论文标题
尼古丁相关电路的生成人工智能动态检测
Generative artificial intelligence-enabled dynamic detection of nicotine-related circuits
论文作者
论文摘要
与成瘾相关的电路的识别对于解释成瘾过程和发展成瘾治疗至关重要。功能成像开发的功能成瘾电路模型是发现和验证成瘾电路的有效工具。但是，分析成瘾和检测功能成瘾电路的功能成像数据仍然有挑战。我们已经开发了一个数据驱动和端到端的生成人工智能（AI）框架来解决这些困难。该框架集成了动态的大脑网络建模和新颖的网络体系结构网络体系结构，包括时间图形变压器和对比度学习模块。我们的生成AI框架形成了完整的工作流程：从神经生物学实验和计算建模到端到端神经网络的功能成像数据被转换为动态尼古丁成瘾相关电路。它可以检测具有动态特性的与成瘾相关的脑回路，并揭示了成瘾的潜在机制。
The identification of addiction-related circuits is critical for explaining addiction processes and developing addiction treatments. And models of functional addiction circuits developed from functional imaging are an effective tool for discovering and verifying addiction circuits. However, analyzing functional imaging data of addiction and detecting functional addiction circuits still have challenges. We have developed a data-driven and end-to-end generative artificial intelligence(AI) framework to address these difficulties. The framework integrates dynamic brain network modeling and novel network architecture networks architecture, including temporal graph Transformer and contrastive learning modules. A complete workflow is formed by our generative AI framework: the functional imaging data, from neurobiological experiments, and computational modeling, to end-to-end neural networks, is transformed into dynamic nicotine addiction-related circuits. It enables the detection of addiction-related brain circuits with dynamic properties and reveals the underlying mechanisms of addiction.
