利用可解释图神经网络对 fMRI、DTI 和 sMRI 进行大脑连接性综合分析。

ArXiv Pub Date : 2024-08-26
Gang Qu, Ziyu Zhou, Vince D Calhoun, Aiying Zhang, Yu-Ping Wang
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引用次数: 0

摘要

多模态神经成像建模已成为一种广泛应用的方法,但由于异质性(包括不同模态的数据类型、规模和格式的差异),它面临着相当大的挑战。这种多变性要求采用先进的计算方法,在一个连贯的分析框架内整合和解释这些不同的数据集。在我们的研究中,我们将功能磁共振成像(fMRI)、弥散张量成像(DTI)和结构磁共振成像(sMRI)整合到一个具有凝聚力的框架中。这种整合利用了每种模式的独特优势及其内在联系,旨在全面了解大脑的连通性和解剖特征。我们利用 Glasser 图集进行解析,将来自不同模式的成像衍生特征--fMRI 的功能连通性、DTI 的结构连通性和 sMRI 的解剖特征--整合到一致的区域中。我们的方法采用掩蔽策略对神经连接进行不同加权,从而促进多模态成像数据的整体融合。这项技术增强了连接层面的可解释性,超越了以单一区域属性为中心的传统分析。该模型被应用于人类连接组计划的发展研究,以阐明多模态成像与青少年认知功能之间的关联。该分析提高了预测准确性,发现了关键的解剖特征和重要的神经连接,加深了我们对大脑结构和功能的理解。这项研究提供了一种对多种成像模式进行综合分析的新方法,不仅推动了多模态神经成像分析的发展,而且加深了人们对大脑结构和功能网络与认知发展之间错综复杂关系的理解。
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Integrated Brain Connectivity Analysis with fMRI, DTI, and sMRI Powered by Interpretable Graph Neural Networks.

Multimodal neuroimaging modeling has become a widely used approach but confronts considerable challenges due to heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitates the deployment of advanced computational methods to integrate and interpret these diverse datasets within a cohesive analytical framework. In our research, we amalgamate functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural MRI (sMRI) into a cohesive framework. This integration capitalizes on the unique strengths of each modality and their inherent interconnections, aiming for a comprehensive understanding of the brain's connectivity and anatomical characteristics. Utilizing the Glasser atlas for parcellation, we integrate imaging-derived features from various modalities-functional connectivity from fMRI, structural connectivity from DTI, and anatomical features from sMRI-within consistent regions. Our approach incorporates a masking strategy to differentially weight neural connections, thereby facilitating a holistic amalgamation of multimodal imaging data. This technique enhances interpretability at connectivity level, transcending traditional analyses centered on singular regional attributes. The model is applied to the Human Connectome Project's Development study to elucidate the associations between multimodal imaging and cognitive functions throughout youth. The analysis demonstrates improved predictive accuracy and uncovers crucial anatomical features and essential neural connections, deepening our understanding of brain structure and function. This study not only advances multi-modal neuroimaging analytics by offering a novel method for the integrated analysis of diverse imaging modalities but also improves the understanding of intricate relationship between the brain's structural and functional networks and cognitive development.

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