BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a brain network disorder closely associated with synaptic loss in the upper and lower motor neurons. However, the in vivo synaptic network changes and their progressive processes remain unclear. Here, we aim to investigate the synaptic density network connectivity and the likely sequences of synaptic loss in patients with ALS.

METHODS

We examined data from 21 patients diagnosed with ALS and 25 sex- and age-matched healthy controls (HCs) who underwent PET imaging with the SV2A radioligand [F]SynVesT-1. The individual synaptic density similarity network was constructed for each patient by calculating the similarity between interregional synaptic density distributions. The synaptic network connectivity changes were investigated, followed by an examination of the local synaptic density in regions that showed significant network alterations. Finally, we constructed the voxel-wise and ROI-wise causal synaptic covariance network (cSCN) by applying Granger causality analysis. This allowed us to identify the sequence of synaptic loss in these brain regions.

RESULTS

We observed an overall decrease in synaptic density network connectivity in ALS patients compared to controls, with the highest nodal degree in the right medulla oblongata. Specifically, the reduced connections were dominantly between the medulla oblongata and the striatum, frontal lobe, occipital lobe, as well as between the striatum and the frontal lobe, occipital lobe. Furthermore, patients with ALS displayed significantly synaptic loss in those brain regions. The cSCN analyses showed that as the disease progresses, the cortical synaptic loss sequences of ALS extend from the medulla oblongata to the regions including the striatum, frontal lobe, occipital lobe, and parietal lobe.

CONCLUSIONS

These findings suggest that synaptic density network degeneration in ALS may follow a bottom-up transmission pattern, primarily involving in the medulla oblongata-striatum-neocortex network, which have the potential to capture new network-based targets for clinical therapy in the progression of ALS.