Integrating Multimodal Neuroimaging and Genetics: A Structurally-Linked Sparse Canonical Correlation Analysis Approach

IF 3.7 3区 医学 Q2 ENGINEERING, BIOMEDICAL
Jiwon Chung;Sunghun Kim;Ji Hye Won;Hyunjin Park
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引用次数: 0

Abstract

Neuroimaging genetics represents a multivariate approach aimed at elucidating the intricate relationships between high-dimensional genetic variations and neuroimaging data. Predominantly, existing methodologies revolve around Sparse Canonical Correlation Analysis (SCCA), a framework we expand to 1) encompass multiple imaging modalities and 2) promote the simultaneous identification of structurally linked features across imaging modalities. The structurally linked brain regions were assessed using diffusion tensor imaging, which quantifies the presence of neuronal fibers, thereby grounding our approach in biologically well-founded prior knowledge within the SCCA model. In our proposed structurally linked SCCA framework, we leverage T1-weighted MRI and functional MRI (fMRI) time series data to delineate both the structural and functional characteristics of the brain. Genetic variations, specifically single nucleotide polymorphisms (SNPs), are also incorporated as a genetic modality. Validation of our methodology was conducted using a simulated dataset and large-scale normative data from the Human Connectome Project (HCP). Our approach demonstrated superior performance compared to existing methods on simulated data and revealed interpretable gene-imaging associations in the real dataset. Thus, our methodology lays the groundwork for elucidating the genetic underpinnings of brain structure and function, thereby providing novel insights into the field of neuroscience. Our code is available at https://github.com/mungegg .
整合多模态神经成像与遗传学:结构关联稀疏典型相关分析方法
神经影像遗传学是一种多变量方法,旨在阐明高维遗传变异与神经影像数据之间错综复杂的关系。现有的方法主要围绕稀疏典型相关分析(SCCA)展开,我们对这一框架进行了扩展:1)涵盖多种成像模式;2)促进跨成像模式同时识别结构关联特征。结构关联的脑区是通过扩散张量成像评估的,扩散张量成像可量化神经元纤维的存在,从而使我们的方法立足于 SCCA 模型中具有生物学基础的先验知识。在我们提出的结构关联 SCCA 框架中,我们利用 T1 加权 MRI 和功能 MRI(fMRI)时间序列数据来划分大脑的结构和功能特征。遗传变异,特别是单核苷酸多态性(SNPs),也作为一种遗传模式被纳入其中。我们使用模拟数据集和来自人类连接组项目(HCP)的大规模标准数据对我们的方法进行了验证。与模拟数据上的现有方法相比,我们的方法表现出更优越的性能,并在真实数据集中揭示了可解释的基因成像关联。因此,我们的方法为阐明大脑结构和功能的基因基础奠定了基础,从而为神经科学领域提供了新的见解。我们的代码见 https://github.com/mungegg。
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来源期刊
CiteScore
7.40
自引率
2.90%
发文量
65
审稿时长
27 weeks
期刊介绍: The IEEE Journal of Translational Engineering in Health and Medicine is an open access product that bridges the engineering and clinical worlds, focusing on detailed descriptions of advanced technical solutions to a clinical need along with clinical results and healthcare relevance. The journal provides a platform for state-of-the-art technology directions in the interdisciplinary field of biomedical engineering, embracing engineering, life sciences and medicine. A unique aspect of the journal is its ability to foster a collaboration between physicians and engineers for presenting broad and compelling real world technological and engineering solutions that can be implemented in the interest of improving quality of patient care and treatment outcomes, thereby reducing costs and improving efficiency. The journal provides an active forum for clinical research and relevant state-of the-art technology for members of all the IEEE societies that have an interest in biomedical engineering as well as reaching out directly to physicians and the medical community through the American Medical Association (AMA) and other clinical societies. The scope of the journal includes, but is not limited, to topics on: Medical devices, healthcare delivery systems, global healthcare initiatives, and ICT based services; Technological relevance to healthcare cost reduction; Technology affecting healthcare management, decision-making, and policy; Advanced technical work that is applied to solving specific clinical needs.
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