedom manipulator with a bird-eye view camera for visual feedback, and by deploying the Neurodynamical Computational Framework (NeuCF). NeuCF is a recently developed bio-inspired model for target tracking based on Dynamic Neural Fields (DNFs) and Stochastic Optimal Control (SOC) theory. It has been trained for reaching actions on a planar surface toward localized visual beacons, and it can re-target or generate stop signals on the fly based on changes in the environment (e.g., a new target has emerged, or an existing one has been removed). We evaluated our system over various target-reaching scenarios. In all experiments, NeuCF had high end-effector positional accuracy, generated smooth trajectories, and provided reduced path lengths compared with a baseline cubic polynomial trajectory generator. In all, the developed system offers a robust and dynamic-aware robotic manipulation approach that affords real-time decision-making.