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脑OSM:为多视图功能性脑网络分析进行异常选

Guiliang Guo¹, Guangqi Wen¹, Lingwen Liu¹

¹College of Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, China.

Computer methods and programs in biomedicine

|October 10, 2025

概括

通过分析功能性大脑网络 (FBNs),BrainOSM提高了对自闭症谱系障碍 (ASD) 和阿尔茨海默病 (AD) 的诊断. 这种新的方法通过处理图形异质性和无关数据来提高分类准确性,有助于早期发现疾病.

科学领域:

神经科学是一个神经科学.
计算生物学计算生物学
医疗信息学医疗信息学

背景情况:

确定精神疾病的可靠生物标志物对于早期诊断和个性化治疗至关重要.
功能性大脑网络 (FBNs),以图形表示,捕捉大脑连接,但面临着异质性和噪音等挑战.
自闭症谱系障碍 (ASD) 和阿尔茨海默病 (AD) 的诊断需要强大的方法来分析复杂的FBN.

研究的目的:

开发和验证一个新的框架,BrainOSM,用于使用FBN诊断ASD和AD.
在FBN分析中解决图形异质性和与疾病无关的信息的挑战.
通过先进的图形分析,提高精神疾病分类的准确性和通用性.

主要方法:

介绍了BrainOSM,这是一个两阶段的框架,结合了异常选和多视图图集.
采用渐进的基于不确定性的异常选,以减轻图表间异质性.
集成的多图组合,多视图学习和事先的子网络规范化,以改进图结构并减少噪音.

主要成果:

与传统的图形卷积网络 (GCN) 方法相比,BrainOSM在ABIDE (ASD) 和ADNI (AD) 数据集上表现出卓越的性能.
在ABIDE上达到70.23%的平均准确度和70.42%的AUC,比GCN的性能高8.55%和7.74%.
在ADNI上达到82.29%的平均精度和83.23%的AUC,比GCN有8.97%和11.78%的改进.

结论:

脑OSM是使用FBN进行精神疾病分类的可通用和有效框架.
该方法成功地识别了与疾病相关的子网络,为临床解释提供了潜力.
异常点查是提高异质神经成像数据集分类准确性的关键组成部分.

关键词:

阿尔茨海默氏症是阿尔茨海默氏症的一种疾病.自闭症谱系障碍自闭症谱系障碍功能性大脑网络功能性大脑网络基于图表的分类是基于图表的分类.异常值选异常值的选

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