Abstract: Research in ALS has gained unprecedented momentum in recent years fueled by important conceptual developments, establishment of international consortia, breakthrough genetic discoveries and relentless technological advances. The first genotype-specific pharmaceutical trials signal the paradigm shift from the notion of ‘one-drug-for-all’ to precision, individualized therapies. The once arcane presymptomatic phase of the disease is gradually unraveled by seminal studies of asymptomatic mutation carriers (Geevasinga et al., 2015; Querin et al., 2019). The meticulous analysis of data from large population-based registries has contributed to the identification of etiological factors, genetic risk profiles, epigenetic and environmental modifiers. Progression patterns have been characterized in vivo by robust clinical, neurophysiology and neuroimaging studies and led to the development of clinical staging systems and biomarkers with practical utility in clinical trials (Chipika et al., 2019). While ALS was once considered a ‘pure’ motor system disorder, it is now widely regarded as multisystem condition with frontotemporal, cerebellar, and subcortical grey matter involvement and a range of extrapyramidal, cognitive, and behavioral manifestations (Elamin et al., 2017). Disease-specific functional rating scales are now routinely used and screening instruments have been developed to assess the most commonly affected cognitive and behavioral domains in ALS. Advances in genetics paved the way for the first large presymptomatic studies which confirmed considerable cerebral and spinal cord alterations decades before symptom manifestation (Vucic et al., 2008; Querin et al., 2019). The characterization of genotype-associated molecular cascades, pathological signatures and clinical features were important milestones for the development of novel therapies, and the first antisense oligonucleotide trials are now underway. The datasets generated by multicenter initiatives offer unprecedented data mining opportunities; clustering patterns, prognostic determinants, and reliable diagnostic indicators were identified using machine-learning approaches that could not have previously been applied to smaller datasets. Technological advances in electrophysiology and the emergence of magnetoencephalography generated important functional insights (Bede et al., 2018). Novel imaging modalities, such as multi-voxel spectroscopy, spinal cord imaging, diffusion kurtosis imaging captured pathological changes that were previously impossible to ascertain in vivo (Bede et al., 2017; Huang et al., 2020). Advanced neurophysiology techniques, such as transcranial magnetic stimulation or motor unit number estimation are now widely used in both clinical and academic settings and contribute to diagnostic clarification and the monitoring of individual patients. In response to the inevitable sample size limitations of single-center studies (Schuster et al., 2016), ambitious international initiatives such as Project MinE established large biobanks to conduct genetic studies with sufficient statistical power. Societies such as NISALS provide pioneering frameworks to conduct large multicenter neuroimaging studies.