Modeling the Propagation of Neurodegeneration in Amyotrophic Lateral Sclerosis

Post by Elisa Guma

What's the science?

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disease that causes muscle weakness, paralysis, and ultimately, respiratory failure. It has been hypothesized that a misfolded protein (phosphorylated 43 kDa TAR DNA-binding protein) spreads through the brain of patients in a ‘prion-like’ manner, causing neurodegeneration as it spreads. Impairment is thought to begin in the motor regions of the brain, but it’s unclear if and how the spreading occurs. Further, there is a lot of heterogeneity in survival times following symptom onset. It is difficult to assess this network spreading hypothesis in vivo or in post-mortem tissue of ALS patients, and therefore computational approaches are appealing. This week in the Annals of Neurology, Meier, and colleagues examine the propagation model for disease progression in ALS and aim to predict disease progression by using network analyses of brain imaging data from patients.

How did they do it?

The authors leveraged a longitudinal dataset of 60 participants with ALS who underwent four magnetic resonance image (MRI) scans (comprising of a structural and a diffusion-weighted scan) over the course of their illness. They also used a different dataset with 120 controls matched at each timepoint for age and sex. To construct the connectome, the authors first parcellated the brain (using the structural MRI scan) into 68 cortical and 14 subcortical regions. Next, white matter connections between parcellated brain regions were estimated based on the diffusion-weighted scans. The authors applied network-based statistics to find the largest grouping of impaired brain regions in ALS patients. Brain region impairment was defined by the number of  impaired white matter connections/streamlines (with lower fractional anisotropy than normal) attached to that brain region

A random walker model was used to model disease progression. They started the model in regions shown to be impaired first in ALS and tested all the possible connections coming out of that region. Regions with a higher number of connections had a greater likelihood of being traversed by the random walker in the model. Upper motor neuron burden was also calculated per brain region. Their models were compared to regions known to be affected at four stages of disease progression. Finally, they used a deep learning model to predict patient survival. They used a previous model they had built but included the random walker aggregation levels to assess whether they could improve their prediction accuracy. They examined 30 ALS patients and 30 controls from an external dataset who were scanned three times longitudinally for validation.

What did they find?

Based on their network-based statistics analysis, as well as the random walker model, the authors found that ALS patients had an impaired connectome that seemed to spread in a spatiotemporal manner, in line with the hypothesis they were testing. Impairment based on the network-based statistics analysis and the random walker was correlated. More importantly, impairment overlapped with known stages of disease progression, suggesting that the computational model is reflective of disease progression. When their computational model was tested on a dataset, they found a similar correlation between simulated disease spread and known stages of disease progression.  They also found that the level of upper motor neuron burden for each region correlated with their model of disease spread, suggesting that patients with higher upper motor neuron burden had more widespread aggregation, and patients with lower burden had higher variance and aggregation. Finally, the authors found that including the random walker model in their prediction algorithm improved their ability to accurately predict survival time for ALS patients from 79.6% to 83.3%.

What's the impact?

This study provides compelling evidence for the utility of computational network models to predict neurodegeneration in ALS. The authors show that a random walker model based on MRI scans could predict degeneration over time in ALS patients, with overlap between known stages of disease progression and upper motor neuron burden. These results support the hypothesis that ALS impairment begins in the motor cortex and spreads along white matter tracts in a spatiotemporal manner. Future work is needed to investigate the impact of various genetic mutations associated with ALS on network degeneration.

Jil M. Meier et al. Connectome-based propagation model in amyotrophic lateral sclerosis. Annals of Neurology (2020). Access the original scientific publication here.