Machine Learning Department, Carnegie Mellon University, Pittsburgh, PA, USA.
Neuroscience Institute, Carnegie Mellon University, Pittsburgh, PA, USA.
Aging Dis. 2024 Aug 1;15(4):1899-1912. doi: 10.14336/AD.2022.1210.
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.
阿尔茨海默病(AD)和肥胖与白质(WM)连接组的紊乱有关。我们通过基于轨迹的方法——边缘密度成像/指数(EDI),来研究 WM 连接组与肥胖和 AD 之间的联系,该方法可描绘轨迹连接的解剖结构。从阿尔茨海默病神经影像学倡议(ADNI)中选择了 60 名参与者,其中 30 名已知在至少 24 个月的随访中从正常认知或轻度认知障碍转为 AD。从基线扫描的弥散加权磁共振图像中提取分数各向异性(FA)和 EDI 图,然后使用基于 Desikan-Killiany 图谱的确定性 WM 轨迹追踪法对其进行平均。采用多元线性和逻辑回归分析来确定与体重指数(BMI)或转为 AD 相关性最大的特定于束的 FA 或 EDI 指数的加权和。使用开放获取成像研究系列(OASIS)的参与者作为 BMI 结果的独立验证。边缘密度丰富的脑室周围、连合和投射纤维是与 BMI 相关的 FA 和 EDI 最重要的 WM 束之一。对与 BMI 相关的回归模型有重要贡献的 WM 纤维与预测转化的纤维重叠;特别是在额桥束、皮质纹状体束和视辐射通路中。这些结果通过在 OASIS-4 数据集测试使用 ADNI 发现的特定于束的系数得到了验证。使用 EDI 进行 WM 映射可识别出与肥胖和转化为 AD 均有关的异常连接组。