个人简介：亓兴勤，山东大学数学与统计学院教授，博士生导师。2006年6月毕业于山东大学数学学院运筹学与控制论专业，获理学博士。2009年5月至2011年5月期间，于美国西弗吉尼亚大学数学系做博士后研究。2006年7月至今在山东大学数学与统计学院任教。目前主要从事图与复杂网络、生物信息学等领域的研究。主要研究兴趣包括网络数据分析及建模等。主持或完成国家自然科学基金3项，山东省自然科学基金项目3项。目前为中国运筹学会图论与组合分会理事，中国工业与应用数学学会信息和通讯技术领域的数学专委会委员。

报告题目:

Extracting interpretable higher-order topological features across multiple scales from fMRI for Alzheimer’s disease classification

报告内容：

Brain network topology, derived from functional magnetic resonance imaging (fMRI), holds promise for improving Alzheimer’s disease (AD) diagnosis. Current methods primarily focus on lower-order topological features, often overlooking the significance of higher-order features such as connected components, cycles, and cavities. These higher-order features are critical for understanding normal brain function and have been increasingly linked to the pathological mechanisms of AD. However, their quantification for diagnosing AD is hindered by their inherent nonlinearity and stochasticity in the brain. In this talk, we  introduce a novel framework for diagnosing AD that uses persistent homology to extract higher-order topological features from fMRI data. It also introduces four quantitative methods that capture subtle, multiscale geometric variations in functional brain networks associated with AD. Our experimental results demonstrate that this framework significantly outperforms existing methods in AD classification, achieving substantial gains in accuracy metrics (typically 10%–30% over baselines on ADNI). This study highlights the potential of higher-order topological features for early AD detection and significantly advances the field of brain topology analysis in neurodegenerative disease research.

报告时间：2026年3月11日 上午08:30

报告地点：8119智慧教室