TY - JOUR
T1 - Connectome-Based Propagation Model in Amyotrophic Lateral Sclerosis
AU - Meier, Jil M.
AU - van der Burgh, Hannelore K.
AU - Nitert, Abram D.
AU - Bede, Peter
AU - de Lange, Siemon C.
AU - Hardiman, Orla
AU - van den Berg, Leonard H.
AU - van den Heuvel, Martijn P.
N1 - This article is protected by copyright. All rights reserved.
PY - 2020/5/1
Y1 - 2020/5/1
N2 - Objective: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression. Methods: Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets. Results: We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients. Interpretation: Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725–738.
AB - Objective: Clinical trials in amyotrophic lateral sclerosis (ALS) continue to rely on survival or functional scales as endpoints, despite the emergence of quantitative biomarkers. Neuroimaging-based biomarkers in ALS have been shown to detect ALS-associated pathology in vivo, although anatomical patterns of disease spread are poorly characterized. The objective of this study is to simulate disease propagation using network analyses of cerebral magnetic resonance imaging (MRI) data to predict disease progression. Methods: Using brain networks of ALS patients (n = 208) and matched controls across longitudinal time points, network-based statistics unraveled progressive network degeneration originating from the motor cortex and expanding in a spatiotemporal manner. We applied a computational model to the MRI scan of patients to simulate this progressive network degeneration. Simulated aggregation levels at the group and individual level were validated with empirical impairment observed at later time points of white matter and clinical decline using both internal and external datasets. Results: We observe that computer-simulated aggregation levels mimic true disease patterns in ALS patients. Simulated patterns of involvement across cortical areas show significant overlap with the patterns of empirically impaired brain regions on later scans, at both group and individual levels. These findings are validated using an external longitudinal dataset of 30 patients. Interpretation: Our results are in accordance with established pathological staging systems and may have implications for patient stratification in future clinical trials. Our results demonstrate the utility of computational models in ALS to predict disease progression and underscore their potential as a prognostic biomarker. ANN NEUROL 2020;87:725–738.
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U2 - https://doi.org/10.1002/ana.25706
DO - https://doi.org/10.1002/ana.25706
M3 - Article
C2 - 32072667
SN - 0364-5134
VL - 87
SP - 725
EP - 738
JO - Annals of neurology
JF - Annals of neurology
IS - 5
ER -