Multidimensional representations in late-life depression: convergence in neuroimaging, cognition, clinical symptomatology and genetics

TL;DR

This study identifies two neurobiological and clinical dimensions of late-life depression through multimodal neuroimaging, cognition, and genetics, revealing heterogeneity that could inform personalized treatment approaches.

Contribution

The paper introduces a semi-supervised clustering approach to delineate distinct neurobiological subtypes of late-life depression based on multimodal data, linking them to genetics and disease progression.

Findings

Two distinct dimensions of LLD were identified, with different neuroanatomical and genetic profiles.

One dimension showed preserved brain structure, the other showed widespread atrophy and cognitive decline.

The dimensions had significant heritability and different longitudinal progression patterns.

Abstract

Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity would aid in elucidating etiological mechanisms and pave the road to precision and individualized medicine. We sought to delineate, cross-sectionally and longitudinally, disease-related heterogeneity in LLD linked to neuroanatomy, cognitive functioning, clinical symptomatology, and genetic profiles. Multimodal data from a multicentre sample (N=996) were analyzed. A semi-supervised clustering method (HYDRA) was applied to regional grey matter (GM) brain volumes to derive dimensional representations. Two dimensions were identified, which accounted for the LLD-related heterogeneity in voxel-wise GM maps, white matter (WM) fractional anisotropy (FA), neurocognitive functioning, clinical phenotype, and genetics. Dimension one (Dim1) demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy controls. In contrast, dimension two (Dim2) showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, one de novo independent genetic variant (rs13120336) was significantly associated with Dim 1 but not with Dim 2. Notably, the two dimensions demonstrated significant SNP-based heritability of 18-27% within the general population (N=12,518 in UKBB). Lastly, in a subset of individuals having longitudinal measurements, Dim2 demonstrated a more rapid longitudinal decrease in GM and brain age, and was more likely to progress to Alzheimers disease, compared to Dim1 (N=1,413 participants and 7,225 scans from ADNI, BLSA, and BIOCARD datasets).