ADVERTISEMENT
Resting-State EEG Reveals Patterns in Amyotrophic Lateral Sclerosis
Recent data show that novel phenotyping using neuroelectric signal analysis can help identify 4 different disease subtypes of amyotrophic lateral sclerosis (ALS), according to findings published in Brain.
Study findings indicated that resting-state EEG could show abnormal patterns of cognitive and motor network disruptions in patients with ALS.
In this study, researchers aimed to use data-driven methods to perform a clustering analysis with the aim to identify subphenotypes of ALS while discovering if different patterns of disease disruption signify different disease outcomes.
A total of 95 patients with ALS within the first 18 months of diagnosis who fulfilled the El Escorial diagnostic criteria for Possible, Probable, or Definite ALS from the National ALS clinic in Beaumont Hospital Dublin were recruited, in addition to 77 healthy controls from an existing population-based control bank.
“We show that ALS patients can be subgrouped into four phenotypes with distinct neurophysiological profiles. These clusters are characterised by varying degrees of disruption in the somatomotor (α-band synchrony), frontotemporal (β-band neural activity and γl-band synchrony) and frontoparietal (γl-band co-modulation) networks, which reliably correlate with distinct clinical profiles and different disease trajectories,” wrote Stefan Dukic, Academic Unit of Neurology, Trinity Biomedical Sciences Institute, Trinity College Dublin, University of Dublin, Ireland, and co-researchers.
The clusters remained stable after re-assessment and follow-up, and data suggest that disease subtypes can be distinguished with novel phenotyping using neuroelectric signal analysis.
“The identification of ALS subtypes based on profiles of differential impairment in neuronal networks has clear potential in future stratification for clinical trials. Advanced network profiling in ALS can also underpin new therapeutic strategies that are based on principles of neurobiology and designed to modulate network disruption,” concluded Dukic et al.
Reference
