Biomarker Identification Through Integrating fMRI and Epigenetics.

Area of Science:

• Neuroscience
• Genetics
• Psychiatry

Background:

• Schizophrenia (SZ) is a complex mental disorder with multifactorial causes.
• While genetic and brain imaging data have been extensively studied, the role of epigenetic factors is increasingly recognized.
• There is a need to integrate epigenetic data, such as DNA methylation, with imaging data for a comprehensive understanding of SZ.

Purpose of the Study:

• To identify novel biomarkers for schizophrenia by combining epigenetics and neuroimaging data.
• To explore the co-expression patterns between DNA methylation and functional magnetic resonance imaging (fMRI) data in SZ.
• To advance the understanding of the underlying mechanisms of schizophrenia.

Main Methods:

• A novel method combining linear regression and canonical correlation analysis (CCA) was developed.
• The method was designed to extract features co-expressed in fMRI and DNA methylation datasets.
• The approach was validated through simulations and applied to real-world data from 184 subjects.

Main Results:

• The study identified 14 significant brain regions and 44 cytosine-phosphate-guanine (CpG) sites associated with SZ.
• The identified CpG sites were linked to genes involved in guanosine nucleotide biosynthesis pathways.
• The method achieved a notable classification accuracy for SZ, with identified biomarkers related to neuron development and memory processing.

Conclusions:

• The imaging epigenetics approach effectively identified novel biomarkers for schizophrenia.
• The findings highlight the importance of integrating epigenetic and imaging data for understanding SZ.
• These biomarkers offer insights into the mechanisms of SZ and may aid in its diagnosis.