Effective Subsets of Fine-Grained Network-based Neurophysiological Biomarkers for Early Stratification in Amyotrophic Lateral Sclerosis
Motor Neurone Disease (MND)/Amyotrophic Lateral Sclerosis (ALS) is a terminal neurological condition in which the neurons (neural cells) that control movement degenerate. The exiting drugs have very limited effects on the disease progression and those affected only survive for about 3-4 years after the symptoms begin.
More than 40 drug candidates to date have failed to provide real benefits to patients and the clinical trials for finding effective treatments face crucial limitations. First, in many patients, the disease has already damaged about 80% of the nerves, therefore even treatments that may have an effect on the progression of ALS may fail to show any effects at this later stage of the disease. Early diagnosis is therefore a crucial component to identifying effective treatments. Second, the disease includes several subtypes that are still being studied by researchers, and these subgroups may respond to different drug candidates.
Our team measures the electrical activity of brain, including brain waves (EEG), muscle activity (EMG) and the brain’s response to non-invasive magnetic stimulation (TMS). We have found that different patterns of networks are affected at different stages of disease, and that the changes in brain networks can be used to divide patients into different subgroups. Here we combine all of our methods using advanced analyses to find the most sensitive and powerful measurements that can be quickly and easily recorded from patients. This combined assessment will help us to direct new clinical trials by facilitating earlier diagnoses and enabling patients to be clustered into groups based on the patterns of brain wave disruption. In this way, we will have better methods of ensuring that the right patient gets the right drug.
- Award Date
- 01 July 2022
- Award Value
- Principal Investigator
- Dr Bahman Nasseroleslami
- Host Institution
- Trinity College Dublin
- Investigator Led Projects