Edge Density Imaging Identifies White Matter Biomarkers of Late-Life Obesity and Cognition.

IF 7 2区 医学 Q1 GERIATRICS & GERONTOLOGY Aging and Disease Pub Date : 2024-08-01 DOI:10.14336/AD.2022.1210
Maxwell Bond Wang, Farzaneh Rahmani, Tammie L S Benzinger, Cyrus A Raji
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Abstract

Alzheimer disease (AD) and obesity are related to disruptions in the white matter (WM) connectome. We examined the link between the WM connectome and obesity and AD through edge-density imaging/index (EDI), a tractography-based method that characterizes the anatomical embedding of tractography connections. A total of 60 participants, 30 known to convert from normal cognition or mild-cognitive impairment to AD within a minimum of 24 months of follow up, were selected from the Alzheimer disease Neuroimaging Initiative (ADNI). Diffusion-weighted MR images from the baseline scans were used to extract fractional anisotropy (FA) and EDI maps that were subsequently averaged using deterministic WM tractography based on the Desikan-Killiany atlas. Multiple linear and logistic regression analysis were used to identify the weighted sum of tract-specific FA or EDI indices that maximized correlation to body-mass-index (BMI) or conversion to AD. Participants from the Open Access Series of Imaging Studies (OASIS) were used as an independent validation for the BMI findings. The edge-density rich, periventricular, commissural and projection fibers were among the most important WM tracts linking BMI to FA as well as to EDI. WM fibers that contributed significantly to the regression model related to BMI overlapped with those that predicted conversion; specifically in the frontopontine, corticostriatal, and optic radiation pathways. These results were replicated by testing the tract-specific coefficients found using ADNI in the OASIS-4 dataset. WM mapping with EDI enables identification of an abnormal connectome implicated in both obesity and conversion to AD.

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边缘密度成像确定晚年肥胖和认知的白质生物标志物
阿尔茨海默病(AD)和肥胖与白质(WM)连接组的破坏有关。我们通过边缘密度成像/指数(EDI)研究了白质连接组与肥胖和阿兹海默症之间的联系,EDI是一种基于牵引成像的方法,可描述牵引成像连接的解剖学嵌入。研究人员从阿尔茨海默病神经影像学倡议(ADNI)中选取了 60 名参与者,其中 30 人已知在至少 24 个月的随访期内会从认知正常或轻度认知障碍转变为注意力缺失症。基线扫描的弥散加权磁共振图像用于提取分数各向异性(FA)和EDI图,随后使用基于Desikan-Killiany图谱的确定性WM束图对其进行平均。多重线性回归分析和逻辑回归分析用于确定与体质指数(BMI)或AD转换相关性最大的特定束FA或EDI指数的加权和。来自 "影像研究开放存取系列"(Open Access Series of Imaging Studies,OASIS)的参与者被用作 BMI 研究结果的独立验证。边缘密度丰富的脑室周围纤维、神经节纤维和投射纤维是将BMI与FA和EDI联系起来的最重要的WM束。在与 BMI 相关的回归模型中起重要作用的 WM 纤维与预测转换的纤维重叠;特别是在额叶、皮层和视辐射通路中。通过在 OASIS-4 数据集中测试使用 ADNI 发现的特定束系数,这些结果得到了验证。利用EDI绘制WM图谱能够识别与肥胖和向AD转化有关的异常连接组。
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来源期刊
Aging and Disease
Aging and Disease GERIATRICS & GERONTOLOGY-
CiteScore
14.60
自引率
2.70%
发文量
138
审稿时长
10 weeks
期刊介绍: Aging & Disease (A&D) is an open-access online journal dedicated to publishing groundbreaking research on the biology of aging, the pathophysiology of age-related diseases, and innovative therapies for conditions affecting the elderly. The scope encompasses various diseases such as Stroke, Alzheimer's disease, Parkinson’s disease, Epilepsy, Dementia, Depression, Cardiovascular Disease, Cancer, Arthritis, Cataract, Osteoporosis, Diabetes, and Hypertension. The journal welcomes studies involving animal models as well as human tissues or cells.
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