IDEEA: information diffusion model for integrating gene expression and EEG data in identifying Alzheimer’s disease markers

Abstract

Understanding the genetic components of Alzheimer’s disease (AD) via transcriptome analysis often necessitates the use of invasive methods. This work focuses on overcoming the difficulties associated with the invasive process of collecting brain tissue samples in order to measure and investigate the transcriptome behavior of AD. Our approach called IDEEA ( I nformation D iffusion model for integrating gene E xpression and E EG data in identifying A lzheimer’s disease markers) involves systematically linking two different but complementary modalities: transcriptomics and electroencephalogram (EEG) data. We preprocess these two data types by calculating the spectral and transcriptional sample distances, over 11 brain regions encompassing 6 distinct frequency bands. Subsequently, we employ a genetic algorithm approach to integrate the distinct features of the preprocessed data. Our experimental results show that IDEEA converges rapidly to local optima gene subsets, in fewer than 250 iterations. Our algorithm identifies novel genes along with genes that have previously been linked to AD. It is also capable of detecting genes with transcription patterns specific to individual EEG bands as well as those with common patterns among bands. In particular, the alpha2 (10–13 Hz) frequency band yielded 8 AD-associated genes out of the top 100 most frequently selected genes by our algorithm, with a p -value of 0.05. Our method not only identifies AD-related genes but also genes that interact with AD genes in terms of transcription regulation. We evaluated various aspects of our approach, including the genetic algorithm performance, band-pair association and gene interaction topology. Our approach reveals AD-relevant genes with transcription patterns inferred from EEG alone, across various frequency bands, avoiding the risky brain tissue collection process. This is a significant advancement toward the early identification of AD using non-invasive EEG recordings.

Keywords

• Alzheimer’s disease
• electroencephalogram
• gene expression
• integrative machine learning