贝叶斯半参数隐马尔可夫随机划分场用于图上因子坍缩：指尖皮层映射研究

中文摘要 AI 辅助

理解大脑皮层区域如何响应指尖敲击等刺激是神经科学中的一个重大挑战，尤其是在使用高分辨率功能磁共振成像数据时。为解决这一问题，我们提出了一种新的统计方法，用于评估因子影响如何在复杂图（如大脑皮层）上局部变化。我们的方法旨在处理大图规模和计算需求带来的复杂性。该方法使用一种新颖的贝叶斯隐马尔可夫随机场，将因子的影响划分为具有相似效应的坍缩状态，每个节点上的结果均如此。这种独特模型促进了划分的稀疏性，并惩罚相邻节点间的大幅变化。结果得到一个高度详细的局部影响图，捕捉因子影响在图上的模式和幅度的细微变化。为确保大数据集的高效分析，我们开发了一种专门的马尔可夫链蒙特卡洛算法。我们的模拟实验表明，在准确性和可扩展性方面，该方法显著优于现有技术。最终，我们的方法为探索刺激与大脑活动之间的复杂关系提供了一个强大的新框架，为指尖的皮层映射提供了更清晰的图景。

英文摘要

Understanding how the brain's cortical regions respond to stimuli like fingertip tapping is a significant challenge in neuroscience, especially with high-resolution functional magnetic resonance imaging data. To address this, we propose a new statistical method for evaluating how a factor's influence locally varies across a complex graph, such as the brain's cortex. Our approach is designed to handle the complexities of large graph sizes and computational demands. Our method uses a novel Bayesian hidden Markov random field to partition the factor's influence into collapsed states with similar effects on the outcome at each node. This unique model promotes sparsity in the partitions and penalizes large variations across adjacent nodes. The result is a highly detailed local influence map that captures subtle changes in the patterns and magnitudes of a factor's influence across the graph. To ensure efficient analysis of large datasets, we developed a specialized Markov chain Monte Carlo algorithm. Our simulation experiments demonstrate significant improvements over existing techniques in both accuracy and scalability. Ultimately, our method provides a powerful new framework for exploring the intricate relationship between stimuli and brain activity, offering a clearer picture of the cortical mapping of fingertips.

发表机构

• University of Texas at Austin（德克萨斯大学奥斯汀分校）
• Northwestern University（西北大学）
• University of Florida（佛罗里达大学）

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