网络扩散模型预测肢体起病型肌萎缩侧索硬化症的神经退行性变。
Network diffusion model predicts neurodegeneration in limb-onset Amyotrophic Lateral Sclerosis.
机构信息
Department of Psychiatry, Monash University, Clayton, Victoria, Australia.
Monash Biomedical Imaging, Monash University, Clayton, Victoria, Australia.
出版信息
PLoS One. 2022 Aug 11;17(8):e0272736. doi: 10.1371/journal.pone.0272736. eCollection 2022.
OBJECTIVE
Emerging evidences suggest that the trans-neural propagation of phosphorylated 43-kDa transactive response DNA-binding protein (pTDP-43) contributes to neurodegeneration in Amyotrophic Lateral Sclerosis (ALS). We investigated whether Network Diffusion Model (NDM), a biophysical model of spread of pathology via the brain connectome, could capture the severity and progression of neurodegeneration (atrophy) in ALS.
METHODS
We measured degeneration in limb-onset ALS patients (n = 14 at baseline, 12 at 6-months, and 9 at 12 months) and controls (n = 12 at baseline) using FreeSurfer analysis on the structural T1-weighted Magnetic Resonance Imaging (MRI) data. The NDM was simulated on the canonical structural connectome from the IIT Human Brain Atlas. To determine whether NDM could predict the atrophy pattern in ALS, the accumulation of pathology modelled by NDM was correlated against atrophy measured using MRI. In order to investigate whether network spread on the brain connectome derived from healthy individuals were significant findings, we compared our findings against network spread simulated on random networks.
RESULTS
The cross-sectional analyses revealed that the network diffusion seeded from the inferior frontal gyrus (pars triangularis and pars orbitalis) significantly predicts the atrophy pattern in ALS compared to controls. Whereas, atrophy over time with-in the ALS group was best predicted by seeding the network diffusion process from the inferior temporal gyrus at 6-month and caudal middle frontal gyrus at 12-month. Network spread simulated on the random networks showed that the findings using healthy brain connectomes are significantly different from null models.
INTERPRETATION
Our findings suggest the involvement of extra-motor regions in seeding the spread of pathology in ALS. Importantly, NDM was able to recapitulate the dynamics of pathological progression in ALS. Understanding the spatial shifts in the seeds of degeneration over time can potentially inform further research in the design of disease modifying therapeutic interventions in ALS.
目的
新出现的证据表明，磷酸化 43kDa 反式反应 DNA 结合蛋白（pTDP-43）的跨神经元传播有助于肌萎缩侧索硬化症（ALS）的神经退行性变。我们研究了网络扩散模型（NDM）是否可以捕捉 ALS 中神经退行性变（萎缩）的严重程度和进展。
方法
我们使用 FreeSurfer 分析了结构性 T1 加权磁共振成像（MRI）数据，对肢体起病的 ALS 患者（基线时 14 例，6 个月时 12 例，12 个月时 9 例）和对照组（基线时 12 例）进行了变性测量。NDM 是在 IIT 人类大脑图谱的典型结构连接组上进行模拟的。为了确定 NDM 是否可以预测 ALS 中的萎缩模式，我们将 NDM 建模的病理积累与 MRI 测量的萎缩进行了相关性分析。为了研究从健康个体衍生的大脑连接组上的网络传播是否是重要的发现，我们将我们的发现与在随机网络上模拟的网络传播进行了比较。
结果
横断面分析表明，与对照组相比，从额下回（三角部和眶部）起始的网络扩散种子显著预测了 ALS 中的萎缩模式。然而，ALS 组随时间的萎缩变化在 6 个月时从颞下回和 12 个月时从中后额回起始的网络扩散过程中得到了最佳预测。在随机网络上模拟的网络传播表明，使用健康大脑连接组的发现与零模型显著不同。
解释
我们的研究结果表明，运动前区域参与了 ALS 中病理传播的启动。重要的是，NDM 能够重现 ALS 中病理进展的动力学。随着时间的推移，了解变性种子在空间上的转移可能会为 ALS 中疾病修饰治疗干预的设计提供进一步的研究信息。
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