Functional connectivity-based prediction of global cognition and motor function in riluzole-naive amyotrophic lateral sclerosis patients

Luqing Wei, Chris Baeken, Daihong Liu, Jiuquan Zhang, Guo-Rong Wu
2021 Network Neuroscience  
Amyotrophic lateral sclerosis (ALS) is increasingly recognized as a multisystem disorder accompanied by cognitive changes. To date, no effective therapy is available for ALS patients, partly due to disease heterogeneity and an imperfect understanding of the underlying pathophysiological processes. Reliable models that can predict cognitive and motor de ficits are needed to improve symptomatic treatment and slow down disease progression. This study aimed to identify individualized functional
more » ... ectivity-based predictors of cognitive and motor function in ALS by using multiple kernel learning (MKL) regression. Resting-state fMRI scanning was performed on 34 riluzole-naive ALS patients. Motor severity and global cognition were separately measured with the revised ALS functional rating scale (ALSFRS-R) and the Montreal Cognitive Assessment (MoCA). Our results showed that functional connectivity within the default mode network (DMN) as well as between the DMN and the sensorimotor network (SMN), fronto-parietal network (FPN), and salience network (SN) were predictive for MoCA scores. Additionally, the observed connectivity patterns were also predictive for the individual ALSFRS-R scores. Our findings demonstrate that cognitive and motor impairments may share common connectivity fingerprints in ALS patients. Furthermore, the identified brain connectivity signatures may serve as novel targets for effective disease-modifying therapies.
doi:10.1162/netn_a_00217 pmid:35356196 pmcid:PMC8959121 fatcat:sphkqs72dvepjdystvrscgyzpi