Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA.
National Taiwan University Cancer Center, Taipei, Taiwan; Department of Neurology, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei, Taiwan.
Neuroimage Clin. 2022;34:102997. doi: 10.1016/j.nicl.2022.102997. Epub 2022 Mar 30.
Multiple system atrophy (MSA) and Parkinson's disease (PD) belong to alpha-synucleinopathy, but they have very different clinical courses and prognoses. An imaging biomarker that can differentiate between the two diseases early in the disease course is desirable for appropriate treatment. Neuroimaging-based brain age paradigm provides an individualized marker to differentiate aberrant brain aging patterns in neurodegenerative diseases. In this study, patients with MSA (N = 23), PD (N = 33), and healthy controls (N = 34; HC) were recruited. A deep learning approach was used to estimate brain-predicted age difference (PAD) of gray matter (GM) and white matter (WM) based on image features extracted from T1-weighted and diffusion-weighted magnetic resonance images, respectively. Spatial normative models of image features were utilized to quantify neuroanatomical impairments in patients, which were then used to estimate the contributions of image features to brain age measures. For PAD of GM (GM-PAD), patients with MSA had significantly older brain age (9.33 years) than those with PD (0.75 years; P = 0.002) and HC (-1.47 years; P < 0.001), and no significant difference was found between PD and HC (P = 1.000). For PAD of WM (WM-PAD), it was significantly greater in MSA (9.27 years) than that in PD (1.90 years; P = 0.037) and HC (-0.74 years; P < 0.001); there was no significant difference between PD and HC (P = 0.087). The most salient image features that contributed to PAD in MSA and PD were different. For GM, they were the orbitofrontal regions and the cuneus in MSA and PD, respectively, and for WM, they were the central corpus callosum and the uncinate fasciculus in MSA and PD, respectively. Our results demonstrated that MSA revealed significantly greater PAD than PD, which might be related to markedly different neuroanatomical contributions to brain aging. The image features with distinct contributions to brain aging might be of value in the differential diagnosis of MSA and PD.
多系统萎缩症(MSA)和帕金森病(PD)属于α-突触核蛋白病,但它们的临床病程和预后有很大的不同。一种能够在疾病早期区分这两种疾病的影像学生物标志物,对于进行适当的治疗是非常理想的。基于神经影像学的大脑年龄范式为区分神经退行性疾病中异常的大脑衰老模式提供了一种个体化的标志物。在这项研究中,共招募了 MSA 患者(N=23)、PD 患者(N=33)和健康对照组(N=34;HC)。使用深度学习方法,基于从 T1 加权和弥散加权磁共振图像中提取的图像特征,分别估计灰质(GM)和白质(WM)的大脑预测年龄差异(PAD)。利用图像特征的空间规范模型来量化患者的神经解剖学损伤,然后用这些模型来估计图像特征对大脑年龄测量的贡献。对于 GM 的 PAD(GM-PAD),MSA 患者的大脑年龄明显比 PD 患者(9.33 岁;P=0.002)和 HC 患者(-1.47 岁;P<0.001)老,而 PD 患者与 HC 患者之间无显著差异(P=1.000)。对于 WM 的 PAD(WM-PAD),MSA 患者明显大于 PD 患者(9.27 岁;P=0.037)和 HC 患者(-0.74 岁;P<0.001),而 PD 患者与 HC 患者之间无显著差异(P=0.087)。MSA 和 PD 中导致 PAD 的最显著的图像特征不同。对于 GM,MSA 是眶额区域和楔前叶,PD 是眶额区域和楔前叶;对于 WM,MSA 是胼胝体中部和钩束,PD 是胼胝体中部和钩束。我们的研究结果表明,MSA 比 PD 显示出明显更大的 PAD,这可能与对大脑衰老有明显不同的神经解剖学贡献有关。对大脑衰老有不同贡献的图像特征可能对 MSA 和 PD 的鉴别诊断有价值。