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Hum Brain Mapp. 2014 Apr;35(4):1351-61. doi: 10.1002/hbm.22258. Epub 2013 Mar 1.

Structural brain network imaging shows expanding disconnection of the motor system in amyotrophic lateral sclerosis.

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Department of Neurology, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands.


Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease, which primarily targets the motor system. The structural integrity of the motor network and the way it is embedded in the overall brain network is essential for motor functioning. We studied the longitudinal effects of ALS on the brain network using diffusion tensor imaging and questioned whether over time an increasing number of connections become involved or whether there is progressive impairment of a limited number of connections. The brain network was reconstructed based on "whole brain" diffusion tensor imaging data. We examined: (1) network integrity in 24 patients with ALS at baseline (T = 1) and at a more advanced stage of the disease (T = 2; interval 5.5 months) compared with a group of healthy controls and (2) progressive brain network impairment comparing patients at two time-points in a paired-analysis. These analyses demonstrated an expanding subnetwork of affected brain connections over time with a central role for the primary motor regions (P-values T = 1 0.003; T = 2 0.001). Loss of structural connectivity mainly propagated to frontal and parietal brain regions at T = 2 compared with T = 1. No progressive impairment of the initially affected (motor) connections could be detected. The main finding of this study is an increasing loss of network structure in patients with ALS. In contrast to the theory of ALS solely affecting a fixed set of primary motor connections, our findings show that the network of impaired connectivity is expanding over time. These results are in support of disease spread along structural brain connections.


amyotrophic lateral sclerosis; connectivity; diffusion tensor imaging; magnetic resonance imaging; neural networks; neuroimaging

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