  Late-life depression (LLD) is a highly prevalent mood disorder occurring in
older adults and is frequently accompanied by cognitive impairment (CI).
Studies have shown that LLD may increase the risk of Alzheimer's disease (AD).
However, the heterogeneity of presentation of geriatric depression suggests
that multiple biological mechanisms may underlie it. Current biological
research on LLD progression incorporates machine learning that combines
neuroimaging data with clinical observations. There are few studies on incident
cognitive diagnostic outcomes in LLD based on structural MRI (sMRI). In this
paper, we describe the development of a hybrid representation learning (HRL)
framework for predicting cognitive diagnosis over 5 years based on T1-weighted
sMRI data. Specifically, we first extract prediction-oriented MRI features via
a deep neural network, and then integrate them with handcrafted MRI features
via a Transformer encoder for cognitive diagnosis prediction. Two tasks are
investigated in this work, including (1) identifying cognitively normal
subjects with LLD and never-depressed older healthy subjects, and (2)
identifying LLD subjects who developed CI (or even AD) and those who stayed
cognitively normal over five years. To the best of our knowledge, this is among
the first attempts to study the complex heterogeneous progression of LLD based
on task-oriented and handcrafted MRI features. We validate the proposed HRL on
294 subjects with T1-weighted MRIs from two clinically harmonized studies.
Experimental results suggest that the HRL outperforms several classical machine
learning and state-of-the-art deep learning methods in LLD identification and
prediction tasks.
