MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study

TL;DR

An innovative multi-omics approach to accurately differentiate AD from VaD is presented, achieving a diagnostic accuracy of 89.25%, setting a new benchmark in classification accuracy on a large public dataset.

Abstract

In the complex realm of cognitive disorders, Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types, presenting entangled symptoms yet requiring distinct treatment approaches. The crux of effective treatment in slowing neurodegeneration lies in early, accurate diagnosis, as this significantly assists doctors in determining the appropriate course of action. However, current diagnostic practices often delay VaD diagnosis, impeding timely intervention and adversely affecting patient prognosis. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%. The proposed method segments the longitudinal MRI scans and extracts advanced radiomics features. Subsequently, it synergistically integrates the radiomics features with an ensemble of clinical, cognitive, and genetic data to provide state-of-the-art diagnostic accuracy, setting a new benchmark in classification accuracy on a large public dataset. The paper's primary contribution is proposing a comprehensive methodology utilizing multi-omics data to provide a nuanced understanding of dementia subtypes. Additionally, the paper introduces an interpretable model to enhance clinical decision-making coupled with a novel model architecture for evaluating treatment efficacy. These advancements lay the groundwork for future work not only aimed at improving differential diagnosis but also mitigating and preventing the progression of dementia.

Figures (5)

• Figure 1: The architecture segments the MRI scans to extract radiomics features and fuses them with other multi-omics features. Feature selection is applied, and then features are passed to the Deep Feature Generation module, where discriminative features are generated and concatenated with the raw features.
• Figure 2: Multi-planar MRI Scans with Annotated Segmentation: The image series showcases axial, coronal, and sagittal views of the temporal brain MRIs at time points 0, 3, 12, both in raw form (left column) and with detailed segmentation overlays (right column). The segmentation highlights various brain structures, including the thalamus and cortical regions, using distinct color codes to differentiate between anatomical areas.
• Figure 3: An example patient data with MRI, the generated segmentation mask, and a sample set of the tabular data, left to right.
• Figure 4: Figure shows the feature importance of MRI radiomics features only versus multi-omics features. Importance is broken down by scan timepoint.
• Figure 8: Feature importance of radiomics MRI features for treated MCI patients, showcasing a notable decrease in the relative importance as time progresses.