Unsupervised definition of two clinical subtypes of Essential tremor and the underlying brain topology

Abstract

Background

Essential tremor (ET) is one of the most prevalent neurological diseases varying considerably in clinical manifestations and prognosis, which indicates the existence of subtypes. Identifying ET subtypes is crucial for explaining clinical heterogeneity. This study aimed to identify ET subtypes using unsupervised clustering analysis based on clinical manifestations and explore underlying brain topology within both functional and structural networks.

Methods

We recruited 103 ET patients and 43 healthy control subjects. K-means clustering analysis was performed to identify ET subtypes based on age of onset, motor and non-motor symptoms. Functional MRI and diffusion tensor imaging data were used to construct functional and structural networks. Global attributes (clustering coefficient, characteristic path length, global efficiency, and local efficiency) and nodal attributes (nodal clustering coefficient, nodal efficiency, and nodal degree centrality) were calculated for topological analysis.

Results

We identified two subtypes: Subtype 1 (earlier age of onset – without nonmotor symptoms subtype) and Subtype 2 (later age of onset – with nonmotor symptoms subtype). Decreased clustering coefficient and global efficiency, increased characteristic path length were observed in Subtype 2 compared to NC, while only decreased global efficiency was observed in Subtype 1. More widespread brain regions with decreased nodal clustering coefficient were specifically observed in Subtype 2.

Conclusion

We identified two ET subtypes based on comprehensive clinical information and revealed that Subtype 2 may be a more malignant subtype. Our study firstly unsupervisedly identifies the clinical heterogeneity of ET and provides neuroimaging evidence for better understanding the underlying disease biology.