Distinct longitudinal changes in EEG measures reflecting functional network disruption in ALS cognitive phenotypes
Amyotrophic lateral sclerosis (ALS) is characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. We have shown previously that resting-state EEG captures dysfunction in motor and cognitive networks in ALS. However, the longitudinal development of these dysfunctional patterns, especially in networks linked with cognitive-behavioural functions, remains unclear. Longitudinal studies on non-motor changes in ALS are essential to further develop our understanding of disease progression, improve care and enhance the evaluation of new treatments. To address this gap, we examined 124 ALS individuals with 128-channel resting-state EEG recordings, categorised by cognitive impairment (ALSci, n = 25), behavioural impairment (ALSbi, n = 58), or non-impaired (ALSncbi, n = 53), with 12 participants meeting the criteria for both ALSci and ALSbi. Using linear mixed-effects models, we characterised the general and phenotype-specific longitudinal changes in brain network, and their association with cognitive performance, behaviour changes, fine motor symptoms, and survival. Our findings revealed a significant decline in θ-band spectral power over time in the temporal region along with increased γl-band power in the fronto-temporal region in the ALS group. ALSncbi participants showed widespread β-band synchrony decrease, while ALSci participants exhibited increased co-modulation correlated with verbal fluency decline. Longitudinal network-level changes were specific of ALS subgroups and correlated with motor, cognitive, and behavioural decline, as well as with survival. Spectral EEG measures can longitudinally track abnormal network patterns, serving as a candidate stratification tool for clinical trials and personalised treatments in ALS.
Funding
Thierry Latran Foundation
Novel biomarkers of ALS subphenotypes using advanced imaging and spectral EEG technology
Health Research Board
Find out more...Novel Neurophysical Biomarkers of Heterogeneous Network Degeneration in Motor Neuron Disease for Quantifying the Progression and Outcome in Clinical Trials
Health Research Board
Find out more...Health Research Board of Ireland HRB ILP-POR-2022-046
Irish/UK Motor Neurone Disease Research Foundation IceBucket Award
Irish/UK Motor Neurone Disease Research Foundation MRCG2018–02
Irish/UK Motor Neurone Disease Research Foundation McManus/Apr22/888 − 791
Development of ALS prognostic biomarkers based on patterned neural network dysfunction
Motor Neurone Disease Association
Find out more...Irish Research Council (Government of Ireland Postdoctoral Research Fellowship GOIPD/2015/213)
Irish Research Council (Government of Ireland Postdoctoral Postgraduate Scholarship GOIPG/2017/1014)
Deciphering ALS Heterogeneity: A Precision Medicine Approach to Network Based Biomarker Development
Science Foundation Ireland
Find out more...Science Foundation Ireland (Royal Society/SFI URF\R1\221917)
ALS Association (multi-year grant 20-IIA-546)
Health Research Board of Ireland (Emerging Investigator Award HRB-EIA-2017–019)
Irish Institute of Clinical Neuroscience (IICN) - Novartis Ireland research grant
Iris O’Brien Foundation
Perrigo clinician-scientist research fellowship
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 424778381-TRR 295
Fondazione Grigioni per il Morbo di Parkinson
History
Data Availability Statement
The data supporting the findings presented above are available from the corresponding author on reasonable request from qualified investigators. Data sharing is subject to the participant’s con¬sent and approvals by the Data Protection Officer and the Office of Corporate Partnership and Knowledge Exchange in Trinity College Dublin.Comments
The original article is available at https://link.springer.com/Published Citation
Metzger M, et al. Distinct longitudinal changes in EEG measures reflecting functional network disruption in ALS cognitive phenotypes. Brain Topogr. 2025;38(1):3.Publication Date
4 October 2024External DOI
PubMed ID
9367160Department/Unit
- Beaumont Hospital
- FutureNeuro Centre
Publisher
Springer NatureVersion
- Published Version (Version of Record)