Resting-state EEG reveals four subphenotypes of amyotrophic lateral sclerosis

Abstract

Amyotrophic lateral sclerosis (ALS) is a devastating disease characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. ALS is both clinically and biologically heterogeneous. Subgrouping is currently undertaken using clinical parameters, such as site of symptom onset (bulbar or spinal), burden of disease (based on the modified El Escorial Research Criteria) and genomics in those with familial disease. However, with the exception of genomics, these subcategories do not take into account underlying disease pathobiology, and are not fully predictive of disease course or prognosis. Recently, we have shown that resting-state EEG can reliably and quantitatively capture abnormal patterns of motor and cognitive network disruption in ALS. These network disruptions have been identified across multiple frequency bands, and using measures of neural activity (spectral power) and connectivity (co-modulation of activity by amplitude envelope correlation and synchrony by imaginary coherence) on source-localised brain oscillations from high-density EEG. Using data-driven methods (similarity network fusion and spectral clustering), we have now undertaken a clustering analysis to identify disease subphenotypes and to determine whether different patterns of disruption are predictive of disease outcome. We show that ALS patients (N = 95) 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. Using an in-depth stability analysis, we show that these clusters are statistically reproducible and robust, remain stable after re-assessment using a follow-up EEG session, and continue to predict the clinical trajectory and disease outcome. Our data demonstrate that novel phenotyping using neuroelectric signal analysis can distinguish disease subtypes based exclusively on different patterns of network disturbances. These patterns may reflect underlying disease neurobiology. 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.

Hippocampal Metabolic Alterations in Amyotrophic Lateral Sclerosis: A Magnetic Resonance Spectroscopy Study

“…Another trend of “big data” interrogation in ALS is the implementation of various clustering approaches to unravel inherent, naturally occurring sub-groups or patient cohorts with distinctive characteristics. A number of recent imaging studies have confirmed the existence of radiological sub-phenotypes using cluster analyses of connectomics [ 108 ], functional [ 109 ], or structural [ 110 ] raw datasets. The inclusion of MRS data into similar clustering pipelines may further help to untangle the heterogeneity of ALS and identify subcohorts with distinctive radiological characteristics.…”

Flexibility of Fast Brain Dynamics and Disease Severity in Amyotrophic Lateral Sclerosis

“…Our results were specific to the delta and the theta frequency bands. However, alterations in regional power or static functional connectivity have been described in all frequency bands 36,37 . Ours is the first M/EEG study directly addressing the dynamic, aperiodic, scalefree activity in ALS.…”

The progressive loss of brain network fingerprints in Amyotrophic Lateral Sclerosis predicts clinical impairment

“…According to Fraschini et al ( Fraschini et al, 2018 ) ALS patients showed reduced brain functional connectivity compared to healthy subjects, which was linked to different synchrony patterns in the alpha band. Nevertheless, alterations in functional connectivity have been reported in all frequency bands ( Dukic et al, 2022 , Nasseroleslami et al, 2019 , Proudfoot et al, 2017 ). Our is the first study which explores the individual connectome features in ALS using the CCF approach.…”