Development of ALS progNOstic biomarkers based on patterned neural network dysfunction
MND Association
Trinity College Dublin
Despite substantial heterogeneity of presentation and progression, ALS is currently managed as though it is a single neurodegenerative disorder. With the exception of gene based therapies, most clinical trial designs rely exclusively on clinical parameters to stratify patients into “fast” and “slow” progressors, and on clinical evaluation (ALS functional rating scale revised, ALSFRS-R) to determine progression and outcome. These parameters are limited in their ability to capture underlying disease heterogeneity, due to low sensitivity of the subscores, variability in the overall slope, and emphasis on motor aspects of disease while excluding NOn-motor components including cognitive and behavioural change (Rooney et al. 2017). By contrast, there is compelling emerging evidence that direct measurements of cortical and corticospinal network activity can provide objective and measures of early ALS pathology. For example, transcranial magnetic stimulation (TMS) which measures of upper motor neuron function and quantitative electroencephalography (EEG), which measures of cognitive network function, correlate with clinical findings and can presage future disease progression(Vucic et al. 2008; Poil et al. 2013; Dukic et al. 2019; McMackin et al. 2019b, 2020). Based on these observations and on my preliminary data, my hypothesis is that ALS subpheNOtypes differ with respect to patterns of cortical and corticospinal network dysfunction which may be captured by a combination of cortical EEG- and TMS-based measures and lower motor neurone EMG-based measures. I will record a battery of EEG, TMS and EMG measures longitudinally in a large cohort of ALS patients and correlate these measurements with indices of clinical and cognitive symptoms. I will cluster patients into subpheNOtypes based on a combination of measures which predict each individual’s concurrent and future symptoms. I will then test my hypothesis that changes in neuroelectric signal patterns alone can define subpheNOtypes with different clinical presentations and disease progression.
4.1 Discovery and preclinical testing of markers and technologies