Deighton Jared, Zhong Shan, Agyeman Kofi, Choi Wooseong, Liu Charles, Lee Darrin, Maroulas Vasileios, Christopoulos Vassilios
Department of Mathematics, University of Tennessee, Knoxville, Knoxville, TN, USA.
Neuroscience graduate program, University of California Riverside, Riverside, CA, USA.
ArXiv. 2025 Aug 19:arXiv:2410.09523v2.
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 rely 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 compared three machine learning approaches-convolutional neural network (CNN), support vector machine (SVM), and vision transformer (ViT)-combined with fUSI to analyze the pharmacodynamics of Dizocilpine (MK-801), a potent non-competitive NMDA receptor antagonist commonly used in preclinical models for memory and learning impairments. While all three machine learning techniques could distinguish between drug and control conditions, CNN proved particularly effective due to their ability to capture hierarchical spatial features while maintaining anatomical specificity. Class activation mapping revealed brain regions, including the prefrontal cortex and hippocampus, that are significantly affected by drug administration, consistent with the literature reporting a high density of NMDA receptors in these areas. Overall, the combination of fUSI and CNN creates a novel analytical framework for examining pharmacological mechanisms, allowing for data-driven identification and regional mapping of drug effects while preserving anatomical context and physiological relevance.
功能超声成像（fUSI）是一项前沿技术，它通过检测在其视野（FOV）内移动的红细胞的反向散射回波来测量脑血容量（CBV）的变化。它具有高时空分辨率和灵敏度，能够详细可视化脑血流动力学。虽然fUSI已被用于临床前药物开发研究，以探索各种针对中枢神经系统的药物的作用机制，但许多这些研究依赖于预先确定的感兴趣区域（ROI）。这种关注可能会忽略这些特定区域之外的相关脑活动，而这可能会影响结果。为了解决这一局限性，我们将三种机器学习方法——卷积神经网络（CNN）、支持向量机（SVM）和视觉Transformer（ViT）——与fUSI相结合，以分析地卓西平（MK-801）的药效学，地卓西平是一种强效的非竞争性NMDA受体拮抗剂，常用于临床前模型中治疗记忆和学习障碍。虽然所有三种机器学习技术都能区分药物和对照条件，但CNN因其能够在保持解剖学特异性的同时捕捉分层空间特征而被证明特别有效。类激活映射揭示了包括前额叶皮层和海马体在内的脑区，这些脑区受药物给药的显著影响，这与文献报道这些区域中NMDA受体的高密度一致。总体而言，fUSI和CNN的结合为研究药理机制创建了一个新的分析框架，允许在保留解剖背景和生理相关性的同时，通过数据驱动识别药物效应并进行区域映射。
