Feng Yixue, Chandio Bramsh Q, Villalon-Reina Julio E, Thomopoulos Sophia I, Nir Talia M, Benavidez Sebastian, Laltoo Emily, Chattopadhyay Tamoghna, Joshi Himanshu, Venkatasubramanian Ganesan, John John P, Jahanshad Neda, Reid Robert I, Jack Clifford R, Weiner Michael W, Thompson Paul M
Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, United States.
Multimodal Brain Image Analysis Laboratory National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, Karnataka, India.
bioRxiv. 2024 Apr 28:2024.04.25.591183. doi: 10.1101/2024.04.25.591183.
Diffusion MRI is sensitive to the microstructural properties of brain tissues, and shows great promise in detecting the effects of degenerative diseases. However, many approaches analyze single measures averaged over regions of interest, without considering the underlying fiber geometry.
Here, we propose a novel Macrostructure-Informed Normative Tractometry (MINT) framework, to investigate how white matter microstructure and macrostructure are jointly altered in mild cognitive impairment (MCI) and dementia. We compare MINT-derived metrics with univariate metrics from diffusion tensor imaging (DTI), to examine how fiber geometry may impact interpretation of microstructure.
In two multi-site cohorts from North America and India, we find consistent patterns of microstructural and macrostructural anomalies implicated in MCI and dementia; we also rank diffusion metrics' sensitivity to dementia.
We show that MINT, by jointly modeling tract shape and microstructure, has potential to disentangle and better interpret the effects of degenerative disease on the brain's neural pathways.
扩散磁共振成像对脑组织的微观结构特性敏感,在检测退行性疾病的影响方面显示出巨大潜力。然而,许多方法分析的是感兴趣区域的单一测量平均值,而未考虑潜在的纤维几何结构。
在此,我们提出一种新颖的基于宏观结构的规范纤维束测量法(MINT)框架,以研究轻度认知障碍(MCI)和痴呆症中白质微观结构和宏观结构是如何共同改变的。我们将MINT得出的指标与扩散张量成像(DTI)的单变量指标进行比较,以研究纤维几何结构如何影响微观结构的解释。
在来自北美和印度的两个多中心队列中,我们发现了与MCI和痴呆症相关的微观结构和宏观结构异常的一致模式;我们还对扩散指标对痴呆症的敏感性进行了排名。
我们表明,通过联合对纤维束形状和微观结构进行建模,MINT有潜力理清并更好地解释退行性疾病对大脑神经通路的影响。