  Alzheimer's disease prognosis is critical for early Mild Cognitive Impairment
patients for timely treatment to improve the patient's quality of life. Whilst
existing prognosis techniques demonstrate potential results, they are highly
limited in terms of using a single modality. Most importantly, they fail in
considering a key element for prognosis: not all features extracted at the
current moment may contribute to the prognosis prediction several years later.
To address the current drawbacks of the literature, we propose a novel
hypergraph framework based on an information bottleneck strategy (HGIB).
Firstly, our framework seeks to discriminate irrelevant information, and
therefore, solely focus on harmonising relevant information for future MCI
conversion prediction e.g., two years later). Secondly, our model
simultaneously accounts for multi-modal data based on imaging and non-imaging
modalities. HGIB uses a hypergraph structure to represent the multi-modality
data and accounts for various data modality types. Thirdly, the key of our
model is based on a new optimisation scheme. It is based on modelling the
principle of information bottleneck into loss functions that can be integrated
into our hypergraph neural network. We demonstrate, through extensive
experiments on ADNI, that our proposed HGIB framework outperforms existing
state-of-the-art hypergraph neural networks for Alzheimer's disease prognosis.
We showcase our model even under fewer labels. Finally, we further support the
robustness and generalisation capabilities of our framework under both
topological and feature perturbations.
