@article{8748718,
  abstract     = {{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 deficits are needed to improve symptomatic treatment and slow down disease progression. This study aimed to identify individualized functional connectivity-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.

Author Summary Amyotrophic lateral sclerosis is recognized as a multisystem disorder, and currently no effective therapy is available for this devastating disease. Reliable models that can predict disease progression may facilitate the development of more efficient symptomatic treatment. This study used multiple kernel learning algorithm to identify a potential functional connectivity-based marker for cognitive and motor functioning in ALS. The results show that cognitive decline and motor progression could be predicted by seed-based functional connectivity from the medial prefrontal cortex/posterior cingulate cortex to the sensorimotor network, fronto-parietal network, and salience network. The identified brain connectivity signatures may serve as novel targets for effective disease-modifying therapies.}},
  author       = {{Wei, Luqing and Baeken, Chris and Liu, Daihong and Zhang, Jiuquan and Wu, Guo-Rong}},
  issn         = {{2472-1751}},
  journal      = {{NETWORK NEUROSCIENCE}},
  keywords     = {{Applied Mathematics,Artificial Intelligence,Computer Science Applications,General Neuroscience,Amyotrophic lateral sclerosis,Cognitive changes,Functional connectivity,Motor severity,DEFAULT-MODE NETWORK,SALIENCE NETWORK,ALZHEIMERS-DISEASE,ALSFRS-R,IMPAIRMENT,STATE,DIAGNOSIS,CORTEX,FRONTOPARIETAL,INDIVIDUALS}},
  language     = {{eng}},
  number       = {{1}},
  pages        = {{161--174}},
  title        = {{Functional connectivity–based prediction of global cognition and motor function in riluzole-naive amyotrophic lateral sclerosis patients}},
  url          = {{http://doi.org/10.1162/netn_a_00217}},
  volume       = {{6}},
  year         = {{2022}},
}

Altmetric

View in Altmetric

Web of Science

Times cited: