Axon degeneration (AxD) is a frequent trait of many neurodegenerative disorders (NDDs). SARM1 (sterile alpha and Toll/interleukin-1 receptor motif-containing 1), a NADase protein, is an executioner of AxD. Injury or stress to the central nervous system leads to an increased NMN/NAD+ (nicotinamide mononucleotide/nicotinamide adenine dinucleotide) ratio in the axons, triggering SARM1 activation. Upon activation, SARM1 rapidly depletes NAD+ in the affected cells and thereby causes axon degeneration. Recently, the inactive octameric ring structure of the human SARM1 (hSARM1) has been solved. The structure shows that the central core of the octamer consists of the tandem SAM domains (hSARM1SAM). The autoregulatory ARM domains (hSARM1ARM) flank around the core and the effector TIR domains (hSARM1TIR) wedge between the ARM domains on the outside of the ring. Both NMN and NAD+ can bind to the ARM domain; however, NMN activates, while NAD+ retains the inhibitory state of the protein. These two molecules compete in stressed cells, and NMN replaces NAD+ to activate the protein. In the active form, the catalytic sites of the TIR domains bind and cleave NAD+ into Nam (nicotinamide) and ADPR/cADPR (adenosine diphosphate ribose/cyclic ADPR). This NADase activity of the TIR domain is self-assembly dependent. Solving the structures of full-length SARM1 and its individual domains has helped us understand the molecular mechanism of SARM1-dependent AxD. However, a few areas still need to be explored. In this study, we tried to solve the cryoEM structure of the activated TIR oligo assembled with a modified substrate, termed 1AD. We have also studied different phases of SARM1 activation using nanobodies and also characterised how these nanobodies influence the function of SARM1. In addition, we have done in silico studies to understand how SARM1 mutations impact ALS progression. We have solved the cryoEM structure of the induced TIR oligo. We have characterised a nanobody, hS-TIR-Nb-F11, that can catalyse the NADase activity of SARM1. We have used machine-learning-based features to explain how SARM1 mutations influence ALS progression. Overall, this study has provided useful insights into SARM1-mediated AxD and ALS progression.